# Plot top genes allowing more variables to color and shape points

`degPlot.Rd`

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 testingdegPlot(dse, genes = rownames(dse)[1:10], xs = "group")#> No genes were mapped to rowData. check ann parameter values.#> Using gene as id variablesdegPlot(dse, genes = rownames(dse)[1:10], xs = "group", color = "orange")#> No genes were mapped to rowData. check ann parameter values.#> Using gene as id variablesdegPlot(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