From 39321abd2f0d4dc887af897ac7561b84eb0ad82e Mon Sep 17 00:00:00 2001 From: Pavankumar Videm Date: Fri, 5 Apr 2024 12:01:44 +0200 Subject: [PATCH] combine get data and generate seurat objects into one section --- .../tutorial.md | 49 +++++++++++-------- 1 file changed, 29 insertions(+), 20 deletions(-) diff --git a/topics/single-cell/tutorials/scrna-case_FilterPlotandExplore_SeuratTools/tutorial.md b/topics/single-cell/tutorials/scrna-case_FilterPlotandExplore_SeuratTools/tutorial.md index 0c3db57fed6f37..9931573c19af75 100644 --- a/topics/single-cell/tutorials/scrna-case_FilterPlotandExplore_SeuratTools/tutorial.md +++ b/topics/single-cell/tutorials/scrna-case_FilterPlotandExplore_SeuratTools/tutorial.md @@ -59,7 +59,9 @@ You’ve previously done all the work to make a single cell matrix. Now it’s t We’ll provided you with experimental data to analyse from a mouse dataset of fetal growth restriction ({% cite Bacon2018 %}). This is the full dataset generated from [this tutorial]({% link topics/single-cell/tutorials/scrna-case_alevin/tutorial.md %}). -# Get Data onto Galaxy +# Get Data onto Galaxy and generate a Seurat object + +## Get Data onto Galaxy To start, let's get our dataset loaded into Galaxy. {% include _includes/cyoa-choices.html option1='EBI Data Retrieval' option2='Importing from a history' option3='Uploading from Zenodo' default='EBI Data Retrieval' text="There are multiple ways in which to collect the data for this tutorial. I find it easiest to do so via the EBI Data Retrieval." %} @@ -85,6 +87,30 @@ To start, let's get our dataset loaded into Galaxy. {:.hands_on} +
+ +## Generating a Seurat object +You now should have imported the `matrix.mtx`, `genes.tsv`, `barcodes.tsv`, and `exp_design.tsv` files into your Galaxy history. In order for Seurat tools to work, we will have to convert the data into a format that Seurat recognizes. To do so, we will add row and column names to our matrix. In the end, this will leave us with a matrix whose rows are gene names, columns are cell barcodes, and each value in the matrix represent the expression value of a given gene in a given cell. + +This can be accomplished via the Read10x step. **Read10x** tool implements Seurat's function to create a matrix and add in feature and barcode names simultaneously: + +> Read10X +> +> Run{% tool [Seurat Read10x](toolshed.g2.bx.psu.edu/repos/ebi-gxa/seurat_read10x/seurat_read10x/4.0.4+galaxy0) %} with the following parameters: +> - *"Expression matrix in sparse matrix format (.mtx)"*: `EBI SCXA Data Retrieval on E-MTAB-6945 matrix.mtx (Raw filtered counts)` +> - *"Gene table"*: `EBI SCXA Data Retrieval on EMTAB-6945 genes.tsv (Raw filtered counts)` +> - *"Barcode/cell table"*: `EBI SCXA Data Retrieval on E-MTAB-6945 barcodes.tsv (Raw filtered counts)` +> - *"Cell Metadata"*: `EBI SCXA Data Retrieval on E-MTAB-6945 exp_design.tsv` +> - *"Minimum cells to include features"*: `5` +> - *"Choose the format of the output"*: `RDS with a Seurat object` +> +> **Rename** {% icon galaxy-pencil %} output `Initial Seurat Object` +{: .hands_on} + +The output of this tool will result in a Seurat object with row/column names as described above. + +
+
> Uploading from Zenodo > @@ -107,6 +133,7 @@ To start, let's get our dataset loaded into Galaxy. > {: .hands_on} +## Generating a Seurat object When you uploaded your data from Zenodo, it came in AnnData format, thus we will need to convert this to a Seurat Object. This can be easily accomplished using the Seurat FilterCells tool. Simply run the tool without any actual filtering thresholds and with the following parameters: @@ -120,25 +147,7 @@ Simply run the tool without any actual filtering thresholds and with the followi
-# Generating a Seurat object -You now should have imported the `matrix.mtx`, `genes.tsv`, `barcodes.tsv`, and `exp_design.tsv` files into your Galaxy history. In order for Seurat tools to work, we will have to convert the data into a format that Seurat recognizes. To do so, we will add row and column names to our matrix. In the end, this will leave us with a matrix whose rows are gene names, columns are cell barcodes, and each value in the matrix represent the expression value of a given gene in a given cell. - -This can be accomplished via the Read10x step. **Read10x** tool implements Seurat's function to create a matrix and add in feature and barcode names simultaneously: - -> Read10X -> -> Run{% tool [Seurat Read10x](toolshed.g2.bx.psu.edu/repos/ebi-gxa/seurat_read10x/seurat_read10x/4.0.4+galaxy0) %} with the following parameters: -> - *"Expression matrix in sparse matrix format (.mtx)"*: `EBI SCXA Data Retrieval on E-MTAB-6945 matrix.mtx (Raw filtered counts)` -> - *"Gene table"*: `EBI SCXA Data Retrieval on EMTAB-6945 genes.tsv (Raw filtered counts)` -> - *"Barcode/cell table"*: `EBI SCXA Data Retrieval on E-MTAB-6945 barcodes.tsv (Raw filtered counts)` -> - *"Cell Metadata"*: `EBI SCXA Data Retrieval on E-MTAB-6945 exp_design.tsv` -> - *"Minimum cells to include features"*: `5` -> - *"Choose the format of the output"*: `RDS with a Seurat object` -> -> **Rename** {% icon galaxy-pencil %} output `Initial Seurat Object` -{: .hands_on} - -The output of this tool will result in a Seurat object with row/column names as described above. You've created a Seurat object, congratulations! +You've created a Seurat object, congratulations! # QC Plots Now that we have a complete Seurat object, we can begin the filtering process.