Description Usage Arguments Value See Also Examples
Combine and compare multiple single cell RNAseq datasets, including
1)combine into one dataset;
2)find highly variable genes, reduce dimensions using PCA and tSNE for the combined dataset;
3)pairwise comparsion between any two datasets to find the top differentially expressed genes;
1 2 3 4 5 6 7 8 9 | Combine_scRNAseq(
sces,
nHVGs = 1000,
nPCs = 10,
logFC = 1,
FDR = 0.01,
sampleRatio = 1,
organism = "mmusculus"
)
|
sces |
a list of SingleCellExperiment objects (results from |
nHVGs |
integer; the number of highly variable genes (default:1000) |
nPCs |
integer; the number of principal components (default:10) |
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) |
sampleRatio |
float; the ratio of cells sampled from each dataset to examine the expression similarity(default: 1) |
organism |
string; the organism of single cell RNAseq datasets;if supported by WebGestaltR, the functional enrichment will be performed; (defeault: mmusculus) |
a SingleCellExperiment object with several slots:
assays; ShallowSimpleListAssays object containing one sparse matrix logcounts (log-transformed normalized counts)
rowRanges@elementMetadata; A Dataframe hvg containining mean, variance and z-score for each gene
colData; A Dataframe containing conidtion for each cell
metadata: A list containing other metadata, including
reducedDims: a list containing two types of reduced dimensions: PCA and tSNE
diffFC: differential expressed genes and pathways from pairwise comparison between any two datasets
other metadata including nHVGs,nPC, logFC, FDR, sampleRatio
Process_scRNAseq
, generateReport
1 2 3 4 5 6 7 8 9 10 11 | library(scRNABatchQC)
sces<-Process_scRNAseq(inputs=c("https://github.com/liuqivandy/scRNABatchQC/raw/master/bioplar1.csv.gz","https://github.com/liuqivandy/scRNABatchQC/raw/master/bioplar5.csv.gz"))
scesMerge <- Combine_scRNAseq(sces)
logcounts(scesMerge)[1:5,1:5]
head(scesMerge@rowRanges@metadata$hvg)
summary(scesMerge@metadata$reducedDims$PCA)
#visualize PCA results
plot(scesMerge@metadata$reducedDims$PCA$x[,1:2],pch=16,col=as.factor(scesMerge@colData$condition),xlab="PCA1",ylab="PCA2")
#visualize tSNE results
plot(scesMerge@metadata$reducedDims$tSNE,pch=16,col=as.factor(scesMerge@colData$condition),xlab="tSNE1",ylab="tSNE2")
scesMerge@metadata$diffFC
|
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