Single cell RNA Sequencing Analysis

Used for filtering, normalization, scaling, integration (optionally), and clustering of single or aggregated single-cell RNA-Seq datasets

The main functional blocks of sc-rna-analyze-wf.cwl workflow are shown below. For detailed workflow structure refer to CWL Viewer.

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To reproduce the analysis of single-cell RNA sequencing data described in the Surumbayeva, Kotliar et al., 2021 paper proceed to the following steps.

1. Create a temporary folder and clone the current repository.

   mkdir sc_rna
   cd sc_rna
   git clone https://github.com/Barski-lab/sc-seq-analysis.git
   

2. Create a folder for input data. Download required input files from the Figshare either using a web browser or commands below.

   mkdir inputs
   cd inputs
   wget -O filtered_feature_bc_matrix.tar.gz https://figshare.com/ndownloader/files/34819513
   wget -O aggregation.csv https://figshare.com/ndownloader/files/34819516
   wget -O condition.csv https://figshare.com/ndownloader/files/34819519
   wget -O mouse_cell_cycle_genes.csv https://figshare.com/ndownloader/files/34822054
   

3. Copy the job definition file into the inputs folder.

   cp ../sc-seq-analysis/jobs/sc-rna-analyze-wf.yaml .
   

4. Create a folder for workflow outputs and execute sc-rna-analyze-wf.cwl workflow with sc-rna-analyze-wf.yaml job definition file.

   cd ..
   mkdir outputs
   cd outputs
   cwltool ../sc-seq-analysis/workflows/sc-rna-analyze-wf.cwl sc-rna-analyze-wf.yaml
   

Step 1. Filters single-cell RNA-Seq datasets based on the common QC metrics.

Raw	Filtered

Step 2. Integrates multiple single-cell RNA-Seq datasets, reduces dimensionality using PCA.

Step 3. Clusters single-cell RNA-Seq datasets, identifies gene markers.