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Plot top transcripts for a gene

Source: R/generate_plots.R
plot_top_transcripts.Rd

Plot top transcripts for a gene

Usage

plot_top_transcripts(
  counts,
  readcounts = NULL,
  gene = NULL,
  samples = NULL,
  coldata = NULL,
  sample_type_col = "sample_type",
  tx2gene = NULL,
  res = NULL,
  top_n = 3,
  pseudocount = 1e-06,
  output_file = NULL,
  metric = c("median", "mean", "variance", "iqr")
)

Arguments

counts

Matrix or data.frame of transcript counts. Rows are transcripts and columns are samples.

readcounts

Optional matrix or data.frame of raw read counts. Used for transcript-level quantification if provided.

gene

Character; gene symbol to inspect.

samples

Character vector of sample group labels (length = ncol(counts)).

coldata

Optional data.frame or file path containing sample metadata. Used to infer sample groups if `samples` is not provided.

sample_type_col

Character; column name in `coldata` or `SummarizedExperiment` colData to use for sample grouping. Default is "sample_type".

tx2gene

Path or data.frame mapping transcripts to genes. Must contain columns `Transcript` and `Gen`.

res

Optional result data.frame from a differential analysis. If provided and `gene` is NULL, top genes are selected by adjusted p-value.

top_n

Integer number of transcripts to show (default = 3). Use NULL to plot all transcripts for the gene.

pseudocount

Numeric pseudocount added before log2 (default = 1e-6) to avoid division by zero.

output_file

Optional file path to save the plot. If `NULL`, the `ggplot` object is returned.

metric

Aggregation metric used to summarize transcript expression per group when plotting. One of c("median", "mean", "variance", "iqr"). Use "iqr" to compute the interquartile range. Defaults to "median".

Value

If output_file is NULL, returns a ggplot object. Otherwise, the plot is saved to the specified file and the function returns NULL invisibly.

Examples

tx_counts <- matrix(sample(1:100, 24, replace = TRUE), nrow = 6)
rownames(tx_counts) <- paste0("tx", seq_len(nrow(tx_counts)))
colnames(tx_counts) <- paste0("S", seq_len(ncol(tx_counts)))
tx2gene <- data.frame(Transcript = rownames(tx_counts), Gen = rep(paste0("G", seq_len(3)), each = 2), stringsAsFactors = FALSE)
samples <- rep(c("Normal", "Tumor"), length.out = ncol(tx_counts))
plot_top_transcripts(tx_counts, gene = c("G1", "G2"), samples = samples, tx2gene = tx2gene, top_n = 2)