Correlation of the standard desviation and the mean of the abundance of a set of genes.

Correlation of the standard desviation and the mean of the abundance of a set of genes.

degMV(group, pvalues, counts, sign = 0.01)

Arguments

group	Character vector with group name for each sample in the same order than counts column names.
pvalues	pvalues of DEG analysis.
counts	Matrix with counts for each samples and each gene.
sign	Defining the cutoff to label significant features. row number should be the same length than pvalues vector.

Value

ggplot2 object

Examples

data(humanGender)
library(DESeq2)
idx <- c(1:10, 75:85)
dds <- DESeqDataSetFromMatrix(assays(humanGender)[[1]][1:1000, idx],
  colData(humanGender)[idx,], design=~group)
dds <- DESeq(dds)
#> using pre-existing size factors
#> estimating dispersions
#> gene-wise dispersion estimates
#> mean-dispersion relationship
#> final dispersion estimates
#> fitting model and testing
#> -- replacing outliers and refitting for 1 genes
#> -- DESeq argument 'minReplicatesForReplace' = 7 
#> -- original counts are preserved in counts(dds)
#> estimating dispersions
#> fitting model and testing
res <- results(dds)
degMV(colData(dds)[["group"]],
      res[, 4],
      counts(dds, normalized = TRUE))
#> Warning: Removed 1 rows containing non-finite values (stat_quantile).
#> Warning: Computation failed in `stat_quantile()`:
#> Package `quantreg` required for `stat_quantile`.
#> Please install and try again.