README.md

In StanfordBioinformatics/SCDEcode:

SCDE-code

This is a general workflow for single cell RNAseq analysis using SCDE R package.

Please provide a raw read count matrix as a input for SCDEcode.r. The raw read count matrix can be generated by below steps Fastqc -> STAR alignment -> generate big matrix containing raw readcount -> PAGODA for pathway and Goterms overdispersion analysis

The below Python (inside exec/) and R script (inside R) implements a set of statistical methods from SCDE for analyzing single-cell RNA-seq data.

1. mergy_STAR_stat.py - This python script will mergy read count from STAR for each gene each sample to make a big matrix for SCDE.
2. SCDEcode.r - The R script implemented in the scde resolves multiple, potentially overlapping aspects of transcriptional heterogeneity by identifying known pathways that show significant excess of coordinated variability among the measured cells.

A sample matrix (sample_data.txt) is saved in data folder which can directly fed into SCDEcode.R for analysis

StanfordBioinformatics/SCDEcode documentation built on May 9, 2019, 1:56 p.m.