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RNAseq

Analyzing scRNA sequencing dataset with the scanpy python package.

retina_10x_smart Jupytert notebook performs an analysis of the cell types and their marker genes on the 10x single cell RNA-seq data from the developing mouse retina, here, and GSE118614_Smart dataset.

10X data files, including count matrix, and Cellular Phenotype Data should be copied into sdata10x folder. Rename count matrix to matrix.mtx before performing the analysis.

Minor modifications were made on the GSE118614_Smart dataset to make it proper to be read with scabpy read_10x_mtx command. All necessary files for smartseq dataset are included in sdata folder, except the matrix.mtx file which I couldn't upload here due to its large size. This file should be downloaded, and copied into sdata folder before running the notebook.

retina_10x_Early performs the analysis of the cell types and their marker genes on the Early RPC cells of the 10x single cell RNA-seq data

retina_10x_Early_E14E18P2 performs the analysis of the cell types and their marker genes on the Early RPC cells of the 10x single cell RNA-seq data filtered to ages E14, E18 and P2

retina_smart_Early performs the analysis of the cell types and their marker genes on the Early RPC cells of the GSE118614_Smart dataset.

Ribosormal genes were highly expressed in Early RPC cells, and were excluded from downstream analysis along with mitochondrial and hemoglobin genes.

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Analyzing scRNA sequencing dataset with the scanpy python package

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