vic3_plotPCA: PCA plot

In anabeloff/vic3PCD: Transcriptome analysis of Cryphonectria parasititca allorecognotion

Description

vic3_plotPCA returns PCA plot for normalized counts output by DESeq2.

Usage

vic3_plotPCA(counts, group_vector, ntop = 500, returnData = FALSE)

Arguments

counts	Counts table output assay(x) or matrix.
group_vector	Column name in DESeq2 colData(x).
ntop	Numeric. Number of top genes with most variance.
returnData	Logical (default FALSE). Return PC1 and PC2 data.frame.

Details

The function works similarly to DESeq2 plotPCA. It accepts count tables which could be accessed with assay function.

Examples

## Load SE dataset
se <- readRDS(system.file("extdata", "se_vic3_2020.RData", package = "vic3PCD"))

## DESeq dataset
dds <- DESeqDataSet(se, design = ~ condition)

## DESeq analysis
dds <- DESeq(dds)
## Filter genes with more than 10 aligned reads
keep <- rowSums(counts(dds)) >= 10
dds <- dds[keep,]
## Normised counts
rld <- rlog(dds, blind = F)

## Manual color
clr = c("dodgerblue3", "coral3")
names(clr) <- unique(colData(rld)[,"condition"])

vic3_plotPCA(counts = assay(rld), group_vector = colData(rld)[,"condition"]) +
scale_colour_manual(values=clr, name = "Condition:") +
geom_text(label = colData(rld)[,"exset"], size = 3, colour = "black", fontface = "italic", hjust = 0, nudge_x = 3) +
xlim(-20,60) +
ylim(-25,25)

anabeloff/vic3PCD documentation built on Dec. 2, 2020, 11:03 a.m.