scProgram

scProgram is a R package for quantifying transcriptional programs at the single-cell resolution

Requirements

install.packages("philentropy")
install.packages("Seurat") #Please use the version ≤4
install.packages("data.table")
install.packages("dplyr")
install.packages("tidyverse")
install.packages("Matrix")
install.packages("pheatmap")
install.packages("RColorBrewer")
install.packages("clusterProfiler")
install.packages("ggplot2")

Install

devtools::install_github("wu-yc/scProgram")

Quick Start

scProgram generally supports the quantification and visualization of transcriptional programs at the single-cell resolution.

1. Load packages and demo data

The demo data is the dataset of Peripheral Blood Mononuclear Cells (PBMC) from 10X Genomics open access dataset (~2,700 single cells, also used by Seurat tutorial). The demo Seurat object can be downloaded from here.

load(file = "pbmc_demo.rda")

library(scProgram)

2. Get the features of each cluster

FeatureMatrix = GetFeatures(obj = countexp.Seurat, group.by = "ident", genenumber = 50, pct_exp = 0.1, mode = "fast")

obj is a Seurat object containing the UMI count matrix.

group.by is the cell cluster or identity column of the given Seurat object.

genenumber is the number of featured genes of each cluster.

pct_exp is the percentage of the gene expressed in each cell cluster.

mode supports fast, standard, in which fast is the default method.

3. Plot the heatmap of the features of each cluster

HeatFeatures(obj = countexp.Seurat, features = FeatureMatrix, group.by = "ident", 
                     show_rownames = F, show_colnames = T, cols = c("white","white", "white", "#52A85F"))

obj is a Seurat object containing the UMI count matrix.

features is the output matrix generated by GetFeatures function.

group.by is the cell cluster or identity column of the given Seurat object.

4. Get the features of each cluster

GetProgram(features = FeatureMatrix, geneset = "KEGG", pvalue_cutoff = 0.05,
                   cols = c("#F47E5D", "#CA3D74", "#7F2880", "#463873"), plot_term_number =3)

features is the output matrix generated by GetFeatures function.

geneset supports KEGG and HALLMARK

pvalue_cutoff is the cutoff value for the enrichment analysis.

Citations

scProgram

Author

Ying-Cheng Wu [email protected]

Copyright (C) 2021-2999 Gao Lab @ Fudan University.