Hello everyone,
I have downloaded the dataset GSEXXXXXX from NCBI which provides single-cell multiome data (scRNA-seq + scATAC-seq). For each sample, the .tar.gz archive (GSEXXXXXX_10x_filtered_matrix.tar.gz) contains the following files: features.tsv.gz matrix.mtx.gz barcodes.tsv.gz features_RNA.tsv.gz features_ATAC.tsv.gz My goal is to perform downstream analysis to obtain a DEG (differentially expressed genes) list from this data. How can I proceed
1 answer
For differential expression you only need the RNA assay, not the chromatin. So load the features, matrix and barcodes files into R or Python, and then consider following workflows such as https://bioconductor.org/books/release/OSCA/, https://satijalab.org/seurat/articles/get_started_v5_new or https://scanpy.readthedocs.io/en/stable/tutorials/index.html
Since you seem to start from the basics, please first read these guides carefully, as right now one would need to basically just tell you what is already written in there.
For starters, you need to load data, say into R, perform basic QC, clustering and/or celltype annotation, maybe sample integration depending on experimental setup, and then determine the differential genes. All is covered in linked resources. Good luck!
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