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Create Gene Exploration Plot

make_gene_exploration_plot.Rd

This function generates a scatter plot that visualizes the relationship between experimental p-values and tKOI-adjusted false discovery rates (FDR) for genes in the network. The plot highlights significantly upregulated and downregulated genes based on user-defined thresholds for log fold change and p-values.

Usage

make_gene_exploration_plot(
  tkoi_list,
  sig_color = "#F39B7FB2",
  non_sig_color = "gray"
)

Arguments

tkoi_list

An object of class tKOIList. This object must contain the following slots:

  • expression_data: A data.frame containing experimental data with columns gene_name, logfc, and pvalue.

  • network_summary_statistics: A list with a data.frame for gene-level statistics, including node_id and fdr.

  • pvalue_threshold: A numeric value specifying the p-value threshold.

  • logfc_threshold: A numeric value specifying the log fold change threshold.

sig_color

A character string specifying the color for significant genes in the plot. Default is "#F39B7FB2".

non_sig_color

A character string specifying the color for non-significant genes in the plot. Default is "gray".

Value

A ggplot object representing the scatter plot.

Details

The function performs the following steps:

  1. Merges the expression_data from the tKOIList object with gene metadata using inner_join.

  2. Merges the result with gene-level network data using right_join.

  3. Filters and annotates the data to classify genes as upregulated or downregulated based on the sign of logfc.

  4. Colors genes based on their significance determined by logfc_threshold.

  5. Generates a scatter plot with ggplot2, where:

    • The x-axis represents -log10(pvalue) (experimental p-value).

    • The y-axis represents -log10(fdr) (tKOI-adjusted FDR).

    • Points are colored and categorized into upregulated and downregulated facets.

The plot includes vertical and horizontal dashed lines to indicate the thresholds for p-value and FDR.

Examples

if (FALSE) { # \dontrun{
# Create a dummy tKOIList object
tkoi_list <- new("tKOIList",
                 expression_data = data.frame(
                   gene_name = c("gene1", "gene2", "gene3"),
                   logfc = c(1.2, -0.8, 0.5),
                   pvalue = c(0.01, 0.03, 0.2)
                 ),
                 network_summary_statistics = list(
                   Gene = data.frame(
                     node_id = c(1, 2, 3),
                     fdr = c(0.02, 0.05, 0.1)
                   )
                 ),
                 pvalue_threshold = 0.05,
                 logfc_threshold = 0.5)

# Generate the plot
plt <- make_gene_exploration_plot(tkoi_list)
print(plt)
} # }