scRNABatchQC is an R package for generating a HTML QC report to check and compare quality of multiple single cell RNA-seq datasets. scRNABatchQC supports multiple types of inputs, including gene-cell count matrices, 10x genomics, SingleCellExperiment or Seurat v3 objects. Please see the manual for the usage of scRNABatchQC and the explanation of the HTML report.
Install pandoc (https://github.com/jgm/pandoc/releases/tag/2.2.1) (pandoc is required to convert files from markup format into html format). After installation , update your path to include the directory where pandoc’s binaries are installed.
library(devtools)
install_github("liuqivandy/scRNABatchQC")or
BiocManager::install("liuqivandy/scRNABatchQC")If the error converted from warning prevents package install when dependency was built under newer R, set following enviroment in R before installing scRNABatchQC.
Sys.setenv(R_REMOTES_NO_ERRORS_FROM_WARNINGS="true")scRNABatchQC: Multi-samples quality control for single cell RNA-seq data. Liu Q, Sheng Q, Ping J, Ramirez MA, Lau KS, Coffey R, Shyr Y. Bioinformatics. 2019 Dec 15;35(24):5306-5308. PMID: 31373345 PMCID: PMC6954654
After installing scRNABatchQC, use following codes to run examples:
Check and compare the quality of two scRNA-seq datasets from Retinal Bipolar Neurons (Cell 2016 Aug 25;166(5):1308-1323 ). The scRNA-seq datasets are provided by the gene(row)-cell(column) matrices (the rowname should be gene symbol). Since one dataset has more than 14,000 cells and 2,4904 genes, memory >= 4Gb and 64 bit system are required.
A QC report named "report.html" will be generated in your working directory. In addition, One SingleCellExperiment object (scesMerge) containing the combined dataset and a list of SingleCellExperiment objects (sces, each object contains the preprocessed dataset and metadata for one dataset) will be returned.
library(scRNABatchQC)
start_time <- Sys.time()
result<-scRNABatchQC(inputs=c("https://github.com/liuqivandy/scRNABatchQC/raw/master/bioplar1.csv.gz",
"https://github.com/liuqivandy/scRNABatchQC/raw/master/bioplar5.csv.gz"),
organism="mmusculus")
end_time <- Sys.time()
end_time - start_time
#the number of genes and the number of cells in the combined dataset
dim(result$scesMerge)
#the number of genes and the number of cells in the first dataset after filtering
dim(result$sces[[1]])
Check the quality of six seminiferous tubule (ST) datasets of murine spermatogenesis from GEO (GSE112393), each dataset has 25,000 genes and 2,600~5,500 cells.
A QC report named "report.html" will be generated in your working directory.
library(scRNABatchQC)
start_time <- Sys.time()
scRNABatchQC(inputs=c("ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM3069nnn/GSM3069439/suppl/GSM3069439_ST1_DGE.txt.gz",
"ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM3069nnn/GSM3069440/suppl/GSM3069440_ST2_DGE.txt.gz",
"ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM3069nnn/GSM3069441/suppl/GSM3069441_ST3_DGE.txt.gz",
"ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM3069nnn/GSM3069442/suppl/GSM3069442_ST4_DGE.txt.gz",
"ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM3069nnn/GSM3069443/suppl/GSM3069443_ST5_DGE.txt.gz",
"ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM3069nnn/GSM3069444/suppl/GSM3069444_ST6_DGE.txt.gz"),
organism="mmusculus")
end_time <- Sys.time()
end_time - start_timeCheck the quality of three scRNA-seq datasets from embryonic development of the thymus.
A QC report named "report.html" will be generated in your working directory.
library(scRNABatchQC)
start_time <- Sys.time()
scRNABatchQC(inputs=c("ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM2883nnn/GSM2883184/suppl/GSM2883184_E12_5_wholeThy_venus_1.dge.txt.gz",
"ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM2883nnn/GSM2883185/suppl/GSM2883185_E12_5_wholeThy_venus_2.dge.txt.gz",
"ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM2883nnn/GSM2883186/suppl/GSM2883186_E12_5_wholeThy_venus_3.dge.txt.gz"),
organism="mmusculus")
end_time <- Sys.time()
end_time - start_timescRNABatchQC also supports SingleCellExperiment, Seurat v3 objects or 10X v3/v2 genomics data as input.
# run on a list of SingleCellExperiment objects
# the result from Example 1 (result$sces) is a list of SingleCellExperiment objects
scRNABatchQC(inputs=result$sces)
# run on a list of Seurat v3 objects (note: v3 is required)
library(Seurat)
S1<-CreateSeuratObject(counts=counts(result$sces[[1]]))
S2<-CreateSeuratObject(counts=counts(result$sces[[2]]))
scRNABatchQC(inputs=list(S1,S2))
# run on 10X genomics data
# assume "c:/usrs/folder1", "c:/usrs/folder2" are folders containing the the barcodes.tsv.gz, features.tsv.gz, and matrix.mtx.gz provided by 10X from CellRanger >=3.0
scRNABatchQC(inputs=c("c:/usrs/folder1","c:/usrs/folder2"))
| Computer | CPU | Memory | System | R version | Example1 | Example2 | Example3 |
|---|---|---|---|---|---|---|---|
| MacBook Pro Laptop | 1.7 GHz Intel Core i5 | 4 Gb | MacOS | 3.4.3 | 39 min | 13 min | 3 min |
| MacBook Pro Laptop | 2.7 GHz Intel Core i5 | 8 Gb | MacOS | 3.4.1 | 6.5 min | 12.5 min | 1.1 min |
| MacBook Pro Laptop | 3.1 GHz Intel Core i5 | 16 Gb | MacOS | 3.6.0 | 2.5 min | 3.6 min | 41 sec |
| Lenovo Laptop | 1.8 GHz Intel(R) i7 8565U | 16 Gb | Windows 10 | 3.6.0 | 3.4 min | 5.1 min | 1.1 min |
| Windows Desktop | 2 GHz Intel(R) Xeon(R) E5-2620 | 32 Gb | Windows 7 | 3.4.3 | 3 min | 5 min | 1 min |
| Windows Desktop | 2.6 GHz Intel(R) Xeon(R) E5-2640 | 64 Gb | Windows 10 | 3.6.0 | 4.1 min | 6 min | 1.3 min |
| ACCRE Cluster Gateway | Intel(R) Xeon(R) Gold 6154 CPU @ 3.00GHz | 1000 Gb | CentOs 7 | 3.5.1 | 3.1 min | 5.2 min | 28.6 sec |