This workflow uses the decoupler tool in Galaxy to generate pseudobulk counts from an annotated AnnData file obtained from scRNA-seq analysis. Following the pseudobulk step, differential expression genes (DEG) are calculated using the edgeR tool. The workflow also includes data sanitation steps to ensure smooth operation of edgeR and minimizing potential issues. Additionally, a Volcano plot tool is used to visualize the results after the DEG analysis.

This Workflow takes a dataset collection of single-cell ATAC-seq fragments and performs:

• preprocessing
• filtering
• concatenation
• dimension reduction
• batch correction (with Harmony and optionally Scanorama and MNC-correct)
• leiden clustering

• new SnapATAC2 version: from 2.5.3 to 2.6.4

Workflow for Single-cell ATAC-seq standard processing with SnapATAC2. This workflow takes a fragment file as input and performs the standard steps of scATAC-seq analysis: filtering, dimension reduction, embedding and visualization of marker genes with SnapATAC2. Finally, the clusters are manually annotated with the help of marker genes. In an alternative step, the fragment file can also be generated from a BAM file.

• newer Version: Updated SnapATAC2 version from 2.5.3 to 2.6.4

This workflow processes the CMO fastqs with CITE-seq-Count and include the translation step required for cellPlex processing. In parallel it processes the Gene Expresion fastqs with STARsolo, filter cells with DropletUtils and reformat all outputs to be easily used by the function 'Read10X' from Seurat.

Take a scRNAseq counts matrix from a single sample, and perform basic QC with scanpy. Then, do further processing by making a UMAP and clustering. Produces a processed AnnData object.

Depreciated: use individual workflows insead for multiple samples

Take an anndata file, and perform basic QC with scanpy. Produces a filtered AnnData object.

Run velocyto to get loom with counts of spliced and unspliced. It will extract the 'barcodes' from the bundled outputs.

From the R1 and R2 fastq files of a single samples, make a scRNAseq counts matrix, and perform basic QC with scanpy. Then, do further processing by making a UMAP and clustering. Produces a processed AnnData Depreciated: use individual workflows insead for multiple samples

From the R1 and R2 fastq files of a single samples, make a scRNAseq counts matrix, and perform basic QC with scanpy. Then, do further processing by making a UMAP and clustering. Produces a processed AnnData

Depreciated: use individual workflows insead for multiple samples