From f20f39baef1bc4555a04bdda00ddb728a19d11d5 Mon Sep 17 00:00:00 2001 From: Wendi Bacon <44605769+nomadscientist@users.noreply.github.com> Date: Fri, 22 Sep 2023 16:00:42 +0100 Subject: [PATCH 1/8] Update metadata.yaml --- topics/single-cell/metadata.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/topics/single-cell/metadata.yaml b/topics/single-cell/metadata.yaml index b1e91db5e7dc29..7fe35a1c271281 100644 --- a/topics/single-cell/metadata.yaml +++ b/topics/single-cell/metadata.yaml @@ -18,10 +18,10 @@ editorial_board: subtopics: - id: scintroduction title: "Introduction" - description: "Start here if you are new to single cell analysis in Galaxy" + description: "Start here if you are new to single cell analysis in Galaxy and want to learn the concepts." - id: firstsc title: "Your first analysis" - description: "Start here if you are new to single cell analysis in Galaxy" + description: "Start here if you are new to single cell analysis in Galaxy and want to try analysing data." - id: single-cell-CS title: "Case study" description: "These tutorials take you from raw scRNA sequencing reads to inferred trajectories to replicate a published analysis. The data is messy. The decisions are tough. The interpretation is meaningful. Come here to advance your single cell skills! Note that you get two options for inferring trajectories." From 5819a1ba67951f9d213c1dfd6694d15830b5d6ac Mon Sep 17 00:00:00 2001 From: Wendi Bacon <44605769+nomadscientist@users.noreply.github.com> Date: Fri, 22 Sep 2023 16:07:25 +0100 Subject: [PATCH 2/8] Move trajectories to slide deck section this way it applies to everything since there are about 3-4 trajectories tutorials --- .../tutorials/scrna-case_monocle3-trajectories/tutorial.md | 2 +- .../slides.bib | 0 .../slides.html | 6 ++++-- 3 files changed, 5 insertions(+), 3 deletions(-) rename topics/single-cell/tutorials/{scrna-case_monocle3-trajectories => scrna-trajectories}/slides.bib (100%) rename topics/single-cell/tutorials/{scrna-case_monocle3-trajectories => scrna-trajectories}/slides.html (99%) diff --git a/topics/single-cell/tutorials/scrna-case_monocle3-trajectories/tutorial.md b/topics/single-cell/tutorials/scrna-case_monocle3-trajectories/tutorial.md index d23edc73e0b1fa..923ed0f826709a 100644 --- a/topics/single-cell/tutorials/scrna-case_monocle3-trajectories/tutorial.md +++ b/topics/single-cell/tutorials/scrna-case_monocle3-trajectories/tutorial.md @@ -57,7 +57,7 @@ contributions: This tutorial is a follow-up to the ['Single-cell RNA-seq: Case Study']({% link topics/single-cell/index.md %}). We will use the same sample from the previous tutorials. If you haven’t done them yet, it’s highly recommended that you go through them to get an idea how to [prepare a single cell matrix]({% link topics/single-cell/tutorials/scrna-case_alevin/tutorial.md %}), [combine datasets]({% link topics/single-cell/tutorials/scrna-case_alevin-combine-datasets/tutorial.md %}) and [filter, plot and process scRNA-seq data]({% link topics/single-cell/tutorials/scrna-case_basic-pipeline/tutorial.md %}) to get the data in the form we’ll be working on today. -In this tutorial we will perform trajectory analysis using [monocle3](https://cole-trapnell-lab.github.io/monocle3/). You can find out more about the theory behind trajectory analysis in our [slide deck]({% link topics/single-cell/tutorials/scrna-case_monocle3-trajectories/slides.html %}). We have already analysed the trajectory of our sample using the ScanPy toolkit in another tutorial: [Trajectory Analysis using Python (Jupyter Notebook) in Galaxy]({% link topics/single-cell/tutorials/scrna-case_JUPYTER-trajectories/tutorial.md %}). However, trajectory analysis is quite sensitive and some methods work better for specific datasets. In this tutorial, you will perform the same steps but using a different method for inferring trajectories. You will then compare the results, usability and outcomes! Sounds exciting, let’s dive into that! +In this tutorial we will perform trajectory analysis using [monocle3](https://cole-trapnell-lab.github.io/monocle3/). You can find out more about the theory behind trajectory analysis in our [slide deck]({% link topics/single-cell/tutorials/scrna-trajectories/slides.html %}). We have already analysed the trajectory of our sample using the ScanPy toolkit in another tutorial: [Trajectory Analysis using Python (Jupyter Notebook) in Galaxy]({% link topics/single-cell/tutorials/scrna-case_JUPYTER-trajectories/tutorial.md %}). However, trajectory analysis is quite sensitive and some methods work better for specific datasets. In this tutorial, you will perform the same steps but using a different method for inferring trajectories. You will then compare the results, usability and outcomes! Sounds exciting, let’s dive into that! {% snippet faqs/galaxy/tutorial_mode.md %} diff --git a/topics/single-cell/tutorials/scrna-case_monocle3-trajectories/slides.bib b/topics/single-cell/tutorials/scrna-trajectories/slides.bib similarity index 100% rename from topics/single-cell/tutorials/scrna-case_monocle3-trajectories/slides.bib rename to topics/single-cell/tutorials/scrna-trajectories/slides.bib diff --git a/topics/single-cell/tutorials/scrna-case_monocle3-trajectories/slides.html b/topics/single-cell/tutorials/scrna-trajectories/slides.html similarity index 99% rename from topics/single-cell/tutorials/scrna-case_monocle3-trajectories/slides.html rename to topics/single-cell/tutorials/scrna-trajectories/slides.html index 1507a037db3f73..4b1acdd3c0caaa 100644 --- a/topics/single-cell/tutorials/scrna-case_monocle3-trajectories/slides.html +++ b/topics/single-cell/tutorials/scrna-trajectories/slides.html @@ -17,7 +17,9 @@ redirect_from: - /topics/transcriptomics/tutorials/scrna-case_monocle3-trajectories/slides -subtopic: single-cell-CS +subtopic: scintroduction +priority: 4 + key_points: - "Trajectory analysis in pseudotime is a powerful way to get insight into the differentiation and development of cells." - "There are multiple methods and algorithms used in trajectory analysis and depending on the dataset, some might work better than others." @@ -509,7 +511,7 @@ - Monocle3 in RStudio (coming soon) - From 8292b1cf532da671e34d93995d5d03332ab83648 Mon Sep 17 00:00:00 2001 From: Saskia Hiltemann Date: Mon, 25 Sep 2023 12:41:01 +0200 Subject: [PATCH 3/8] fix link --- .../tutorials/scrna-case_monocle3-rstudio/preamble.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/preamble.md b/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/preamble.md index 6f7478b345a953..59091b4dfe1ea5 100644 --- a/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/preamble.md +++ b/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/preamble.md @@ -1,6 +1,6 @@ # Introduction -This tutorial is the next one in the [Single-cell RNA-seq: Case Study]({% link topics/single-cell/index.md %}) series. This tutorial focuses on trajectory analysis using [monocle3](https://cole-trapnell-lab.github.io/monocle3/), similar to the [Monocle3 in Galaxy tutorial]({% link topics/single-cell/tutorials/scrna-case_monocle3-trajectories/tutorial.md %}). However, in this tutorial we will use the R programming language that hides behind the user-friendly Galaxy tools. Sometimes you might encounter limitations when working with Galaxy tools, or you might want to make a wee modification that has to be done manually. It is therefore useful to be able to switch to R. If you do not feel confident using R, [this tutorial]({% link topics/data-science/tutorials/r-basics/tutorial.md %}) is a good place to start. However, our tutorial is quite straightforward to follow and at the end you will feel like a programmer! On the other hand, if you are not confident with the biological or statistical theory behind trajectory analysis, check out the [slide deck]({% link topics/single-cell/tutorials/scrna-case_monocle3-trajectories/slides.html %}). With those resources (including the previous case study tutorials) you are well-equipped to go through this tutorial with ease. Let’s get started! +This tutorial is the next one in the [Single-cell RNA-seq: Case Study]({% link topics/single-cell/index.md %}) series. This tutorial focuses on trajectory analysis using [monocle3](https://cole-trapnell-lab.github.io/monocle3/), similar to the [Monocle3 in Galaxy tutorial]({% link topics/single-cell/tutorials/scrna-case_monocle3-trajectories/tutorial.md %}). However, in this tutorial we will use the R programming language that hides behind the user-friendly Galaxy tools. Sometimes you might encounter limitations when working with Galaxy tools, or you might want to make a wee modification that has to be done manually. It is therefore useful to be able to switch to R. If you do not feel confident using R, [this tutorial]({% link topics/data-science/tutorials/r-basics/tutorial.md %}) is a good place to start. However, our tutorial is quite straightforward to follow and at the end you will feel like a programmer! On the other hand, if you are not confident with the biological or statistical theory behind trajectory analysis, check out the [slide deck]({% link topics/single-cell/tutorials/scrna-case-trajectories/slides.html %}). With those resources (including the previous case study tutorials) you are well-equipped to go through this tutorial with ease. Let’s get started! > > This tutorial is significantly based on the [Monocle3 documentation](https://cole-trapnell-lab.github.io/monocle3/docs/introduction/). @@ -14,7 +14,7 @@ First, we need to retrieve the appropriate data. We will continue to work on the > Optional data upload into Galaxy history > > You have three options for importing the input data into a Galaxy history. -> +> > 1. You can import a history from: [input history](https://usegalaxy.eu/u/wendi.bacon.training/h/cs4trajectories--monocle3--rstudio---input); Import the files from [Zenodo]({{ page.zenodo_link }}); or Import the files from the shared data library (`GTN - Material` -> `{{ page.topic_name }}` > -> `{{ page.title }}`): > From 4ba71eb62d9fd4d22b6a935827589e3d07be4782 Mon Sep 17 00:00:00 2001 From: Wendi Bacon <44605769+nomadscientist@users.noreply.github.com> Date: Mon, 25 Sep 2023 11:48:47 +0100 Subject: [PATCH 4/8] internal link fix --- .../tutorials/scrna-case_monocle3-trajectories/tutorial.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/topics/single-cell/tutorials/scrna-case_monocle3-trajectories/tutorial.md b/topics/single-cell/tutorials/scrna-case_monocle3-trajectories/tutorial.md index 923ed0f826709a..30e8612b0fcb7b 100644 --- a/topics/single-cell/tutorials/scrna-case_monocle3-trajectories/tutorial.md +++ b/topics/single-cell/tutorials/scrna-case_monocle3-trajectories/tutorial.md @@ -621,7 +621,7 @@ Here we used a priori knowledge regarding the marker genes. If we wanted to appr > > > > > -> > By looking at the table, you might give the 5 top gene IDs expressed in DP-M1. To save you some time and make the analysis more readable, we converted the gene IDs to gene names and they are as follows: Rps17, Rpl41, Rps26, Rps29, Rps28. They are all ribosomal! [You can do this yourself if you want by following this section of a previous tutorial that [uses the gene names in one object to add to a table of Ensembl IDs](https://training.galaxyproject.org/training-material/topics/single-cell/tutorials/scrna-case_basic-pipeline/tutorial.html#findmarkers). These ribosomal differences might be due to housekeeping background, cell cycling, or even something more bioligically interesting...or all three! +> > By looking at the table, you might give the 5 top gene IDs expressed in DP-M1. To save you some time and make the analysis more readable, we converted the gene IDs to gene names and they are as follows: Rps17, Rpl41, Rps26, Rps29, Rps28. They are all ribosomal! [You can do this yourself if you want by following this section of a previous tutorial that [uses the gene names in one object to add to a table of Ensembl IDs]{% link topics/single-cell/tutorials/scrna-case_basic-pipeline/tutorial.html#findmarkers %}. These ribosomal differences might be due to housekeeping background, cell cycling, or even something more bioligically interesting...or all three! > > The plot also indicates other specifically expressed genes, such as Hmgb2, Pclaf, Rpl13, Rps19, Ybx1, Ncl, Hsp90ab1, Npm1. > > > > Whenever you want to explore what might be the function of a particular cluster or why it branches out from the trajectory, check the top markers for that cluster to draw biological conclusions. Thank you Maths! From 34651185b286aa80f09410693be35d24c97fcc8f Mon Sep 17 00:00:00 2001 From: Wendi Bacon <44605769+nomadscientist@users.noreply.github.com> Date: Mon, 25 Sep 2023 22:56:24 +0100 Subject: [PATCH 5/8] awkward fix of internal link to subsection --- .../tutorials/scrna-case_monocle3-trajectories/tutorial.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/topics/single-cell/tutorials/scrna-case_monocle3-trajectories/tutorial.md b/topics/single-cell/tutorials/scrna-case_monocle3-trajectories/tutorial.md index 30e8612b0fcb7b..42f131b2f051d5 100644 --- a/topics/single-cell/tutorials/scrna-case_monocle3-trajectories/tutorial.md +++ b/topics/single-cell/tutorials/scrna-case_monocle3-trajectories/tutorial.md @@ -621,7 +621,7 @@ Here we used a priori knowledge regarding the marker genes. If we wanted to appr > > > > > -> > By looking at the table, you might give the 5 top gene IDs expressed in DP-M1. To save you some time and make the analysis more readable, we converted the gene IDs to gene names and they are as follows: Rps17, Rpl41, Rps26, Rps29, Rps28. They are all ribosomal! [You can do this yourself if you want by following this section of a previous tutorial that [uses the gene names in one object to add to a table of Ensembl IDs]{% link topics/single-cell/tutorials/scrna-case_basic-pipeline/tutorial.html#findmarkers %}. These ribosomal differences might be due to housekeeping background, cell cycling, or even something more bioligically interesting...or all three! +> > By looking at the table, you might give the 5 top gene IDs expressed in DP-M1. To save you some time and make the analysis more readable, we converted the gene IDs to gene names and they are as follows: Rps17, Rpl41, Rps26, Rps29, Rps28. They are all ribosomal! [You can do this yourself if you want by following this section of a previous tutorial that [uses the gene names in one object to add to a table of Ensembl IDs]{% link topics/single-cell/tutorials/scrna-case_basic-pipeline/tutorial.md %} in the "FindMarkers" subsection. These ribosomal differences might be due to housekeeping background, cell cycling, or even something more bioligically interesting...or all three! > > The plot also indicates other specifically expressed genes, such as Hmgb2, Pclaf, Rpl13, Rps19, Ybx1, Ncl, Hsp90ab1, Npm1. > > > > Whenever you want to explore what might be the function of a particular cluster or why it branches out from the trajectory, check the top markers for that cluster to draw biological conclusions. Thank you Maths! From d9d3a4c9902f76f8c3761903545c60ad5f0bd755 Mon Sep 17 00:00:00 2001 From: Wendi Bacon <44605769+nomadscientist@users.noreply.github.com> Date: Mon, 25 Sep 2023 22:57:26 +0100 Subject: [PATCH 6/8] saskia is a genius --- .../tutorials/scrna-case_monocle3-trajectories/tutorial.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/topics/single-cell/tutorials/scrna-case_monocle3-trajectories/tutorial.md b/topics/single-cell/tutorials/scrna-case_monocle3-trajectories/tutorial.md index 42f131b2f051d5..83151934d68ba3 100644 --- a/topics/single-cell/tutorials/scrna-case_monocle3-trajectories/tutorial.md +++ b/topics/single-cell/tutorials/scrna-case_monocle3-trajectories/tutorial.md @@ -621,7 +621,7 @@ Here we used a priori knowledge regarding the marker genes. If we wanted to appr > > > > > -> > By looking at the table, you might give the 5 top gene IDs expressed in DP-M1. To save you some time and make the analysis more readable, we converted the gene IDs to gene names and they are as follows: Rps17, Rpl41, Rps26, Rps29, Rps28. They are all ribosomal! [You can do this yourself if you want by following this section of a previous tutorial that [uses the gene names in one object to add to a table of Ensembl IDs]{% link topics/single-cell/tutorials/scrna-case_basic-pipeline/tutorial.md %} in the "FindMarkers" subsection. These ribosomal differences might be due to housekeeping background, cell cycling, or even something more bioligically interesting...or all three! +> > By looking at the table, you might give the 5 top gene IDs expressed in DP-M1. To save you some time and make the analysis more readable, we converted the gene IDs to gene names and they are as follows: Rps17, Rpl41, Rps26, Rps29, Rps28. They are all ribosomal! [You can do this yourself if you want by following this section of a previous tutorial that [uses the gene names in one object to add to a table of Ensembl IDs]({% link topics/single-cell/tutorials/scrna-case_basic-pipeline/tutorial.md %}#findmarkers). These ribosomal differences might be due to housekeeping background, cell cycling, or even something more bioligically interesting...or all three! > > The plot also indicates other specifically expressed genes, such as Hmgb2, Pclaf, Rpl13, Rps19, Ybx1, Ncl, Hsp90ab1, Npm1. > > > > Whenever you want to explore what might be the function of a particular cluster or why it branches out from the trajectory, check the top markers for that cluster to draw biological conclusions. Thank you Maths! From 962d477f9c9f2b9c403a5277df294865f702b506 Mon Sep 17 00:00:00 2001 From: Wendi Bacon <44605769+nomadscientist@users.noreply.github.com> Date: Tue, 26 Sep 2023 09:24:45 +0100 Subject: [PATCH 7/8] fixed internal link --- .../tutorials/scrna-case_monocle3-rstudio/preamble.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/preamble.md b/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/preamble.md index 59091b4dfe1ea5..134445044c3be4 100644 --- a/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/preamble.md +++ b/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/preamble.md @@ -1,6 +1,6 @@ # Introduction -This tutorial is the next one in the [Single-cell RNA-seq: Case Study]({% link topics/single-cell/index.md %}) series. This tutorial focuses on trajectory analysis using [monocle3](https://cole-trapnell-lab.github.io/monocle3/), similar to the [Monocle3 in Galaxy tutorial]({% link topics/single-cell/tutorials/scrna-case_monocle3-trajectories/tutorial.md %}). However, in this tutorial we will use the R programming language that hides behind the user-friendly Galaxy tools. Sometimes you might encounter limitations when working with Galaxy tools, or you might want to make a wee modification that has to be done manually. It is therefore useful to be able to switch to R. If you do not feel confident using R, [this tutorial]({% link topics/data-science/tutorials/r-basics/tutorial.md %}) is a good place to start. However, our tutorial is quite straightforward to follow and at the end you will feel like a programmer! On the other hand, if you are not confident with the biological or statistical theory behind trajectory analysis, check out the [slide deck]({% link topics/single-cell/tutorials/scrna-case-trajectories/slides.html %}). With those resources (including the previous case study tutorials) you are well-equipped to go through this tutorial with ease. Let’s get started! +This tutorial is the next one in the [Single-cell RNA-seq: Case Study]({% link topics/single-cell/index.md %}) series. This tutorial focuses on trajectory analysis using [monocle3](https://cole-trapnell-lab.github.io/monocle3/), similar to the [Monocle3 in Galaxy tutorial]({% link topics/single-cell/tutorials/scrna-case_monocle3-trajectories/tutorial.md %}). However, in this tutorial we will use the R programming language that hides behind the user-friendly Galaxy tools. Sometimes you might encounter limitations when working with Galaxy tools, or you might want to make a wee modification that has to be done manually. It is therefore useful to be able to switch to R. If you do not feel confident using R, [this tutorial]({% link topics/data-science/tutorials/r-basics/tutorial.md %}) is a good place to start. However, our tutorial is quite straightforward to follow and at the end you will feel like a programmer! On the other hand, if you are not confident with the biological or statistical theory behind trajectory analysis, check out the [slide deck]({% link topics/single-cell/tutorials/scrna-trajectories/slides.html %}). With those resources (including the previous case study tutorials) you are well-equipped to go through this tutorial with ease. Let’s get started! > > This tutorial is significantly based on the [Monocle3 documentation](https://cole-trapnell-lab.github.io/monocle3/docs/introduction/). From b13794657708af6804be79c225c18bbfbdfebfe1 Mon Sep 17 00:00:00 2001 From: Wendi Bacon <44605769+nomadscientist@users.noreply.github.com> Date: Tue, 26 Sep 2023 09:25:14 +0100 Subject: [PATCH 8/8] rearrange headers --- topics/single-cell/metadata.yaml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/topics/single-cell/metadata.yaml b/topics/single-cell/metadata.yaml index 7fe35a1c271281..88189ed0d2f8fc 100644 --- a/topics/single-cell/metadata.yaml +++ b/topics/single-cell/metadata.yaml @@ -28,12 +28,12 @@ subtopics: - id: single-cell-CS-code title: "Case study: Reloaded" description: "These tutorials let you follow the same case study analysis of real, messy data but in a programming environment, hosted on Galaxy. So if you want more flexibility, but the same guided steps as the Case Study, you can skip the Case Study and start here instead. Alternatively, try these after completing the Case Study for an easier jump to a coding environment." - - id: deconvo - title: "Deconvolution" - description: "These tutorials infer cell compositions from bulk RNA-seq data using a scRNA-seq reference" - id: end-to-end title: "End-to-end scRNA-seq Analyses" description: "These tutorials use different methods to analyse scRNA-seq samples" + - id: deconvo + title: "Deconvolution" + description: "These tutorials infer cell compositions from bulk RNA-seq data using a scRNA-seq reference" - id: scmultiomics title: "Multiomic Analyses" description: "This section lets you build on mere scRNA analyses into a multiomic future!"