deploy: 6e23eae

_sources/air_repertoire/clonotype.ipynb

@@ -47,7 +47,15 @@
    "id": "3adf7e04",
    "metadata": {},
    "source": [
-    "Here, as well as in the pre-processing step, we will use the utilities from the *Scirpy* library to perform the analysis and locate the results in the *AnnData* object."
+    "Here, as well as in the pre-processing step, we will use the utilities from the *Scirpy* library to perform the analysis and locate the results in the *AnnData* object.\n",
+    "\n",
+    ":::{warning}\n",
+    "Scirpy changed the format of [its datastructure](https://scirpy.scverse.org/en/latest/data-structure.html#storing-airr-rearrangement-data-in-anndata)\n",
+    "with v0.13. While the overall anlaysis workflow has not changed, some outputs shown in this chapter might not be accurate anymore. \n",
+    "\n",
+    "See [the scirpy release notes](https://scirpy.scverse.org/en/latest/changelog.html#v0-13-0-new-data-structure-based-on-awkward-arrays) for more details about this change. \n",
+    "Until we update this chapter, please also refer to the [official scirpy documentation](https://scirpy.scverse.org).\n",
+    ":::"
    ]
   },
   {

_sources/air_repertoire/ir_profiling.ipynb

@@ -199,7 +199,15 @@
     "- **Scanpy**: general package for single cell analysis (https://github.com/theislab/scanpy, {cite}`wolf2018scanpy`)\n",
     "- **Scirpy**: scanpy extension for immune receptor analysis (https://github.com/scverse/scirpy, {cite}`sturm2020scirpy`)\n",
     "\n",
-    "Here, we only showcase IR analysis with Scirpy. However, there exist several tools with similar functionality such as immunarch(R, {cite}`immunomind2019`), scRepertoire (R, {cite}`borcherding2020screpertoire`), and dandelion (R, {cite}`stephenson2021single`), and Platypus (R, {cite}`yermanos2021platypus`) reviewed in {cite}`valkiers2022recent`. \n"
+    "Here, we only showcase IR analysis with Scirpy. However, there exist several tools with similar functionality such as immunarch(R, {cite}`immunomind2019`), scRepertoire (R, {cite}`borcherding2020screpertoire`), and dandelion (R, {cite}`stephenson2021single`), and Platypus (R, {cite}`yermanos2021platypus`) reviewed in {cite}`valkiers2022recent`. \n",
+    "\n",
+    ":::{warning}\n",
+    "Scirpy changed the format of [its datastructure](https://scirpy.scverse.org/en/latest/data-structure.html#storing-airr-rearrangement-data-in-anndata)\n",
+    "with v0.13. While the overall anlaysis workflow has not changed, some outputs shown in this chapter might not be accurate anymore. \n",
+    "\n",
+    "See [the scirpy release notes](https://scirpy.scverse.org/en/latest/changelog.html#v0-13-0-new-data-structure-based-on-awkward-arrays) for more details about this change. \n",
+    "Until we update this chapter, please also refer to the [official scirpy documentation](https://scirpy.scverse.org).\n",
+    ":::\n"
    ]
   },
   {

_sources/air_repertoire/multimodal_integration.ipynb

@@ -25,7 +25,15 @@
    "source": [
     "## Data Preparation\n",
     "\n",
-    "First, we will combine the GEX with AIR data. Note, that we can do this also at earlier stages of single cell analysis: e.g. we can fuse both modalities already before preprocessing to filter GEX doublets. When fusing GEX and AIR data, a left join is often performed. I.e., only cells with GEX are kept for analysis. Therefore, the order of fusing and filtering is not of great importance. However, it can be convenient to visualize the AIR information in an GEX UMAP in early stages. E.g. missing AIR information in clusters can help during cell type assignment."
+    "First, we will combine the GEX with AIR data. Note, that we can do this also at earlier stages of single cell analysis: e.g. we can fuse both modalities already before preprocessing to filter GEX doublets. When fusing GEX and AIR data, a left join is often performed. I.e., only cells with GEX are kept for analysis. Therefore, the order of fusing and filtering is not of great importance. However, it can be convenient to visualize the AIR information in an GEX UMAP in early stages. E.g. missing AIR information in clusters can help during cell type assignment.\n",
+    "\n",
+    ":::{warning}\n",
+    "Scirpy changed the format of [its datastructure](https://scirpy.scverse.org/en/latest/data-structure.html#storing-airr-rearrangement-data-in-anndata)\n",
+    "with v0.13. While the overall anlaysis workflow has not changed, some outputs shown in this chapter might not be accurate anymore. \n",
+    "\n",
+    "See [the scirpy release notes](https://scirpy.scverse.org/en/latest/changelog.html#v0-13-0-new-data-structure-based-on-awkward-arrays) for more details about this change. \n",
+    "Until we update this chapter, please also refer to the [official scirpy documentation](https://scirpy.scverse.org).\n",
+    ":::"
    ]
   },
   {

_sources/air_repertoire/specificity.ipynb

@@ -41,7 +41,15 @@
     "\n",
     "CDR3β > V- and J-gene > CDR3α > MHC > cell type\n",
     "\n",
-    "We can assume that this importance is similar not only for prediction, but also for querying, clustering, and distance calculation."
+    "We can assume that this importance is similar not only for prediction, but also for querying, clustering, and distance calculation.\n",
+    "\n",
+    ":::{warning}\n",
+    "Scirpy changed the format of [its datastructure](https://scirpy.scverse.org/en/latest/data-structure.html#storing-airr-rearrangement-data-in-anndata)\n",
+    "with v0.13. While the overall anlaysis workflow has not changed, some outputs shown in this chapter might not be accurate anymore. \n",
+    "\n",
+    "See [the scirpy release notes](https://scirpy.scverse.org/en/latest/changelog.html#v0-13-0-new-data-structure-based-on-awkward-arrays) for more details about this change. \n",
+    "Until we update this chapter, please also refer to the [official scirpy documentation](https://scirpy.scverse.org).\n",
+    ":::"
    ]
   },
   {

_sources/conditions/perturbation_modeling.ipynb

@@ -2355,7 +2355,7 @@
     }
    ],
    "source": [
-    "pt.pl.ms.barplot(mdata[\"rna\"])"
+    "pt.pl.ms.barplot(mdata[\"rna\"], guide_rna_column=\"guide_ID\")"
    ]
   },
   {

_sources/introduction/raw_data_processing.md

@@ -499,7 +499,7 @@ When running `simpleaf index`, if a genome FASTA file (`-f`) and a gene annotati
 ```bash
 # simpleaf needs the environment variable ALEVIN_FRY_HOME to store configuration and data.
 # For example, the paths to the underlying programs it uses and the CB permit list
-mkdir alevin_fry_home & export ALEVIN_FRY_HOME='alevin_fry_home'
+mkdir alevin_fry_home && export ALEVIN_FRY_HOME='alevin_fry_home'
 
 # the simpleaf set-paths command finds the path to the required tools and write a configuration JSON file in the ALEVIN_FRY_HOME folder.
 simpleaf set-paths

_sources/preprocessing_visualization/normalization.ipynb

@@ -31,7 +31,7 @@
     "\n",
     "The preprocessing step of \"normalization\" aims to adjust the raw counts in the dataset for variable sampling effects by scaling the observable variance to a specified range. Several normalization techniques are used in practice varying in complexity. They are mostly designed in such a way that subsequent analysis tasks and their underlying statistical methods are applicable. \n",
     "\n",
-    "A recent benchmark published by Ahlmann-Eltze and Huber{cite}`Ahlmann-Eltze2021.06.24.449781` compared 22 different transformations for single-cell data. The benchmark compared the performance of the different normalization techniques based on the cell graph overlap with the ground truth. We would like to highlight that a complete benchmark which also compares the impact of the normalization on a variety of different downstream analysis tasks is still outstanding. We advise analysts to choose the normalization carefully and always depend on the subsequent analysis task. \n",
+    "A recent benchmark published by Ahlmann-Eltze and Huber{cite}`Ahlmann-Eltze2023` compared 22 different transformations for single-cell data. The benchmark compared the performance of the different normalization techniques based on the cell graph overlap with the ground truth. We would like to highlight that a complete benchmark which also compares the impact of the normalization on a variety of different downstream analysis tasks is still outstanding. We advise analysts to choose the normalization carefully and always depend on the subsequent analysis task. \n",
     "\n",
     "This chapter will introduce the reader to three different normalization techniques, the shifted logarithm transformation, scran normalization and analytic approximation of Pearson residuals. The shifted logarithm works beneficial for stabilizing variance for subsequent dimensionality reduction and identification of differentially expressed genes. Scran was extensively tested and used for batch correction tasks and analytic Pearson residuals are well suited for selecting biologically variable genes and identification of rare cell types. \n",
     "\n",

_static/pygments.css

@@ -17,6 +17,7 @@ span.linenos.special { color: #000000; background-color: #ffffc0; padding-left:
 .highlight .cs { color: #408090; background-color: #fff0f0 } /* Comment.Special */
 .highlight .gd { color: #A00000 } /* Generic.Deleted */
 .highlight .ge { font-style: italic } /* Generic.Emph */
+.highlight .ges { font-weight: bold; font-style: italic } /* Generic.EmphStrong */
 .highlight .gr { color: #FF0000 } /* Generic.Error */
 .highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
 .highlight .gi { color: #00A000 } /* Generic.Inserted */

air_repertoire/clonotype.html

@@ -910,6 +910,13 @@ <h2><span class="section-number">42.2. </span>Gene segment usage and spectratype
 <section id="tcr-data-preparation">
 <h2><span class="section-number">42.3. </span>TCR data preparation<a class="headerlink" href="#tcr-data-preparation" title="Permalink to this headline">#</a></h2>
 <p>Here, as well as in the pre-processing step, we will use the utilities from the <em>Scirpy</em> library to perform the analysis and locate the results in the <em>AnnData</em> object.</p>
+<div class="admonition warning">
+<p class="admonition-title">Warning</p>
+<p>Scirpy changed the format of <a class="reference external" href="https://scirpy.scverse.org/en/latest/data-structure.html#storing-airr-rearrangement-data-in-anndata">its datastructure</a>
+with v0.13. While the overall anlaysis workflow has not changed, some outputs shown in this chapter might not be accurate anymore.</p>
+<p>See <a class="reference external" href="https://scirpy.scverse.org/en/latest/changelog.html#v0-13-0-new-data-structure-based-on-awkward-arrays">the scirpy release notes</a> for more details about this change.
+Until we update this chapter, please also refer to the <a class="reference external" href="https://scirpy.scverse.org">official scirpy documentation</a>.</p>
+</div>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython2 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">warnings</span>

air_repertoire/ir_profiling.html

@@ -1045,6 +1045,13 @@ <h2><span class="section-number">41.7. </span>Load data<a class="headerlink" hre
 <li><p><strong>Scirpy</strong>: scanpy extension for immune receptor analysis (<a class="reference external" href="https://github.com/scverse/scirpy">https://github.com/scverse/scirpy</a>, <span id="id11">[<a class="reference internal" href="#id480" title="Gregor Sturm, Tamas Szabo, Georgios Fotakis, Marlene Haider, Dietmar Rieder, Zlatko Trajanoski, and Francesca Finotello. Scirpy: a scanpy extension for analyzing single-cell t-cell receptor-sequencing data. Bioinformatics, 36(18):4817–4818, 2020.">Sturm <em>et al.</em>, 2020</a>]</span>)</p></li>
 </ul>
 <p>Here, we only showcase IR analysis with Scirpy. However, there exist several tools with similar functionality such as immunarch(R, <span id="id12">[<a class="reference internal" href="#id481" title="ImmunoMind Team. immunarch: An R Package for Painless Bioinformatics Analysis of T-Cell and B-Cell Immune Repertoires. August 2019. URL: https://doi.org/10.5281/zenodo.3367200, doi:10.5281/zenodo.3367200.">ImmunoMind Team, 2019</a>]</span>), scRepertoire (R, <span id="id13">[<a class="reference internal" href="#id482" title="Nicholas Borcherding, Nicholas L Bormann, and Gloria Kraus. Screpertoire: an r-based toolkit for single-cell immune receptor analysis. F1000Research, 2020.">Borcherding <em>et al.</em>, 2020</a>]</span>), and dandelion (R, <span id="id14">[<a class="reference internal" href="#id478" title="Emily Stephenson, Gary Reynolds, Rachel A Botting, Fernando J Calero-Nieto, Michael D Morgan, Zewen Kelvin Tuong, Karsten Bach, Waradon Sungnak, Kaylee B Worlock, Masahiro Yoshida, and others. Single-cell multi-omics analysis of the immune response in covid-19. Nature medicine, 27(5):904–916, 2021.">Stephenson <em>et al.</em>, 2021</a>]</span>), and Platypus (R, <span id="id15">[<a class="reference internal" href="#id484" title="Alexander Yermanos, Andreas Agrafiotis, Raphael Kuhn, Damiano Robbiani, Josephine Yates, Chrysa Papadopoulou, Jiami Han, Ioana Sandu, Cédric Weber, Florian Bieberich, and others. Platypus: an open-access software for integrating lymphocyte single-cell immune repertoires with transcriptomes. NAR genomics and bioinformatics, 3(2):lqab023, 2021.">Yermanos <em>et al.</em>, 2021</a>]</span>) reviewed in <span id="id16">[<a class="reference internal" href="#id483" title="Sebastiaan Valkiers, Nicky de Vrij, Sofie Gielis, Sara Verbandt, Benson Ogunjimi, Kris Laukens, and Pieter Meysman. Recent advances in t-cell receptor repertoire analysis: bridging the gap with multimodal single-cell rna sequencing. ImmunoInformatics, pages 100009, 2022.">Valkiers <em>et al.</em>, 2022</a>]</span>.</p>
+<div class="admonition warning">
+<p class="admonition-title">Warning</p>
+<p>Scirpy changed the format of <a class="reference external" href="https://scirpy.scverse.org/en/latest/data-structure.html#storing-airr-rearrangement-data-in-anndata">its datastructure</a>
+with v0.13. While the overall anlaysis workflow has not changed, some outputs shown in this chapter might not be accurate anymore.</p>
+<p>See <a class="reference external" href="https://scirpy.scverse.org/en/latest/changelog.html#v0-13-0-new-data-structure-based-on-awkward-arrays">the scirpy release notes</a> for more details about this change.
+Until we update this chapter, please also refer to the <a class="reference external" href="https://scirpy.scverse.org">official scirpy documentation</a>.</p>
+</div>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">warnings</span>

air_repertoire/multimodal_integration.html

@@ -974,6 +974,13 @@ <h2><span class="section-number">47.1. </span>Motivation<a class="headerlink" hr
 <section id="data-preparation">
 <h2><span class="section-number">47.2. </span>Data Preparation<a class="headerlink" href="#data-preparation" title="Permalink to this headline">#</a></h2>
 <p>First, we will combine the GEX with AIR data. Note, that we can do this also at earlier stages of single cell analysis: e.g. we can fuse both modalities already before preprocessing to filter GEX doublets. When fusing GEX and AIR data, a left join is often performed. I.e., only cells with GEX are kept for analysis. Therefore, the order of fusing and filtering is not of great importance. However, it can be convenient to visualize the AIR information in an GEX UMAP in early stages. E.g. missing AIR information in clusters can help during cell type assignment.</p>
+<div class="admonition warning">
+<p class="admonition-title">Warning</p>
+<p>Scirpy changed the format of <a class="reference external" href="https://scirpy.scverse.org/en/latest/data-structure.html#storing-airr-rearrangement-data-in-anndata">its datastructure</a>
+with v0.13. While the overall anlaysis workflow has not changed, some outputs shown in this chapter might not be accurate anymore.</p>
+<p>See <a class="reference external" href="https://scirpy.scverse.org/en/latest/changelog.html#v0-13-0-new-data-structure-based-on-awkward-arrays">the scirpy release notes</a> for more details about this change.
+Until we update this chapter, please also refer to the <a class="reference external" href="https://scirpy.scverse.org">official scirpy documentation</a>.</p>
+</div>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">warnings</span>

air_repertoire/specificity.html

@@ -947,6 +947,13 @@ <h2><span class="section-number">46.2. </span>TCR specificity analysis<a class="
 <p>In the following, we will showcase these approaches for TCRs. Previous studies <span id="id3">[<a class="reference internal" href="#id498" title="Jacob Glanville, Huang Huang, Allison Nau, Olivia Hatton, Lisa E Wagar, Florian Rubelt, Xuhuai Ji, Arnold Han, Sheri M Krams, Christina Pettus, and others. Identifying specificity groups in the t cell receptor repertoire. Nature, 547(7661):94–98, 2017.">Glanville <em>et al.</em>, 2017</a>, <a class="reference internal" href="#id499" title="Mark M Davis and Pamela J Bjorkman. T-cell antigen receptor genes and t-cell recognition. Nature, 334(6181):395–402, 1988.">Davis and Bjorkman, 1988</a>, <a class="reference internal" href="#id500" title="Markus G Rudolph, Robyn L Stanfield, and Ian A Wilson. How tcrs bind mhcs, peptides, and coreceptors. Annual review of immunology, 24(1):419–466, 2006.">Rudolph <em>et al.</em>, 2006</a>]</span> showed that the TCR is in close contact with the pMHC at the CDR3, specifically, of the β-chain. This is in line with the regions of high variability of the TCR sequence. In <span id="id4">[<a class="reference internal" href="#id486" title="Ido Springer, Nili Tickotsky, and Yoram Louzoun. Contribution of t cell receptor alpha and beta cdr3, mhc typing, v and j genes to peptide binding prediction. Frontiers in immunology, 2021.">Springer <em>et al.</em>, 2021</a>]</span> by Springer et al., the importance of different information (elements of the TCR and MHC type) was reported for training a sequence-based classifier, which are mostly in line with these findings:</p>
 <p>CDR3β &gt; V- and J-gene &gt; CDR3α &gt; MHC &gt; cell type</p>
 <p>We can assume that this importance is similar not only for prediction, but also for querying, clustering, and distance calculation.</p>
+<div class="admonition warning">
+<p class="admonition-title">Warning</p>
+<p>Scirpy changed the format of <a class="reference external" href="https://scirpy.scverse.org/en/latest/data-structure.html#storing-airr-rearrangement-data-in-anndata">its datastructure</a>
+with v0.13. While the overall anlaysis workflow has not changed, some outputs shown in this chapter might not be accurate anymore.</p>
+<p>See <a class="reference external" href="https://scirpy.scverse.org/en/latest/changelog.html#v0-13-0-new-data-structure-based-on-awkward-arrays">the scirpy release notes</a> for more details about this change.
+Until we update this chapter, please also refer to the <a class="reference external" href="https://scirpy.scverse.org">official scirpy documentation</a>.</p>
+</div>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">warnings</span>