Plot top genes allowing more variables to color and shape points

Plot top genes allowing more variables to color and shape points

degPlot(dds, xs, res = NULL, n = 9, genes = NULL, group = NULL,
  batch = NULL, metadata = NULL, ann = c("geneID", "symbol"),
  slot = 1L, log2 = TRUE, xsLab = xs, ysLab = "abundance",
  color = "black", groupLab = group, batchLab = batch)

Arguments

dds	DESeq2::DESeqDataSet object or SummarizedExperiment or Matrix or data.frame. In case of a DESeqDataSet object, always the normalized expression will be used from counts(dds, normalized = TRUE).
xs	Character, colname in colData that will be used as X-axes.
res	DESeq2::DESeqResults object.
n	Integer number of genes to plot from the res object. It will take the top N using padj values to order the table.
genes	Character of gene names matching rownames of count data.
group	Character, colname in colData to color points and add different lines for each level.
batch	Character, colname in colData to shape points, normally used by batch effect visualization.
metadata	Metadata in case dds is a matrix.
ann	Columns in rowData (if available) used to print gene names. First element in the vector is the column name in rowData that matches the row.names of the dds or count object. Second element in the vector is the column name in rowData that it will be used as the title for each gene or feature figure.
slot	Name of the slot to use to get count data.
log2	Whether to apply or not log2 transformation.
xsLab	Character, alternative label for x-axis (default: same as xs)
ysLab	Character, alternative label for y-axis..
color	Color to use to plot groups. It can be one color, or a palette compatible with ggplot2::scale_color_brewer().
groupLab	Character, alternative label for group (default: same as group).
batchLab	Character, alternative label for batch (default: same as batch).

Value

ggplot showing the expresison of the genes

Examples

data(humanGender)
library(DESeq2)
idx <- c(1:10, 75:85)
dse <- DESeqDataSetFromMatrix(assays(humanGender)[[1]][1:1000, idx],
  colData(humanGender)[idx,], design=~group)
dse <- DESeq(dse)
#> 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
degPlot(dse, genes = rownames(dse)[1:10], xs = "group")
#> No genes were mapped to rowData. check ann parameter values.
#> Using gene as id variables
degPlot(dse, genes = rownames(dse)[1:10], xs = "group", color = "orange")
#> No genes were mapped to rowData. check ann parameter values.
#> Using gene as id variables
degPlot(dse, genes = rownames(dse)[1:10], xs = "group", group = "group",
        color = "Accent")
#> No genes were mapped to rowData. check ann parameter values.
#> Using gene as id variables