EnrichMet performs pathway enrichment analysis using Fisher’s exact test, computes betweenness centrality for metabolites, and performs Metabolite Set Enrichment Analysis (MetSEA). It also generates plots for pathway enrichment, MetSEA, relative betweenness centrality (RBC), and and interaction plot.
You can install enrichmet as:
install.packages("remotes")
remotes::install_git("https://github.com/biodatalab/enrichmet.git")
or
install.packages("devtools")
devtools::install_git("https://github.com/biodatalab/enrichmet.git")
or
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("enrichmet")This is a basic example
library(igraph)
#>
#> Attaching package: 'igraph'
#> The following objects are masked from 'package:stats':
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#> decompose, spectrum
#> The following object is masked from 'package:base':
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#> union
library(ggraph)
#> Loading required package: ggplot2
library(tidygraph)
#>
#> Attaching package: 'tidygraph'
#> The following object is masked from 'package:igraph':
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#> groups
#> The following object is masked from 'package:stats':
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#> filter
library(ggplot2)
library(openxlsx)
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:igraph':
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#> as_data_frame, groups, union
#> The following objects are masked from 'package:stats':
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#> filter, lag
#> The following objects are masked from 'package:base':
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#> intersect, setdiff, setequal, union
library(tidyr)
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#> Attaching package: 'tidyr'
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#> crossing
library(fgsea)
library(purrr)
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#> Attaching package: 'purrr'
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#> compose, simplify
library(ggrepel)
library(stringr)
library(pheatmap)
library(ComplexHeatmap)
#> Loading required package: grid
#> ========================================
#> ComplexHeatmap version 2.24.0
#> Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/
#> Github page: https://github.com/jokergoo/ComplexHeatmap
#> Documentation: http://jokergoo.github.io/ComplexHeatmap-reference
#>
#> If you use it in published research, please cite either one:
#> - Gu, Z. Complex Heatmap Visualization. iMeta 2022.
#> - Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional
#> genomic data. Bioinformatics 2016.
#>
#>
#> The new InteractiveComplexHeatmap package can directly export static
#> complex heatmaps into an interactive Shiny app with zero effort. Have a try!
#>
#> This message can be suppressed by:
#> suppressPackageStartupMessages(library(ComplexHeatmap))
#> ========================================
#> ! pheatmap() has been masked by ComplexHeatmap::pheatmap(). Most of the arguments
#> in the original pheatmap() are identically supported in the new function. You
#> can still use the original function by explicitly calling pheatmap::pheatmap().
#>
#> Attaching package: 'ComplexHeatmap'
#> The following object is masked from 'package:pheatmap':
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#> pheatmap
library(ggplotify)
library(enrichmet)
## basic example code
## ** Examples
# Generate example data with at least n=50 metabolites
set.seed(1234)
# Create 50 unique metabolites
inputMetabolites <- paste0("M", 1:20)
# ---- 1. Generate 10 pathways with random metabolites assigned ----
pathway_names <- paste0("Pathway", 1:50)
PathwayVsMetabolites <- data.frame(
Pathway = rep(pathway_names, each = 1),
Metabolites = sapply(1:50, function(x) paste(sample(inputMetabolites, sample(5:15, 1)), collapse = ","))
)
# ---- 2. Add new pathway entries (Pathway101 and Pathway102) ----
new_rows <- data.frame(
Pathway = c("Pathway101", "Pathway102", "Pathway103", "Pathway104", "pathway105"),
Metabolites = c(
"M12,M13,M14,M15,M16,M1,M18,M3,M29,M6,M16,M4",
"M6,M7,M8,M9,M10,M11,M9,M29,M6,M6,M16,M4",
"M24,M25,M26,M27,M28,M29,M30,M29,M26,M5",
"M13,M14,M15,M16,M17,M24,M27,M14",
"M15,M16,M17,M18,M19,M20,M21,M4,M8,M10"
)
)
# Combine with existing PathwayVsMetabolites
PathwayVsMetabolites <- rbind(PathwayVsMetabolites, new_rows)
# ---- 3. Generate example metabolite-level data ----
example_data <- data.frame(
met_id = inputMetabolites,
pval = runif(20, 0.001, 0.05), # Random p-values between 0.001 and 0.05
log2fc = rnorm(20, mean = 0, sd = 1) # Log2 fold changes from normal distribution
)
# ---- 4. Create mapping_df ----
set.seed(42)
mapping_df <- data.frame(
KEGG_ID = inputMetabolites,
PubChem_CID = as.character(sample(10000:99999, length(inputMetabolites))),
STITCH_ID = paste0("CIDs", str_pad(sample(1000:9999, length(inputMetabolites)), 8, pad = "0"))
)
# ---- 5. Create synthetic STITCH interaction data ----
stitch_ids <- mapping_df$STITCH_ID
stitch_pairs <- expand.grid(chemical1 = stitch_ids, chemical2 = stitch_ids) %>%
filter(chemical1 != chemical2)
set.seed(123)
stitch_df <- stitch_pairs %>%
slice_sample(n = 200) %>%
mutate(
similarity = runif(n(), 0, 1),
experimental = sample(0:500, n(), replace = TRUE),
database = sample(c(0, 300, 600, 900), n(), replace = TRUE),
textmining = sample(0:1000, n(), replace = TRUE),
combined_score = similarity * 200 + experimental + database + textmining
) %>%
as_tibble()
# ---- 6. Run enrichment analysis ----
enrichmet(
inputMetabolites = inputMetabolites,
PathwayVsMetabolites = PathwayVsMetabolites, example_data,
top_n = 20,
mapping_df = mapping_df,
stitch_df = stitch_df
)
#> | | | 0% | |======================================================================| 100%
#> $pathway_plot
#>
#> $impact_plot
#>
#> $gsea_plot
#>
#> $rbc_plot
#>
#> $network_plot
#> Warning: Removed 55 rows containing missing values or values outside the scale range
#> (`geom_point()`).
#>
#> $heatmap_plot
#>
#> $membership_plot
#>
#> $interaction_plot
