diff --git a/docs/lab05.html b/docs/lab05.html
index 53651eb..237ee67 100644
--- a/docs/lab05.html
+++ b/docs/lab05.html
@@ -566,16 +566,16 @@ <h4 class="anchored" data-anchor-id="the-subject-meta-data">The Subject Meta Dat
 <pre><code># A tibble: 10 × 30
    Experiment Subject `Cell Type` `Target Type` Cohort          Age Gender Race 
    &lt;chr&gt;        &lt;dbl&gt; &lt;chr&gt;       &lt;chr&gt;         &lt;chr&gt;         &lt;dbl&gt; &lt;chr&gt;  &lt;chr&gt;
- 1 ePD85         5869 naive_CD8   C19_cI        Healthy (No …    27 F      &lt;NA&gt; 
- 2 ePD81         2922 PBMC        C19_cI        COVID-19-Con…    64 M      &lt;NA&gt; 
- 3 eQD129        2283 PBMC        C19_cI        COVID-19-Con…    60 F      White
- 4 eHO125        4611 PBMC        C19_cI        COVID-19-Con…    52 M      &lt;NA&gt; 
- 5 eNL192        1524 B-CD8-_PBMC C19_cII       COVID-19-Con…    NA &lt;NA&gt;   &lt;NA&gt; 
- 6 eJL151        3270 PBMC        C19_cI        COVID-19-Con…    79 F      &lt;NA&gt; 
- 7 eHO126        6913 PBMC        C19_cI        COVID-19-Con…    37 F      &lt;NA&gt; 
- 8 eQD124         361 PBMC        C19_cI        COVID-19-B-N…    40 F      White
- 9 eQD136        2818 PBMC        C19_cI        COVID-19-Con…    NA &lt;NA&gt;   &lt;NA&gt; 
-10 eXL31        20300 naive_CD8   C19_cI        Healthy (No …    28 M      White
+ 1 eLH43         3565 PBMC        C19_cI        COVID-19-Con…    57 M      &lt;NA&gt; 
+ 2 eQD134        2903 PBMC        C19_cI        COVID-19-Con…    NA &lt;NA&gt;   &lt;NA&gt; 
+ 3 ePD100        1811 PBMC        C19_cI        COVID-19-Con…    66 M      &lt;NA&gt; 
+ 4 eAV100        1995 PBMC        C19_cII       COVID-19-Con…    29 F      &lt;NA&gt; 
+ 5 ePD91          169 PBMC        C19_cI        COVID-19-Con…    52 M      White
+ 6 eHO133     1005699 PBMC        C19_cI        COVID-19-Acu…    67 M      White
+ 7 eHO129         142 PBMC        C19_cI        COVID-19-Con…    66 F      Asian
+ 8 eMR22      1565927 PBMC        C19_cI        COVID-19-Con…    65 M      &lt;NA&gt; 
+ 9 eMR14         2845 PBMC        C19_cI        COVID-19-Con…    NA &lt;NA&gt;   &lt;NA&gt; 
+10 eQD124         361 PBMC        C19_cI        COVID-19-B-N…    40 F      White
 # ℹ 22 more variables: `HLA-A...9` &lt;chr&gt;, `HLA-A...10` &lt;chr&gt;,
 #   `HLA-B...11` &lt;chr&gt;, `HLA-B...12` &lt;chr&gt;, `HLA-C...13` &lt;chr&gt;,
 #   `HLA-C...14` &lt;chr&gt;, DPA1...15 &lt;chr&gt;, DPA1...16 &lt;chr&gt;, DPB1...17 &lt;chr&gt;,
@@ -639,16 +639,16 @@ <h5 class="anchored" data-anchor-id="stop-make-sure-you-handled-how-nas-are-deno
 <pre><code># A tibble: 10 × 27
    Experiment Cohort      Age Gender Race  `HLA-A...9` `HLA-A...10` `HLA-B...11`
    &lt;chr&gt;      &lt;chr&gt;     &lt;dbl&gt; &lt;chr&gt;  &lt;chr&gt; &lt;chr&gt;       &lt;chr&gt;        &lt;chr&gt;       
- 1 eMR14      COVID-19…    NA &lt;NA&gt;   &lt;NA&gt;  "A*03:01:0… "A*24:02:01" "B*07:02:01"
- 2 eXL36      Healthy …    37 F      White "A*01:01"   "A*02:01"    "B*15:01"   
- 3 eJL161     COVID-19…    31 F      White "A*01:01:0… "A*02:01:01" "B*08:01:01"
- 4 ePD90      COVID-19…    29 M      &lt;NA&gt;  ""          ""           ""          
- 5 eQD115     COVID-19…    48 M      &lt;NA&gt;  "A*02:01:0… "A*03:01:01" "B*07:02:01"
- 6 ePD73      Healthy …    37 F      White "A*01:01"   "A*02:01"    "B*15:01"   
- 7 eOX56      Healthy …    30 M      Blac… "A*02:01"   "A*33:03"    "B*53:01"   
- 8 eEE240     Healthy …    23 M      White "A*02:01"   "A*02:01"    "B*40:01"   
- 9 eLH45      COVID-19…    53 M      &lt;NA&gt;  "A*02:01:0… "A*03:01:01" "B*07:02:01"
-10 eJL148     COVID-19…    41 F      &lt;NA&gt;  "A*02:01:0… "A*02:01:01" "B*07:02:01"
+ 1 eQD111     COVID-19…    51 M      &lt;NA&gt;  "A*01:01:0… "A*11:01:01" "B*07:02:01"
+ 2 eLH50      COVID-19…    28 M      &lt;NA&gt;  "A*02:01:0… "A*26:01:01" "B*15:01:01"
+ 3 eEE224     Healthy …    24 M      White "A*02:01"   "A*03:01"    "B*27:05"   
+ 4 eQD113     COVID-19…    36 M      &lt;NA&gt;  "A*03:01:0… "A*11:01:01" "B*51:01:01"
+ 5 ePD73      Healthy …    37 F      White "A*01:01"   "A*02:01"    "B*15:01"   
+ 6 eQD119     COVID-19…    51 M      &lt;NA&gt;  "A*01:01:0… "A*01:01:01" "B*08:01:01"
+ 7 eXL30      Healthy …    21 F      White "A*01:01"   "A*02:01"    "B*35:02"   
+ 8 eAV105     COVID-19…    29 F      &lt;NA&gt;  "A*02:01:0… "A*68:01:02" "B*07:02:01"
+ 9 eHO141     COVID-19…    NA &lt;NA&gt;   &lt;NA&gt;  ""          ""           ""          
+10 eXL43      Healthy …    36 F      White "A*32:01"   "A*32:01"    "B*07:02"   
 # ℹ 19 more variables: `HLA-B...12` &lt;chr&gt;, `HLA-C...13` &lt;chr&gt;,
 #   `HLA-C...14` &lt;chr&gt;, DPA1...15 &lt;chr&gt;, DPA1...16 &lt;chr&gt;, DPB1...17 &lt;chr&gt;,
 #   DPB1...18 &lt;chr&gt;, DQA1...19 &lt;chr&gt;, DQA1...20 &lt;chr&gt;, DQB1...21 &lt;chr&gt;,
@@ -863,16 +863,16 @@ <h5 class="anchored" data-anchor-id="stop-make-sure-you-handled-how-nas-are-deno
 <pre><code># A tibble: 10 × 11
    Experiment Cohort        Age Gender Race  A1    A2    B1    B2    C1    C2   
    &lt;chr&gt;      &lt;chr&gt;       &lt;dbl&gt; &lt;chr&gt;  &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; &lt;chr&gt;
- 1 eQD115     COVID-19-C…    48 M      &lt;NA&gt;  "A*0… "A*0… "B*0… "B*4… "C*0… "C*0…
- 2 eHO140     COVID-19-C…    NA &lt;NA&gt;   &lt;NA&gt;  ""    ""    ""    ""    ""    ""   
- 3 eGK120     COVID-19-C…    46 F      White "A*1… "A*6… "B*0… "B*5… "C*0… "C*1…
- 4 eLH42      COVID-19-C…    63 M      &lt;NA&gt;  "A*0… "A*2… "B*0… "B*1… "C*0… "C*0…
- 5 ePD73      Healthy (N…    37 F      White "A*0… "A*0… "B*1… "B*4… "C*0… "C*0…
- 6 eEE226     Healthy (N…    21 F      White "A*0… "A*0… "B*3… "B*3… "C*0… "C*0…
- 7 eHH173     Healthy (N…    50 M      White "A*0… "A*0… "B*3… "B*4… "C*0… "C*0…
- 8 eHH169     Healthy (N…    24 F      Blac… "A*0… "A*7… "B*3… "B*3… "C*0… "C*0…
- 9 eJL147     Healthy (N…    40 M      Mixe… "A*0… "A*1… "B*0… "B*3… "C*0… "C*0…
-10 eQD120     COVID-19-C…    62 F      &lt;NA&gt;  "A*0… "A*3… "B*0… "B*4… "C*0… "C*0…</code></pre>
+ 1 eEE217     Healthy (N…    32 F      White "A*0… "A*0… "B*1… "B*4… "C*0… "C*0…
+ 2 eLH41      COVID-19-C…    71 F      &lt;NA&gt;  "A*0… "A*0… "B*1… "B*1… "C*0… "C*0…
+ 3 ePD73      Healthy (N…    37 F      White "A*0… "A*0… "B*1… "B*4… "C*0… "C*0…
+ 4 eHO136     COVID-19-C…    51 M      Hisp… ""    ""    ""    ""    ""    ""   
+ 5 eHO125     COVID-19-C…    52 M      &lt;NA&gt;  "A*0… "A*0… "B*3… "B*4… "C*0… "C*0…
+ 6 eOX46      Healthy (N…    45 M      White "A*0… "A*0… "B*3… "B*4… "C*0… "C*0…
+ 7 eQD113     COVID-19-C…    36 M      &lt;NA&gt;  "A*0… "A*1… "B*5… "B*5… "C*0… "C*1…
+ 8 eOX56      Healthy (N…    30 M      Blac… "A*0… "A*3… "B*5… "B*5… "C*0… "C*0…
+ 9 eQD108     COVID-19-C…    NA &lt;NA&gt;   &lt;NA&gt;  "A*1… "A*6… "B*0… "B*5… "C*0… "C*1…
+10 eXL43      Healthy (N…    36 F      White "A*3… "A*3… "B*0… "B*1… "C*0… "C*0…</code></pre>
 </div>
 </div>
 <p>Now, we have a beautiful <code>tidy</code> dataset, recall that this entails, that each row is an observation, each column is a variable and each cell holds one value.</p>
@@ -888,16 +888,16 @@ <h4 class="anchored" data-anchor-id="the-peptide-details-data">The Peptide Detai
 <pre><code># A tibble: 10 × 7
    `TCR BioIdentity`            TCR Nucleotide Seque…¹ Experiment `ORF Coverage`
    &lt;chr&gt;                        &lt;chr&gt;                  &lt;chr&gt;      &lt;chr&gt;         
- 1 unproductive+TCRBV03-01/03-… TCACATCAATTCCCTGGAGCT… eHO141     surface glyco…
- 2 CASSRRDMGGGPTDTQYF+TCRBV19-… CAAAAGAACCCGACAGCTTTC… eEE226     ORF7b         
- 3 CSASLESETQYF+TCRBV20-01+TCR… CTGACAGTGACCAGTGCCCAT… eEE226     ORF7b         
- 4 CASSLTGALGRSIANQPQHF+TCRBV1… AGGGACTCAGCTGTGTACTTC… eXL31      ORF3a         
- 5 CASRKGSTNEKLFF+TCRBV05-06+T… GTGAACGCCTTGTTGCTGGGG… eEE226     ORF7b         
- 6 CASSPGLVIEQFF+TCRBV13-01+TC… AACATGAGCTCCTTGGAGCTG… eHO136     ORF1ab        
- 7 CASTPGLAVYEQYF+TCRBV12-X+TC… ATCCAGCCCTCAGAACCCAGG… eEE228     ORF1ab        
- 8 CSARAGGYSNQPQHF+TCRBV20-X+T… ACCAGTGCCCATCCTGAAGAC… eOX52      surface glyco…
- 9 CASSPDWGGGEQYF+TCRBV05-04+T… GTGAACGCCTTGGAGCTGGAC… eEE228     ORF1ab        
-10 CASDEGVDNEQFF+TCRBV02-01+TC… AAGATCCGGTCCACAAAGCTG… eEE226     surface glyco…
+ 1 CASSLWASGPNEQFF+TCRBV28-01+… GAGTCCGCCAGCACCAACCAG… eHH175     surface glyco…
+ 2 CASSPQGSLTEAFF+TCRBV06-05+T… NNNNTGTCGGCTGCTCCCTCC… eQD126     ORF1ab        
+ 3 CASSVGQGVIAGGPSEQFF+TCRBV09… CTGGGGGACTCAGCTTTGTAT… eOX52      ORF1ab        
+ 4 CASSPQLAMRGNEQFF+TCRBV07-08… CGCACACAGCAGGAGGACTCC… eQD124     membrane glyc…
+ 5 CASSFTTGAGANVLTF+TCRBV05-04… GCCTTGGAGCTGGACGACTCG… eEE226     surface glyco…
+ 6 CSATGRAGINEQFF+TCRBV20-X+TC… GTGACCAGTGCCCATCCTGAA… eEE240     ORF1ab        
+ 7 CSASLGLYEQYF+TCRBV20-01+TCR… CTGACAGTGACCAGTGCCCAT… eOX49      ORF1ab        
+ 8 CASSFGQVGQPQHF+TCRBV28-01+T… CTGGAGTCCGCCAGCACCAAC… eXL31      membrane glyc…
+ 9 CSVGTGDGETQYF+TCRBV20-X+TCR… ACAGTGACCAGTGCCCATCCT… eXL32      ORF7b         
+10 CSADTRGVEETQYF+TCRBV20-X+TC… GTGACCAGTGCCCATCCTGAA… eLH49      ORF1ab        
 # ℹ abbreviated name: ¹​`TCR Nucleotide Sequence`
 # ℹ 3 more variables: `Amino Acids` &lt;chr&gt;, `Start Index in Genome` &lt;dbl&gt;,
 #   `End Index in Genome` &lt;dbl&gt;</code></pre>
@@ -933,18 +933,18 @@ <h4 class="anchored" data-anchor-id="the-peptide-details-data">The Peptide Detai
 <span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">sample_n</span>(<span class="dv">10</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code># A tibble: 10 × 3
-   Experiment `TCR BioIdentity`                       `Amino Acids`             
-   &lt;chr&gt;      &lt;chr&gt;                                   &lt;chr&gt;                     
- 1 eLH51      CASSTSGNTGELFF+TCRBV19-01+TCRBJ02-02    APSASAFFGM,AQFAPSASA,ASAF…
- 2 eHO140     CASNDLNTGELFF+TCRBV02-01+TCRBJ02-02     YLQPRTFL,YLQPRTFLL,YYVGYL…
- 3 eEE240     CSASGSLIGTNEKLFF+TCRBV20-X+TCRBJ01-04   AFLLFLVLI,FLAFLLFLV,FYLCF…
- 4 eEE240     CSLALADPDTQYF+TCRBV29-01+TCRBJ02-03     DFLEYHDVR,EDFLEYHDVR,LEYH…
- 5 eMR23      CSARGWGGPKTSTDTQYF+TCRBV20-X+TCRBJ02-03 ALNTPKDHI,ATEGALNTPK      
- 6 eOX54      CASSQGVGTEAFF+TCRBV04-03+TCRBJ01-01     AFPFTIYSL,GYINVFAFPF,INVF…
- 7 eOX49      CASSLGQGAYEQYF+TCRBV04-01+TCRBJ02-07    FLNGSCGSV                 
- 8 eEE224     CASSQDLGMGEQFF+TCRBV06-X+TCRBJ02-01     TVLSFCAFA,VLSFCAFAV       
- 9 eHH175     CASSQPSYEQYF+TCRBV04-02+TCRBJ02-07      AFLLFLVLI,FLAFLLFLV,FYLCF…
-10 eMR15      CASDQTGGRGTDTQYF+TCRBV28-01+TCRBJ02-03  FPRGQGVPI,KFPRGQGVPI      </code></pre>
+   Experiment `TCR BioIdentity`                           `Amino Acids`         
+   &lt;chr&gt;      &lt;chr&gt;                                       &lt;chr&gt;                 
+ 1 eQD137     CASSPLDEDEAFF+TCRBV18-01+TCRBJ01-01         HTTDPSFLGRY           
+ 2 eQD112     CASS*PED*RGLNRDTQYF+TCRBV13-01+TCRBJ02-03   LSPRWYFYY,SPRWYFYYL   
+ 3 eQD131     CASSLSGAPGVGTDTQYF+TCRBV05-06+TCRBJ02-03    FLQSINFVR,FLQSINFVRI,…
+ 4 eDH105     CASSRTGTGPYEQYF+TCRBV28-01+TCRBJ02-07       ALALLLLDR,GDAALALLLL,…
+ 5 eEE224     RASSLESYNEQFF+TCRBV07-03+TCRBJ02-01         AFPFTIYSL,GYINVFAFPF,…
+ 6 eOX54      CASSLGGVTGGELFF+TCRBV04-02+TCRBJ02-02       YLDAYNMMI             
+ 7 eEE228     CASSPVTGTNQPQHF+TCRBV03-01/03-02+TCRBJ01-05 AFLLFLVLI,FLAFLLFLV,F…
+ 8 eQD121     CASSLSSSQETQYF+TCRBV27-01+TCRBJ02-05        HTTDPSFLGRY           
+ 9 eXL30      CASSYSAAANTGELFF+TCRBV06-06+TCRBJ02-02      AEAELAKNVSL,AELAKNVSL…
+10 eLH47      CASSPDIQAFF+TCRBV07-09+TCRBJ01-01           YLQPRTFL,YLQPRTFLL,YY…</code></pre>
 </div>
 </div>
 <ul>
@@ -961,16 +961,16 @@ <h4 class="anchored" data-anchor-id="the-peptide-details-data">The Peptide Detai
 <pre><code># A tibble: 10 × 5
    Experiment CDR3b            V_gene           J_gene     `Amino Acids`        
    &lt;chr&gt;      &lt;chr&gt;            &lt;chr&gt;            &lt;chr&gt;      &lt;chr&gt;                
- 1 eEE226     CASSSFGTGGAGELFF TCRBV05-01       TCRBJ02-02 IVDTVSALV            
- 2 eOX54      CASIQGMNTEAFF    TCRBV07-09       TCRBJ01-01 FVCNLLLLFV,LLFVTVYSH…
- 3 eAV88      CASSFMGGNQPQHF   TCRBV05-06       TCRBJ01-05 KLSYGIATV            
- 4 eEE240     CASSSADRVKSNGYTF TCRBV28-01       TCRBJ01-02 TLDSKTQSL            
- 5 eOX43      CASSLRRDHTDTQYF  TCRBV28-01       TCRBJ02-03 REGVFVSNGTHW         
- 6 eOX43      CSASSSLGYNEQFF   TCRBV20-X        TCRBJ02-01 AFLLFLVLI,FLAFLLFLV,…
- 7 eOX52      CASSLGVLAGADTQYF TCRBV27-01       TCRBJ02-03 QYIKWPWYI,YEQYIKWPW,…
- 8 eOX54      CASGLAEYEQYF     TCRBV10-02       TCRBJ02-07 FKVSIWNLDY,ILLIIMRTF…
- 9 eQD128     CASRVELEPQHF     TCRBV12-03/12-04 TCRBJ01-05 ALALLLLDR,GDAALALLLL…
-10 ePD84      CSAPDRVNTGELFF   TCRBV20-X        TCRBJ02-02 AFLLFLVLI,FLAFLLFLV,…</code></pre>
+ 1 eJL149     CASSLAGTANYEQYF  TCRBV07-02       TCRBJ02-07 FAYANRNRF,LQFAYANRNR…
+ 2 eXL32      CASSEEEGAGAYEQYF TCRBV02-01       TCRBJ02-07 RQLLFVVEV            
+ 3 eLH48      CASSSRAASGDTQYF  TCRBV07-06       TCRBJ02-03 VLAWLYAAV            
+ 4 eXL27      CASGAGANVLTF     TCRBV25-01       TCRBJ02-06 LLDDFVEII,LLLDDFVEI  
+ 5 eEE226     CASSPGSGTGFHEQYF TCRBV07-06       TCRBJ02-07 AFLLFLVLI,FLAFLLFLV,…
+ 6 eXL30      CASSSGTGEAFF     TCRBV04-01       TCRBJ01-01 AFLLFLVLI,FLAFLLFLV,…
+ 7 eEE240     CSADDRGGEQYF     TCRBV20-X        TCRBJ02-07 AFLLFLVLI,FLAFLLFLV,…
+ 8 eXL31      CASSYLQGWETQYF   TCRBV06-02/06-03 TCRBJ02-05 AFLLFLVLI,FLAFLLFLV,…
+ 9 eEE224     CATTRGHYTDTQYF   TCRBV06-06       TCRBJ02-03 ELYSPIFLI,LYSPIFLIV,…
+10 ePD83      CASSLRLGLAYEQYF  TCRBV28-01       TCRBJ02-07 SEHDYQIGGYTEKW,YQIGG…</code></pre>
 </div>
 </div>
 <details>
@@ -1002,16 +1002,16 @@ <h4 class="anchored" data-anchor-id="the-peptide-details-data">The Peptide Detai
 <pre><code># A tibble: 10 × 6
    Experiment CDR3b             V_gene           J_gene `Amino Acids` n_peptides
    &lt;chr&gt;      &lt;chr&gt;             &lt;chr&gt;            &lt;chr&gt;  &lt;chr&gt;              &lt;dbl&gt;
- 1 eXL30      CASSQPPGEHNYGYTF  TCRBV04-01       TCRBJ… AEAELAKNVSL,…          2
- 2 eOX43      CASSLRGEQYF       TCRBV28-01       TCRBJ… KLSYGIATV              1
- 3 eEE240     CASSFGYEQYF       TCRBV12-03/12-04 TCRBJ… FLNGSCGSV              1
- 4 eQD110     CATSRETESSTDTQYF  TCRBV15-01       TCRBJ… HTTDPSFLGRY            1
- 5 eXL27      CASSFPGLAGEQFF    TCRBV11-02       TCRBJ… KPLEFGATSAAL           1
- 6 eEE224     CASSFLRPDRGNNEQFF TCRBV28-01       TCRBJ… TVLSFCAFA,VL…          2
- 7 eOX52      CASSLTSWDRAYGYTF  TCRBV05-08       TCRBJ… APKEIIFL,KEI…          2
- 8 eXL32      CASSLAGNQPQHF     TCRBV05-06       TCRBJ… FVCNLLLLFV,L…          3
- 9 eEE228     CASSPGPYTGELFF    TCRBV06-04       TCRBJ… IMLIIFWFSL,M…          2
-10 eOX43      CSVGLAGGPYEQYF    TCRBV29-01       TCRBJ… DFLEYHDVR,ED…          5</code></pre>
+ 1 eXL30      CASSLGLNTEAFF     TCRBV13-01       TCRBJ… HLVDFQVTI              1
+ 2 eEE226     CASSYPRTGYYGYTF   TCRBV06-02/06-03 TCRBJ… APKEIIFL,KEI…          2
+ 3 eOX54      CSARAGVRETQYF     TCRBV20-X        TCRBJ… NYLYRLFRK,NY…          2
+ 4 eHO130     CATGFRPNTEAFF     TCRBV28-01       TCRBJ… FVDGVPFVV              1
+ 5 eOX46      CASSSPGGTFYNEQFF  TCRBV09-01       TCRBJ… FLNRFTTTL              1
+ 6 eAV88      CASSFTGGILDTQYF   TCRBV05-04       TCRBJ… ELYSPIFLI,LY…          5
+ 7 eXL37      CASSVFEGTGGNSPLHF TCRBV09-01       TCRBJ… GMEVTPSGTWL,…          4
+ 8 eHO141     CASSQQPGQGLNYGYTF TCRBV04-01       TCRBJ… AYKTFPPTEPK,…          2
+ 9 eEE226     CASSYMGGAASTDTQYF TCRBV06-X        TCRBJ… KLSYGIATV              1
+10 eEE226     CASSEASLNTEAFF    TCRBV06-01       TCRBJ… LPAADLDDF              1</code></pre>
 </div>
 </div>
 <ul>
@@ -1060,16 +1060,16 @@ <h4 class="anchored" data-anchor-id="the-peptide-details-data">The Peptide Detai
 <pre><code># A tibble: 10 × 18
    Experiment CDR3b        V_gene J_gene peptide_1 peptide_2 peptide_3 peptide_4
    &lt;chr&gt;      &lt;chr&gt;        &lt;chr&gt;  &lt;chr&gt;  &lt;chr&gt;     &lt;chr&gt;     &lt;chr&gt;     &lt;chr&gt;    
- 1 eEE240     CASSDSSGELFF TCRBV… TCRBJ… FVCNLLLL… LLFVTVYS… TVYSHLLLV &lt;NA&gt;     
- 2 eEE228     CASSHRTGRVL… TCRBV… TCRBJ… APAHISTI  LIVNSVLL… LLFLAFVV… SVLLFLAFV
- 3 eEE224     CASSLLAGGHN… TCRBV… TCRBJ… AFLLFLVLI FLAFLLFLV FYLCFLAFL FYLCFLAF…
- 4 eQD111     CASSPDGNTEA… TCRBV… TCRBJ… HTTDPSFL… &lt;NA&gt;      &lt;NA&gt;      &lt;NA&gt;     
- 5 eEE228     CASSFAGTDYE… TCRBV… TCRBJ… FLNGSCGSV &lt;NA&gt;      &lt;NA&gt;      &lt;NA&gt;     
- 6 eMR14      CASSLDDRGLP… TCRBV… TCRBJ… KAYNVTQAF &lt;NA&gt;      &lt;NA&gt;      &lt;NA&gt;     
- 7 eXL31      CSARDSQLAGY… TCRBV… TCRBJ… FLYIIKLI… FLYIIKLV… LYIIKLIFL LYIIKLIF…
- 8 eQD129     CASSDKPGPGA… TCRBV… TCRBJ… ALSKGVHFV &lt;NA&gt;      &lt;NA&gt;      &lt;NA&gt;     
- 9 ePD83      CASSIGQGFAN… TCRBV… TCRBJ… SEHDYQIG… YQIGGYTEK YQIGGYTE… &lt;NA&gt;     
-10 eXL30      CASSDIRTEVY… TCRBV… TCRBJ… RQLLFVVEV &lt;NA&gt;      &lt;NA&gt;      &lt;NA&gt;     
+ 1 eQD111     CASTLAGGNEQ… TCRBV… TCRBJ… LSPRWYFYY SPRWYFYYL &lt;NA&gt;      &lt;NA&gt;     
+ 2 eQD132     CASGVGQYEQYF TCRBV… TCRBJ… AEIRASANL AEIRASAN… ASANLAATK &lt;NA&gt;     
+ 3 eMR16      CAISDPTSGTD… TCRBV… TCRBJ… FLLNKEMYL &lt;NA&gt;      &lt;NA&gt;      &lt;NA&gt;     
+ 4 eXL31      CSAPRGGVSSY… TCRBV… TCRBJ… AFLLFLVLI FLAFLLFLV FYLCFLAFL FYLCFLAF…
+ 5 eOX46      CASSYPDSTGE… TCRBV… TCRBJ… HPLADNKF… SPFHPLAD… &lt;NA&gt;      &lt;NA&gt;     
+ 6 ePD83      CASSKGSGGPS… TCRBV… TCRBJ… SEHDYQIG… YQIGGYTEK YQIGGYTE… &lt;NA&gt;     
+ 7 eHH173     CASEIWLAGPR… TCRBV… TCRBJ… AFLLFLVLI FLAFLLFLV FYLCFLAFL FYLCFLAF…
+ 8 eOX46      CSARGGTSGSH… TCRBV… TCRBJ… APAHISTI  LIVNSVLL… LLFLAFVV… SVLLFLAFV
+ 9 eQD114     RASSRLEHRGA… TCRBV… TCRBJ… HTTDPSFL… &lt;NA&gt;      &lt;NA&gt;      &lt;NA&gt;     
+10 eXL30      CASSQRTGADE… TCRBV… TCRBJ… LPAADLDDF &lt;NA&gt;      &lt;NA&gt;      &lt;NA&gt;     
 # ℹ 10 more variables: peptide_5 &lt;chr&gt;, peptide_6 &lt;chr&gt;, peptide_7 &lt;chr&gt;,
 #   peptide_8 &lt;chr&gt;, peptide_9 &lt;chr&gt;, peptide_10 &lt;chr&gt;, peptide_11 &lt;chr&gt;,
 #   peptide_12 &lt;chr&gt;, peptide_13 &lt;chr&gt;, n_peptides &lt;dbl&gt;</code></pre>
@@ -1108,18 +1108,18 @@ <h4 class="anchored" data-anchor-id="the-peptide-details-data">The Peptide Detai
 <span id="cb20-2"><a href="#cb20-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">sample_n</span>(<span class="dv">10</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code># A tibble: 10 × 7
-   Experiment CDR3b             V_gene       J_gene n_peptides peptide_n peptide
-   &lt;chr&gt;      &lt;chr&gt;             &lt;chr&gt;        &lt;chr&gt;       &lt;dbl&gt; &lt;chr&gt;     &lt;chr&gt;  
- 1 eOX54      CASSAETRMGNTIYF   TCRBV21-01   TCRBJ…          3 peptide_9 &lt;NA&gt;   
- 2 eMR12      CASGSWTAGYEQYF    TCRBV03-01/… TCRBJ…          2 peptide_5 &lt;NA&gt;   
- 3 eEE224     CASSEEQADNEQFF    TCRBV10-01   TCRBJ…          2 peptide_9 &lt;NA&gt;   
- 4 eOX46      CSASGGSKDTQYF     TCRBV20-X    TCRBJ…          2 peptide_9 &lt;NA&gt;   
- 5 eAM13      CASSYGGGQETQYF    TCRBV06-05   TCRBJ…          3 peptide_4 &lt;NA&gt;   
- 6 eQD125     CASSAGDASGYTF     TCRBV09-01   TCRBJ…          1 peptide_8 &lt;NA&gt;   
- 7 eOX54      CASGGYEQYF        TCRBV12-03/… TCRBJ…          1 peptide_… &lt;NA&gt;   
- 8 ePD83      CASSMTSGRSSYNEQFF TCRBV19-01   TCRBJ…          3 peptide_9 &lt;NA&gt;   
- 9 eEE226     CASSLSKDRSYNEQFF  TCRBV13-01   TCRBJ…          1 peptide_8 &lt;NA&gt;   
-10 eQD108     CSVVRGGDTAPDTQYF  TCRBV29-01   TCRBJ…          3 peptide_… &lt;NA&gt;   </code></pre>
+   Experiment CDR3b                  V_gene  J_gene n_peptides peptide_n peptide
+   &lt;chr&gt;      &lt;chr&gt;                  &lt;chr&gt;   &lt;chr&gt;       &lt;dbl&gt; &lt;chr&gt;     &lt;chr&gt;  
+ 1 eEE224     CASSPTQTKDVHPYEQYF     TCRBV0… TCRBJ…          5 peptide_4 VQELYS…
+ 2 eXL31      CSARDMEQTQYF           TCRBV2… TCRBJ…          1 peptide_… &lt;NA&gt;   
+ 3 eJL164     CASSDQENRALAGGRRPDTQYF TCRBV0… TCRBJ…          4 peptide_3 YPDKVF…
+ 4 eOX49      CSATKLAGPTSNEQFF       TCRBV2… TCRBJ…         11 peptide_… &lt;NA&gt;   
+ 5 eMR25      CASSWTSGALQNIQYF       TCRBV0… TCRBJ…          1 peptide_1 KLWAQC…
+ 6 eOX54      CASSHHGLAGVRQYNEQFF    TCRBV1… TCRBJ…         11 peptide_… &lt;NA&gt;   
+ 7 eQD137     CASTRTQIRDRVDTEAFF     TCRBV1… TCRBJ…          1 peptide_6 &lt;NA&gt;   
+ 8 eEE224     CASSLGHSYEQYF          TCRBV0… TCRBJ…         11 peptide_5 IDFYLC…
+ 9 eHO136     CASSLGGVYNEQFF         TCRBV0… TCRBJ…          1 peptide_4 &lt;NA&gt;   
+10 eQD137     CASSITGTYEQYF          TCRBV1… TCRBJ…          2 peptide_… &lt;NA&gt;   </code></pre>
 </div>
 </div>
 <ul>
@@ -1135,18 +1135,18 @@ <h4 class="anchored" data-anchor-id="the-peptide-details-data">The Peptide Detai
 <span id="cb22-2"><a href="#cb22-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">sample_n</span>(<span class="dv">10</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code># A tibble: 10 × 5
-   Experiment CDR3b             V_gene     J_gene     peptide    
-   &lt;chr&gt;      &lt;chr&gt;             &lt;chr&gt;      &lt;chr&gt;      &lt;chr&gt;      
- 1 eGK111     CASSYEGMLGYTF     TCRBV06-05 TCRBJ01-02 HTTDPSFLGRY
- 2 eXL30      CASSSAGPNYEQYF    TCRBV27-01 TCRBJ02-07 AFPFTIYSL  
- 3 eEE240     CSASGVSGRYQETQYF  TCRBV20-01 TCRBJ02-05 FLAFLLFLV  
- 4 eOX49      CSATQRDRVENEQFF   TCRBV20-X  TCRBJ02-01 LIDFYLCFL  
- 5 eOX54      CASSGRDSVTPGELFF  TCRBV09-01 TCRBJ02-02 IDFYLCFLAF 
- 6 eEE226     CASSPTPGQGSYEQYF  TCRBV05-04 TCRBJ02-07 FYLCFLAFL  
- 7 eHO136     CSVEDRNTGELFF     TCRBV29-01 TCRBJ02-02 YLQPRTFLL  
- 8 ePD84      CASSPQGSTDPNYGYTF TCRBV07-09 TCRBJ01-02 GLEAPFLYLY 
- 9 eHH175     CASSLPLSYEQYF     TCRBV28-01 TCRBJ02-07 AFLLFLVLI  
-10 eXL31      CSADGEGDGTDTQYF   TCRBV20-X  TCRBJ02-03 AFLLFLVLI  </code></pre>
+   Experiment CDR3b             V_gene     J_gene     peptide   
+   &lt;chr&gt;      &lt;chr&gt;             &lt;chr&gt;      &lt;chr&gt;      &lt;chr&gt;     
+ 1 eJL158     CASSERVIRRGQEAFF  TCRBV25-01 TCRBJ01-01 KLWAQCVQL 
+ 2 eXL37      CASSPDRDNQPQHF    TCRBV27-01 TCRBJ01-05 AFLLFLVLI 
+ 3 eXL30      CASSQDITGELFF     TCRBV04-03 TCRBJ02-02 WPVTLACFVL
+ 4 eOX52      CSAQWASGGAGTGELFF TCRBV20-X  TCRBJ02-02 MIELSLIDFY
+ 5 eOX52      CASSLAGVVEQYF     TCRBV07-08 TCRBJ02-07 APKEIIFL  
+ 6 eEE224     CSARQTRTGAWEQYF   TCRBV20-X  TCRBJ02-07 IDFYLCFLAF
+ 7 eQD137     CASSLGTGAMETQYF   TCRBV27-01 TCRBJ02-05 VYFLQSINF 
+ 8 eAV88      CSAETSGSSNEQFF    TCRBV20-X  TCRBJ02-01 IDFYLCFLAF
+ 9 eOX46      CASSGLAGGRSQFF    TCRBV12-X  TCRBJ02-01 YLCFLAFLL 
+10 eXL30      CASSLGAYEGVLTF    TCRBV12-X  TCRBJ02-06 MIELSLIDFY</code></pre>
 </div>
 </div>
 <ul>
@@ -1186,18 +1186,18 @@ <h4 class="anchored" data-anchor-id="the-peptide-details-data">The Peptide Detai
 <span id="cb24-2"><a href="#cb24-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">sample_n</span>(<span class="dv">10</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code># A tibble: 10 × 7
-   Experiment CDR3b             V_gene          J_gene peptide k_CDR3b k_peptide
-   &lt;chr&gt;      &lt;chr&gt;             &lt;chr&gt;           &lt;chr&gt;  &lt;chr&gt;     &lt;int&gt;     &lt;int&gt;
- 1 eLH47      CASSLGTDTQYF      TCRBV05-01      TCRBJ… SVLLFL…      12         9
- 2 eEE240     CASSLWGYNEQFF     TCRBV05-01      TCRBJ… LYIIKL…      13        10
- 3 eEE224     CASSGDGTYEQYF     TCRBV02-01      TCRBJ… SINFVR…      13        10
- 4 eXL30      CASSLYGTSGGQETQYF TCRBV03-01/03-… TCRBJ… RFLYII…      17        10
- 5 eOX52      CASSGLAGASTDTQYF  TCRBV06-X       TCRBJ… AFLLFL…      16         9
- 6 eEE228     CASSQKCLAAYEQYF   TCRBV07-07      TCRBJ… LWPVTL…      15         9
- 7 eOX52      CASSQAGPGTEAFF    TCRBV04-01      TCRBJ… YEDFLE…      14        14
- 8 eMR25      CASSHGGSSYNEQFF   TCRBV06-02/06-… TCRBJ… QWNLVI…      15        10
- 9 eEE226     CASSLGDEQYF       TCRBV07-09      TCRBJ… YLCFLA…      11         9
-10 eAV88      CASSFQGLASNEQFF   TCRBV27-01      TCRBJ… FLAFLL…      15         9</code></pre>
+   Experiment CDR3b            V_gene     J_gene     peptide   k_CDR3b k_peptide
+   &lt;chr&gt;      &lt;chr&gt;            &lt;chr&gt;      &lt;chr&gt;      &lt;chr&gt;       &lt;int&gt;     &lt;int&gt;
+ 1 eHO136     CSARDFVQSYGYTF   TCRBV20-X  TCRBJ01-02 CNDPFLGV…      14        10
+ 2 eEE240     CSAGGTSDYGYTF    TCRBV20-X  TCRBJ01-02 YLCFLAFLL      13         9
+ 3 eXL27      CASSPRDRPVYEQYF  TCRBV18-01 TCRBJ02-07 RFLYIIKL…      15        10
+ 4 ePD76      CASSHRLAGFPYEQYF TCRBV11-02 TCRBJ02-07 SLIDFYLC…      16        10
+ 5 eOX46      CSVHGQHTEAFF     TCRBV29-01 TCRBJ01-01 LLFLVLIML      12         9
+ 6 eXL37      CSATGGGQPQHF     TCRBV20-X  TCRBJ01-05 YLCFLAFLL      12         9
+ 7 eEE228     CASSLPGSEAFF     TCRBV07-09 TCRBJ01-01 MIELSLID…      12        10
+ 8 eEE240     CASSRGAGELFF     TCRBV04-01 TCRBJ02-02 SINFVRII…      12        10
+ 9 eAV91      CASSEGGNQPQHF    TCRBV10-02 TCRBJ01-05 HVTFFIYNK      13         9
+10 eXL30      CSLMGSSYNSPLHF   TCRBV20-X  TCRBJ01-06 AFPFTIYSL      14         9</code></pre>
 </div>
 </div>
 <ul>
@@ -1226,18 +1226,18 @@ <h4 class="anchored" data-anchor-id="the-peptide-details-data">The Peptide Detai
 <span id="cb26-2"><a href="#cb26-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">sample_n</span>(<span class="dv">10</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code># A tibble: 10 × 7
-   Experiment CDR3b            V_gene           J_gene peptide k_CDR3b k_peptide
-   &lt;chr&gt;      &lt;chr&gt;            &lt;chr&gt;            &lt;chr&gt;  &lt;chr&gt;     &lt;int&gt;     &lt;int&gt;
- 1 eEE224     CSAVRGLSSYEQYF   TCRBV20-X        TCRBJ… NVFAFP…      14         9
- 2 eXL27      CASGNEQFF        TCRBV12-03/12-04 TCRBJ… NVFAFP…       9        10
- 3 eEE228     CASSQRWAVTYNEQFF TCRBV21-01       TCRBJ… YLYALV…      16         9
- 4 eEE226     CASSDLPGGVYEQYF  TCRBV10-02       TCRBJ… FLAFLL…      15         9
- 5 eOX54      CASSPAGGWVTDTQYF TCRBV07-03       TCRBJ… KLSYGI…      16         9
- 6 eQD110     CASMAEASNTGELFF  TCRBV19-01       TCRBJ… YFLQSI…      15        10
- 7 ePD82      CASSDLASETQYF    TCRBV25-01       TCRBJ… INVFAF…      13        10
- 8 eXL27      CASSFLGPAYEQYF   TCRBV12-X        TCRBJ… WLLWPV…      14         9
- 9 eEE226     CASSIDQENTEAFF   TCRBV19-01       TCRBJ… LIIMRT…      14         9
-10 eXL30      CASSPVHPGNTEAFF  TCRBV27-01       TCRBJ… TLACFV…      15        10</code></pre>
+   Experiment CDR3b             V_gene     J_gene     peptide  k_CDR3b k_peptide
+   &lt;chr&gt;      &lt;chr&gt;             &lt;chr&gt;      &lt;chr&gt;      &lt;chr&gt;      &lt;int&gt;     &lt;int&gt;
+ 1 eHH175     CSADIAGAAYEQYF    TCRBV20-X  TCRBJ02-07 SLIDFYL…      14        10
+ 2 eEE228     CASSEGLAGEEEQFF   TCRBV11-01 TCRBJ02-01 HLVDFQV…      15         9
+ 3 eAV93      CASSAGESNEQYF     TCRBV07-09 TCRBJ02-07 FLWLLWP…      13        10
+ 4 eOX52      CSVTLASEQYF       TCRBV29-01 TCRBJ02-07 FYLCFLA…      11         9
+ 5 eOX52      CASSSHPASGDSNIQYF TCRBV07-08 TCRBJ02-04 LYSPIFL…      17         9
+ 6 eQD123     CASSPGEGSTGELFF   TCRBV09-01 TCRBJ02-02 QSINFVR…      15         9
+ 7 eLH47      CASSSPLEGVRYGYTF  TCRBV07-09 TCRBJ01-02 TLKKRWQ…      16         9
+ 8 eQD109     CASIPAPRQGLDGYTF  TCRBV06-X  TCRBJ01-02 NVFAFPF…      16         9
+ 9 eLH46      CASSGDSYEQYF      TCRBV09-01 TCRBJ02-07 IMRTFKV…      12         9
+10 eHH175     CASSVEGISLYETQYF  TCRBV02-01 TCRBJ02-05 FLAFLLF…      16         9</code></pre>
 </div>
 </div>
 </section>
@@ -1251,16 +1251,16 @@ <h4 class="anchored" data-anchor-id="creating-one-data-set-from-two-data-sets">C
 <pre><code># A tibble: 10 × 11
    Experiment Cohort        Age Gender Race  A1    A2    B1    B2    C1    C2   
    &lt;chr&gt;      &lt;chr&gt;       &lt;dbl&gt; &lt;chr&gt;  &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; &lt;chr&gt;
- 1 ePD82      COVID-19-C…    60 F      &lt;NA&gt;  A*26… A*33… B*40… B*44… C*08… C*14…
- 2 eHH173     Healthy (N…    50 M      White A*02… A*03… B*35… B*44… C*04… C*05…
- 3 eQD121     COVID-19-C…    38 M      &lt;NA&gt;  A*01… A*24… B*18… B*57… C*05… C*07…
- 4 eQD108     COVID-19-C…    NA &lt;NA&gt;   &lt;NA&gt;  A*11… A*68… B*08… B*52… C*07… C*12…
- 5 eEE228     Healthy (N…    45 M      White A*02… A*02… B*35… B*44… C*04… C*05…
- 6 eQD127     COVID-19-C…    61 F      &lt;NA&gt;  A*02… A*03… B*27… B*35… C*02… C*04…
- 7 ePD84      Healthy (N…    29 F      Asian A*02… A*02… B*13… B*44… C*03… C*07…
- 8 eQD114     COVID-19-C…    73 M      &lt;NA&gt;  A*01… A*24… B*08… B*41… C*07… C*17…
- 9 eLH50      COVID-19-C…    28 M      &lt;NA&gt;  A*02… A*26… B*15… B*27… C*01… C*03…
-10 eHO124     Healthy (N…    62 M      &lt;NA&gt;  A*02… A*03… B*07… B*44… C*07… C*07…</code></pre>
+ 1 eHO136     COVID-19-C…    51 M      Hisp… ""    ""    ""    ""    ""    ""   
+ 2 eHH175     Healthy (N…    28 M      White "A*0… "A*2… "B*0… "B*4… "C*0… "C*1…
+ 3 eQD115     COVID-19-C…    48 M      &lt;NA&gt;  "A*0… "A*0… "B*0… "B*4… "C*0… "C*0…
+ 4 ePD73      Healthy (N…    37 F      White "A*0… "A*0… "B*1… "B*4… "C*0… "C*0…
+ 5 eHO127     COVID-19-C…    28 M      &lt;NA&gt;  "A*2… "A*2… "B*4… "B*5… "C*0… "C*1…
+ 6 eHO131     COVID-19-C…    58 F      &lt;NA&gt;  "A*0… "A*0… "B*1… "B*5… "C*0… "C*1…
+ 7 eMR16      COVID-19-C…    NA &lt;NA&gt;   &lt;NA&gt;  "A*0… "A*0… "B*1… "B*1… "C*0… "C*0…
+ 8 eLH44      COVID-19-C…    61 F      &lt;NA&gt;  "A*0… "A*6… "B*1… "B*3… "C*0… "C*1…
+ 9 eQD129     COVID-19-C…    60 F      White "A*0… "A*0… "B*4… "B*5… "C*0… "C*0…
+10 eQD126     COVID-19-C…    54 F      &lt;NA&gt;  "A*0… "A*0… "B*0… "B*0… "C*0… "C*0…</code></pre>
 </div>
 </div>
 <p>Remember you can scroll in the data.</p>
@@ -1279,18 +1279,18 @@ <h4 class="anchored" data-anchor-id="creating-one-data-set-from-two-data-sets">C
 <span id="cb30-2"><a href="#cb30-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">sample_n</span>(<span class="dv">10</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code># A tibble: 10 × 7
-   Experiment Cohort                        Age Gender Race       Gene  Allele  
-   &lt;chr&gt;      &lt;chr&gt;                       &lt;dbl&gt; &lt;chr&gt;  &lt;chr&gt;      &lt;chr&gt; &lt;chr&gt;   
- 1 eQD119     COVID-19-Convalescent          51 M      &lt;NA&gt;       B1    "B*08:0…
- 2 eMR14      COVID-19-Convalescent          NA &lt;NA&gt;   &lt;NA&gt;       B1    "B*07:0…
- 3 eDH107     COVID-19-Convalescent          72 F      &lt;NA&gt;       A1    "A*03:0…
- 4 eHO141     COVID-19-Acute                 NA &lt;NA&gt;   &lt;NA&gt;       C1    ""      
- 5 eQD131     COVID-19-Exposed               NA &lt;NA&gt;   &lt;NA&gt;       C1    "C*02:0…
- 6 eNL192     COVID-19-Convalescent          NA &lt;NA&gt;   &lt;NA&gt;       A2    ""      
- 7 eJL147     Healthy (No known exposure)    40 M      Mixed Race B1    "B*07:0…
- 8 eEE240     Healthy (No known exposure)    23 M      White      B1    "B*40:0…
- 9 eNL187     COVID-19-Convalescent          NA &lt;NA&gt;   &lt;NA&gt;       C1    ""      
-10 eJL147     Healthy (No known exposure)    40 M      Mixed Race A2    "A*11:0…</code></pre>
+   Experiment Cohort                        Age Gender Race  Gene  Allele      
+   &lt;chr&gt;      &lt;chr&gt;                       &lt;dbl&gt; &lt;chr&gt;  &lt;chr&gt; &lt;chr&gt; &lt;chr&gt;       
+ 1 eHO129     COVID-19-Convalescent          66 F      Asian A1    "A*24:02:01"
+ 2 eLH51      COVID-19-Convalescent          55 M      Asian C2    "C*12:04:02"
+ 3 eQD138     COVID-19-Convalescent          NA &lt;NA&gt;   &lt;NA&gt;  B2    "B*44:03:01"
+ 4 eXL31      Healthy (No known exposure)    28 M      White B2    "B*44:03"   
+ 5 eLH57      COVID-19-Convalescent          NA &lt;NA&gt;   &lt;NA&gt;  B1    "B*07:02:01"
+ 6 ePD83      Healthy (No known exposure)    29 F      Asian C1    "C*03:04"   
+ 7 eHO138     COVID-19-B-Non-Acute           NA &lt;NA&gt;   &lt;NA&gt;  B1    ""          
+ 8 eJL148     COVID-19-Convalescent          41 F      &lt;NA&gt;  C1    "C*03:03:01"
+ 9 eXL27      Healthy (No known exposure)    24 M      White C1    "C*03:04"   
+10 eXL32      Healthy (No known exposure)    37 F      White C2    "C*04:01"   </code></pre>
 </div>
 </div>
 <p>Remember, what we are aiming for here, is to create one data set from two. So:</p>
@@ -1306,18 +1306,18 @@ <h4 class="anchored" data-anchor-id="creating-one-data-set-from-two-data-sets">C
 <span id="cb32-2"><a href="#cb32-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">sample_n</span>(<span class="dv">10</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code># A tibble: 10 × 2
-   Experiment Allele      
-   &lt;chr&gt;      &lt;chr&gt;       
- 1 eJL151     "A*68:01:01"
- 2 eJL143     "B*14:01"   
- 3 eJL153     "B*15:01:01"
- 4 eLH53      "B*57:01:01"
- 5 eQD120     "A*31:01:02"
- 6 eHO131     "B*51:01:01"
- 7 eNL189     ""          
- 8 eOX49      "A*26:01"   
- 9 eQD129     "C*06:02:01"
-10 eEE240     "A*02:01"   </code></pre>
+   Experiment Allele    
+   &lt;chr&gt;      &lt;chr&gt;     
+ 1 eAV93      B*35:01   
+ 2 eHO134     A*01:01:01
+ 3 eQD127     C*04:01:01
+ 4 eQD114     B*08:01:01
+ 5 eXL30      B*39:01   
+ 6 eJL152     C*07:02:01
+ 7 eJL149     B*44:03:01
+ 8 eQD112     B*07:02:01
+ 9 eHO131     A*02:01:01
+10 eJL164     A*02:01:01</code></pre>
 </div>
 </div>
 <p>Use the <code>View()</code> function again, to look at the <code>meta_data</code>. Notice something? Some alleles are e.g.&nbsp;<code>A*11:01</code>, whereas others are <code>B*51:01:02</code>. You can find information on why, by visiting <a href="http://hla.alleles.org/nomenclature/naming.html">Nomenclature for Factors of the HLA System</a>.</p>
@@ -1343,16 +1343,16 @@ <h4 class="anchored" data-anchor-id="creating-one-data-set-from-two-data-sets">C
 <pre><code># A tibble: 10 × 3
    Experiment Allele     Allele_F_1_2
    &lt;chr&gt;      &lt;chr&gt;      &lt;chr&gt;       
- 1 eXL32      C*04:01    C*04:01     
- 2 eJL151     A*24:02:01 A*24:02     
- 3 eDH105     A*24:02:01 A*24:02     
- 4 eJL158     A*24:02:01 A*24:02     
- 5 eEE224     B*40:01    B*40:01     
- 6 eJL161     C*07:01:01 C*07:01     
- 7 eQD108     A*68:01:02 A*68:01     
- 8 eJL160     A*02:01:01 A*02:01     
- 9 ePD84      C*07:06    C*07:06     
-10 eJL149     C*16:01:01 C*16:01     </code></pre>
+ 1 eQD127     C*02:02:02 C*02:02     
+ 2 eQD136     B*15:01:01 B*15:01     
+ 3 eQD121     C*07:01:01 C*07:01     
+ 4 eLH41      B*13:02:01 B*13:02     
+ 5 eLH59      A*02:01:01 A*02:01     
+ 6 eLH47      C*07:01:01 C*07:01     
+ 7 eLH42      A*23:01:01 A*23:01     
+ 8 eQD113     A*11:01:01 A*11:01     
+ 9 eLH48      A*03:01:01 A*03:01     
+10 eQD121     B*57:01:01 B*57:01     </code></pre>
 </div>
 </div>
 <p>The asterisk, i.e.&nbsp;<code>*</code> is a rather annoying character because of ambiguity, so:</p>
@@ -1369,16 +1369,16 @@ <h4 class="anchored" data-anchor-id="creating-one-data-set-from-two-data-sets">C
 <pre><code># A tibble: 10 × 2
    Experiment Allele
    &lt;chr&gt;      &lt;chr&gt; 
- 1 eLH49      A03:01
- 2 eMR22      A03:01
- 3 eQD128     C08:01
- 4 eQD108     B52:01
- 5 eEE226     C04:01
- 6 eLH47      A01:01
- 7 eEE228     C04:01
- 8 eJL160     A02:01
- 9 eQD108     C07:01
-10 eEE226     A01:01</code></pre>
+ 1 eDH105     C08:01
+ 2 eQD131     C02:02
+ 3 eQD132     C07:02
+ 4 eJL157     B07:02
+ 5 eJL151     B40:01
+ 6 eHO129     C08:01
+ 7 eOX46      C05:01
+ 8 ePD86      A02:01
+ 9 ePD80      A02:01
+10 eJL154     C04:03</code></pre>
 </div>
 </div>
 <details>
@@ -1403,18 +1403,18 @@ <h4 class="anchored" data-anchor-id="creating-one-data-set-from-two-data-sets">C
 <span id="cb38-2"><a href="#cb38-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">sample_n</span>(<span class="dv">10</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code># A tibble: 10 × 7
-   Experiment CDR3b            V_gene           J_gene peptide k_CDR3b k_peptide
-   &lt;chr&gt;      &lt;chr&gt;            &lt;chr&gt;            &lt;chr&gt;  &lt;chr&gt;     &lt;int&gt;     &lt;int&gt;
- 1 eXL30      CASSGLAVDEQFF    TCRBV06-02/06-03 TCRBJ… LIDFYL…      13         9
- 2 ePD83      CASSQDMGIYNEQFF  TCRBV03-01/03-02 TCRBJ… YQIGGY…      15         9
- 3 eOX49      CASSQSLDPDAYEQYF TCRBV07-09       TCRBJ… LIDFYL…      16         9
- 4 eXL31      CSLDENYGYTF      TCRBV20-01       TCRBJ… FYLCFL…      11        10
- 5 eQD125     CASSLDVGSPLHF    TCRBV11-02       TCRBJ… LQIPFA…      13         9
- 6 eXL32      CASRGDRGRGYGYTF  TCRBV25-01       TCRBJ… LIDFYL…      15         9
- 7 eXL30      CSASTNEGGQETQYF  TCRBV20-X        TCRBJ… MIELSL…      15        10
- 8 eEE226     CASSHNIARGPYEQYF TCRBV14-01       TCRBJ… FYLCFL…      16        10
- 9 eAV88      CASSLRTDNSPLHF   TCRBV06-06       TCRBJ… FYLCFL…      14         9
-10 eOX54      CASSQGPALHF      TCRBV03-01/03-02 TCRBJ… GYINVF…      11        10</code></pre>
+   Experiment CDR3b             V_gene     J_gene     peptide  k_CDR3b k_peptide
+   &lt;chr&gt;      &lt;chr&gt;             &lt;chr&gt;      &lt;chr&gt;      &lt;chr&gt;      &lt;int&gt;     &lt;int&gt;
+ 1 eLH47      CASSLAGGPAGELFF   TCRBV05-01 TCRBJ02-02 LSPRWYF…      15         9
+ 2 ePD83      CASSARSTGELFF     TCRBV19-01 TCRBJ02-02 YQIGGYT…      13        10
+ 3 eOX52      CASSFGTITAQETQYF  TCRBV28-01 TCRBJ02-05 NYNYLYR…      16         9
+ 4 eEE228     CASSLEEGVLGYEQYF  TCRBV07-02 TCRBJ02-07 RNPANNA…      16        10
+ 5 eOX56      CASSLDSYTSTDTQYF  TCRBV07-06 TCRBJ02-03 MIELSLI…      16        10
+ 6 eOX52      CASSLEVLPRETQYF   TCRBV11-03 TCRBJ02-05 FYLCFLA…      15         9
+ 7 eEE224     CASSLTGMNTEAFF    TCRBV27-01 TCRBJ01-01 VTPSGTW…      14        10
+ 8 eEE224     CASSLTSGTSGNQPQHF TCRBV05-06 TCRBJ01-05 LIDFYLC…      17         9
+ 9 eEE224     CSASIEGQETQYF     TCRBV20-01 TCRBJ02-05 YLCFLAF…      13         9
+10 ePD76      CASSQRWGADTEAFF   TCRBV04-01 TCRBJ01-01 FVCNLLL…      15        10</code></pre>
 </div>
 </div>
 <ul>
@@ -1444,18 +1444,18 @@ <h4 class="anchored" data-anchor-id="creating-one-data-set-from-two-data-sets">C
 <span id="cb40-2"><a href="#cb40-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">sample_n</span>(<span class="dv">10</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code># A tibble: 10 × 8
-   Experiment CDR3b               V_gene J_gene peptide k_CDR3b k_peptide Allele
-   &lt;chr&gt;      &lt;chr&gt;               &lt;chr&gt;  &lt;chr&gt;  &lt;chr&gt;     &lt;int&gt;     &lt;int&gt; &lt;chr&gt; 
- 1 eXL30      CASSYFSGETQYF       TCRBV… TCRBJ… TESIVR…      13        10 B35:02
- 2 eOX52      CASSQEGTGTYEQYF     TCRBV… TCRBJ… NATRFA…      15         9 A24:02
- 3 eEE228     CASSLMQGATEAFF      TCRBV… TCRBJ… LWLLWP…      14         9 C04:01
- 4 ePD85      CASSAGQGLIYEQYF     TCRBV… TCRBJ… YQIGGY…      15         9 A29:01
- 5 eHH173     CASSNPGWAAGGPVIGTE… TCRBV… TCRBJ… YIIKLI…      21         9 C05:01
- 6 eEE226     CASKEGRGQPPYEQYF    TCRBV… TCRBJ… RNPANN…      16        10 C04:01
- 7 eXL37      CASSPTSGARREQYF     TCRBV… TCRBJ… DFLEYH…      15         9 B40:01
- 8 eQD110     CASRPGIHTIYF        TCRBV… TCRBJ… FLQSIN…      12        10 A11:01
- 9 eXL31      CSATGTSGFEQYF       TCRBV… TCRBJ… AFLLFL…      13         9 A29:02
-10 eEE226     CASSSEGQGRAYEQYF    TCRBV… TCRBJ… TFKVSI…      16         9 B35:02</code></pre>
+   Experiment CDR3b              V_gene  J_gene peptide k_CDR3b k_peptide Allele
+   &lt;chr&gt;      &lt;chr&gt;              &lt;chr&gt;   &lt;chr&gt;  &lt;chr&gt;     &lt;int&gt;     &lt;int&gt; &lt;chr&gt; 
+ 1 eEE224     CASSGATSGITGELFF   TCRBV0… TCRBJ… MIELSL…      16        10 C03:04
+ 2 eEE228     CSARDYMVASGKGYEQYF TCRBV2… TCRBJ… FPPTSF…      18         9 C04:01
+ 3 eEE224     CASSTTGGGEQFF      TCRBV0… TCRBJ… SVLLFL…      13         9 B27:05
+ 4 eLH51      CASPLGTGSSYEQYF    TCRBV2… TCRBJ… APSASA…      15        10 A34:01
+ 5 eOX54      CASNLRLTGNSPLHF    TCRBV2… TCRBJ… SLIDFY…      15        10 B15:03
+ 6 eOX52      CASSGGDTYEQYF      TCRBV1… TCRBJ… VQELYS…      13         9 B40:01
+ 7 eEE226     CSVYLALAKNIQYF     TCRBV2… TCRBJ… FLWLLW…      14         9 C07:02
+ 8 eLH51      CASSESILGYNEQFF    TCRBV1… TCRBJ… SLIDFY…      15        10 C12:04
+ 9 eXL30      CASSLNEQYF         TCRBV0… TCRBJ… FYLCFL…      10        10 A01:01
+10 eXL31      CASSLEDYNEQFF      TCRBV1… TCRBJ… INFVRI…      13         9 B44:03</code></pre>
 </div>
 </div>
 </section>
diff --git a/docs/lab06_files/figure-html/unnamed-chunk-27-1.png b/docs/lab06_files/figure-html/unnamed-chunk-27-1.png
index 9ff9f78..dbf2bd6 100644
Binary files a/docs/lab06_files/figure-html/unnamed-chunk-27-1.png and b/docs/lab06_files/figure-html/unnamed-chunk-27-1.png differ
diff --git a/docs/lab10.html b/docs/lab10.html
index 1cbd99c..b695763 100644
--- a/docs/lab10.html
+++ b/docs/lab10.html
@@ -305,7 +305,8 @@ <h2 class="anchored" data-anchor-id="schedule">Schedule</h2>
 </section>
 <section id="sec-symposium" class="level2">
 <h2 class="anchored" data-anchor-id="sec-symposium">Mini Symposium: Applications of R for Bio Data Science in Industry</h2>
-<p><em>This mini symposium on Applications of R for Bio Data Science in Industry aims to give students a glimpse into how modern bio data science is transforming value creation in the pharmaceutical industry and healthcare sector.</em></p>
+<p><em>This mini symposium on Applications of R for Bio Data Science in Industry offers participants a glimpse into how modern Bio Data Science is transforming value creation in the pharmaceutical and healthcare sectors.</em></p>
+<p><em>Each talk features a Bio Data Science professional sharing unique insights into real-world projects that apply cutting-edge Bio Data Science techniques, while also providing a glimpse into the career paths that have enabled their exciting work. Every talk will be followed by a Q&amp;A session.</em></p>
 <section id="section" class="level3">
 <h3 class="anchored" data-anchor-id="section">2024</h3>
 <section id="companies-represented" class="level4">
diff --git a/docs/primer_on_linear_models_in_r.html b/docs/primer_on_linear_models_in_r.html
index 36ff5b7..04d4b8b 100644
--- a/docs/primer_on_linear_models_in_r.html
+++ b/docs/primer_on_linear_models_in_r.html
@@ -390,7 +390,7 @@ <h3 class="anchored" data-anchor-id="data">Data</h3>
 <div class="cell">
 <div class="sourceCode cell-code" id="cb2"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="fu">run_simulation</span>(<span class="at">temp =</span> <span class="fu">c</span>(<span class="dv">15</span>, <span class="dv">20</span>, <span class="dv">25</span>, <span class="dv">30</span>, <span class="dv">35</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
-<pre><code>[1] 28.97596 41.86122 51.98388 60.55778 75.83143</code></pre>
+<pre><code>[1] 33.45604 42.73547 49.41673 60.40195 72.35998</code></pre>
 </div>
 </div>
 <p>Let’s just go ahead and create some data, we can work with. For this example, we take samples starting at 5 degree celsius and then in increments of 1 up to 50 degrees:</p>
diff --git a/docs/search.json b/docs/search.json
index d4b137a..fe5df39 100644
--- a/docs/search.json
+++ b/docs/search.json
@@ -340,7 +340,7 @@
     "href": "lab05.html#creating-the-micro-report",
     "title": "Lab 5: Data Wrangling II",
     "section": "Creating the Micro-Report",
-    "text": "Creating the Micro-Report\n\nBackground\nFeel free to copy paste the one stated in the background-section above\n\n\nAim\nState the aim of the micro-report, i.e. what are the questions you are addressing?\n\n\nLoad Libraries\n\n\n\nLoad the libraries needed\n\n\nLoad Data\nRead the two data sets into variables peptide_data and meta_data.\n\n\n\nClick here for hint\n\n\nThink about which Tidyverse package deals with reading data and what are the file types we want to read here?\n\n\n\n\n\n\nData Description\nIt is customary to include a description of the data, helping the reader if the report, i.e. your stakeholder, to get an easy overview\n\nThe Subject Meta Data\nLet’s take a look at the meta data:\n\nmeta_data |> \n  sample_n(10)\n\n# A tibble: 10 × 30\n   Experiment Subject `Cell Type` `Target Type` Cohort          Age Gender Race \n   <chr>        <dbl> <chr>       <chr>         <chr>         <dbl> <chr>  <chr>\n 1 ePD85         5869 naive_CD8   C19_cI        Healthy (No …    27 F      <NA> \n 2 ePD81         2922 PBMC        C19_cI        COVID-19-Con…    64 M      <NA> \n 3 eQD129        2283 PBMC        C19_cI        COVID-19-Con…    60 F      White\n 4 eHO125        4611 PBMC        C19_cI        COVID-19-Con…    52 M      <NA> \n 5 eNL192        1524 B-CD8-_PBMC C19_cII       COVID-19-Con…    NA <NA>   <NA> \n 6 eJL151        3270 PBMC        C19_cI        COVID-19-Con…    79 F      <NA> \n 7 eHO126        6913 PBMC        C19_cI        COVID-19-Con…    37 F      <NA> \n 8 eQD124         361 PBMC        C19_cI        COVID-19-B-N…    40 F      White\n 9 eQD136        2818 PBMC        C19_cI        COVID-19-Con…    NA <NA>   <NA> \n10 eXL31        20300 naive_CD8   C19_cI        Healthy (No …    28 M      White\n# ℹ 22 more variables: `HLA-A...9` <chr>, `HLA-A...10` <chr>,\n#   `HLA-B...11` <chr>, `HLA-B...12` <chr>, `HLA-C...13` <chr>,\n#   `HLA-C...14` <chr>, DPA1...15 <chr>, DPA1...16 <chr>, DPB1...17 <chr>,\n#   DPB1...18 <chr>, DQA1...19 <chr>, DQA1...20 <chr>, DQB1...21 <chr>,\n#   DQB1...22 <chr>, DRB1...23 <chr>, DRB1...24 <chr>, DRB3...25 <chr>,\n#   DRB3...26 <chr>, DRB4...27 <chr>, DRB4...28 <chr>, DRB5...29 <chr>,\n#   DRB5...30 <chr>\n\n\n\nQ1: How many observations of how many variables are in the data?\nQ2: Are there groupings in the variables, i.e. do certain variables “go together” somehow?\nT1: Re-create this plot\n\nRead this first:\n\nThink about: What is on the x-axis? What is on the y-axis? And also, it looks like we need to do some counting stratified by Cohort and Gender. Recall, that we can stick together a dplyr pipeline with a call to ggplot.\n\n\n\n\n\n\nDoes your plot look different somehow? Consider peeking at the hint…\n\n\n\nClick here for hint\n\n\nPerhaps not everyone agrees on how to denote NAs in data. I have seen -99, -11, _ and so on… Perhaps this can be dealt with in the instance we read the data from the file? I.e. in the actual function call to your read_csv() function. Recall, how can we get information on the parameters of a ?function\n\n\nT2: Re-create this plot\n\n\n\n\n\n\n\n\n\nClick here for hint\n\n\nPerhaps there is a function, which can cut continuous observations into a set of bins?\n\n\nSTOP! Make sure you handled how NAs are denoted in the data before proceeding, see hint below T1\n\nT3: Look at the data and create yet another plot as you see fit. Also skip the redundant variables Subject, Cell Type and Target Type\n\n\n\n\n\nmeta_data |> \n  sample_n(10)\n\n# A tibble: 10 × 27\n   Experiment Cohort      Age Gender Race  `HLA-A...9` `HLA-A...10` `HLA-B...11`\n   <chr>      <chr>     <dbl> <chr>  <chr> <chr>       <chr>        <chr>       \n 1 eMR14      COVID-19…    NA <NA>   <NA>  \"A*03:01:0… \"A*24:02:01\" \"B*07:02:01\"\n 2 eXL36      Healthy …    37 F      White \"A*01:01\"   \"A*02:01\"    \"B*15:01\"   \n 3 eJL161     COVID-19…    31 F      White \"A*01:01:0… \"A*02:01:01\" \"B*08:01:01\"\n 4 ePD90      COVID-19…    29 M      <NA>  \"\"          \"\"           \"\"          \n 5 eQD115     COVID-19…    48 M      <NA>  \"A*02:01:0… \"A*03:01:01\" \"B*07:02:01\"\n 6 ePD73      Healthy …    37 F      White \"A*01:01\"   \"A*02:01\"    \"B*15:01\"   \n 7 eOX56      Healthy …    30 M      Blac… \"A*02:01\"   \"A*33:03\"    \"B*53:01\"   \n 8 eEE240     Healthy …    23 M      White \"A*02:01\"   \"A*02:01\"    \"B*40:01\"   \n 9 eLH45      COVID-19…    53 M      <NA>  \"A*02:01:0… \"A*03:01:01\" \"B*07:02:01\"\n10 eJL148     COVID-19…    41 F      <NA>  \"A*02:01:0… \"A*02:01:01\" \"B*07:02:01\"\n# ℹ 19 more variables: `HLA-B...12` <chr>, `HLA-C...13` <chr>,\n#   `HLA-C...14` <chr>, DPA1...15 <chr>, DPA1...16 <chr>, DPB1...17 <chr>,\n#   DPB1...18 <chr>, DQA1...19 <chr>, DQA1...20 <chr>, DQB1...21 <chr>,\n#   DQB1...22 <chr>, DRB1...23 <chr>, DRB1...24 <chr>, DRB3...25 <chr>,\n#   DRB3...26 <chr>, DRB4...27 <chr>, DRB4...28 <chr>, DRB5...29 <chr>,\n#   DRB5...30 <chr>\n\n\nNow, a classic way of describing a cohort, i.e. the group of subjects used for the study, is the so-called table1 and while we could build this ourselves, this one time, in the interest of exercise focus and time, we are going to “cheat” and use an R-package, like so:\nNB!: This may look a bit odd initially, but if you render your document, you should be all good!\n\nlibrary(\"table1\") # <= Yes, this should normally go at the beginning!\nmeta_data |>\n  mutate(Gender = factor(Gender),\n         Cohort = factor(Cohort)) |>\n  table1(x = formula(~ Gender + Age + Race | Cohort),\n         data = _)\n\n\n\n\n\n\nCOVID-19-Acute(N=4)\nCOVID-19-B-Non-Acute(N=8)\nCOVID-19-Convalescent(N=90)\nCOVID-19-Exposed(N=3)\nHealthy (No known exposure)(N=39)\nOverall(N=144)\n\n\n\n\nGender\n\n\n\n\n\n\n\n\nF\n1 (25.0%)\n4 (50.0%)\n33 (36.7%)\n1 (33.3%)\n17 (43.6%)\n56 (38.9%)\n\n\nM\n2 (50.0%)\n3 (37.5%)\n36 (40.0%)\n0 (0%)\n21 (53.8%)\n62 (43.1%)\n\n\nMissing\n1 (25.0%)\n1 (12.5%)\n21 (23.3%)\n2 (66.7%)\n1 (2.6%)\n26 (18.1%)\n\n\nAge\n\n\n\n\n\n\n\n\nMean (SD)\n50.7 (17.0)\n43.7 (7.74)\n51.5 (15.3)\n35.0 (NA)\n33.3 (9.93)\n44.9 (15.7)\n\n\nMedian [Min, Max]\n52.0 [33.0, 67.0]\n42.0 [33.0, 53.0]\n53.0 [21.0, 79.0]\n35.0 [35.0, 35.0]\n31.0 [21.0, 62.0]\n42.0 [21.0, 79.0]\n\n\nMissing\n1 (25.0%)\n1 (12.5%)\n21 (23.3%)\n2 (66.7%)\n0 (0%)\n25 (17.4%)\n\n\nRace\n\n\n\n\n\n\n\n\nAfrican American\n1 (25.0%)\n0 (0%)\n0 (0%)\n0 (0%)\n1 (2.6%)\n2 (1.4%)\n\n\nWhite\n2 (50.0%)\n7 (87.5%)\n13 (14.4%)\n0 (0%)\n28 (71.8%)\n50 (34.7%)\n\n\nAsian\n0 (0%)\n0 (0%)\n3 (3.3%)\n0 (0%)\n2 (5.1%)\n5 (3.5%)\n\n\nHispanic or Latino/a\n0 (0%)\n0 (0%)\n1 (1.1%)\n0 (0%)\n0 (0%)\n1 (0.7%)\n\n\nNative Hawaiian or Other Pacific Islander\n0 (0%)\n0 (0%)\n0 (0%)\n1 (33.3%)\n0 (0%)\n1 (0.7%)\n\n\nBlack or African American\n0 (0%)\n0 (0%)\n0 (0%)\n0 (0%)\n3 (7.7%)\n3 (2.1%)\n\n\nMixed Race\n0 (0%)\n0 (0%)\n0 (0%)\n0 (0%)\n1 (2.6%)\n1 (0.7%)\n\n\nMissing\n1 (25.0%)\n1 (12.5%)\n73 (81.1%)\n2 (66.7%)\n4 (10.3%)\n81 (56.3%)\n\n\n\n\n\n\nNote how good this looks! If you have ever done a “Table 1” before, you know how painful they can be and especially if something changes in your cohort - Dynamic reporting to the rescue!\nLastly, before we proceed, the meta_data contains HLA data for both class I and class II (see background), but here we are only interested in class I, recall these are denoted HLA-A, HLA-B and HLA-C, so make sure to remove any non-class I, i.e. the one after, denoted D-something.\n\nT4: Create a new version of the meta_data, which with respect to allele-data only contains information on class I and also fix the odd naming, e.g. HLA-A...9 becomes A1 oand HLA-A...10 becomes A2 and so on for B1, B2, C1 and C2 (Think: How can we rename variables? And here, just do it “manually” per variable). Remember to assign this new data to the same meta_data variable\n\n\n\n\nClick here for hint\n\n\nWhich tidyverse function subsets variables? Perhaps there is a function, which somehow matches a set of variables? And perhaps for the initiated this is compatible with regular expressions (If you don’t know what this means - No worries! If you do, see if you utilise this to simplify your variable selection)\n\n\n\n\nBefore we proceed, this is the data we will carry on with:\n\nmeta_data |> \n  sample_n(10)\n\n# A tibble: 10 × 11\n   Experiment Cohort        Age Gender Race  A1    A2    B1    B2    C1    C2   \n   <chr>      <chr>       <dbl> <chr>  <chr> <chr> <chr> <chr> <chr> <chr> <chr>\n 1 eQD115     COVID-19-C…    48 M      <NA>  \"A*0… \"A*0… \"B*0… \"B*4… \"C*0… \"C*0…\n 2 eHO140     COVID-19-C…    NA <NA>   <NA>  \"\"    \"\"    \"\"    \"\"    \"\"    \"\"   \n 3 eGK120     COVID-19-C…    46 F      White \"A*1… \"A*6… \"B*0… \"B*5… \"C*0… \"C*1…\n 4 eLH42      COVID-19-C…    63 M      <NA>  \"A*0… \"A*2… \"B*0… \"B*1… \"C*0… \"C*0…\n 5 ePD73      Healthy (N…    37 F      White \"A*0… \"A*0… \"B*1… \"B*4… \"C*0… \"C*0…\n 6 eEE226     Healthy (N…    21 F      White \"A*0… \"A*0… \"B*3… \"B*3… \"C*0… \"C*0…\n 7 eHH173     Healthy (N…    50 M      White \"A*0… \"A*0… \"B*3… \"B*4… \"C*0… \"C*0…\n 8 eHH169     Healthy (N…    24 F      Blac… \"A*0… \"A*7… \"B*3… \"B*3… \"C*0… \"C*0…\n 9 eJL147     Healthy (N…    40 M      Mixe… \"A*0… \"A*1… \"B*0… \"B*3… \"C*0… \"C*0…\n10 eQD120     COVID-19-C…    62 F      <NA>  \"A*0… \"A*3… \"B*0… \"B*4… \"C*0… \"C*0…\n\n\nNow, we have a beautiful tidy dataset, recall that this entails, that each row is an observation, each column is a variable and each cell holds one value.\n\n\n\nThe Peptide Details Data\nLet’s start with simply having a look see:\n\npeptide_data |> \n  sample_n(10)\n\n# A tibble: 10 × 7\n   `TCR BioIdentity`            TCR Nucleotide Seque…¹ Experiment `ORF Coverage`\n   <chr>                        <chr>                  <chr>      <chr>         \n 1 unproductive+TCRBV03-01/03-… TCACATCAATTCCCTGGAGCT… eHO141     surface glyco…\n 2 CASSRRDMGGGPTDTQYF+TCRBV19-… CAAAAGAACCCGACAGCTTTC… eEE226     ORF7b         \n 3 CSASLESETQYF+TCRBV20-01+TCR… CTGACAGTGACCAGTGCCCAT… eEE226     ORF7b         \n 4 CASSLTGALGRSIANQPQHF+TCRBV1… AGGGACTCAGCTGTGTACTTC… eXL31      ORF3a         \n 5 CASRKGSTNEKLFF+TCRBV05-06+T… GTGAACGCCTTGTTGCTGGGG… eEE226     ORF7b         \n 6 CASSPGLVIEQFF+TCRBV13-01+TC… AACATGAGCTCCTTGGAGCTG… eHO136     ORF1ab        \n 7 CASTPGLAVYEQYF+TCRBV12-X+TC… ATCCAGCCCTCAGAACCCAGG… eEE228     ORF1ab        \n 8 CSARAGGYSNQPQHF+TCRBV20-X+T… ACCAGTGCCCATCCTGAAGAC… eOX52      surface glyco…\n 9 CASSPDWGGGEQYF+TCRBV05-04+T… GTGAACGCCTTGGAGCTGGAC… eEE228     ORF1ab        \n10 CASDEGVDNEQFF+TCRBV02-01+TC… AAGATCCGGTCCACAAAGCTG… eEE226     surface glyco…\n# ℹ abbreviated name: ¹​`TCR Nucleotide Sequence`\n# ℹ 3 more variables: `Amino Acids` <chr>, `Start Index in Genome` <dbl>,\n#   `End Index in Genome` <dbl>\n\n\n\nQ3: How many observations of how many variables are in the data?\n\nThis is a rather big data set, so let us start with two “tricks” to handle this, first:\n\nWrite the data back into your data folder, using the filename peptide-detail-ci.csv.gz, note the appending of .gz, which is automatically recognised and results in gz-compression\nNow, check in your data folder, that you have two files peptide-detail-ci.csv and peptide-detail-ci.csv.gz, delete the former\nAdjust your reading-the-data-code in the “Load Data”-section, to now read in the peptide-detail-ci.csv.gz file\n\n\n\n\nClick here for hint\n\n\nJust as you can read a file, you can of course also write a file. Note the filetype we want to write here is csv. If you in the console type e.g. readr::wr and then hit the Tab key, you will see the different functions for writing different filetypes\n\nThen:\n\nT5: As before, let’s immediately subset the peptide_data to the variables of interest: TCR BioIdentity, Experiment and Amino Acids. Remember to assign this new data to the same peptide_data variable to avoid cluttering your environment with redundant variables. Bonus: Did you know you can click the Environment pane and see which variables you have?\n\n\n\n\nOnce again, before we proceed, this is the data we will carry on with:\n\npeptide_data |> \n  sample_n(10)\n\n# A tibble: 10 × 3\n   Experiment `TCR BioIdentity`                       `Amino Acids`             \n   <chr>      <chr>                                   <chr>                     \n 1 eLH51      CASSTSGNTGELFF+TCRBV19-01+TCRBJ02-02    APSASAFFGM,AQFAPSASA,ASAF…\n 2 eHO140     CASNDLNTGELFF+TCRBV02-01+TCRBJ02-02     YLQPRTFL,YLQPRTFLL,YYVGYL…\n 3 eEE240     CSASGSLIGTNEKLFF+TCRBV20-X+TCRBJ01-04   AFLLFLVLI,FLAFLLFLV,FYLCF…\n 4 eEE240     CSLALADPDTQYF+TCRBV29-01+TCRBJ02-03     DFLEYHDVR,EDFLEYHDVR,LEYH…\n 5 eMR23      CSARGWGGPKTSTDTQYF+TCRBV20-X+TCRBJ02-03 ALNTPKDHI,ATEGALNTPK      \n 6 eOX54      CASSQGVGTEAFF+TCRBV04-03+TCRBJ01-01     AFPFTIYSL,GYINVFAFPF,INVF…\n 7 eOX49      CASSLGQGAYEQYF+TCRBV04-01+TCRBJ02-07    FLNGSCGSV                 \n 8 eEE224     CASSQDLGMGEQFF+TCRBV06-X+TCRBJ02-01     TVLSFCAFA,VLSFCAFAV       \n 9 eHH175     CASSQPSYEQYF+TCRBV04-02+TCRBJ02-07      AFLLFLVLI,FLAFLLFLV,FYLCF…\n10 eMR15      CASDQTGGRGTDTQYF+TCRBV28-01+TCRBJ02-03  FPRGQGVPI,KFPRGQGVPI      \n\n\n\nQ4: Is this tidy data? Why/why not?\nT6: See if you can find a way to create the below data, from the above\n\n\n\n\n\npeptide_data |> \n  sample_n(size = 10)\n\n# A tibble: 10 × 5\n   Experiment CDR3b            V_gene           J_gene     `Amino Acids`        \n   <chr>      <chr>            <chr>            <chr>      <chr>                \n 1 eEE226     CASSSFGTGGAGELFF TCRBV05-01       TCRBJ02-02 IVDTVSALV            \n 2 eOX54      CASIQGMNTEAFF    TCRBV07-09       TCRBJ01-01 FVCNLLLLFV,LLFVTVYSH…\n 3 eAV88      CASSFMGGNQPQHF   TCRBV05-06       TCRBJ01-05 KLSYGIATV            \n 4 eEE240     CASSSADRVKSNGYTF TCRBV28-01       TCRBJ01-02 TLDSKTQSL            \n 5 eOX43      CASSLRRDHTDTQYF  TCRBV28-01       TCRBJ02-03 REGVFVSNGTHW         \n 6 eOX43      CSASSSLGYNEQFF   TCRBV20-X        TCRBJ02-01 AFLLFLVLI,FLAFLLFLV,…\n 7 eOX52      CASSLGVLAGADTQYF TCRBV27-01       TCRBJ02-03 QYIKWPWYI,YEQYIKWPW,…\n 8 eOX54      CASGLAEYEQYF     TCRBV10-02       TCRBJ02-07 FKVSIWNLDY,ILLIIMRTF…\n 9 eQD128     CASRVELEPQHF     TCRBV12-03/12-04 TCRBJ01-05 ALALLLLDR,GDAALALLLL…\n10 ePD84      CSAPDRVNTGELFF   TCRBV20-X        TCRBJ02-02 AFLLFLVLI,FLAFLLFLV,…\n\n\n\n\n\nClick here for hint\n\n\nFirst: Compare the two datasets and identify what happened? Did any variables “disappear” and did any “appear”? Ok, so this is a bit tricky, but perhaps there is a function to separate a composite (untidy) column into a set of new variables based on a separator? But what is a separator? Just like when you read a file with Comma Separated Values, a separator denotes how a composite string is divided into fields. So, look for such a repeated value, which seem to indeed separate such fields. Also, be aware, that character, which can mean more than one thing, may need to be “escaped” using an initial two backslashed, i.e. “\\x”, where x denotes the character needing to be “escaped”\n\n\nT7: Add a variable, which counts how many peptides are in each observation of Amino Acids\n\n\n\n\n\n\n\nClick here for hint\n\n\nWe have been working with the stringr package, perhaps the contains a function to somehow count the number of occurrences of a given character in a string? Again, remember you can type e.g. stringr::str_ and then hit the Tab key to see relevant functions\n\n\npeptide_data |> \n  sample_n(size = 10)\n\n# A tibble: 10 × 6\n   Experiment CDR3b             V_gene           J_gene `Amino Acids` n_peptides\n   <chr>      <chr>             <chr>            <chr>  <chr>              <dbl>\n 1 eXL30      CASSQPPGEHNYGYTF  TCRBV04-01       TCRBJ… AEAELAKNVSL,…          2\n 2 eOX43      CASSLRGEQYF       TCRBV28-01       TCRBJ… KLSYGIATV              1\n 3 eEE240     CASSFGYEQYF       TCRBV12-03/12-04 TCRBJ… FLNGSCGSV              1\n 4 eQD110     CATSRETESSTDTQYF  TCRBV15-01       TCRBJ… HTTDPSFLGRY            1\n 5 eXL27      CASSFPGLAGEQFF    TCRBV11-02       TCRBJ… KPLEFGATSAAL           1\n 6 eEE224     CASSFLRPDRGNNEQFF TCRBV28-01       TCRBJ… TVLSFCAFA,VL…          2\n 7 eOX52      CASSLTSWDRAYGYTF  TCRBV05-08       TCRBJ… APKEIIFL,KEI…          2\n 8 eXL32      CASSLAGNQPQHF     TCRBV05-06       TCRBJ… FVCNLLLLFV,L…          3\n 9 eEE228     CASSPGPYTGELFF    TCRBV06-04       TCRBJ… IMLIIFWFSL,M…          2\n10 eOX43      CSVGLAGGPYEQYF    TCRBV29-01       TCRBJ… DFLEYHDVR,ED…          5\n\n\n\nT8: Re-create the following plot\n\n\n\n\n\n\n\nQ4: What is the maximum number of peptides assigned to one observation?\nT9: Using the str_c() and the seq() functions, re-create the below\n\n\n\n[1] \"peptide_1\" \"peptide_2\" \"peptide_3\" \"peptide_4\" \"peptide_5\"\n\n\n\n\n\nClick here for hint\n\n\nIf you’re uncertain on how a function works, try going into the console and in this case e.g. type str_c(\"a\", \"b\") and seq(from = 1, to = 3) and see if you combine these?\n\n\nT10: Use, what you learned about separating in T6 and the vector-of-strings you created in T9 adjusted to the number from Q4 to create the below data\n\n\n\n\n\n\n\nClick here for hint\n\n\nIn the console, write ?separate and think about how you used it earlier. Perhaps you can not only specify a vector to separate into, but also specify a function, which returns a vector?\n\n\npeptide_data |> \n  sample_n(size = 10)\n\n# A tibble: 10 × 18\n   Experiment CDR3b        V_gene J_gene peptide_1 peptide_2 peptide_3 peptide_4\n   <chr>      <chr>        <chr>  <chr>  <chr>     <chr>     <chr>     <chr>    \n 1 eEE240     CASSDSSGELFF TCRBV… TCRBJ… FVCNLLLL… LLFVTVYS… TVYSHLLLV <NA>     \n 2 eEE228     CASSHRTGRVL… TCRBV… TCRBJ… APAHISTI  LIVNSVLL… LLFLAFVV… SVLLFLAFV\n 3 eEE224     CASSLLAGGHN… TCRBV… TCRBJ… AFLLFLVLI FLAFLLFLV FYLCFLAFL FYLCFLAF…\n 4 eQD111     CASSPDGNTEA… TCRBV… TCRBJ… HTTDPSFL… <NA>      <NA>      <NA>     \n 5 eEE228     CASSFAGTDYE… TCRBV… TCRBJ… FLNGSCGSV <NA>      <NA>      <NA>     \n 6 eMR14      CASSLDDRGLP… TCRBV… TCRBJ… KAYNVTQAF <NA>      <NA>      <NA>     \n 7 eXL31      CSARDSQLAGY… TCRBV… TCRBJ… FLYIIKLI… FLYIIKLV… LYIIKLIFL LYIIKLIF…\n 8 eQD129     CASSDKPGPGA… TCRBV… TCRBJ… ALSKGVHFV <NA>      <NA>      <NA>     \n 9 ePD83      CASSIGQGFAN… TCRBV… TCRBJ… SEHDYQIG… YQIGGYTEK YQIGGYTE… <NA>     \n10 eXL30      CASSDIRTEVY… TCRBV… TCRBJ… RQLLFVVEV <NA>      <NA>      <NA>     \n# ℹ 10 more variables: peptide_5 <chr>, peptide_6 <chr>, peptide_7 <chr>,\n#   peptide_8 <chr>, peptide_9 <chr>, peptide_10 <chr>, peptide_11 <chr>,\n#   peptide_12 <chr>, peptide_13 <chr>, n_peptides <dbl>\n\n\n\nQ5: Now, presumable you got a warning, discuss in your group why that is?\nQ6: With respect to peptide_n, discuss in your group, if this is wide- or long-data?\n\nNow, finally we will use the what we prepared for today, data pivoting. There are two functions, namely pivot_wider() and pivot_longer(). Also, now, we will use a trick when developing ones data pipeline, while working with new functions, that on might not be completely comfortable with. You have seen the sample_n() function several times above and we can use that to randomly sample n observations from data. This we can utilise to work with a smaller data set in the development face and once we are ready, we can increase this n gradually to see if everything continues to work as anticipated.\n\nT11: Using the peptide_data, run a few sample_n() calls with varying degree of n to make sure, that you get a feeling for what is going on\nT12: From the peptide_data data above, with peptide_1, peptide_2, etc. create this data set using one of the data pivoting functions. Remember to start initially with sampling a smaller data set and then work on that first! Also, once you’re sure you’re good to go, reuse the peptide_data variable as we don’t want huge redundant data sets floating around in our environment\n\n\n\n\n\n\n\nClick here for hint\n\n\nIf the pivoting is not clear at all, then do what I do, create some example data:\n\nmy_data <- tibble(\n  id = str_c(\"id_\", 1:10),\n  var_1 = round(rnorm(10),1),\n  var_2 = round(rnorm(10),1),\n  var_3 = round(rnorm(10),1))\n\n…and then play around with that. A small set like the one above is easy to handle, so perhaps start with that and then pivot back and forth a few times using pivot_wider()/pivot_longer(). Use View() to inspect and get a better overview of the results of pivoting.\n\n\npeptide_data |> \n  sample_n(10)\n\n# A tibble: 10 × 7\n   Experiment CDR3b             V_gene       J_gene n_peptides peptide_n peptide\n   <chr>      <chr>             <chr>        <chr>       <dbl> <chr>     <chr>  \n 1 eOX54      CASSAETRMGNTIYF   TCRBV21-01   TCRBJ…          3 peptide_9 <NA>   \n 2 eMR12      CASGSWTAGYEQYF    TCRBV03-01/… TCRBJ…          2 peptide_5 <NA>   \n 3 eEE224     CASSEEQADNEQFF    TCRBV10-01   TCRBJ…          2 peptide_9 <NA>   \n 4 eOX46      CSASGGSKDTQYF     TCRBV20-X    TCRBJ…          2 peptide_9 <NA>   \n 5 eAM13      CASSYGGGQETQYF    TCRBV06-05   TCRBJ…          3 peptide_4 <NA>   \n 6 eQD125     CASSAGDASGYTF     TCRBV09-01   TCRBJ…          1 peptide_8 <NA>   \n 7 eOX54      CASGGYEQYF        TCRBV12-03/… TCRBJ…          1 peptide_… <NA>   \n 8 ePD83      CASSMTSGRSSYNEQFF TCRBV19-01   TCRBJ…          3 peptide_9 <NA>   \n 9 eEE226     CASSLSKDRSYNEQFF  TCRBV13-01   TCRBJ…          1 peptide_8 <NA>   \n10 eQD108     CSVVRGGDTAPDTQYF  TCRBV29-01   TCRBJ…          3 peptide_… <NA>   \n\n\n\nQ7: You will see some NAs in the peptide variable, discuss in your group from where these arise?\nQ8: How many rows and columns now and how does this compare with Q3? Discuss why/why not it is different?\nT13: Now, lose the redundant variables n_peptides and peptide_n, get rid of the NAs in the peptide column, and make sure that we only have unique observations (i.e. there are no repeated rows/observations).\n\n\n\n\n\npeptide_data |> \n  sample_n(10)\n\n# A tibble: 10 × 5\n   Experiment CDR3b             V_gene     J_gene     peptide    \n   <chr>      <chr>             <chr>      <chr>      <chr>      \n 1 eGK111     CASSYEGMLGYTF     TCRBV06-05 TCRBJ01-02 HTTDPSFLGRY\n 2 eXL30      CASSSAGPNYEQYF    TCRBV27-01 TCRBJ02-07 AFPFTIYSL  \n 3 eEE240     CSASGVSGRYQETQYF  TCRBV20-01 TCRBJ02-05 FLAFLLFLV  \n 4 eOX49      CSATQRDRVENEQFF   TCRBV20-X  TCRBJ02-01 LIDFYLCFL  \n 5 eOX54      CASSGRDSVTPGELFF  TCRBV09-01 TCRBJ02-02 IDFYLCFLAF \n 6 eEE226     CASSPTPGQGSYEQYF  TCRBV05-04 TCRBJ02-07 FYLCFLAFL  \n 7 eHO136     CSVEDRNTGELFF     TCRBV29-01 TCRBJ02-02 YLQPRTFLL  \n 8 ePD84      CASSPQGSTDPNYGYTF TCRBV07-09 TCRBJ01-02 GLEAPFLYLY \n 9 eHH175     CASSLPLSYEQYF     TCRBV28-01 TCRBJ02-07 AFLLFLVLI  \n10 eXL31      CSADGEGDGTDTQYF   TCRBV20-X  TCRBJ02-03 AFLLFLVLI  \n\n\n\nQ8: Now how many rows and columns and is this data tidy? Discuss in your group why/why not?\n\nAgain, we turn to the stringr package, as we need to make sure that the sequence data does indeed only contain valid characters. There are a total of 20 proteogenic amino acids, which we symbolise using ARNDCQEGHILKMFPSTWYV.\n\nT14: Use the str_detect() function to filter the CDR3b and peptide variables using a pattern of [^ARNDCQEGHILKMFPSTWYV] and then play with the negate parameter so see what happens\n\n\n\n\n\n\n\nClick here for hint\n\n\nAgain, try to play a bit around with the function in the console, type e.g. str_detect(string = \"ARND\", pattern = \"A\") and str_detect(string = \"ARND\", pattern = \"C\") and then recall, that the filter() function requires a logical vector, i.e. a vector of TRUE and FALSE to filter the rows\n\n\nT15: Add two new variables to the data, k_CDR3b and k_peptide each signifying the length of the respective sequences\n\n\n\n\n\n\n\nClick here for hint\n\n\nAgain, we’re working with strings, so perhaps there is a package of interest and perhaps in that package, there is a function, which can get the length of a string?\n\n\npeptide_data |> \n  sample_n(10)\n\n# A tibble: 10 × 7\n   Experiment CDR3b             V_gene          J_gene peptide k_CDR3b k_peptide\n   <chr>      <chr>             <chr>           <chr>  <chr>     <int>     <int>\n 1 eLH47      CASSLGTDTQYF      TCRBV05-01      TCRBJ… SVLLFL…      12         9\n 2 eEE240     CASSLWGYNEQFF     TCRBV05-01      TCRBJ… LYIIKL…      13        10\n 3 eEE224     CASSGDGTYEQYF     TCRBV02-01      TCRBJ… SINFVR…      13        10\n 4 eXL30      CASSLYGTSGGQETQYF TCRBV03-01/03-… TCRBJ… RFLYII…      17        10\n 5 eOX52      CASSGLAGASTDTQYF  TCRBV06-X       TCRBJ… AFLLFL…      16         9\n 6 eEE228     CASSQKCLAAYEQYF   TCRBV07-07      TCRBJ… LWPVTL…      15         9\n 7 eOX52      CASSQAGPGTEAFF    TCRBV04-01      TCRBJ… YEDFLE…      14        14\n 8 eMR25      CASSHGGSSYNEQFF   TCRBV06-02/06-… TCRBJ… QWNLVI…      15        10\n 9 eEE226     CASSLGDEQYF       TCRBV07-09      TCRBJ… YLCFLA…      11         9\n10 eAV88      CASSFQGLASNEQFF   TCRBV27-01      TCRBJ… FLAFLL…      15         9\n\n\n\nT16: Re-create this plot\n\n\n\n\n\n\n\nQ9: What is the most predominant length of the CDR3b-sequences?\nT17: Re-create this plot\n\n\n\n\n\n\n\nQ10: What is the most predominant length of the peptide-sequences?\nQ11: Discuss in your group, if this data set is tidy or not?\n\n\npeptide_data |> \n  sample_n(10)\n\n# A tibble: 10 × 7\n   Experiment CDR3b            V_gene           J_gene peptide k_CDR3b k_peptide\n   <chr>      <chr>            <chr>            <chr>  <chr>     <int>     <int>\n 1 eEE224     CSAVRGLSSYEQYF   TCRBV20-X        TCRBJ… NVFAFP…      14         9\n 2 eXL27      CASGNEQFF        TCRBV12-03/12-04 TCRBJ… NVFAFP…       9        10\n 3 eEE228     CASSQRWAVTYNEQFF TCRBV21-01       TCRBJ… YLYALV…      16         9\n 4 eEE226     CASSDLPGGVYEQYF  TCRBV10-02       TCRBJ… FLAFLL…      15         9\n 5 eOX54      CASSPAGGWVTDTQYF TCRBV07-03       TCRBJ… KLSYGI…      16         9\n 6 eQD110     CASMAEASNTGELFF  TCRBV19-01       TCRBJ… YFLQSI…      15        10\n 7 ePD82      CASSDLASETQYF    TCRBV25-01       TCRBJ… INVFAF…      13        10\n 8 eXL27      CASSFLGPAYEQYF   TCRBV12-X        TCRBJ… WLLWPV…      14         9\n 9 eEE226     CASSIDQENTEAFF   TCRBV19-01       TCRBJ… LIIMRT…      14         9\n10 eXL30      CASSPVHPGNTEAFF  TCRBV27-01       TCRBJ… TLACFV…      15        10\n\n\n\n\nCreating one data set from two data sets\nBefore we move onto using the family of *_join() functions you prepared for today, we will just take a quick peek at the meta data again:\n\nmeta_data |> \n  sample_n(10)\n\n# A tibble: 10 × 11\n   Experiment Cohort        Age Gender Race  A1    A2    B1    B2    C1    C2   \n   <chr>      <chr>       <dbl> <chr>  <chr> <chr> <chr> <chr> <chr> <chr> <chr>\n 1 ePD82      COVID-19-C…    60 F      <NA>  A*26… A*33… B*40… B*44… C*08… C*14…\n 2 eHH173     Healthy (N…    50 M      White A*02… A*03… B*35… B*44… C*04… C*05…\n 3 eQD121     COVID-19-C…    38 M      <NA>  A*01… A*24… B*18… B*57… C*05… C*07…\n 4 eQD108     COVID-19-C…    NA <NA>   <NA>  A*11… A*68… B*08… B*52… C*07… C*12…\n 5 eEE228     Healthy (N…    45 M      White A*02… A*02… B*35… B*44… C*04… C*05…\n 6 eQD127     COVID-19-C…    61 F      <NA>  A*02… A*03… B*27… B*35… C*02… C*04…\n 7 ePD84      Healthy (N…    29 F      Asian A*02… A*02… B*13… B*44… C*03… C*07…\n 8 eQD114     COVID-19-C…    73 M      <NA>  A*01… A*24… B*08… B*41… C*07… C*17…\n 9 eLH50      COVID-19-C…    28 M      <NA>  A*02… A*26… B*15… B*27… C*01… C*03…\n10 eHO124     Healthy (N…    62 M      <NA>  A*02… A*03… B*07… B*44… C*07… C*07…\n\n\nRemember you can scroll in the data.\n\nQ12: Discuss in your group, if this data with respect to the A1, A2, B1, B2, C1 and C2 variables is a wide or a long data format?\n\nAs with the peptide_data, we will now have to use data pivoting again. I.e.:\n\nT18: use either pivot_wider() or pivot_longer() to create the following data:\n\n\n\n\n\nmeta_data |> \n  sample_n(10)\n\n# A tibble: 10 × 7\n   Experiment Cohort                        Age Gender Race       Gene  Allele  \n   <chr>      <chr>                       <dbl> <chr>  <chr>      <chr> <chr>   \n 1 eQD119     COVID-19-Convalescent          51 M      <NA>       B1    \"B*08:0…\n 2 eMR14      COVID-19-Convalescent          NA <NA>   <NA>       B1    \"B*07:0…\n 3 eDH107     COVID-19-Convalescent          72 F      <NA>       A1    \"A*03:0…\n 4 eHO141     COVID-19-Acute                 NA <NA>   <NA>       C1    \"\"      \n 5 eQD131     COVID-19-Exposed               NA <NA>   <NA>       C1    \"C*02:0…\n 6 eNL192     COVID-19-Convalescent          NA <NA>   <NA>       A2    \"\"      \n 7 eJL147     Healthy (No known exposure)    40 M      Mixed Race B1    \"B*07:0…\n 8 eEE240     Healthy (No known exposure)    23 M      White      B1    \"B*40:0…\n 9 eNL187     COVID-19-Convalescent          NA <NA>   <NA>       C1    \"\"      \n10 eJL147     Healthy (No known exposure)    40 M      Mixed Race A2    \"A*11:0…\n\n\nRemember, what we are aiming for here, is to create one data set from two. So:\n\nQ13: Discuss in your group, which variable(s?) define the same observations between the peptide_data and the meta_data?\n\nOnce you have agreed upon Experiment, then use that knowledge to subset the meta_data to the variables-of-interest:\n\n\n\n\nmeta_data |> \n  sample_n(10)\n\n# A tibble: 10 × 2\n   Experiment Allele      \n   <chr>      <chr>       \n 1 eJL151     \"A*68:01:01\"\n 2 eJL143     \"B*14:01\"   \n 3 eJL153     \"B*15:01:01\"\n 4 eLH53      \"B*57:01:01\"\n 5 eQD120     \"A*31:01:02\"\n 6 eHO131     \"B*51:01:01\"\n 7 eNL189     \"\"          \n 8 eOX49      \"A*26:01\"   \n 9 eQD129     \"C*06:02:01\"\n10 eEE240     \"A*02:01\"   \n\n\nUse the View() function again, to look at the meta_data. Notice something? Some alleles are e.g. A*11:01, whereas others are B*51:01:02. You can find information on why, by visiting Nomenclature for Factors of the HLA System.\nLong story short, we only want to include Field 1 (allele group) and Field 2 (Specific HLA protein). You have prepared the stringr package for today. See if you can find a way to reduce e.g. B*51:01:02 to B*51:01 and then create a new variable Allele_F_1_2 accordingly, while also removing the ...x (where x is a number) subscripts from the Gene variable (It is an artifact from having the data in a wide format, where you cannot have two variables with the same name) and also, remove any NAs and \"\"s, denoting empty entries.\n\n\n\nClick here for hint\n\n\nThere are several ways this can be achieved, the easiest being to consider if perhaps a part of the string based on indices could be of interest. This term “a part of a string” is called a substring, perhaps the stringr package contains a function work with substring? In the console, type stringr:: and hit tab. This will display the functions available in the stringr package. Scroll down and find the functionst starting with str_ and look for on, which might be relevant and remember you can use ?function_name to get more information on how a given function works.\n\n\n\n\n\nT19: Create the following data, according to specifications above:\n\n\nmeta_data |> \n  sample_n(10)\n\n# A tibble: 10 × 3\n   Experiment Allele     Allele_F_1_2\n   <chr>      <chr>      <chr>       \n 1 eXL32      C*04:01    C*04:01     \n 2 eJL151     A*24:02:01 A*24:02     \n 3 eDH105     A*24:02:01 A*24:02     \n 4 eJL158     A*24:02:01 A*24:02     \n 5 eEE224     B*40:01    B*40:01     \n 6 eJL161     C*07:01:01 C*07:01     \n 7 eQD108     A*68:01:02 A*68:01     \n 8 eJL160     A*02:01:01 A*02:01     \n 9 ePD84      C*07:06    C*07:06     \n10 eJL149     C*16:01:01 C*16:01     \n\n\nThe asterisk, i.e. * is a rather annoying character because of ambiguity, so:\n\nT20: Clean the data a bit more, by removing the asterisk and redundant variables:\n\n\n\n\n\nmeta_data |> \n  sample_n(size = 10)\n\n# A tibble: 10 × 2\n   Experiment Allele\n   <chr>      <chr> \n 1 eLH49      A03:01\n 2 eMR22      A03:01\n 3 eQD128     C08:01\n 4 eQD108     B52:01\n 5 eEE226     C04:01\n 6 eLH47      A01:01\n 7 eEE228     C04:01\n 8 eJL160     A02:01\n 9 eQD108     C07:01\n10 eEE226     A01:01\n\n\n\n\n\nClick here for hint 1\n\n\nAgain, the stringr package may come in handy. Perhaps there is a function remove, one or more such pesky characters?\n\n\n\n\nClick here for hint 2\n\n\nGetting a weird error? Recall, that character ambiguity needs to be “escaped”, you did this somehow earlier on…\n\nRecall the peptide_data?\n\npeptide_data |>\n  sample_n(10)\n\n# A tibble: 10 × 7\n   Experiment CDR3b            V_gene           J_gene peptide k_CDR3b k_peptide\n   <chr>      <chr>            <chr>            <chr>  <chr>     <int>     <int>\n 1 eXL30      CASSGLAVDEQFF    TCRBV06-02/06-03 TCRBJ… LIDFYL…      13         9\n 2 ePD83      CASSQDMGIYNEQFF  TCRBV03-01/03-02 TCRBJ… YQIGGY…      15         9\n 3 eOX49      CASSQSLDPDAYEQYF TCRBV07-09       TCRBJ… LIDFYL…      16         9\n 4 eXL31      CSLDENYGYTF      TCRBV20-01       TCRBJ… FYLCFL…      11        10\n 5 eQD125     CASSLDVGSPLHF    TCRBV11-02       TCRBJ… LQIPFA…      13         9\n 6 eXL32      CASRGDRGRGYGYTF  TCRBV25-01       TCRBJ… LIDFYL…      15         9\n 7 eXL30      CSASTNEGGQETQYF  TCRBV20-X        TCRBJ… MIELSL…      15        10\n 8 eEE226     CASSHNIARGPYEQYF TCRBV14-01       TCRBJ… FYLCFL…      16        10\n 9 eAV88      CASSLRTDNSPLHF   TCRBV06-06       TCRBJ… FYLCFL…      14         9\n10 eOX54      CASSQGPALHF      TCRBV03-01/03-02 TCRBJ… GYINVF…      11        10\n\n\n\nT21: Create a dplyr pipeline, starting with the peptide_data, which joins it with the meta_data and remember to make sure that you get only unqiue observations of rows. Save this data into a new variable names peptide_meta_data (If you get a warning, discuss in your group what it means?)\n\n\n\n\n\n\n\nClick here for hint 1\n\n\nWhich family of functions do we use to join data? Also, perhaps here it would be prudent to start with working on a smaller data set, recall we could sample a number of rows yielding a smaller development data set\n\n\n\n\nClick here for hint 2\n\n\nYou should get a data set of around +3.000.000, take a moment to consider how that would have been to work with in Excel? Also, in case the servers are not liking this, you can consider subsetting the peptide_data prior to joining to e.g. 100,000 or 10,000 rows.\n\n\npeptide_meta_data |>\n  sample_n(10)\n\n# A tibble: 10 × 8\n   Experiment CDR3b               V_gene J_gene peptide k_CDR3b k_peptide Allele\n   <chr>      <chr>               <chr>  <chr>  <chr>     <int>     <int> <chr> \n 1 eXL30      CASSYFSGETQYF       TCRBV… TCRBJ… TESIVR…      13        10 B35:02\n 2 eOX52      CASSQEGTGTYEQYF     TCRBV… TCRBJ… NATRFA…      15         9 A24:02\n 3 eEE228     CASSLMQGATEAFF      TCRBV… TCRBJ… LWLLWP…      14         9 C04:01\n 4 ePD85      CASSAGQGLIYEQYF     TCRBV… TCRBJ… YQIGGY…      15         9 A29:01\n 5 eHH173     CASSNPGWAAGGPVIGTE… TCRBV… TCRBJ… YIIKLI…      21         9 C05:01\n 6 eEE226     CASKEGRGQPPYEQYF    TCRBV… TCRBJ… RNPANN…      16        10 C04:01\n 7 eXL37      CASSPTSGARREQYF     TCRBV… TCRBJ… DFLEYH…      15         9 B40:01\n 8 eQD110     CASRPGIHTIYF        TCRBV… TCRBJ… FLQSIN…      12        10 A11:01\n 9 eXL31      CSATGTSGFEQYF       TCRBV… TCRBJ… AFLLFL…      13         9 A29:02\n10 eEE226     CASSSEGQGRAYEQYF    TCRBV… TCRBJ… TFKVSI…      16         9 B35:02\n\n\n\n\n\nAnalysis\nNow, that we have the data in a prepared and ready-to-analyse format, let us return to the two burning questions we had:\n\nWhat characterises the peptides binding to the HLAs?\nWhat characterises T-cell Receptors binding to the pMHC-complexes?\n\n\nPeptides binding to HLA\nAs we have touched upon multiple times, R is very flexible and naturally you can also create sequence logos. Finally, let us create a binding motif using the package ggseqlogo (More info here).\n\nT22: Subset the final peptide_meta_data data to A02:01 and unique observations of peptides of length 9 and re-create the below sequence logo\n\n\n\n\nClick here for hint\n\n\nYou can pipe a vector of peptides into ggseqlogo, but perhaps you first need to pull that vector from the relevant variable in your tibble? Also, consider before that, that you’ll need to make sure, you are only looking at peptides of length 9\n\n\n\n\n\n\n\n\n\n\n\nT23: Repeat for e.g. B07:02 or another of your favourite alleles\n\nNow, let’s take a closer look at the sequence logo:\n\nQ14: Which positions in the peptide determines binding to HLA?\n\n\n\n\nClick here for hint\n\n\nRecall your Introduction to Bioinformatics course? And/or perhaps ask your fellow group members if they know?\n\n\n\nCDR3b-sequences binding to pMHC\n\nT24: Subset the peptide_meta_data, such that the length of the CDR3b is 15, the allele is A02:01 and the peptide is LLFLVLIML and re-create the below sequence logo of the CDR3b sequences:\n\n\n\n\n\n\n\n\n\n\n\nQ15: In your group, discuss what you see?\nT25: Play around with other combinations of k_CDR3b, Allele, and peptide and inspect how the logo changes\n\nDisclaimer: In this data set, we only get: A given CDR3b was found to recognise a given peptide in a given subject and that subject had a given haplotype - Something’s missing… Perhaps if you have had immunology, then you can spot it? There is a trick to get around this missing information, but that’s beyond scope of what we’re working with here."
+    "text": "Creating the Micro-Report\n\nBackground\nFeel free to copy paste the one stated in the background-section above\n\n\nAim\nState the aim of the micro-report, i.e. what are the questions you are addressing?\n\n\nLoad Libraries\n\n\n\nLoad the libraries needed\n\n\nLoad Data\nRead the two data sets into variables peptide_data and meta_data.\n\n\n\nClick here for hint\n\n\nThink about which Tidyverse package deals with reading data and what are the file types we want to read here?\n\n\n\n\n\n\nData Description\nIt is customary to include a description of the data, helping the reader if the report, i.e. your stakeholder, to get an easy overview\n\nThe Subject Meta Data\nLet’s take a look at the meta data:\n\nmeta_data |> \n  sample_n(10)\n\n# A tibble: 10 × 30\n   Experiment Subject `Cell Type` `Target Type` Cohort          Age Gender Race \n   <chr>        <dbl> <chr>       <chr>         <chr>         <dbl> <chr>  <chr>\n 1 eLH43         3565 PBMC        C19_cI        COVID-19-Con…    57 M      <NA> \n 2 eQD134        2903 PBMC        C19_cI        COVID-19-Con…    NA <NA>   <NA> \n 3 ePD100        1811 PBMC        C19_cI        COVID-19-Con…    66 M      <NA> \n 4 eAV100        1995 PBMC        C19_cII       COVID-19-Con…    29 F      <NA> \n 5 ePD91          169 PBMC        C19_cI        COVID-19-Con…    52 M      White\n 6 eHO133     1005699 PBMC        C19_cI        COVID-19-Acu…    67 M      White\n 7 eHO129         142 PBMC        C19_cI        COVID-19-Con…    66 F      Asian\n 8 eMR22      1565927 PBMC        C19_cI        COVID-19-Con…    65 M      <NA> \n 9 eMR14         2845 PBMC        C19_cI        COVID-19-Con…    NA <NA>   <NA> \n10 eQD124         361 PBMC        C19_cI        COVID-19-B-N…    40 F      White\n# ℹ 22 more variables: `HLA-A...9` <chr>, `HLA-A...10` <chr>,\n#   `HLA-B...11` <chr>, `HLA-B...12` <chr>, `HLA-C...13` <chr>,\n#   `HLA-C...14` <chr>, DPA1...15 <chr>, DPA1...16 <chr>, DPB1...17 <chr>,\n#   DPB1...18 <chr>, DQA1...19 <chr>, DQA1...20 <chr>, DQB1...21 <chr>,\n#   DQB1...22 <chr>, DRB1...23 <chr>, DRB1...24 <chr>, DRB3...25 <chr>,\n#   DRB3...26 <chr>, DRB4...27 <chr>, DRB4...28 <chr>, DRB5...29 <chr>,\n#   DRB5...30 <chr>\n\n\n\nQ1: How many observations of how many variables are in the data?\nQ2: Are there groupings in the variables, i.e. do certain variables “go together” somehow?\nT1: Re-create this plot\n\nRead this first:\n\nThink about: What is on the x-axis? What is on the y-axis? And also, it looks like we need to do some counting stratified by Cohort and Gender. Recall, that we can stick together a dplyr pipeline with a call to ggplot.\n\n\n\n\n\n\nDoes your plot look different somehow? Consider peeking at the hint…\n\n\n\nClick here for hint\n\n\nPerhaps not everyone agrees on how to denote NAs in data. I have seen -99, -11, _ and so on… Perhaps this can be dealt with in the instance we read the data from the file? I.e. in the actual function call to your read_csv() function. Recall, how can we get information on the parameters of a ?function\n\n\nT2: Re-create this plot\n\n\n\n\n\n\n\n\n\nClick here for hint\n\n\nPerhaps there is a function, which can cut continuous observations into a set of bins?\n\n\nSTOP! Make sure you handled how NAs are denoted in the data before proceeding, see hint below T1\n\nT3: Look at the data and create yet another plot as you see fit. Also skip the redundant variables Subject, Cell Type and Target Type\n\n\n\n\n\nmeta_data |> \n  sample_n(10)\n\n# A tibble: 10 × 27\n   Experiment Cohort      Age Gender Race  `HLA-A...9` `HLA-A...10` `HLA-B...11`\n   <chr>      <chr>     <dbl> <chr>  <chr> <chr>       <chr>        <chr>       \n 1 eQD111     COVID-19…    51 M      <NA>  \"A*01:01:0… \"A*11:01:01\" \"B*07:02:01\"\n 2 eLH50      COVID-19…    28 M      <NA>  \"A*02:01:0… \"A*26:01:01\" \"B*15:01:01\"\n 3 eEE224     Healthy …    24 M      White \"A*02:01\"   \"A*03:01\"    \"B*27:05\"   \n 4 eQD113     COVID-19…    36 M      <NA>  \"A*03:01:0… \"A*11:01:01\" \"B*51:01:01\"\n 5 ePD73      Healthy …    37 F      White \"A*01:01\"   \"A*02:01\"    \"B*15:01\"   \n 6 eQD119     COVID-19…    51 M      <NA>  \"A*01:01:0… \"A*01:01:01\" \"B*08:01:01\"\n 7 eXL30      Healthy …    21 F      White \"A*01:01\"   \"A*02:01\"    \"B*35:02\"   \n 8 eAV105     COVID-19…    29 F      <NA>  \"A*02:01:0… \"A*68:01:02\" \"B*07:02:01\"\n 9 eHO141     COVID-19…    NA <NA>   <NA>  \"\"          \"\"           \"\"          \n10 eXL43      Healthy …    36 F      White \"A*32:01\"   \"A*32:01\"    \"B*07:02\"   \n# ℹ 19 more variables: `HLA-B...12` <chr>, `HLA-C...13` <chr>,\n#   `HLA-C...14` <chr>, DPA1...15 <chr>, DPA1...16 <chr>, DPB1...17 <chr>,\n#   DPB1...18 <chr>, DQA1...19 <chr>, DQA1...20 <chr>, DQB1...21 <chr>,\n#   DQB1...22 <chr>, DRB1...23 <chr>, DRB1...24 <chr>, DRB3...25 <chr>,\n#   DRB3...26 <chr>, DRB4...27 <chr>, DRB4...28 <chr>, DRB5...29 <chr>,\n#   DRB5...30 <chr>\n\n\nNow, a classic way of describing a cohort, i.e. the group of subjects used for the study, is the so-called table1 and while we could build this ourselves, this one time, in the interest of exercise focus and time, we are going to “cheat” and use an R-package, like so:\nNB!: This may look a bit odd initially, but if you render your document, you should be all good!\n\nlibrary(\"table1\") # <= Yes, this should normally go at the beginning!\nmeta_data |>\n  mutate(Gender = factor(Gender),\n         Cohort = factor(Cohort)) |>\n  table1(x = formula(~ Gender + Age + Race | Cohort),\n         data = _)\n\n\n\n\n\n\nCOVID-19-Acute(N=4)\nCOVID-19-B-Non-Acute(N=8)\nCOVID-19-Convalescent(N=90)\nCOVID-19-Exposed(N=3)\nHealthy (No known exposure)(N=39)\nOverall(N=144)\n\n\n\n\nGender\n\n\n\n\n\n\n\n\nF\n1 (25.0%)\n4 (50.0%)\n33 (36.7%)\n1 (33.3%)\n17 (43.6%)\n56 (38.9%)\n\n\nM\n2 (50.0%)\n3 (37.5%)\n36 (40.0%)\n0 (0%)\n21 (53.8%)\n62 (43.1%)\n\n\nMissing\n1 (25.0%)\n1 (12.5%)\n21 (23.3%)\n2 (66.7%)\n1 (2.6%)\n26 (18.1%)\n\n\nAge\n\n\n\n\n\n\n\n\nMean (SD)\n50.7 (17.0)\n43.7 (7.74)\n51.5 (15.3)\n35.0 (NA)\n33.3 (9.93)\n44.9 (15.7)\n\n\nMedian [Min, Max]\n52.0 [33.0, 67.0]\n42.0 [33.0, 53.0]\n53.0 [21.0, 79.0]\n35.0 [35.0, 35.0]\n31.0 [21.0, 62.0]\n42.0 [21.0, 79.0]\n\n\nMissing\n1 (25.0%)\n1 (12.5%)\n21 (23.3%)\n2 (66.7%)\n0 (0%)\n25 (17.4%)\n\n\nRace\n\n\n\n\n\n\n\n\nAfrican American\n1 (25.0%)\n0 (0%)\n0 (0%)\n0 (0%)\n1 (2.6%)\n2 (1.4%)\n\n\nWhite\n2 (50.0%)\n7 (87.5%)\n13 (14.4%)\n0 (0%)\n28 (71.8%)\n50 (34.7%)\n\n\nAsian\n0 (0%)\n0 (0%)\n3 (3.3%)\n0 (0%)\n2 (5.1%)\n5 (3.5%)\n\n\nHispanic or Latino/a\n0 (0%)\n0 (0%)\n1 (1.1%)\n0 (0%)\n0 (0%)\n1 (0.7%)\n\n\nNative Hawaiian or Other Pacific Islander\n0 (0%)\n0 (0%)\n0 (0%)\n1 (33.3%)\n0 (0%)\n1 (0.7%)\n\n\nBlack or African American\n0 (0%)\n0 (0%)\n0 (0%)\n0 (0%)\n3 (7.7%)\n3 (2.1%)\n\n\nMixed Race\n0 (0%)\n0 (0%)\n0 (0%)\n0 (0%)\n1 (2.6%)\n1 (0.7%)\n\n\nMissing\n1 (25.0%)\n1 (12.5%)\n73 (81.1%)\n2 (66.7%)\n4 (10.3%)\n81 (56.3%)\n\n\n\n\n\n\nNote how good this looks! If you have ever done a “Table 1” before, you know how painful they can be and especially if something changes in your cohort - Dynamic reporting to the rescue!\nLastly, before we proceed, the meta_data contains HLA data for both class I and class II (see background), but here we are only interested in class I, recall these are denoted HLA-A, HLA-B and HLA-C, so make sure to remove any non-class I, i.e. the one after, denoted D-something.\n\nT4: Create a new version of the meta_data, which with respect to allele-data only contains information on class I and also fix the odd naming, e.g. HLA-A...9 becomes A1 oand HLA-A...10 becomes A2 and so on for B1, B2, C1 and C2 (Think: How can we rename variables? And here, just do it “manually” per variable). Remember to assign this new data to the same meta_data variable\n\n\n\n\nClick here for hint\n\n\nWhich tidyverse function subsets variables? Perhaps there is a function, which somehow matches a set of variables? And perhaps for the initiated this is compatible with regular expressions (If you don’t know what this means - No worries! If you do, see if you utilise this to simplify your variable selection)\n\n\n\n\nBefore we proceed, this is the data we will carry on with:\n\nmeta_data |> \n  sample_n(10)\n\n# A tibble: 10 × 11\n   Experiment Cohort        Age Gender Race  A1    A2    B1    B2    C1    C2   \n   <chr>      <chr>       <dbl> <chr>  <chr> <chr> <chr> <chr> <chr> <chr> <chr>\n 1 eEE217     Healthy (N…    32 F      White \"A*0… \"A*0… \"B*1… \"B*4… \"C*0… \"C*0…\n 2 eLH41      COVID-19-C…    71 F      <NA>  \"A*0… \"A*0… \"B*1… \"B*1… \"C*0… \"C*0…\n 3 ePD73      Healthy (N…    37 F      White \"A*0… \"A*0… \"B*1… \"B*4… \"C*0… \"C*0…\n 4 eHO136     COVID-19-C…    51 M      Hisp… \"\"    \"\"    \"\"    \"\"    \"\"    \"\"   \n 5 eHO125     COVID-19-C…    52 M      <NA>  \"A*0… \"A*0… \"B*3… \"B*4… \"C*0… \"C*0…\n 6 eOX46      Healthy (N…    45 M      White \"A*0… \"A*0… \"B*3… \"B*4… \"C*0… \"C*0…\n 7 eQD113     COVID-19-C…    36 M      <NA>  \"A*0… \"A*1… \"B*5… \"B*5… \"C*0… \"C*1…\n 8 eOX56      Healthy (N…    30 M      Blac… \"A*0… \"A*3… \"B*5… \"B*5… \"C*0… \"C*0…\n 9 eQD108     COVID-19-C…    NA <NA>   <NA>  \"A*1… \"A*6… \"B*0… \"B*5… \"C*0… \"C*1…\n10 eXL43      Healthy (N…    36 F      White \"A*3… \"A*3… \"B*0… \"B*1… \"C*0… \"C*0…\n\n\nNow, we have a beautiful tidy dataset, recall that this entails, that each row is an observation, each column is a variable and each cell holds one value.\n\n\n\nThe Peptide Details Data\nLet’s start with simply having a look see:\n\npeptide_data |> \n  sample_n(10)\n\n# A tibble: 10 × 7\n   `TCR BioIdentity`            TCR Nucleotide Seque…¹ Experiment `ORF Coverage`\n   <chr>                        <chr>                  <chr>      <chr>         \n 1 CASSLWASGPNEQFF+TCRBV28-01+… GAGTCCGCCAGCACCAACCAG… eHH175     surface glyco…\n 2 CASSPQGSLTEAFF+TCRBV06-05+T… NNNNTGTCGGCTGCTCCCTCC… eQD126     ORF1ab        \n 3 CASSVGQGVIAGGPSEQFF+TCRBV09… CTGGGGGACTCAGCTTTGTAT… eOX52      ORF1ab        \n 4 CASSPQLAMRGNEQFF+TCRBV07-08… CGCACACAGCAGGAGGACTCC… eQD124     membrane glyc…\n 5 CASSFTTGAGANVLTF+TCRBV05-04… GCCTTGGAGCTGGACGACTCG… eEE226     surface glyco…\n 6 CSATGRAGINEQFF+TCRBV20-X+TC… GTGACCAGTGCCCATCCTGAA… eEE240     ORF1ab        \n 7 CSASLGLYEQYF+TCRBV20-01+TCR… CTGACAGTGACCAGTGCCCAT… eOX49      ORF1ab        \n 8 CASSFGQVGQPQHF+TCRBV28-01+T… CTGGAGTCCGCCAGCACCAAC… eXL31      membrane glyc…\n 9 CSVGTGDGETQYF+TCRBV20-X+TCR… ACAGTGACCAGTGCCCATCCT… eXL32      ORF7b         \n10 CSADTRGVEETQYF+TCRBV20-X+TC… GTGACCAGTGCCCATCCTGAA… eLH49      ORF1ab        \n# ℹ abbreviated name: ¹​`TCR Nucleotide Sequence`\n# ℹ 3 more variables: `Amino Acids` <chr>, `Start Index in Genome` <dbl>,\n#   `End Index in Genome` <dbl>\n\n\n\nQ3: How many observations of how many variables are in the data?\n\nThis is a rather big data set, so let us start with two “tricks” to handle this, first:\n\nWrite the data back into your data folder, using the filename peptide-detail-ci.csv.gz, note the appending of .gz, which is automatically recognised and results in gz-compression\nNow, check in your data folder, that you have two files peptide-detail-ci.csv and peptide-detail-ci.csv.gz, delete the former\nAdjust your reading-the-data-code in the “Load Data”-section, to now read in the peptide-detail-ci.csv.gz file\n\n\n\n\nClick here for hint\n\n\nJust as you can read a file, you can of course also write a file. Note the filetype we want to write here is csv. If you in the console type e.g. readr::wr and then hit the Tab key, you will see the different functions for writing different filetypes\n\nThen:\n\nT5: As before, let’s immediately subset the peptide_data to the variables of interest: TCR BioIdentity, Experiment and Amino Acids. Remember to assign this new data to the same peptide_data variable to avoid cluttering your environment with redundant variables. Bonus: Did you know you can click the Environment pane and see which variables you have?\n\n\n\n\nOnce again, before we proceed, this is the data we will carry on with:\n\npeptide_data |> \n  sample_n(10)\n\n# A tibble: 10 × 3\n   Experiment `TCR BioIdentity`                           `Amino Acids`         \n   <chr>      <chr>                                       <chr>                 \n 1 eQD137     CASSPLDEDEAFF+TCRBV18-01+TCRBJ01-01         HTTDPSFLGRY           \n 2 eQD112     CASS*PED*RGLNRDTQYF+TCRBV13-01+TCRBJ02-03   LSPRWYFYY,SPRWYFYYL   \n 3 eQD131     CASSLSGAPGVGTDTQYF+TCRBV05-06+TCRBJ02-03    FLQSINFVR,FLQSINFVRI,…\n 4 eDH105     CASSRTGTGPYEQYF+TCRBV28-01+TCRBJ02-07       ALALLLLDR,GDAALALLLL,…\n 5 eEE224     RASSLESYNEQFF+TCRBV07-03+TCRBJ02-01         AFPFTIYSL,GYINVFAFPF,…\n 6 eOX54      CASSLGGVTGGELFF+TCRBV04-02+TCRBJ02-02       YLDAYNMMI             \n 7 eEE228     CASSPVTGTNQPQHF+TCRBV03-01/03-02+TCRBJ01-05 AFLLFLVLI,FLAFLLFLV,F…\n 8 eQD121     CASSLSSSQETQYF+TCRBV27-01+TCRBJ02-05        HTTDPSFLGRY           \n 9 eXL30      CASSYSAAANTGELFF+TCRBV06-06+TCRBJ02-02      AEAELAKNVSL,AELAKNVSL…\n10 eLH47      CASSPDIQAFF+TCRBV07-09+TCRBJ01-01           YLQPRTFL,YLQPRTFLL,YY…\n\n\n\nQ4: Is this tidy data? Why/why not?\nT6: See if you can find a way to create the below data, from the above\n\n\n\n\n\npeptide_data |> \n  sample_n(size = 10)\n\n# A tibble: 10 × 5\n   Experiment CDR3b            V_gene           J_gene     `Amino Acids`        \n   <chr>      <chr>            <chr>            <chr>      <chr>                \n 1 eJL149     CASSLAGTANYEQYF  TCRBV07-02       TCRBJ02-07 FAYANRNRF,LQFAYANRNR…\n 2 eXL32      CASSEEEGAGAYEQYF TCRBV02-01       TCRBJ02-07 RQLLFVVEV            \n 3 eLH48      CASSSRAASGDTQYF  TCRBV07-06       TCRBJ02-03 VLAWLYAAV            \n 4 eXL27      CASGAGANVLTF     TCRBV25-01       TCRBJ02-06 LLDDFVEII,LLLDDFVEI  \n 5 eEE226     CASSPGSGTGFHEQYF TCRBV07-06       TCRBJ02-07 AFLLFLVLI,FLAFLLFLV,…\n 6 eXL30      CASSSGTGEAFF     TCRBV04-01       TCRBJ01-01 AFLLFLVLI,FLAFLLFLV,…\n 7 eEE240     CSADDRGGEQYF     TCRBV20-X        TCRBJ02-07 AFLLFLVLI,FLAFLLFLV,…\n 8 eXL31      CASSYLQGWETQYF   TCRBV06-02/06-03 TCRBJ02-05 AFLLFLVLI,FLAFLLFLV,…\n 9 eEE224     CATTRGHYTDTQYF   TCRBV06-06       TCRBJ02-03 ELYSPIFLI,LYSPIFLIV,…\n10 ePD83      CASSLRLGLAYEQYF  TCRBV28-01       TCRBJ02-07 SEHDYQIGGYTEKW,YQIGG…\n\n\n\n\n\nClick here for hint\n\n\nFirst: Compare the two datasets and identify what happened? Did any variables “disappear” and did any “appear”? Ok, so this is a bit tricky, but perhaps there is a function to separate a composite (untidy) column into a set of new variables based on a separator? But what is a separator? Just like when you read a file with Comma Separated Values, a separator denotes how a composite string is divided into fields. So, look for such a repeated value, which seem to indeed separate such fields. Also, be aware, that character, which can mean more than one thing, may need to be “escaped” using an initial two backslashed, i.e. “\\x”, where x denotes the character needing to be “escaped”\n\n\nT7: Add a variable, which counts how many peptides are in each observation of Amino Acids\n\n\n\n\n\n\n\nClick here for hint\n\n\nWe have been working with the stringr package, perhaps the contains a function to somehow count the number of occurrences of a given character in a string? Again, remember you can type e.g. stringr::str_ and then hit the Tab key to see relevant functions\n\n\npeptide_data |> \n  sample_n(size = 10)\n\n# A tibble: 10 × 6\n   Experiment CDR3b             V_gene           J_gene `Amino Acids` n_peptides\n   <chr>      <chr>             <chr>            <chr>  <chr>              <dbl>\n 1 eXL30      CASSLGLNTEAFF     TCRBV13-01       TCRBJ… HLVDFQVTI              1\n 2 eEE226     CASSYPRTGYYGYTF   TCRBV06-02/06-03 TCRBJ… APKEIIFL,KEI…          2\n 3 eOX54      CSARAGVRETQYF     TCRBV20-X        TCRBJ… NYLYRLFRK,NY…          2\n 4 eHO130     CATGFRPNTEAFF     TCRBV28-01       TCRBJ… FVDGVPFVV              1\n 5 eOX46      CASSSPGGTFYNEQFF  TCRBV09-01       TCRBJ… FLNRFTTTL              1\n 6 eAV88      CASSFTGGILDTQYF   TCRBV05-04       TCRBJ… ELYSPIFLI,LY…          5\n 7 eXL37      CASSVFEGTGGNSPLHF TCRBV09-01       TCRBJ… GMEVTPSGTWL,…          4\n 8 eHO141     CASSQQPGQGLNYGYTF TCRBV04-01       TCRBJ… AYKTFPPTEPK,…          2\n 9 eEE226     CASSYMGGAASTDTQYF TCRBV06-X        TCRBJ… KLSYGIATV              1\n10 eEE226     CASSEASLNTEAFF    TCRBV06-01       TCRBJ… LPAADLDDF              1\n\n\n\nT8: Re-create the following plot\n\n\n\n\n\n\n\nQ4: What is the maximum number of peptides assigned to one observation?\nT9: Using the str_c() and the seq() functions, re-create the below\n\n\n\n[1] \"peptide_1\" \"peptide_2\" \"peptide_3\" \"peptide_4\" \"peptide_5\"\n\n\n\n\n\nClick here for hint\n\n\nIf you’re uncertain on how a function works, try going into the console and in this case e.g. type str_c(\"a\", \"b\") and seq(from = 1, to = 3) and see if you combine these?\n\n\nT10: Use, what you learned about separating in T6 and the vector-of-strings you created in T9 adjusted to the number from Q4 to create the below data\n\n\n\n\n\n\n\nClick here for hint\n\n\nIn the console, write ?separate and think about how you used it earlier. Perhaps you can not only specify a vector to separate into, but also specify a function, which returns a vector?\n\n\npeptide_data |> \n  sample_n(size = 10)\n\n# A tibble: 10 × 18\n   Experiment CDR3b        V_gene J_gene peptide_1 peptide_2 peptide_3 peptide_4\n   <chr>      <chr>        <chr>  <chr>  <chr>     <chr>     <chr>     <chr>    \n 1 eQD111     CASTLAGGNEQ… TCRBV… TCRBJ… LSPRWYFYY SPRWYFYYL <NA>      <NA>     \n 2 eQD132     CASGVGQYEQYF TCRBV… TCRBJ… AEIRASANL AEIRASAN… ASANLAATK <NA>     \n 3 eMR16      CAISDPTSGTD… TCRBV… TCRBJ… FLLNKEMYL <NA>      <NA>      <NA>     \n 4 eXL31      CSAPRGGVSSY… TCRBV… TCRBJ… AFLLFLVLI FLAFLLFLV FYLCFLAFL FYLCFLAF…\n 5 eOX46      CASSYPDSTGE… TCRBV… TCRBJ… HPLADNKF… SPFHPLAD… <NA>      <NA>     \n 6 ePD83      CASSKGSGGPS… TCRBV… TCRBJ… SEHDYQIG… YQIGGYTEK YQIGGYTE… <NA>     \n 7 eHH173     CASEIWLAGPR… TCRBV… TCRBJ… AFLLFLVLI FLAFLLFLV FYLCFLAFL FYLCFLAF…\n 8 eOX46      CSARGGTSGSH… TCRBV… TCRBJ… APAHISTI  LIVNSVLL… LLFLAFVV… SVLLFLAFV\n 9 eQD114     RASSRLEHRGA… TCRBV… TCRBJ… HTTDPSFL… <NA>      <NA>      <NA>     \n10 eXL30      CASSQRTGADE… TCRBV… TCRBJ… LPAADLDDF <NA>      <NA>      <NA>     \n# ℹ 10 more variables: peptide_5 <chr>, peptide_6 <chr>, peptide_7 <chr>,\n#   peptide_8 <chr>, peptide_9 <chr>, peptide_10 <chr>, peptide_11 <chr>,\n#   peptide_12 <chr>, peptide_13 <chr>, n_peptides <dbl>\n\n\n\nQ5: Now, presumable you got a warning, discuss in your group why that is?\nQ6: With respect to peptide_n, discuss in your group, if this is wide- or long-data?\n\nNow, finally we will use the what we prepared for today, data pivoting. There are two functions, namely pivot_wider() and pivot_longer(). Also, now, we will use a trick when developing ones data pipeline, while working with new functions, that on might not be completely comfortable with. You have seen the sample_n() function several times above and we can use that to randomly sample n observations from data. This we can utilise to work with a smaller data set in the development face and once we are ready, we can increase this n gradually to see if everything continues to work as anticipated.\n\nT11: Using the peptide_data, run a few sample_n() calls with varying degree of n to make sure, that you get a feeling for what is going on\nT12: From the peptide_data data above, with peptide_1, peptide_2, etc. create this data set using one of the data pivoting functions. Remember to start initially with sampling a smaller data set and then work on that first! Also, once you’re sure you’re good to go, reuse the peptide_data variable as we don’t want huge redundant data sets floating around in our environment\n\n\n\n\n\n\n\nClick here for hint\n\n\nIf the pivoting is not clear at all, then do what I do, create some example data:\n\nmy_data <- tibble(\n  id = str_c(\"id_\", 1:10),\n  var_1 = round(rnorm(10),1),\n  var_2 = round(rnorm(10),1),\n  var_3 = round(rnorm(10),1))\n\n…and then play around with that. A small set like the one above is easy to handle, so perhaps start with that and then pivot back and forth a few times using pivot_wider()/pivot_longer(). Use View() to inspect and get a better overview of the results of pivoting.\n\n\npeptide_data |> \n  sample_n(10)\n\n# A tibble: 10 × 7\n   Experiment CDR3b                  V_gene  J_gene n_peptides peptide_n peptide\n   <chr>      <chr>                  <chr>   <chr>       <dbl> <chr>     <chr>  \n 1 eEE224     CASSPTQTKDVHPYEQYF     TCRBV0… TCRBJ…          5 peptide_4 VQELYS…\n 2 eXL31      CSARDMEQTQYF           TCRBV2… TCRBJ…          1 peptide_… <NA>   \n 3 eJL164     CASSDQENRALAGGRRPDTQYF TCRBV0… TCRBJ…          4 peptide_3 YPDKVF…\n 4 eOX49      CSATKLAGPTSNEQFF       TCRBV2… TCRBJ…         11 peptide_… <NA>   \n 5 eMR25      CASSWTSGALQNIQYF       TCRBV0… TCRBJ…          1 peptide_1 KLWAQC…\n 6 eOX54      CASSHHGLAGVRQYNEQFF    TCRBV1… TCRBJ…         11 peptide_… <NA>   \n 7 eQD137     CASTRTQIRDRVDTEAFF     TCRBV1… TCRBJ…          1 peptide_6 <NA>   \n 8 eEE224     CASSLGHSYEQYF          TCRBV0… TCRBJ…         11 peptide_5 IDFYLC…\n 9 eHO136     CASSLGGVYNEQFF         TCRBV0… TCRBJ…          1 peptide_4 <NA>   \n10 eQD137     CASSITGTYEQYF          TCRBV1… TCRBJ…          2 peptide_… <NA>   \n\n\n\nQ7: You will see some NAs in the peptide variable, discuss in your group from where these arise?\nQ8: How many rows and columns now and how does this compare with Q3? Discuss why/why not it is different?\nT13: Now, lose the redundant variables n_peptides and peptide_n, get rid of the NAs in the peptide column, and make sure that we only have unique observations (i.e. there are no repeated rows/observations).\n\n\n\n\n\npeptide_data |> \n  sample_n(10)\n\n# A tibble: 10 × 5\n   Experiment CDR3b             V_gene     J_gene     peptide   \n   <chr>      <chr>             <chr>      <chr>      <chr>     \n 1 eJL158     CASSERVIRRGQEAFF  TCRBV25-01 TCRBJ01-01 KLWAQCVQL \n 2 eXL37      CASSPDRDNQPQHF    TCRBV27-01 TCRBJ01-05 AFLLFLVLI \n 3 eXL30      CASSQDITGELFF     TCRBV04-03 TCRBJ02-02 WPVTLACFVL\n 4 eOX52      CSAQWASGGAGTGELFF TCRBV20-X  TCRBJ02-02 MIELSLIDFY\n 5 eOX52      CASSLAGVVEQYF     TCRBV07-08 TCRBJ02-07 APKEIIFL  \n 6 eEE224     CSARQTRTGAWEQYF   TCRBV20-X  TCRBJ02-07 IDFYLCFLAF\n 7 eQD137     CASSLGTGAMETQYF   TCRBV27-01 TCRBJ02-05 VYFLQSINF \n 8 eAV88      CSAETSGSSNEQFF    TCRBV20-X  TCRBJ02-01 IDFYLCFLAF\n 9 eOX46      CASSGLAGGRSQFF    TCRBV12-X  TCRBJ02-01 YLCFLAFLL \n10 eXL30      CASSLGAYEGVLTF    TCRBV12-X  TCRBJ02-06 MIELSLIDFY\n\n\n\nQ8: Now how many rows and columns and is this data tidy? Discuss in your group why/why not?\n\nAgain, we turn to the stringr package, as we need to make sure that the sequence data does indeed only contain valid characters. There are a total of 20 proteogenic amino acids, which we symbolise using ARNDCQEGHILKMFPSTWYV.\n\nT14: Use the str_detect() function to filter the CDR3b and peptide variables using a pattern of [^ARNDCQEGHILKMFPSTWYV] and then play with the negate parameter so see what happens\n\n\n\n\n\n\n\nClick here for hint\n\n\nAgain, try to play a bit around with the function in the console, type e.g. str_detect(string = \"ARND\", pattern = \"A\") and str_detect(string = \"ARND\", pattern = \"C\") and then recall, that the filter() function requires a logical vector, i.e. a vector of TRUE and FALSE to filter the rows\n\n\nT15: Add two new variables to the data, k_CDR3b and k_peptide each signifying the length of the respective sequences\n\n\n\n\n\n\n\nClick here for hint\n\n\nAgain, we’re working with strings, so perhaps there is a package of interest and perhaps in that package, there is a function, which can get the length of a string?\n\n\npeptide_data |> \n  sample_n(10)\n\n# A tibble: 10 × 7\n   Experiment CDR3b            V_gene     J_gene     peptide   k_CDR3b k_peptide\n   <chr>      <chr>            <chr>      <chr>      <chr>       <int>     <int>\n 1 eHO136     CSARDFVQSYGYTF   TCRBV20-X  TCRBJ01-02 CNDPFLGV…      14        10\n 2 eEE240     CSAGGTSDYGYTF    TCRBV20-X  TCRBJ01-02 YLCFLAFLL      13         9\n 3 eXL27      CASSPRDRPVYEQYF  TCRBV18-01 TCRBJ02-07 RFLYIIKL…      15        10\n 4 ePD76      CASSHRLAGFPYEQYF TCRBV11-02 TCRBJ02-07 SLIDFYLC…      16        10\n 5 eOX46      CSVHGQHTEAFF     TCRBV29-01 TCRBJ01-01 LLFLVLIML      12         9\n 6 eXL37      CSATGGGQPQHF     TCRBV20-X  TCRBJ01-05 YLCFLAFLL      12         9\n 7 eEE228     CASSLPGSEAFF     TCRBV07-09 TCRBJ01-01 MIELSLID…      12        10\n 8 eEE240     CASSRGAGELFF     TCRBV04-01 TCRBJ02-02 SINFVRII…      12        10\n 9 eAV91      CASSEGGNQPQHF    TCRBV10-02 TCRBJ01-05 HVTFFIYNK      13         9\n10 eXL30      CSLMGSSYNSPLHF   TCRBV20-X  TCRBJ01-06 AFPFTIYSL      14         9\n\n\n\nT16: Re-create this plot\n\n\n\n\n\n\n\nQ9: What is the most predominant length of the CDR3b-sequences?\nT17: Re-create this plot\n\n\n\n\n\n\n\nQ10: What is the most predominant length of the peptide-sequences?\nQ11: Discuss in your group, if this data set is tidy or not?\n\n\npeptide_data |> \n  sample_n(10)\n\n# A tibble: 10 × 7\n   Experiment CDR3b             V_gene     J_gene     peptide  k_CDR3b k_peptide\n   <chr>      <chr>             <chr>      <chr>      <chr>      <int>     <int>\n 1 eHH175     CSADIAGAAYEQYF    TCRBV20-X  TCRBJ02-07 SLIDFYL…      14        10\n 2 eEE228     CASSEGLAGEEEQFF   TCRBV11-01 TCRBJ02-01 HLVDFQV…      15         9\n 3 eAV93      CASSAGESNEQYF     TCRBV07-09 TCRBJ02-07 FLWLLWP…      13        10\n 4 eOX52      CSVTLASEQYF       TCRBV29-01 TCRBJ02-07 FYLCFLA…      11         9\n 5 eOX52      CASSSHPASGDSNIQYF TCRBV07-08 TCRBJ02-04 LYSPIFL…      17         9\n 6 eQD123     CASSPGEGSTGELFF   TCRBV09-01 TCRBJ02-02 QSINFVR…      15         9\n 7 eLH47      CASSSPLEGVRYGYTF  TCRBV07-09 TCRBJ01-02 TLKKRWQ…      16         9\n 8 eQD109     CASIPAPRQGLDGYTF  TCRBV06-X  TCRBJ01-02 NVFAFPF…      16         9\n 9 eLH46      CASSGDSYEQYF      TCRBV09-01 TCRBJ02-07 IMRTFKV…      12         9\n10 eHH175     CASSVEGISLYETQYF  TCRBV02-01 TCRBJ02-05 FLAFLLF…      16         9\n\n\n\n\nCreating one data set from two data sets\nBefore we move onto using the family of *_join() functions you prepared for today, we will just take a quick peek at the meta data again:\n\nmeta_data |> \n  sample_n(10)\n\n# A tibble: 10 × 11\n   Experiment Cohort        Age Gender Race  A1    A2    B1    B2    C1    C2   \n   <chr>      <chr>       <dbl> <chr>  <chr> <chr> <chr> <chr> <chr> <chr> <chr>\n 1 eHO136     COVID-19-C…    51 M      Hisp… \"\"    \"\"    \"\"    \"\"    \"\"    \"\"   \n 2 eHH175     Healthy (N…    28 M      White \"A*0… \"A*2… \"B*0… \"B*4… \"C*0… \"C*1…\n 3 eQD115     COVID-19-C…    48 M      <NA>  \"A*0… \"A*0… \"B*0… \"B*4… \"C*0… \"C*0…\n 4 ePD73      Healthy (N…    37 F      White \"A*0… \"A*0… \"B*1… \"B*4… \"C*0… \"C*0…\n 5 eHO127     COVID-19-C…    28 M      <NA>  \"A*2… \"A*2… \"B*4… \"B*5… \"C*0… \"C*1…\n 6 eHO131     COVID-19-C…    58 F      <NA>  \"A*0… \"A*0… \"B*1… \"B*5… \"C*0… \"C*1…\n 7 eMR16      COVID-19-C…    NA <NA>   <NA>  \"A*0… \"A*0… \"B*1… \"B*1… \"C*0… \"C*0…\n 8 eLH44      COVID-19-C…    61 F      <NA>  \"A*0… \"A*6… \"B*1… \"B*3… \"C*0… \"C*1…\n 9 eQD129     COVID-19-C…    60 F      White \"A*0… \"A*0… \"B*4… \"B*5… \"C*0… \"C*0…\n10 eQD126     COVID-19-C…    54 F      <NA>  \"A*0… \"A*0… \"B*0… \"B*0… \"C*0… \"C*0…\n\n\nRemember you can scroll in the data.\n\nQ12: Discuss in your group, if this data with respect to the A1, A2, B1, B2, C1 and C2 variables is a wide or a long data format?\n\nAs with the peptide_data, we will now have to use data pivoting again. I.e.:\n\nT18: use either pivot_wider() or pivot_longer() to create the following data:\n\n\n\n\n\nmeta_data |> \n  sample_n(10)\n\n# A tibble: 10 × 7\n   Experiment Cohort                        Age Gender Race  Gene  Allele      \n   <chr>      <chr>                       <dbl> <chr>  <chr> <chr> <chr>       \n 1 eHO129     COVID-19-Convalescent          66 F      Asian A1    \"A*24:02:01\"\n 2 eLH51      COVID-19-Convalescent          55 M      Asian C2    \"C*12:04:02\"\n 3 eQD138     COVID-19-Convalescent          NA <NA>   <NA>  B2    \"B*44:03:01\"\n 4 eXL31      Healthy (No known exposure)    28 M      White B2    \"B*44:03\"   \n 5 eLH57      COVID-19-Convalescent          NA <NA>   <NA>  B1    \"B*07:02:01\"\n 6 ePD83      Healthy (No known exposure)    29 F      Asian C1    \"C*03:04\"   \n 7 eHO138     COVID-19-B-Non-Acute           NA <NA>   <NA>  B1    \"\"          \n 8 eJL148     COVID-19-Convalescent          41 F      <NA>  C1    \"C*03:03:01\"\n 9 eXL27      Healthy (No known exposure)    24 M      White C1    \"C*03:04\"   \n10 eXL32      Healthy (No known exposure)    37 F      White C2    \"C*04:01\"   \n\n\nRemember, what we are aiming for here, is to create one data set from two. So:\n\nQ13: Discuss in your group, which variable(s?) define the same observations between the peptide_data and the meta_data?\n\nOnce you have agreed upon Experiment, then use that knowledge to subset the meta_data to the variables-of-interest:\n\n\n\n\nmeta_data |> \n  sample_n(10)\n\n# A tibble: 10 × 2\n   Experiment Allele    \n   <chr>      <chr>     \n 1 eAV93      B*35:01   \n 2 eHO134     A*01:01:01\n 3 eQD127     C*04:01:01\n 4 eQD114     B*08:01:01\n 5 eXL30      B*39:01   \n 6 eJL152     C*07:02:01\n 7 eJL149     B*44:03:01\n 8 eQD112     B*07:02:01\n 9 eHO131     A*02:01:01\n10 eJL164     A*02:01:01\n\n\nUse the View() function again, to look at the meta_data. Notice something? Some alleles are e.g. A*11:01, whereas others are B*51:01:02. You can find information on why, by visiting Nomenclature for Factors of the HLA System.\nLong story short, we only want to include Field 1 (allele group) and Field 2 (Specific HLA protein). You have prepared the stringr package for today. See if you can find a way to reduce e.g. B*51:01:02 to B*51:01 and then create a new variable Allele_F_1_2 accordingly, while also removing the ...x (where x is a number) subscripts from the Gene variable (It is an artifact from having the data in a wide format, where you cannot have two variables with the same name) and also, remove any NAs and \"\"s, denoting empty entries.\n\n\n\nClick here for hint\n\n\nThere are several ways this can be achieved, the easiest being to consider if perhaps a part of the string based on indices could be of interest. This term “a part of a string” is called a substring, perhaps the stringr package contains a function work with substring? In the console, type stringr:: and hit tab. This will display the functions available in the stringr package. Scroll down and find the functionst starting with str_ and look for on, which might be relevant and remember you can use ?function_name to get more information on how a given function works.\n\n\n\n\n\nT19: Create the following data, according to specifications above:\n\n\nmeta_data |> \n  sample_n(10)\n\n# A tibble: 10 × 3\n   Experiment Allele     Allele_F_1_2\n   <chr>      <chr>      <chr>       \n 1 eQD127     C*02:02:02 C*02:02     \n 2 eQD136     B*15:01:01 B*15:01     \n 3 eQD121     C*07:01:01 C*07:01     \n 4 eLH41      B*13:02:01 B*13:02     \n 5 eLH59      A*02:01:01 A*02:01     \n 6 eLH47      C*07:01:01 C*07:01     \n 7 eLH42      A*23:01:01 A*23:01     \n 8 eQD113     A*11:01:01 A*11:01     \n 9 eLH48      A*03:01:01 A*03:01     \n10 eQD121     B*57:01:01 B*57:01     \n\n\nThe asterisk, i.e. * is a rather annoying character because of ambiguity, so:\n\nT20: Clean the data a bit more, by removing the asterisk and redundant variables:\n\n\n\n\n\nmeta_data |> \n  sample_n(size = 10)\n\n# A tibble: 10 × 2\n   Experiment Allele\n   <chr>      <chr> \n 1 eDH105     C08:01\n 2 eQD131     C02:02\n 3 eQD132     C07:02\n 4 eJL157     B07:02\n 5 eJL151     B40:01\n 6 eHO129     C08:01\n 7 eOX46      C05:01\n 8 ePD86      A02:01\n 9 ePD80      A02:01\n10 eJL154     C04:03\n\n\n\n\n\nClick here for hint 1\n\n\nAgain, the stringr package may come in handy. Perhaps there is a function remove, one or more such pesky characters?\n\n\n\n\nClick here for hint 2\n\n\nGetting a weird error? Recall, that character ambiguity needs to be “escaped”, you did this somehow earlier on…\n\nRecall the peptide_data?\n\npeptide_data |>\n  sample_n(10)\n\n# A tibble: 10 × 7\n   Experiment CDR3b             V_gene     J_gene     peptide  k_CDR3b k_peptide\n   <chr>      <chr>             <chr>      <chr>      <chr>      <int>     <int>\n 1 eLH47      CASSLAGGPAGELFF   TCRBV05-01 TCRBJ02-02 LSPRWYF…      15         9\n 2 ePD83      CASSARSTGELFF     TCRBV19-01 TCRBJ02-02 YQIGGYT…      13        10\n 3 eOX52      CASSFGTITAQETQYF  TCRBV28-01 TCRBJ02-05 NYNYLYR…      16         9\n 4 eEE228     CASSLEEGVLGYEQYF  TCRBV07-02 TCRBJ02-07 RNPANNA…      16        10\n 5 eOX56      CASSLDSYTSTDTQYF  TCRBV07-06 TCRBJ02-03 MIELSLI…      16        10\n 6 eOX52      CASSLEVLPRETQYF   TCRBV11-03 TCRBJ02-05 FYLCFLA…      15         9\n 7 eEE224     CASSLTGMNTEAFF    TCRBV27-01 TCRBJ01-01 VTPSGTW…      14        10\n 8 eEE224     CASSLTSGTSGNQPQHF TCRBV05-06 TCRBJ01-05 LIDFYLC…      17         9\n 9 eEE224     CSASIEGQETQYF     TCRBV20-01 TCRBJ02-05 YLCFLAF…      13         9\n10 ePD76      CASSQRWGADTEAFF   TCRBV04-01 TCRBJ01-01 FVCNLLL…      15        10\n\n\n\nT21: Create a dplyr pipeline, starting with the peptide_data, which joins it with the meta_data and remember to make sure that you get only unqiue observations of rows. Save this data into a new variable names peptide_meta_data (If you get a warning, discuss in your group what it means?)\n\n\n\n\n\n\n\nClick here for hint 1\n\n\nWhich family of functions do we use to join data? Also, perhaps here it would be prudent to start with working on a smaller data set, recall we could sample a number of rows yielding a smaller development data set\n\n\n\n\nClick here for hint 2\n\n\nYou should get a data set of around +3.000.000, take a moment to consider how that would have been to work with in Excel? Also, in case the servers are not liking this, you can consider subsetting the peptide_data prior to joining to e.g. 100,000 or 10,000 rows.\n\n\npeptide_meta_data |>\n  sample_n(10)\n\n# A tibble: 10 × 8\n   Experiment CDR3b              V_gene  J_gene peptide k_CDR3b k_peptide Allele\n   <chr>      <chr>              <chr>   <chr>  <chr>     <int>     <int> <chr> \n 1 eEE224     CASSGATSGITGELFF   TCRBV0… TCRBJ… MIELSL…      16        10 C03:04\n 2 eEE228     CSARDYMVASGKGYEQYF TCRBV2… TCRBJ… FPPTSF…      18         9 C04:01\n 3 eEE224     CASSTTGGGEQFF      TCRBV0… TCRBJ… SVLLFL…      13         9 B27:05\n 4 eLH51      CASPLGTGSSYEQYF    TCRBV2… TCRBJ… APSASA…      15        10 A34:01\n 5 eOX54      CASNLRLTGNSPLHF    TCRBV2… TCRBJ… SLIDFY…      15        10 B15:03\n 6 eOX52      CASSGGDTYEQYF      TCRBV1… TCRBJ… VQELYS…      13         9 B40:01\n 7 eEE226     CSVYLALAKNIQYF     TCRBV2… TCRBJ… FLWLLW…      14         9 C07:02\n 8 eLH51      CASSESILGYNEQFF    TCRBV1… TCRBJ… SLIDFY…      15        10 C12:04\n 9 eXL30      CASSLNEQYF         TCRBV0… TCRBJ… FYLCFL…      10        10 A01:01\n10 eXL31      CASSLEDYNEQFF      TCRBV1… TCRBJ… INFVRI…      13         9 B44:03\n\n\n\n\n\nAnalysis\nNow, that we have the data in a prepared and ready-to-analyse format, let us return to the two burning questions we had:\n\nWhat characterises the peptides binding to the HLAs?\nWhat characterises T-cell Receptors binding to the pMHC-complexes?\n\n\nPeptides binding to HLA\nAs we have touched upon multiple times, R is very flexible and naturally you can also create sequence logos. Finally, let us create a binding motif using the package ggseqlogo (More info here).\n\nT22: Subset the final peptide_meta_data data to A02:01 and unique observations of peptides of length 9 and re-create the below sequence logo\n\n\n\n\nClick here for hint\n\n\nYou can pipe a vector of peptides into ggseqlogo, but perhaps you first need to pull that vector from the relevant variable in your tibble? Also, consider before that, that you’ll need to make sure, you are only looking at peptides of length 9\n\n\n\n\n\n\n\n\n\n\n\nT23: Repeat for e.g. B07:02 or another of your favourite alleles\n\nNow, let’s take a closer look at the sequence logo:\n\nQ14: Which positions in the peptide determines binding to HLA?\n\n\n\n\nClick here for hint\n\n\nRecall your Introduction to Bioinformatics course? And/or perhaps ask your fellow group members if they know?\n\n\n\nCDR3b-sequences binding to pMHC\n\nT24: Subset the peptide_meta_data, such that the length of the CDR3b is 15, the allele is A02:01 and the peptide is LLFLVLIML and re-create the below sequence logo of the CDR3b sequences:\n\n\n\n\n\n\n\n\n\n\n\nQ15: In your group, discuss what you see?\nT25: Play around with other combinations of k_CDR3b, Allele, and peptide and inspect how the logo changes\n\nDisclaimer: In this data set, we only get: A given CDR3b was found to recognise a given peptide in a given subject and that subject had a given haplotype - Something’s missing… Perhaps if you have had immunology, then you can spot it? There is a trick to get around this missing information, but that’s beyond scope of what we’re working with here."
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@@ -578,7 +578,7 @@
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     "title": "Lab 10 Project Startup & Industry Talks",
     "section": "Mini Symposium: Applications of R for Bio Data Science in Industry",
-    "text": "Mini Symposium: Applications of R for Bio Data Science in Industry\nThis mini symposium on Applications of R for Bio Data Science in Industry aims to give students a glimpse into how modern bio data science is transforming value creation in the pharmaceutical industry and healthcare sector.\n\n2024\n\nCompanies Represented\n\nGubra\nQIAGEN\nUCPH Center for Health Data Science\nTBA\n\n\n\nSymposium Programme\n\nAbril Diosdado Hernández, Associate Research Scientist, Gubra\nRuth Stoney, Senior Field Applications Scientist, QIAGEN\nHelene Wegener, Junior Data Scientist, UCPH Center for Health Data Science\nTBA\n\n\n\n\n2023\n\nCompanies Represented\n\nClinical Microbiomics\nGubra\nLundbeck\nNovo Nordisk\nZS\n\n\n\nSymposium Programme\n\n09.00 – 09:25: “Harnessing R Packages for Microbiome Analysis and Reporting” by Maria Novosolov, Data Scientist, Clinical Microbiomics\n09.25 – 09:50: “Plotly and Shiny apps for exploratory analysis of transcriptomics data” by Christina Bligaard Pedersen, Senior Bioinformatician & Mikhail Osipovitch, Lead Bioinformatician, ZS\n09.50 – 10:00: Break\n10.00 – 10:25: “An ADA monitoring tool developed with Rshiny” by Carlotta Porcelli, Senior Statistical Programmer, Novo Nordisk\n10.25 – 10:50: “The R in R&D efficiency” by Victor A. O. Carmelo, Principal Bioinformatician, Lundbeck\n10.50 – 11:00: Break\n11.00 – 11:25: “From data analysis to infrastructure: A journey with R” by Sebastian Tølbøl Thrane, Senior Scientist, Gubra\n11.25 – 11:50: “The long and winding road to use R in a pharmaceutical company” by Claus Dethlefsen, Statistical Director, Novo Nordisk\n11.50 – 12:00: Symposium round-off\n\n\n\n\n2022\n\nCompanies Represented\n\nAbzu\nBristol Myers Squibb\nClinical Microbiomics\nNovo Nordisk Bioinformatics\nNovo Nordisk Biostatistics\nSteno Diabetes Center Copenhagen\n\n\n\nSpeaker List\n\nAndrea Marquard, Head of Data Science and Automation, Clinical Microbiomics: “How we use internal R packages, and why you should learn to make one”\nAnders Gorst-Rasmussen, Statistical Director, Novo Nordisk Biostatistics, Novo Nordisk: “How we use R in Novo Nordisk Biostatistics”\nAnne-Mette Bjerregaard, Senior Scientist in Computational Biology, Novo Nordisk: “The Evolution of a Computational Biologist”\nSam Demharter, Bioinformatics Specialist, Abzu: “Explainable AI in Precision Medicine - Closing the Loop from Patient to Drug”\nLykke Pedersen, Head of RNA Therapeutics, Abzu: “Explainable AI in Precision Medicine - Closing the Loop from Patient to Drug”\nSimon Papillon-Cavanagh, Principal Scientist, Bristol Myers Squibb (USA): “A Career Built on Mistakes”\nSofie Olund Villumsen, Bioinformatician, Steno Diabetes Center Copenhagen: “Using R for Data Analysis in Clinical Studies”\nTarun Veer Singh Ahluwalia, Assoc. Prof., Steno Diabetes Center Copenhagen: “Using R for Data Analysis in Clinical Studies”\n\n\n\n\n2021\n\nCompanies Represented\n\nAbzu\nALK\nChr. Hansen\nLundbeck\nNovozymes\nTeraData\n\n\n\nSpeaker List\n\nSamuel Demharter, Senior Bioinformatician, Abzu\nLykke Pedersen, Senior Bioinformatician, Abzu\nMarie-Catherine Le Bihan, Senior Data Scientist, Chr. Hansen\nThomas Strantzl, Head of Bioinformatics, ALK\nMaria Dalby, Postdoctoral Researcher, Lundbeck\nThomas Poulsen, Science Manager, Novozymes\nMikkel Freltoft Krogsholm, Team lead, Senior Data Scientist, TeraData"
+    "text": "Mini Symposium: Applications of R for Bio Data Science in Industry\nThis mini symposium on Applications of R for Bio Data Science in Industry offers participants a glimpse into how modern Bio Data Science is transforming value creation in the pharmaceutical and healthcare sectors.\nEach talk features a Bio Data Science professional sharing unique insights into real-world projects that apply cutting-edge Bio Data Science techniques, while also providing a glimpse into the career paths that have enabled their exciting work. Every talk will be followed by a Q&A session.\n\n2024\n\nCompanies Represented\n\nGubra\nQIAGEN\nUCPH Center for Health Data Science\nTBA\n\n\n\nSymposium Programme\n\nAbril Diosdado Hernández, Associate Research Scientist, Gubra\nRuth Stoney, Senior Field Applications Scientist, QIAGEN\nHelene Wegener, Junior Data Scientist, UCPH Center for Health Data Science\nTBA\n\n\n\n\n2023\n\nCompanies Represented\n\nClinical Microbiomics\nGubra\nLundbeck\nNovo Nordisk\nZS\n\n\n\nSymposium Programme\n\n09.00 – 09:25: “Harnessing R Packages for Microbiome Analysis and Reporting” by Maria Novosolov, Data Scientist, Clinical Microbiomics\n09.25 – 09:50: “Plotly and Shiny apps for exploratory analysis of transcriptomics data” by Christina Bligaard Pedersen, Senior Bioinformatician & Mikhail Osipovitch, Lead Bioinformatician, ZS\n09.50 – 10:00: Break\n10.00 – 10:25: “An ADA monitoring tool developed with Rshiny” by Carlotta Porcelli, Senior Statistical Programmer, Novo Nordisk\n10.25 – 10:50: “The R in R&D efficiency” by Victor A. O. Carmelo, Principal Bioinformatician, Lundbeck\n10.50 – 11:00: Break\n11.00 – 11:25: “From data analysis to infrastructure: A journey with R” by Sebastian Tølbøl Thrane, Senior Scientist, Gubra\n11.25 – 11:50: “The long and winding road to use R in a pharmaceutical company” by Claus Dethlefsen, Statistical Director, Novo Nordisk\n11.50 – 12:00: Symposium round-off\n\n\n\n\n2022\n\nCompanies Represented\n\nAbzu\nBristol Myers Squibb\nClinical Microbiomics\nNovo Nordisk Bioinformatics\nNovo Nordisk Biostatistics\nSteno Diabetes Center Copenhagen\n\n\n\nSpeaker List\n\nAndrea Marquard, Head of Data Science and Automation, Clinical Microbiomics: “How we use internal R packages, and why you should learn to make one”\nAnders Gorst-Rasmussen, Statistical Director, Novo Nordisk Biostatistics, Novo Nordisk: “How we use R in Novo Nordisk Biostatistics”\nAnne-Mette Bjerregaard, Senior Scientist in Computational Biology, Novo Nordisk: “The Evolution of a Computational Biologist”\nSam Demharter, Bioinformatics Specialist, Abzu: “Explainable AI in Precision Medicine - Closing the Loop from Patient to Drug”\nLykke Pedersen, Head of RNA Therapeutics, Abzu: “Explainable AI in Precision Medicine - Closing the Loop from Patient to Drug”\nSimon Papillon-Cavanagh, Principal Scientist, Bristol Myers Squibb (USA): “A Career Built on Mistakes”\nSofie Olund Villumsen, Bioinformatician, Steno Diabetes Center Copenhagen: “Using R for Data Analysis in Clinical Studies”\nTarun Veer Singh Ahluwalia, Assoc. Prof., Steno Diabetes Center Copenhagen: “Using R for Data Analysis in Clinical Studies”\n\n\n\n\n2021\n\nCompanies Represented\n\nAbzu\nALK\nChr. Hansen\nLundbeck\nNovozymes\nTeraData\n\n\n\nSpeaker List\n\nSamuel Demharter, Senior Bioinformatician, Abzu\nLykke Pedersen, Senior Bioinformatician, Abzu\nMarie-Catherine Le Bihan, Senior Data Scientist, Chr. Hansen\nThomas Strantzl, Head of Bioinformatics, ALK\nMaria Dalby, Postdoctoral Researcher, Lundbeck\nThomas Poulsen, Science Manager, Novozymes\nMikkel Freltoft Krogsholm, Team lead, Senior Data Scientist, TeraData"
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@@ -767,7 +767,7 @@
     "href": "primer_on_linear_models_in_r.html#example",
     "title": "Primer on Linear Models in R",
     "section": "Example",
-    "text": "Example\n\n\n\n\nBackground\nLet’s say we wanted to study the genetic mechanism protecting a plant from heat shock, then:\n\nIndependent: Environmental Condition (temperature)\nDependent: Gene Expression Level (related to heat shock protection)\n\nHere, the independent variable is the temperature and the dependent variable is the gene expression level. It is clear, that the temperature, does not rely on the gene expression level, but the gene expression level of heat shock related genes, does rely on the temperature.\nSo, we keep plants under different temperatures and collect samples, from which we can extract RNA and run a transcriptomics analysis uncovering gene expression levels.\n\n\nData\nFor the data here, we are going to simulate the relationship between gene expression levels and temperature, as a function in R:\n\nrun_simulation <- function(temp){\n  measurement_error <- rnorm(n = length(temp), mean = 0, sd = 3)\n  gene_expression_level <- 2 * temp + 3 + measurement_error\n  return( gene_expression_level )\n}\n\nNote, how we’re adding some measurement error to our simulation, otherwise we would get a perfect relationship, which we all know never happens.\nNow, we can easily run simulations:\n\nrun_simulation(temp = c(15, 20, 25, 30, 35))\n\n[1] 28.97596 41.86122 51.98388 60.55778 75.83143\n\n\nLet’s just go ahead and create some data, we can work with. For this example, we take samples starting at 5 degree celsius and then in increments of 1 up to 50 degrees:\n\nset.seed(806017)\nexperiment_data <- tibble(\n  temperature = seq(from = 5, to = 50, by = 1),\n  gene_expression_level = run_simulation(temp = temperature)\n)\nexperiment_data |> \n  sample_n(10) |> \n  arrange(temperature)\n\n# A tibble: 10 × 2\n   temperature gene_expression_level\n         <dbl>                 <dbl>\n 1          12                  28.7\n 2          13                  30.2\n 3          16                  35.6\n 4          19                  44.0\n 5          22                  48.8\n 6          27                  64.6\n 7          32                  61.1\n 8          33                  73.9\n 9          35                  76.7\n10          40                  84.9\n\n\n\n\nVisualising\nNow, that we have the data, we can visualise the relationship between the temperature- and gene_expression_level-variables:\n\nmy_viz <- experiment_data |> \n  ggplot(aes(x = temperature,\n             y = gene_expression_level)) +\n  geom_point() +\n  geom_vline(xintercept = 0) +\n  geom_hline(yintercept = 0)\nmy_viz\n\n\n\n\n\n\n\n\nNow, we can easily add the best fit line using the geom_smooth()-function, where we specify that we want to use method = \"lm\" and for now, we exclude the confidence interval, by setting se = FALSE:\n\nmy_viz +\n  geom_smooth(method = \"lm\",\n              se = FALSE)\n\n\n\n\n\n\n\n\nWhat happens here, is that a best-fit line is added to the plot by calculating the line, such that the sum of the squared errors is as small as possible, where the error is the distance from the line to a given point. This is a basic linear regression and is known as Ordinary Least Squares (OLS). But what if we want to work with this regression model, beyond just adding a line to a plot?\n\n\nModelling\nOne of the super powers of R is the build in capability to do modelling. Because we simulated the data (see above), we know that the true intercept is 3 and the true slope of the temperature variable is 2. Let see what we get, if we run a linear model:\n\nmy_lm_mdl <- lm(formula = gene_expression_level ~ temperature,\n   data = experiment_data)\nmy_lm_mdl\n\n\nCall:\nlm(formula = gene_expression_level ~ temperature, data = experiment_data)\n\nCoefficients:\n(Intercept)  temperature  \n      2.816        2.021  \n\n\nImportant, the formula notation gene_expression_level ~ temperature is central to R and should be read as: “gene_expression_level modelled as a function of temperature”, i.e. gene_expression_level is the dependent variable often denoted y and temperature is the independendt variable often denoted x.\nOkay that’s pretty close! Recall the reason for the difference is, that we are adding measurement error, when we run the simulation (see above).\nIn other words our model says, that:\n\\[gene\\_expression\\_level = 2.816 + 2.021 \\cdot temperature\\]\nI.e. the estimate of the intercept is 2.816 and the estimate of the slope is 2.021, meaning that when the temperature = 0, we estimate that the gene_expression_level is 2.816 and for each 1 degree increase in temperature, we estimate, that the increase in gene_expression_level is 2.021.\nThese estimates are pretty close to the true model underlying our simulation:\n\\[gene\\_expression\\_level = 3 + 2 \\cdot temperature\\]\nIn general form, such a linear model can be written like so:\n\\[y = \\beta_{0} + \\beta_{1} \\cdot x_{1}\\]\nWhere the \\(\\beta\\)-coefficients are termed estimates, because that is exactly what we do, given the observed data, we estimate their values.\n\n\nWorking with a lm-object:\nThe model format you saw above, is a bit quirky, but luckily, there is a really nice way to get these kind of model object into a more tidy-format:\n\nlibrary(\"broom\")\nmy_lm_mdl |> \n  tidy()\n\n# A tibble: 2 × 5\n  term        estimate std.error statistic  p.value\n  <chr>          <dbl>     <dbl>     <dbl>    <dbl>\n1 (Intercept)     2.82    1.16        2.44 1.89e- 2\n2 temperature     2.02    0.0378     53.4  1.16e-41\n\n\nBriefly, here we term, estimate, std.error, statistic and p.value. We discussed the term and estimate. The std.error pertains to the estimate and the statistic is used to calculate the p.value.\n\nThe P-value\nNow, because we now have a tidy object, we can simply plug-‘n’-play with other tidyverse tools, so let us visualise the p.value. Note, because of the often vary large differences in p.values, we use a -log10-transformation, this means that larger values are “more significant”. Below, the dashed line signifies \\(p=0.05\\), so anything above that line is considered “statistically significant”:\n\nmy_lm_mdl |> \n  tidy() |> \n  ggplot(aes(x = term,\n             y = -log10(p.value))) +\n  geom_point() +\n  geom_hline(yintercept = -log10(0.05),\n             linetype = \"dashed\")\n\n\n\n\n\n\n\n\nNow, as mentioned the p-values are computed based on the statistic and are defined as: “The probability of observing a statistic as or more extreme given, that the null-hypothesis is true”. Where the null-hypothesis it that there is no effect, i.e. the estimate for the term is zero.\nFrom this, it is quite clear, that there very likely is a relationship between the gene_expression_level and temperature. In fact, we know there is, because we simulated the data.\n\n\nThe Confidence Intervals\nWe can further easily include the confidence intervals of the estimates:\n\nmy_lm_mdl_tidy <- my_lm_mdl |> \n  tidy(conf.int = TRUE,\n       conf.level = 0.95)\nmy_lm_mdl_tidy\n\n# A tibble: 2 × 7\n  term        estimate std.error statistic  p.value conf.low conf.high\n  <chr>          <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>\n1 (Intercept)     2.82    1.16        2.44 1.89e- 2    0.488      5.14\n2 temperature     2.02    0.0378     53.4  1.16e-41    1.94       2.10\n\n\n…and as before easily do a plug’n’play into ggplot:\n\nmy_lm_mdl_tidy |> \n  ggplot(aes(x = estimate,\n             y = term,\n             xmin = conf.low,\n             xmax = conf.high)) +\n  geom_errorbarh(height = 0.1) +\n  geom_point()\n\n\n\n\n\n\n\n\nNote, what the 0.95 = 95% confidence intervals means is that: “If we were to repeat this experiment 100 times, then 95 of the times, the generated confidence interval would contain the true value”.\n\n\n\nSummary\nWhat we have gone through here is a basic linear regression, where we are aiming to model the continuous variable gene_expression_level as a function of yet another continous variable temperature. We simulated data, where the true intercept and slope were 3 and 2 respectively and by fitting a linear regression model, the estimates of the intercept and slope respectively were 2.82 [0.49;5.14] and 2.02 [1.94;2.10].\nLinear models allow us to gain insights into data, by modelling relationships."
+    "text": "Example\n\n\n\n\nBackground\nLet’s say we wanted to study the genetic mechanism protecting a plant from heat shock, then:\n\nIndependent: Environmental Condition (temperature)\nDependent: Gene Expression Level (related to heat shock protection)\n\nHere, the independent variable is the temperature and the dependent variable is the gene expression level. It is clear, that the temperature, does not rely on the gene expression level, but the gene expression level of heat shock related genes, does rely on the temperature.\nSo, we keep plants under different temperatures and collect samples, from which we can extract RNA and run a transcriptomics analysis uncovering gene expression levels.\n\n\nData\nFor the data here, we are going to simulate the relationship between gene expression levels and temperature, as a function in R:\n\nrun_simulation <- function(temp){\n  measurement_error <- rnorm(n = length(temp), mean = 0, sd = 3)\n  gene_expression_level <- 2 * temp + 3 + measurement_error\n  return( gene_expression_level )\n}\n\nNote, how we’re adding some measurement error to our simulation, otherwise we would get a perfect relationship, which we all know never happens.\nNow, we can easily run simulations:\n\nrun_simulation(temp = c(15, 20, 25, 30, 35))\n\n[1] 33.45604 42.73547 49.41673 60.40195 72.35998\n\n\nLet’s just go ahead and create some data, we can work with. For this example, we take samples starting at 5 degree celsius and then in increments of 1 up to 50 degrees:\n\nset.seed(806017)\nexperiment_data <- tibble(\n  temperature = seq(from = 5, to = 50, by = 1),\n  gene_expression_level = run_simulation(temp = temperature)\n)\nexperiment_data |> \n  sample_n(10) |> \n  arrange(temperature)\n\n# A tibble: 10 × 2\n   temperature gene_expression_level\n         <dbl>                 <dbl>\n 1          12                  28.7\n 2          13                  30.2\n 3          16                  35.6\n 4          19                  44.0\n 5          22                  48.8\n 6          27                  64.6\n 7          32                  61.1\n 8          33                  73.9\n 9          35                  76.7\n10          40                  84.9\n\n\n\n\nVisualising\nNow, that we have the data, we can visualise the relationship between the temperature- and gene_expression_level-variables:\n\nmy_viz <- experiment_data |> \n  ggplot(aes(x = temperature,\n             y = gene_expression_level)) +\n  geom_point() +\n  geom_vline(xintercept = 0) +\n  geom_hline(yintercept = 0)\nmy_viz\n\n\n\n\n\n\n\n\nNow, we can easily add the best fit line using the geom_smooth()-function, where we specify that we want to use method = \"lm\" and for now, we exclude the confidence interval, by setting se = FALSE:\n\nmy_viz +\n  geom_smooth(method = \"lm\",\n              se = FALSE)\n\n\n\n\n\n\n\n\nWhat happens here, is that a best-fit line is added to the plot by calculating the line, such that the sum of the squared errors is as small as possible, where the error is the distance from the line to a given point. This is a basic linear regression and is known as Ordinary Least Squares (OLS). But what if we want to work with this regression model, beyond just adding a line to a plot?\n\n\nModelling\nOne of the super powers of R is the build in capability to do modelling. Because we simulated the data (see above), we know that the true intercept is 3 and the true slope of the temperature variable is 2. Let see what we get, if we run a linear model:\n\nmy_lm_mdl <- lm(formula = gene_expression_level ~ temperature,\n   data = experiment_data)\nmy_lm_mdl\n\n\nCall:\nlm(formula = gene_expression_level ~ temperature, data = experiment_data)\n\nCoefficients:\n(Intercept)  temperature  \n      2.816        2.021  \n\n\nImportant, the formula notation gene_expression_level ~ temperature is central to R and should be read as: “gene_expression_level modelled as a function of temperature”, i.e. gene_expression_level is the dependent variable often denoted y and temperature is the independendt variable often denoted x.\nOkay that’s pretty close! Recall the reason for the difference is, that we are adding measurement error, when we run the simulation (see above).\nIn other words our model says, that:\n\\[gene\\_expression\\_level = 2.816 + 2.021 \\cdot temperature\\]\nI.e. the estimate of the intercept is 2.816 and the estimate of the slope is 2.021, meaning that when the temperature = 0, we estimate that the gene_expression_level is 2.816 and for each 1 degree increase in temperature, we estimate, that the increase in gene_expression_level is 2.021.\nThese estimates are pretty close to the true model underlying our simulation:\n\\[gene\\_expression\\_level = 3 + 2 \\cdot temperature\\]\nIn general form, such a linear model can be written like so:\n\\[y = \\beta_{0} + \\beta_{1} \\cdot x_{1}\\]\nWhere the \\(\\beta\\)-coefficients are termed estimates, because that is exactly what we do, given the observed data, we estimate their values.\n\n\nWorking with a lm-object:\nThe model format you saw above, is a bit quirky, but luckily, there is a really nice way to get these kind of model object into a more tidy-format:\n\nlibrary(\"broom\")\nmy_lm_mdl |> \n  tidy()\n\n# A tibble: 2 × 5\n  term        estimate std.error statistic  p.value\n  <chr>          <dbl>     <dbl>     <dbl>    <dbl>\n1 (Intercept)     2.82    1.16        2.44 1.89e- 2\n2 temperature     2.02    0.0378     53.4  1.16e-41\n\n\nBriefly, here we term, estimate, std.error, statistic and p.value. We discussed the term and estimate. The std.error pertains to the estimate and the statistic is used to calculate the p.value.\n\nThe P-value\nNow, because we now have a tidy object, we can simply plug-‘n’-play with other tidyverse tools, so let us visualise the p.value. Note, because of the often vary large differences in p.values, we use a -log10-transformation, this means that larger values are “more significant”. Below, the dashed line signifies \\(p=0.05\\), so anything above that line is considered “statistically significant”:\n\nmy_lm_mdl |> \n  tidy() |> \n  ggplot(aes(x = term,\n             y = -log10(p.value))) +\n  geom_point() +\n  geom_hline(yintercept = -log10(0.05),\n             linetype = \"dashed\")\n\n\n\n\n\n\n\n\nNow, as mentioned the p-values are computed based on the statistic and are defined as: “The probability of observing a statistic as or more extreme given, that the null-hypothesis is true”. Where the null-hypothesis it that there is no effect, i.e. the estimate for the term is zero.\nFrom this, it is quite clear, that there very likely is a relationship between the gene_expression_level and temperature. In fact, we know there is, because we simulated the data.\n\n\nThe Confidence Intervals\nWe can further easily include the confidence intervals of the estimates:\n\nmy_lm_mdl_tidy <- my_lm_mdl |> \n  tidy(conf.int = TRUE,\n       conf.level = 0.95)\nmy_lm_mdl_tidy\n\n# A tibble: 2 × 7\n  term        estimate std.error statistic  p.value conf.low conf.high\n  <chr>          <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>\n1 (Intercept)     2.82    1.16        2.44 1.89e- 2    0.488      5.14\n2 temperature     2.02    0.0378     53.4  1.16e-41    1.94       2.10\n\n\n…and as before easily do a plug’n’play into ggplot:\n\nmy_lm_mdl_tidy |> \n  ggplot(aes(x = estimate,\n             y = term,\n             xmin = conf.low,\n             xmax = conf.high)) +\n  geom_errorbarh(height = 0.1) +\n  geom_point()\n\n\n\n\n\n\n\n\nNote, what the 0.95 = 95% confidence intervals means is that: “If we were to repeat this experiment 100 times, then 95 of the times, the generated confidence interval would contain the true value”.\n\n\n\nSummary\nWhat we have gone through here is a basic linear regression, where we are aiming to model the continuous variable gene_expression_level as a function of yet another continous variable temperature. We simulated data, where the true intercept and slope were 3 and 2 respectively and by fitting a linear regression model, the estimates of the intercept and slope respectively were 2.82 [0.49;5.14] and 2.02 [1.94;2.10].\nLinear models allow us to gain insights into data, by modelling relationships."
   },
   {
     "objectID": "external_resources.html",
diff --git a/lab10.qmd b/lab10.qmd
index 5e3cafd..04d3223 100644
--- a/lab10.qmd
+++ b/lab10.qmd
@@ -9,7 +9,9 @@
 
 ## Mini Symposium: Applications of R for Bio Data Science in Industry {#sec-symposium}
 
-*This mini symposium on Applications of R for Bio Data Science in Industry aims to give students a glimpse into how modern bio data science is transforming value creation in the pharmaceutical industry and healthcare sector.*
+_This mini symposium on Applications of R for Bio Data Science in Industry offers participants a glimpse into how modern Bio Data Science is transforming value creation in the pharmaceutical and healthcare sectors._
+
+_Each talk features a Bio Data Science professional sharing unique insights into real-world projects that apply cutting-edge Bio Data Science techniques, while also providing a glimpse into the career paths that have enabled their exciting work. Every talk will be followed by a Q&A session._
 
 ### 2024