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Prepare Data for Gene and Transcript Expression Profile Plot

Source: R/prepare_profile_data.R
prepare_profile_data.Rd

This function processes gene and transcript-level expression data, along with differential expression results, to prepare a tidy data frame suitable for plotting expression profiles across different sample groups.

Usage

prepare_profile_data(
  txi_gene = NULL,
  txi_transcript,
  sample_metadata,
  tx_to_gene,
  de_result_gene,
  de_result_transcript,
  var,
  var_levels,
  gene_col = "gene_name",
  tx_col = "transcript_name",
  pvalue_cutoff = 0.05,
  lfc_cutoff = 1,
  use_fdr = TRUE
)

Arguments

txi_gene

A tibble or tximport output containing gene-level expression abundances. If NULL, gene-level abundances will be summarized from txi_transcript. Default is NULL.

txi_transcript

A tibble or tximport output containing transcript-level expression abundances.

sample_metadata

A data.frame or tibble containing sample metadata. The first column should contain sample names matching the column names in txi_gene and txi_transcript.

tx_to_gene

A data.frame or tibble containing transcript-to-gene mapping information. Must include columns specified by gene_col and tx_col.

de_result_gene

A data.frame or tibble containing differential expression results at the gene level. Must include gene_name, log2FC, and qvalue columns.

de_result_transcript

A data.frame or tibble containing differential expression results at the transcript level. Must include transcript_name, log2FC, and qvalue columns.

var

A string specifying the column name in sample_metadata that indicates the grouping variable (e.g., treatment, condition).

var_levels

A character vector specifying the levels of var to include in the contrasts.

gene_col

A string specifying the column name in tx_to_gene that contains gene names. Default is "gene_name".

tx_col

A string specifying the column name in tx_to_gene that contains transcript names. Default is "transcript_name".

pvalue_cutoff

A numeric value specifying the p-value cutoff for determining significant differential expression. Default is 0.05.

lfc_cutoff

A numeric value specifying the log2 fold-change cutoff for determining significant differential expression. Default is 1.

use_fdr

A logical value indicating whether to use the false discovery rate (qvalue) instead of p-value for significance cutoff. Default is TRUE.

Value

A tibble containing processed expression data and differential expression flags, ready for plotting.

Details

The function combines gene and transcript expression data with differential expression results to generate a tidy data frame. It filters significant genes and transcripts based on specified cutoffs and prepares the data for plotting expression profiles across specified sample groups.

Examples

if (FALSE) { # \dontrun{
# Assuming txi_gene, txi_transcript, sample_metadata, tx_to_gene, de_result_gene,
# and de_result_transcript are pre-loaded data frames:

# Prepare data for plotting
expr_df <- prepare_profile_data(
  txi_gene = txi_gene,
  txi_transcript = txi_transcript,
  sample_metadata = sample_metadata,
  tx_to_gene = tx_to_gene,
  de_result_gene = de_result_gene,
  de_result_transcript = de_result_transcript,
  var = "condition",
  var_levels = c("control", "treatment"),
  gene_col = "gene_name",
  tx_col = "transcript_name",
  pvalue_cutoff = 0.05,
  lfc_cutoff = 1,
  use_fdr = TRUE
)

# View the prepared data
utils::head(expr_df)

# Plotting example (assuming ggplot2 is installed)
library(ggplot2)
ggplot(expr_df, aes(x = condition, y = mean_TPM, fill = DE)) +
  geom_bar(stat = "identity", position = position_dodge()) +
  facet_wrap(~ parent_gene + transcript_type)
} # }