MA-plot from base means and log fold changes

MA-plot addaptation to show the shrinking effect.

degMA(results, title = NULL, label_points = NULL,
  label_column = "symbol", limit = NULL, diff = 5, raw = FALSE,
  correlation = FALSE)

Arguments

results

DEGSet class.
title

Optional. Plot title.
label_points

Optionally label these particular points.
label_column

Match label_points to this column in the results.
limit

Absolute maximum to plot on the log2FoldChange.
diff

Minimum difference between logFoldChange before and after shrinking.
raw

Whether to plot just the unshrunken log2FC.
correlation

Whether to plot the correlation of the two logFCs.

Value

MA-plot ggplot.

Examples

library(DESeq2)
dds <- makeExampleDESeqDataSet(betaSD=1)
dds <- DESeq(dds)
#> estimating size factors
#> estimating dispersions
#> gene-wise dispersion estimates
#> mean-dispersion relationship
#> final dispersion estimates
#> fitting model and testing
res <- degComps(dds, contrast = list("condition_B_vs_A"))
#> Doing 1 element(s).
#> Doing results() for each element.
#> Doing lcfSrink() for each element.
#> using 'normal' for LFC shrinkage, the Normal prior from Love et al (2014).
#> 
#> Note that type='apeglm' and type='ashr' have shown to have less bias than type='normal'.
#> See ?lfcShrink for more details on shrinkage type, and the DESeq2 vignette.
#> Reference: https://doi.org/10.1093/bioinformatics/bty895
degMA(res)