We present DIscBIO, an open, multi-algorithmic pipeline for easy, fast and efficient analysis of cellular sub-populations and the molecular signatures that characterize them. The pipeline consists of four successive steps: data pre-processing, cellular clustering with pseudo-temporal ordering, defining differential expressed genes and biomarker identification. All the steps are integrated into an interactive notebook where comprehensive explanatory text, code, output data and figures are displayed in a coherent and sequential narrative. Advanced users can fully personalise DIscBIO with new algorithms and outputs. We also provide a user-friendly, cloud version of the notebook that allows non-programmers to efficiently go from raw scRNAseq data to biomarker discovery without the need of downloading any software or packages used in the pipeline.
Try it with Binder:
In case the user would like to run the notebook using Jupyter, this code should be run first: source("https://bioconductor.org/biocLite.R") biocLite(c("pheatmap","MASS","cluster","mclust","flexmix","lattice","fpc", "amap", "RColorBrewer","locfit","TSCAN","genefilter","statmod", "ggplot2","gplots","DESeq2","matrixStats","robustbase","philentropy", "igraph","boot","biomaRt","tidyr","calibrate","partykit","RWeka", "rpart","rpart.plot","imager","png","NetIndices","httr","jsonlite","tidyverse", "samr","tidyverse"))