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

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



Character vector with group name for each sample in the same order than counts column names.


pvalues of DEG analysis.


Matrix with counts for each samples and each gene.


Defining the cutoff to label significant features. row number should be the same length than pvalues vector.


ggplot2 object


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.