OutlierDetection.Rd
Detect outlier with robust PCA
OutlierDetection( deobj, var.genes = NULL, remove.sample = NULL, transform.method = c("rlog", "vst", "ntd"), rpca.method = c("PcaGrid", "PcaHubert"), k = 2, ... )
deobj | Object created by DESeq2 or edgeR. |
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var.genes | Select genes with larger variance for PCA analysis. Default: all genes. |
remove.sample | Sample(s) to remove. Default: NULL. |
transform.method | Data transformation methods, chosen from rlog, vst and ntd. Default: rlog. |
rpca.method | Robust PCA method, chosen from |
k | Number of principal components to compute, for |
... | Parameter for |
A ggplot2 object
library(DESeq2) library(DEbPeak) count.file <- system.file("extdata", "snon_count.txt", package = "DEbPeak") meta.file <- system.file("extdata", "snon_meta.txt", package = "DEbPeak") count.matrix <- read.table(file = count.file, header = TRUE, sep = "\t") meta.info <- read.table(file = meta.file, header = TRUE) dds <- DESeq2::DESeqDataSetFromMatrix(countData = count.matrix, colData = meta.info, design = ~condition)#> Warning: some variables in design formula are characters, converting to factorskeep.genes <- rowSums(DESeq2::counts(dds, normalized = FALSE)) >= 10 dds <- dds[keep.genes, ] outlier.res <- OutlierDetection(deobj = dds, var.genes = NULL, transform.method = "rlog")#>#>#>outlier.res$outlier#> [1] "KO3" "WT1"outlier.res$plot