README.md

In liuqivandy/scRNABatchQC: a package for quality control of multiple single cell RNAseq data

scRNABatchQC

• Introduction
• Installation
• Citation
• Usage
• Speed test

Introduction

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.

Installation

Step 1: Install pandoc

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.

Step 2: Install scRNABatchQC

library(devtools)
install_github("liuqivandy/scRNABatchQC")

or

BiocManager::install("liuqivandy/scRNABatchQC")

Trouble shooting

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")

Citation

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

Usage

After installing scRNABatchQC, use following codes to run examples:

Example 1:

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]])

Example 2:

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_time

Example 3:

Check 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_time

Examples for other types of input:

scRNABatchQC 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"))

Speed test

|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|

liuqivandy/scRNABatchQC documentation built on March 24, 2021, 11:01 p.m.