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

Takes fastqs and reference data, to produce a single cell counts matrix into and save in annData format - adding a column called sample with the sample name.

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

Takes fastqs and reference data, to produce a single cell counts matrix into and save in annData format - adding a column called sample with the sample name.

Loads a single cell counts matrix into an annData format - adding a column called sample with the sample name. (Input format - matrix.mtx, features.tsv and barcodes.tsv)

Basic processing of a QC-filtered Anndata Object. UMAP, clustering e.t.c

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

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