bump x.y.z version to odd y following creation of RELEASE_3_23 branch
Set up a SummarizedExperiment object containing a matrix of raw count data, rowData on gRNAs and genes and colData on the sample type.
counts_matrix <- cbind(raw_counts$library0, raw_counts$R0_0, raw_counts$R1_0) rowData <- data.frame(sgRNA_id = raw_counts$sgrna_id, gene = raw_counts$Gene) colData <- data.frame(samplename = c("library", "R1", "R2"), # timepoint naming convention: # T0 -> reference, # T1 -> selected timepoint = c("T0", "T1", "T1")) se <- SummarizedExperiment(assays=list(counts=counts_matrix), rowData=rowData, colData=colData)
Create a PoolScreenExp object
pse <- createPoolScreenExp(se)
Run gscreend
pse <- RunGscreend(pse)
用于 CRISPR pooled screening 数据的质控和差异分析。
gscreend - analysis of pooled CRISPR screens
Run gscreend, as also explained in the vignette section
Set up a SummarizedExperiment object containing a matrix of raw count data, rowData on gRNAs and genes and colData on the sample type.
Create a PoolScreenExp object
Run gscreend