Skip to contents

Plot fold-change barplots across comparisons for selected genes

Source: R/bioccheck_roxygen_fixes.R, R/viz_related.R
get_foldchange_barplot.Rd

Plots log2 fold changes for selected genes across one or more comparisons. facet_by controls per-gene or per-comparison layout explicitly.

Usage

get_foldchange_barplot(
  x,
  genes,
  sample_comparisons = NULL,
  coord_flip = FALSE,
  display_id = NULL,
  display_from = NULL,
  display_orgdb = NULL,
  sort_by = c("input", "log2fc", "abs_log2fc"),
  facet_scales = "free_y",
  facet_nrow = NULL,
  facet_ncol = NULL,
  facet_by = c("auto", "gene", "comparison", "none")
)

Arguments

x

A VISTA object containing differential expression results.

genes

Character vector of gene IDs to plot.

sample_comparisons

Optional character vector of comparison names to include; defaults to all available.

coord_flip

Logical; flip axes when TRUE.

display_id

Optional column in rowData(x) to use for gene labels. Input gene matching still uses gene_id.

display_from

Optional source ID type for mapping when display_id is not present in rowData(x).

display_orgdb

Optional OrgDb object used for identifier mapping when display_id is not present in rowData(x).

sort_by

How to order genes when faceting: "input" (use supplied order), "log2fc" (descending log2FC of the first comparison), or "abs_log2fc" (descending max absolute log2FC across comparisons).

facet_scales

Facet scales argument passed to facet_wrap() when faceting (default "free_y").

facet_nrow, facet_ncol

Optional layout passed to facet_wrap() when faceting.

facet_by

Faceting mode: "auto" (default), "gene", "comparison", or "none".

Value

An object returned by this function.

A ggplot2 object.

Examples

v <- example_vista()
comp <- names(comparisons(v))[1]
genes <- head(as.character(comparisons(v)[[comp]]$gene_id), 10)
p <- get_foldchange_barplot(v, sample_comparison = comp, genes = genes)
print(p)

data("count_data", package = "VISTA")
data("sample_metadata", package = "VISTA")

vista <- create_vista(
  counts = count_data[seq_len(200), ],
  sample_info = sample_metadata[seq_len(6), ],
  column_geneid = "gene_id",
  group_column = "cond_long",
  group_numerator = "treatment1",
  group_denominator = "control"
)
#> estimating size factors
#> estimating dispersions
#> gene-wise dispersion estimates
#> mean-dispersion relationship
#> final dispersion estimates
#> fitting model and testing

genes <- rownames(vista)[seq_len(3)]
get_foldchange_barplot(vista, genes = genes)

get_foldchange_barplot(vista, genes = genes, facet_by = "gene")