ggpicrust2 (version 1.7.3)

pathway_heatmap: Create pathway heatmap

Description

This function creates a heatmap of the predicted functional pathway abundance data. The function first makes the abundance data relative, then converts the abundance data to a long format and orders the samples based on the environment information. The heatmap is then created using the `ggplot2` library. The color palette, appearance and the color bar of the heatmap can be customized using the `scale_fill_gradientn`, `theme` and `guides` functions respectively.

Value

A ggplot heatmap object. The output is a ggplot object representing the heatmap of the predicted functional pathway abundance data. The heatmap visualizes the z score of pathways in different samples.

Arguments

abundance

A matrix or data frame of pathway abundance data, where columns correspond to samples and rows correspond to pathways.

metadata

A data frame of metadata, where each row corresponds to a sample and each column corresponds to a metadata variable.

group

A character string specifying the column name in the metadata data frame that contains the group variable.

Examples

# \donttest{
library(ggpicrust2)
library(ggh4x)
library(dplyr)
library(tidyr)
library(tibble)
library(magrittr)
# Create example functional pathway abundance data
kegg_abundance_example <- matrix(rnorm(30), nrow = 3, ncol = 10)
colnames(kegg_abundance_example) <- paste0("Sample", 1:10)
rownames(kegg_abundance_example) <- c("PathwayA", "PathwayB", "PathwayC")

# Create example metadata
# Please ensure the sample IDs in the metadata have the column name "sample_name"
metadata_example <- data.frame(sample_name = colnames(kegg_abundance_example),
                               group = factor(rep(c("Control", "Treatment"), each = 5)))

# Create a heatmap
pathway_heatmap(kegg_abundance_example, metadata_example, "group")
# Use real dataset
data("metacyc_abundance")
data("metadata")
metacyc_daa_results_df <- pathway_daa(abundance = metacyc_abundance %>%
column_to_rownames("pathway"),
metadata = metadata, group = "Environment", daa_method = "LinDA")
annotated_metacyc_daa_results_df <- pathway_annotation(pathway = "MetaCyc",
daa_results_df = metacyc_daa_results_df, ko_to_kegg = FALSE)
feature_with_p_0.05 <- metacyc_daa_results_df %>% filter(p_adjust < 0.05)
pathway_heatmap(abundance = metacyc_abundance %>%
right_join(annotated_metacyc_daa_results_df %>%
select(all_of(c("feature","description"))), by = c("pathway" = "feature")) %>%
filter(pathway %in% feature_with_p_0.05$feature) %>%
select(-"pathway") %>%
column_to_rownames("description"), metadata = metadata, group = "Environment")
# }