scRNAseq_NatComm2020

Chris Scharer and Tian Mi

Repository Info

Rscripts used for processing scRNA-seq and bulk RNA-seq data and making figures associated with the following publication:

Scharer & Patterson et al. Antibody-secreting cell destiny emerges during the initial stages of B cell activation. Nature Communications 2020

https://www.nature.com/articles/s41467-020-17798-x

single cell RNA-seq Scripts

• scRNAseq.R: Main scRNAseq analysis pipeline based on Monocle2. Inlcude importing matrix from 10X CellRanger output, QC of cell detection, data normalization, dpFeature feature selection, t-SNE dimension reduction, density peaks clustering and single cell trajectory analysis.

• magic_transform.R: Script to do MAGIC imputation on both WT and IRF4 KO data sets.

• scRNAseq_lib.R: Customized plotting functions based on Monocle2. Functions inlcude t-SNE plots with different color schemes, t-SNE plots using MAGIC transformed data, SCT plot using different color schemas, expression pattern plot over pseudotime.

• scRNAseq_replotting: Usage examples of data loading and various plotting functions using the above library.

• KNN_predict.R: Script to perform KNN prediction based on bulk RNA seq data.

• running_scenic.R: Scripts to run SCENIC on scRNA data, including functions to plot heat maps and perform differential tests between score results for various clusters identified in Monocle2.

• plot.GSEA.pathway.R: script to replot the GSEA output for figures.

Software Versions

• R 3.4.0
• Monocle 2.9.0
• cellrangerRkit 2.0.0
• dplyr 0.7.7
• ggplot2 3.3.2
• viridis 0.5.1
• tibble 1.4.2
• Rmagic 2.0.3
• FNN 1.1.3
• preprocessCore 1.40.0
• SCENIC 1.1.1-10
• Biobase 2.38.0
• AUCell 1.7.1

bulk RNA-seq Scripts

• RNAseq.sample.manifest.txt: key file for all samples and contains metadata for each

• RNAseq.pipeline.R: script for initial data organization, fastq qc/trimming, mapping, duplicate marking

coverage scripts extract gene coverage, ercc coverage, and normalize the data

• geneCounts.exon.PE.R: extracts coverage for all Entrez gene exons and normalizes data

• ERCC_coverage.R: extracts coverage for the ERCC spike in controls

• geneTsNorm.updated.R: normalizes FPKM data to mRNAs/cells based on ERCC coverage

Differential analysis, PCA, and plotting scripts

• diff.glm_v3.R: pairwise differential analysis with edgeR

• basicPlots.R: function for making barplots

• makeBarPlots.R: driver script for making barplots and grouping

• makeMPCBarPlots.R: driver script to plot the total mRNAs for each sample from “Mouse.total.mpc.txt” file.

• Mouse.total.mpc.txt: companion file to above script

• pca.confidenceCircles.R: plot PCA using differentia

Common functions written for many data analysis routines

• bisTools.R: common functions used for processing files

Software Versions

• R 3.5.2
• skewer version: 0.2.2
• FastQC v0.11.4
• Tophat v2.0.13
• samtools 1.9
• PICARD v1.127
• GenomicRanges v1.34
• edgeR v3.24.3
• vegan v2.5.5