scRNABatchQC: QC across multiple scRNAseq datasets

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

Description

Compare multiple scRNA-seq datasets simultaneously on numerous technical and biological features

Usage

scRNABatchQC(
  inputs,
  names = NULL,
  nHVGs = 1000,
  nPCs = 10,
  sf = 10000,
  mincounts = 500,
  mingenes = 200,
  maxmito = 0.2,
  PCind = 1,
  mtRNA = "^mt-|^MT-",
  rRNA = "^Rp[sl][[:digit:]]|^RP[SL][[:digit:]]",
  sampleRatio = 1,
  logFC = 1,
  FDR = 0.01,
  organism = "mmusculus",
  outputFile = "report.html",
  lineSize = 1,
  pointSize = 0.8,
  chunk.size = NULL,
  createReport = TRUE
)

Arguments

inputs	string vector of file or path names, or a list of SingleCellExperiment or Seurat v3 objects; inputs can be a vector of file names (or a URL starting http://, file://, etc.) of gene-by-cell count matrices, the rowname should be gene symbol; each file should be regular delimited file; Compressed files ending .gz and .bz2 are supported. inputs can be a vector of path names, each of which contains the barcodes.tsv.gz, features.tsv.gz, and matrix.mtx.gz provided by 10X from CellRanger >=3.0 inputs can also be a list of SingleCellExperiment or Seurat v3 objects
names	string vector; giving names of each sample (default: NULL); names should have the same length of inputs; if NULL, the names are S1, S2...
nHVGs	integer; the number of highly variable genes (default: 1000)
nPCs	integer; the number of principal components (default: 10)
sf	integer; Scale factor to normalize the single cell RNA-seq data (default: 10000)
mincounts	integer; the cutoff of filtering the cell if the total number of counts in the cell less than the mincounts (default:500)
mingenes	integer; the cutoff of filtering the cell if the total number of genes detected in the cell less than the mingenes (default: 200)
maxmito	float; the cutoff of filtering the cell if the percentage of mtRNA reads in the cell larger than the minmito (default: 0.2)
PCind	integer; which principal component for exploring biological featues (default: 1; the first principal component will be used to find genes highly correlated with PCA 1); PCind should be less than nPC
mtRNA	string; the pattern of genenames for mitochondrial encoded RNAs ; (default: "^mt-|^MT-", the default is mtRNA genenames in human or mouse); If not human or mouse, give the gene name pattern of mtRNA
rRNA	string; the pattern of genenames for ribosomal proteins; (default: "^Rp[sl][[:digit:]]|^RP[SL][[:digit:]]", the default is ribosomal protein genenames in human or mouse); If not human or mouse, give the gene name pattern of ribosomal proteins
sampleRatio	float; the ratio of cells sampled from each dataset to examine the expression similarity (default: 1)
logFC	float; log fold change cutoff to select differentially expressed genes (default: 1)
FDR	float; FDR cutoff to select differentially expressed genes (default: 0.01)
organism	string; the organism of single cell RNAseq datasets; if supported by WebGestaltR, functional enrichment analysis will be performed (default: mmusculus);WebGestaltR supports 12 organisms, including athaliana, btaurus,celegans, cfamiliaris, drerio, sscrofa, dmelanogaster, ggallus, hsapiens, mmusculus, rnorvegicus, and scerevisiae.
outputFile	string; the name of the output file (default: report.html)
lineSize	float; the line size of figures in the report (default: 1)
pointSize	float; the point size of figures in the report (default: 0.8)
chunk.size	NULL or integer; default is NULL, suggesting data will be loaded into memory at one time, otherwise, the data will be loaded into memory by chunks with chunk.size
createReport	logical; default is TRUE, suggesting html report file will be created

Value

a list of SingleCellExperiment objects;

• sces: a list of SingleCellExperiment objects; each object contains technical and biological metadata for one scRNAseq dataset; see the output of Process_scRNAseq

• scesMerge: a SingleCellExperiment object containing bilogical metadata for the combined dataset and the pairwise difference across datasets; see the output of Combine_scRNAseq

See Also

Process_scRNAseq, Combine_scRNAseq , generateReport

Examples

library(scRNABatchQC)  
output<-scRNABatchQC(inputs=c("https://github.com/liuqivandy/scRNABatchQC/raw/master/bioplar1.csv.gz","https://github.com/liuqivandy/scRNABatchQC/raw/master/bioplar5.csv.gz"))

# a list of SingleCellExperiment objects, one for each individual dataset
output$sces
# a SingleCellExperiment object for the combined dataset
output$scesMerge

plotDensity(output$sces, "total_counts")
output$sces[[1]]@metadata$hvgPathway
plotHVGs(output$sces)
output$scesMerge@metadata$diffFC$genes

#scRNABatchQC can run on a list of SingleCellExperiment objects
scRNABatchQC(inputs=output$sces)

#scRNABatchQC can run on a list of Seurat v3 objects
library(Seurat)
S1<-CreateSeuratObject(counts=counts(output$sces[[1]]))
S2<-CreateSeuratObject(counts=counts(output$sces[[2]]))
scRNABatchQC(inputs=list(S1,S2))

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