enrichmet simplifies pathway enrichment analysis by allowing the complete workflow to be executed through a single R function call. This design eliminates repetitive steps such as data reformatting and parameter configuration, improving efficiency, reducing the risk of errors, and supporting reproducible analysis. For users who wish to run only a specific analysis or generate selected plots rather than executing the full workflow, enrichmet also provides dedicated modules for each individual analysis and visualization.
**enrichmet performs pathway enrichment analysis using Fisher’s exact test, computes betweenness centrality for metabolites, and performs Metabolite Set Enrichment Analysis (MetSEA). The **enrichmet**() function produces three tables (S3 data.frame objects), which may include the MetSEA table, metabolite centrality, and pathway enrichment results. In addition, it generates eight plots (S3/S4 plot objects):
- Pathway enrichment plot
- Pathway impact plot
- Metabolite Set Enrichment Analysis (MetSEA plot)
- Relative Betweenness Centrality (RBC) plot
- Network graph
- Pathway heatmap
- Pathway membership plot
- Interaction network plot
You can install enrichmet as:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("enrichmet")This is a basic example
## ** Example showing ALL 8 plots
# Load required libraries
library(enrichmet)
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(stringr)
library(tidyr)
## Generate example data with proper KEGG IDs
set.seed(1234)
# Create 20 unique metabolites WITH KEGG IDs
inputMetabolites <- paste0("C", sprintf("%05d", 1:20))
# ---- 1. Generate 50 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 ----
new_rows <- data.frame(
Pathway = c("Pathway101", "Pathway102", "Pathway103", "Pathway104", "Pathway105"),
Metabolites = c(
"C00012,C00013,C00014,C00015,C00016,C00001,C00018,C00003,C00006,C00004",
"C00006,C00007,C00008,C00009,C00010,C00011,C00009,C00006,C00016,C00004",
"C00024,C00025,C00026,C00027,C00028,C00029,C00030,C00026,C00005",
"C00013,C00014,C00015,C00016,C00017,C00024,C00027,C00014",
"C00015,C00016,C00017,C00018,C00019,C00020,C00021,C00004,C00008,C00010"
)
)
# Combine with existing PathwayVsMetabolites
PathwayVsMetabolites <- rbind(PathwayVsMetabolites, new_rows)
# ---- 3. Generate example metabolite-level data (with KEGG IDs) ----
example_data <- data.frame(
met_id = c(inputMetabolites, "C00021", "C00022", "C00023"), # Add extra metabolites
pval = c(runif(20, 0.001, 0.05), 0.0001, 0.0002, 0.0003),
log2fc = c(rnorm(20, mean = 0, sd = 1), 2.5, 2.3, -2.1),
mz = rnorm(23, mean = 200, sd = 50),
rt = rnorm(23, mean = 10, sd = 3)
)
# ---- 4. Create KEGG lookup table (ESSENTIAL) ----
kegg_lookup <- data.frame(
kegg_id = c(inputMetabolites, "C00021", "C00022", "C00023"),
name = c(
"Glucose", "Lactate", "Pyruvate", "Alanine", "Valine",
"Leucine", "Isoleucine", "Proline", "Serine", "Threonine",
"Cysteine", "Methionine", "Aspartate", "Glutamate", "Asparagine",
"Glutamine", "Lysine", "Arginine", "Histidine", "Phenylalanine",
"Tyrosine", "Tryptophan", "Creatine"
)
)
# ---- 5. Create mapping_df with additional IDs ----
set.seed(42)
mapping_df <- data.frame(
KEGG_ID = inputMetabolites,
HMDB_ID = paste0("HMDB", stringr::str_pad(sample(10000:99999, length(inputMetabolites)), 6, pad = "0")),
PubChem_CID = as.character(sample(10000:99999, length(inputMetabolites))),
CHEBI_ID = paste0("CHEBI:", sample(10000:99999, length(inputMetabolites))),
STITCH_ID = paste0("CIDs", stringr::str_pad(sample(1000:9999, length(inputMetabolites)), 8, pad = "0")),
Compound_Name = paste("Compound", sample(LETTERS, length(inputMetabolites), replace = TRUE),
sample(100:999, length(inputMetabolites), replace = TRUE))
)
# ---- 6. Create synthetic STITCH interaction data ----
stitch_ids <- mapping_df$STITCH_ID
stitch_pairs <- expand.grid(chemical1 = stitch_ids, chemical2 = stitch_ids) %>%
dplyr::filter(chemical1 != chemical2)
set.seed(123)
stitch_df <- stitch_pairs %>%
dplyr::slice_sample(n = 50) %>% # Reduced to 50 for faster processing
dplyr::mutate(
similarity = runif(dplyr::n(), 0.6, 0.95), # Higher similarity for better networks
experimental = sample(200:800, dplyr::n(), replace = TRUE),
database = sample(c(0, 300, 600, 900), dplyr::n(), replace = TRUE),
textmining = sample(0:900, dplyr::n(), replace = TRUE),
combined_score = similarity * 200 + experimental + database + textmining
)
# ---- 7. Run comprehensive enrichment analysis with ALL features ----
results <- enrichmet(
inputMetabolites = inputMetabolites,
PathwayVsMetabolites = PathwayVsMetabolites,
example_data = example_data,
kegg_lookup = kegg_lookup,
top_n = 15,
p_value_cutoff = 0.1, # Use a reasonable cutoff
mapping_df = mapping_df,
stitch_df = stitch_df,
analysis_type = c("enrichment", "gsea", "centrality", "network",
"heatmap", "membership", "interaction"),
network_top_n = 10,
heatmap_top_n = 10,
membership_top_n = 10,
min_pathway_occurrence = 2,
min_metabolite_occurrence = 2
)
#> Using 20 metabolites from inputMetabolites character vector
#> Processing complex KEGG IDs (splitting by |)...
#> Split 20 complex IDs into 20 unique KEGG IDs
#> Running pathway enrichment analysis...
#> Enrichment analysis completed: 55 pathways tested, 55 pathways passed filtering (p <= 1.000000)
#> Running GSEA analysis...
#> Extracting KEGG IDs from 'met_id' column...
#> Successfully extracted KEGG IDs for 23 metabolites
#> Sample extracted KEGG IDs: C00001, C00002, C00003, C00004, C00005, C00006
#> Prepared 23 metabolites with KEGG IDs for GSEA
#> Created rankings for 23 KEGG metabolites using 'signed_pval' method
#> Ranking range: -3.523 to 4
#> Preparing GMT data from 55 pathways
#> GMT preparation summary:
#> Total pathways processed: 55
#> Pathways kept (size >= 5): 54
#> Pathways removed (size < 5): 1
#> Pathway size distribution:
#> Min: 5
#> Median: 10
#> Max: 15
#> Mean: 10.2
#> Testing 54 pathways with GSEA
#> GSEA completed: 54 pathways tested, 0 significant at FDR < 0.05
#> Applied KEGG pathway name mapping using 'kegg_id' and 'name' columns
#> Running centrality analysis...
#> Generating metabolite-pathway network visualization...
#> Using top 10 metabolites by centrality for network plot
#> Generating enrichment heatmap...
#> Selected top 10 metabolites by enrichment significance for heatmap
#> Generating pathway membership plot...
#> === MEMBERSHIP PLOT DEBUG ===
#> Input metabolites: 20
#> Top_n parameter: 10
#> Initial matched metabolites: 20
#> Filtering to top 10 metabolites by centrality...
#> Total metabolites with centrality: 28
#> Top metabolites selected: 10
#> Sample top metabolites: C00005, C00015, C00013, C00004, C00017, C00014
#> After centrality filtering - unique metabolites: 10
#> Matrix before occurrence filtering: 10 x 55
#> Pathway counts range: 1 to 10
#> Metabolite counts range: 27 to 31
#> Pathways to keep: 54/55
#> Metabolites to keep: 10/10
#> Final matrix dimensions: 10 x 54
#> === END DEBUG ===
#> Generating STITCH interaction network...
#> Extracting KEGG IDs from input metabolites...
#> Successfully extracted 20 KEGG IDs from 20 input metabolites
#> Sample extracted KEGG IDs: C00001, C00002, C00003, C00004, C00005, C00006
#> Found 20 metabolites in mapping_df with valid PubChem CIDs
#> Applied KEGG pathway name mapping
#> Display names sample: Glucose, Lactate, Pyruvate, Alanine, Valine, Leucine
#> Found 50 valid interactions between 20 metabolites
#> Creating graph with 20 vertices
#> Vertex attributes: name, display_name, KEGG_ID, PubChem_CID
#> Graph vertex attributes: name, display_name, KEGG_ID, PubChem_CID
#> Sample vertex display_names: Glucose, Lactate, Pyruvate, Alanine, Valine, Leucine
#> Using layout: gem (spacing score: 388.19)
results
#> $input_metabolites_used
#> [1] "C00001" "C00002" "C00003" "C00004" "C00005" "C00006" "C00007" "C00008"
#> [9] "C00009" "C00010" "C00011" "C00012" "C00013" "C00014" "C00015" "C00016"
#> [17] "C00017" "C00018" "C00019" "C00020"
#>
#> $pathway_enrichment_all
#> Pathway P_value Log_P_value Impact Coverage Count Pathway_Size
#> 1 Pathway1 0.0009661836 3.014940e+00 1.0000000 1.0000000 14 14
#> 2 Pathway2 0.0405295188 1.392229e+00 1.0000000 1.0000000 8 8
#> 3 Pathway3 0.0009661836 3.014940e+00 1.0000000 1.0000000 14 14
#> 4 Pathway4 0.0004140787 3.382917e+00 1.0000000 1.0000000 15 15
#> 5 Pathway5 0.0140786749 1.851438e+00 1.0000000 1.0000000 10 10
#> 6 Pathway6 0.0020703934 2.683947e+00 1.0000000 1.0000000 13 13
#> 7 Pathway7 0.1577533578 8.020214e-01 1.0000000 1.0000000 5 5
#> 8 Pathway8 0.0140786749 1.851438e+00 1.0000000 1.0000000 10 10
#> 9 Pathway9 0.0405295188 1.392229e+00 1.0000000 1.0000000 8 8
#> 10 Pathway10 0.0020703934 2.683947e+00 1.0000000 1.0000000 13 13
#> 11 Pathway11 0.0243177113 1.614077e+00 1.0000000 1.0000000 9 9
#> 12 Pathway12 0.0009661836 3.014940e+00 1.0000000 1.0000000 14 14
#> 13 Pathway13 0.0654707611 1.183953e+00 1.0000000 1.0000000 7 7
#> 14 Pathway14 0.0009661836 3.014940e+00 1.0000000 1.0000000 14 14
#> 15 Pathway15 0.0654707611 1.183953e+00 1.0000000 1.0000000 7 7
#> 16 Pathway16 0.0004140787 3.382917e+00 1.0000000 1.0000000 15 15
#> 17 Pathway17 0.0243177113 1.614077e+00 1.0000000 1.0000000 9 9
#> 18 Pathway18 0.0405295188 1.392229e+00 1.0000000 1.0000000 8 8
#> 19 Pathway19 0.0654707611 1.183953e+00 1.0000000 1.0000000 7 7
#> 20 Pathway20 0.0140786749 1.851438e+00 1.0000000 1.0000000 10 10
#> 21 Pathway21 0.0004140787 3.382917e+00 1.0000000 1.0000000 15 15
#> 22 Pathway22 0.0243177113 1.614077e+00 1.0000000 1.0000000 9 9
#> 23 Pathway23 0.0243177113 1.614077e+00 1.0000000 1.0000000 9 9
#> 24 Pathway24 0.0243177113 1.614077e+00 1.0000000 1.0000000 9 9
#> 25 Pathway25 0.1028826246 9.876580e-01 1.0000000 1.0000000 6 6
#> 26 Pathway26 0.0405295188 1.392229e+00 1.0000000 1.0000000 8 8
#> 27 Pathway27 0.0004140787 3.382917e+00 1.0000000 1.0000000 15 15
#> 28 Pathway28 0.0405295188 1.392229e+00 1.0000000 1.0000000 8 8
#> 29 Pathway29 0.0009661836 3.014940e+00 1.0000000 1.0000000 14 14
#> 30 Pathway30 0.1028826246 9.876580e-01 1.0000000 1.0000000 6 6
#> 31 Pathway31 0.0020703934 2.683947e+00 1.0000000 1.0000000 13 13
#> 32 Pathway32 0.0004140787 3.382917e+00 1.0000000 1.0000000 15 15
#> 33 Pathway33 0.0009661836 3.014940e+00 1.0000000 1.0000000 14 14
#> 34 Pathway34 0.0654707611 1.183953e+00 1.0000000 1.0000000 7 7
#> 35 Pathway35 0.0405295188 1.392229e+00 1.0000000 1.0000000 8 8
#> 36 Pathway36 0.0243177113 1.614077e+00 1.0000000 1.0000000 9 9
#> 37 Pathway37 0.0020703934 2.683947e+00 1.0000000 1.0000000 13 13
#> 38 Pathway38 0.0078214861 2.106711e+00 1.0000000 1.0000000 11 11
#> 39 Pathway39 0.0405295188 1.392229e+00 1.0000000 1.0000000 8 8
#> 40 Pathway40 0.0140786749 1.851438e+00 1.0000000 1.0000000 10 10
#> 41 Pathway41 0.0140786749 1.851438e+00 1.0000000 1.0000000 10 10
#> 42 Pathway42 0.0078214861 2.106711e+00 1.0000000 1.0000000 11 11
#> 43 Pathway43 0.1577533578 8.020214e-01 1.0000000 1.0000000 5 5
#> 44 Pathway44 0.0041407867 2.382917e+00 1.0000000 1.0000000 12 12
#> 45 Pathway45 0.0004140787 3.382917e+00 1.0000000 1.0000000 15 15
#> 46 Pathway46 0.0140786749 1.851438e+00 1.0000000 1.0000000 10 10
#> 47 Pathway47 0.1028826246 9.876580e-01 1.0000000 1.0000000 6 6
#> 48 Pathway48 0.0405295188 1.392229e+00 1.0000000 1.0000000 8 8
#> 49 Pathway49 0.0009661836 3.014940e+00 1.0000000 1.0000000 14 14
#> 50 Pathway50 0.0140786749 1.851438e+00 1.0000000 1.0000000 10 10
#> 51 Pathway101 0.0140786749 1.851438e+00 1.0000000 1.0000000 10 10
#> 52 Pathway102 0.0405295188 1.392229e+00 1.0000000 1.0000000 8 8
#> 53 Pathway103 0.9999996783 1.397297e-07 0.9676646 0.1250000 1 8
#> 54 Pathway104 0.6939900679 1.586467e-01 0.9775252 0.7142857 5 7
#> 55 Pathway105 0.1164690382 9.337895e-01 1.0000000 0.9000000 9 10
#> Input_Size Adjusted_P_value Q_value Enrichment_Ratio
#> 1 20 0.004087700 0.0008861659 1.400
#> 2 20 0.051840082 0.0112383292 1.400
#> 3 20 0.004087700 0.0008861659 1.400
#> 4 20 0.003795721 0.0008228684 1.400
#> 5 20 0.027654540 0.0059951839 1.400
#> 6 20 0.006698332 0.0014521207 1.400
#> 7 20 0.163706315 0.0354896324 1.400
#> 8 20 0.027654540 0.0059951839 1.400
#> 9 20 0.051840082 0.0112383292 1.400
#> 10 20 0.006698332 0.0014521207 1.400
#> 11 20 0.039337474 0.0085279087 1.400
#> 12 20 0.004087700 0.0008861659 1.400
#> 13 20 0.076614720 0.0166091838 1.400
#> 14 20 0.004087700 0.0008861659 1.400
#> 15 20 0.076614720 0.0166091838 1.400
#> 16 20 0.003795721 0.0008228684 1.400
#> 17 20 0.039337474 0.0085279087 1.400
#> 18 20 0.051840082 0.0112383292 1.400
#> 19 20 0.076614720 0.0166091838 1.400
#> 20 20 0.027654540 0.0059951839 1.400
#> 21 20 0.003795721 0.0008228684 1.400
#> 22 20 0.039337474 0.0085279087 1.400
#> 23 20 0.039337474 0.0085279087 1.400
#> 24 20 0.039337474 0.0085279087 1.400
#> 25 20 0.113170887 0.0245341372 1.400
#> 26 20 0.051840082 0.0112383292 1.400
#> 27 20 0.003795721 0.0008228684 1.400
#> 28 20 0.051840082 0.0112383292 1.400
#> 29 20 0.004087700 0.0008861659 1.400
#> 30 20 0.113170887 0.0245341372 1.400
#> 31 20 0.006698332 0.0014521207 1.400
#> 32 20 0.003795721 0.0008228684 1.400
#> 33 20 0.004087700 0.0008861659 1.400
#> 34 20 0.076614720 0.0166091838 1.400
#> 35 20 0.051840082 0.0112383292 1.400
#> 36 20 0.039337474 0.0085279087 1.400
#> 37 20 0.006698332 0.0014521207 1.400
#> 38 20 0.021509087 0.0046629208 1.400
#> 39 20 0.051840082 0.0112383292 1.400
#> 40 20 0.027654540 0.0059951839 1.400
#> 41 20 0.027654540 0.0059951839 1.400
#> 42 20 0.021509087 0.0046629208 1.400
#> 43 20 0.163706315 0.0354896324 1.400
#> 44 20 0.012652404 0.0027428946 1.400
#> 45 20 0.003795721 0.0008228684 1.400
#> 46 20 0.027654540 0.0059951839 1.400
#> 47 20 0.113170887 0.0245341372 1.400
#> 48 20 0.051840082 0.0112383292 1.400
#> 49 20 0.004087700 0.0008861659 1.400
#> 50 20 0.027654540 0.0059951839 1.400
#> 51 20 0.027654540 0.0059951839 1.400
#> 52 20 0.051840082 0.0112383292 1.400
#> 53 20 0.999999678 0.2167883450 0.175
#> 54 20 0.706841736 0.1532350994 1.000
#> 55 20 0.125603865 0.0272294627 1.260
#> Metabolite_List
#> 1 C00005,C00012,C00015,C00009,C00020,C00006,C00004,C00002,C00007,C00018,C00010,C00011,C00019,C00017
#> 2 C00014,C00004,C00019,C00008,C00018,C00017,C00003,C00016
#> 3 C00005,C00002,C00015,C00008,C00011,C00004,C00012,C00003,C00007,C00009,C00013,C00006,C00018,C00019
#> 4 C00002,C00015,C00017,C00006,C00008,C00003,C00018,C00001,C00013,C00009,C00016,C00012,C00007,C00019,C00020
#> 5 C00003,C00009,C00016,C00020,C00006,C00019,C00010,C00007,C00018,C00008
#> 6 C00003,C00019,C00018,C00006,C00012,C00004,C00009,C00007,C00020,C00017,C00005,C00008,C00014
#> 7 C00004,C00019,C00009,C00017,C00006
#> 8 C00006,C00013,C00017,C00002,C00014,C00020,C00010,C00018,C00005,C00011
#> 9 C00003,C00004,C00010,C00006,C00017,C00009,C00011,C00020
#> 10 C00014,C00008,C00017,C00013,C00009,C00002,C00006,C00019,C00011,C00020,C00003,C00007,C00016
#> 11 C00014,C00019,C00007,C00009,C00011,C00012,C00016,C00008,C00003
#> 12 C00016,C00002,C00005,C00020,C00018,C00009,C00003,C00008,C00014,C00013,C00012,C00015,C00004,C00011
#> 13 C00007,C00017,C00003,C00002,C00005,C00015,C00014
#> 14 C00017,C00003,C00016,C00009,C00004,C00012,C00005,C00001,C00019,C00006,C00014,C00011,C00002,C00020
#> 15 C00018,C00011,C00016,C00006,C00007,C00008,C00004
#> 16 C00015,C00017,C00011,C00007,C00010,C00008,C00018,C00003,C00016,C00002,C00020,C00001,C00012,C00006,C00014
#> 17 C00020,C00012,C00010,C00001,C00013,C00006,C00015,C00011,C00003
#> 18 C00004,C00011,C00003,C00001,C00018,C00013,C00017,C00009
#> 19 C00010,C00013,C00012,C00001,C00005,C00019,C00020
#> 20 C00013,C00008,C00018,C00002,C00019,C00006,C00009,C00005,C00015,C00020
#> 21 C00014,C00009,C00001,C00018,C00016,C00015,C00006,C00005,C00010,C00002,C00019,C00011,C00004,C00003,C00007
#> 22 C00002,C00006,C00020,C00005,C00014,C00007,C00013,C00010,C00004
#> 23 C00009,C00006,C00015,C00020,C00002,C00011,C00008,C00012,C00014
#> 24 C00019,C00007,C00006,C00005,C00015,C00016,C00004,C00020,C00012
#> 25 C00010,C00004,C00018,C00020,C00002,C00001
#> 26 C00010,C00019,C00013,C00014,C00009,C00018,C00011,C00007
#> 27 C00019,C00010,C00013,C00001,C00006,C00005,C00012,C00016,C00003,C00014,C00017,C00020,C00002,C00007,C00015
#> 28 C00014,C00020,C00017,C00010,C00016,C00007,C00011,C00006
#> 29 C00017,C00014,C00006,C00015,C00007,C00005,C00020,C00012,C00011,C00019,C00018,C00010,C00004,C00003
#> 30 C00016,C00012,C00015,C00008,C00004,C00013
#> 31 C00019,C00001,C00004,C00014,C00015,C00010,C00008,C00013,C00011,C00016,C00003,C00005,C00007
#> 32 C00004,C00017,C00014,C00018,C00015,C00002,C00006,C00008,C00020,C00011,C00003,C00019,C00007,C00010,C00016
#> 33 C00018,C00003,C00004,C00015,C00005,C00006,C00002,C00008,C00014,C00016,C00017,C00013,C00020,C00001
#> 34 C00008,C00012,C00003,C00015,C00001,C00017,C00018
#> 35 C00011,C00007,C00004,C00013,C00016,C00015,C00001,C00020
#> 36 C00004,C00015,C00018,C00007,C00002,C00005,C00006,C00003,C00010
#> 37 C00007,C00016,C00012,C00011,C00010,C00009,C00013,C00015,C00020,C00008,C00019,C00018,C00017
#> 38 C00005,C00006,C00001,C00019,C00013,C00010,C00014,C00008,C00004,C00017,C00002
#> 39 C00010,C00015,C00001,C00018,C00017,C00004,C00003,C00016
#> 40 C00013,C00011,C00017,C00015,C00012,C00006,C00020,C00009,C00001,C00003
#> 41 C00020,C00018,C00017,C00002,C00001,C00005,C00011,C00010,C00013,C00007
#> 42 C00005,C00011,C00014,C00007,C00010,C00018,C00015,C00017,C00013,C00012,C00019
#> 43 C00003,C00015,C00010,C00001,C00013
#> 44 C00017,C00008,C00015,C00016,C00010,C00002,C00001,C00012,C00003,C00018,C00007,C00014
#> 45 C00008,C00019,C00015,C00007,C00013,C00014,C00020,C00005,C00003,C00006,C00011,C00001,C00009,C00017,C00012
#> 46 C00003,C00020,C00007,C00011,C00012,C00005,C00016,C00009,C00004,C00013
#> 47 C00014,C00008,C00005,C00004,C00013,C00007
#> 48 C00013,C00014,C00009,C00005,C00011,C00006,C00018,C00002
#> 49 C00005,C00001,C00007,C00009,C00004,C00018,C00010,C00015,C00012,C00019,C00017,C00020,C00002,C00008
#> 50 C00018,C00004,C00010,C00014,C00012,C00013,C00005,C00006,C00016,C00019
#> 51 C00012,C00013,C00014,C00015,C00016,C00001,C00018,C00003,C00006,C00004
#> 52 C00006,C00007,C00008,C00009,C00010,C00011,C00016,C00004
#> 53 C00005
#> 54 C00013,C00014,C00015,C00016,C00017
#> 55 C00015,C00016,C00017,C00018,C00019,C00020,C00004,C00008,C00010
#>
#> $pathway_enrichment_results
#> Pathway P_value Log_P_value Impact Coverage Count Pathway_Size
#> 1 Pathway4 0.0004140787 3.382917 1 1 15 15
#> 2 Pathway16 0.0004140787 3.382917 1 1 15 15
#> 3 Pathway21 0.0004140787 3.382917 1 1 15 15
#> 4 Pathway27 0.0004140787 3.382917 1 1 15 15
#> 5 Pathway32 0.0004140787 3.382917 1 1 15 15
#> 6 Pathway45 0.0004140787 3.382917 1 1 15 15
#> 7 Pathway1 0.0009661836 3.014940 1 1 14 14
#> 8 Pathway3 0.0009661836 3.014940 1 1 14 14
#> 9 Pathway12 0.0009661836 3.014940 1 1 14 14
#> 10 Pathway14 0.0009661836 3.014940 1 1 14 14
#> 11 Pathway29 0.0009661836 3.014940 1 1 14 14
#> 12 Pathway33 0.0009661836 3.014940 1 1 14 14
#> 13 Pathway49 0.0009661836 3.014940 1 1 14 14
#> 14 Pathway6 0.0020703934 2.683947 1 1 13 13
#> 15 Pathway10 0.0020703934 2.683947 1 1 13 13
#> Input_Size Adjusted_P_value Q_value Enrichment_Ratio
#> 1 20 0.003795721 0.0008228684 1.4
#> 2 20 0.003795721 0.0008228684 1.4
#> 3 20 0.003795721 0.0008228684 1.4
#> 4 20 0.003795721 0.0008228684 1.4
#> 5 20 0.003795721 0.0008228684 1.4
#> 6 20 0.003795721 0.0008228684 1.4
#> 7 20 0.004087700 0.0008861659 1.4
#> 8 20 0.004087700 0.0008861659 1.4
#> 9 20 0.004087700 0.0008861659 1.4
#> 10 20 0.004087700 0.0008861659 1.4
#> 11 20 0.004087700 0.0008861659 1.4
#> 12 20 0.004087700 0.0008861659 1.4
#> 13 20 0.004087700 0.0008861659 1.4
#> 14 20 0.006698332 0.0014521207 1.4
#> 15 20 0.006698332 0.0014521207 1.4
#> Metabolite_List
#> 1 C00002,C00015,C00017,C00006,C00008,C00003,C00018,C00001,C00013,C00009,C00016,C00012,C00007,C00019,C00020
#> 2 C00015,C00017,C00011,C00007,C00010,C00008,C00018,C00003,C00016,C00002,C00020,C00001,C00012,C00006,C00014
#> 3 C00014,C00009,C00001,C00018,C00016,C00015,C00006,C00005,C00010,C00002,C00019,C00011,C00004,C00003,C00007
#> 4 C00019,C00010,C00013,C00001,C00006,C00005,C00012,C00016,C00003,C00014,C00017,C00020,C00002,C00007,C00015
#> 5 C00004,C00017,C00014,C00018,C00015,C00002,C00006,C00008,C00020,C00011,C00003,C00019,C00007,C00010,C00016
#> 6 C00008,C00019,C00015,C00007,C00013,C00014,C00020,C00005,C00003,C00006,C00011,C00001,C00009,C00017,C00012
#> 7 C00005,C00012,C00015,C00009,C00020,C00006,C00004,C00002,C00007,C00018,C00010,C00011,C00019,C00017
#> 8 C00005,C00002,C00015,C00008,C00011,C00004,C00012,C00003,C00007,C00009,C00013,C00006,C00018,C00019
#> 9 C00016,C00002,C00005,C00020,C00018,C00009,C00003,C00008,C00014,C00013,C00012,C00015,C00004,C00011
#> 10 C00017,C00003,C00016,C00009,C00004,C00012,C00005,C00001,C00019,C00006,C00014,C00011,C00002,C00020
#> 11 C00017,C00014,C00006,C00015,C00007,C00005,C00020,C00012,C00011,C00019,C00018,C00010,C00004,C00003
#> 12 C00018,C00003,C00004,C00015,C00005,C00006,C00002,C00008,C00014,C00016,C00017,C00013,C00020,C00001
#> 13 C00005,C00001,C00007,C00009,C00004,C00018,C00010,C00015,C00012,C00019,C00017,C00020,C00002,C00008
#> 14 C00003,C00019,C00018,C00006,C00012,C00004,C00009,C00007,C00020,C00017,C00005,C00008,C00014
#> 15 C00014,C00008,C00017,C00013,C00009,C00002,C00006,C00019,C00011,C00020,C00003,C00007,C00016
#>
#> $pathway_plot
#>
#> $impact_plot
#>
#> $gsea_results
#> pathway pval padj log2err ES NES size
#> <char> <num> <num> <num> <num> <num> <int>
#> 1: Pathway14 0.03438555 0.7948800 0.32177592 -0.5555556 -1.6626293 14
#> 2: Pathway36 0.05400982 0.7948800 0.24891111 0.5757292 1.5332087 9
#> 3: Pathway35 0.05505109 0.7948800 0.32177592 -0.5353320 -1.5107447 8
#> 4: Pathway104 0.06997743 0.7948800 0.25720647 -0.6111111 -1.5165348 5
#> 5: Pathway44 0.07360000 0.7948800 0.20895503 0.5478224 1.4848700 12
#> 6: Pathway40 0.11413043 0.8915094 0.21925035 -0.4615385 -1.3763556 10
#> 7: Pathway18 0.13131313 0.8915094 0.19578900 -0.4698021 -1.3258147 8
#> 8: Pathway10 0.13207547 0.8915094 0.20207171 -0.4262116 -1.2924156 13
#> 9: Pathway4 0.16825397 0.9145161 0.13284630 0.5000000 1.3163859 15
#> 10: Pathway45 0.16935484 0.9145161 0.17669427 -0.4207094 -1.2303829 15
#> 11: Pathway34 0.20000000 0.9782609 0.12443417 0.5000000 1.2562309 7
#> 12: Pathway105 0.24921136 0.9782609 0.10552094 0.4530527 1.2168795 10
#> 13: Pathway46 0.26630435 0.9782609 0.13880511 -0.3846154 -1.1469630 10
#> 14: Pathway16 0.28888889 0.9782609 0.09688777 0.4293675 1.1304266 15
#> 15: Pathway7 0.29119639 0.9782609 0.11881504 -0.4595606 -1.1404467 5
#> 16: Pathway27 0.29301075 0.9782609 0.13077714 -0.3884136 -1.1359325 15
#> 17: Pathway5 0.40063091 0.9782609 0.07829552 0.3846154 1.0330599 10
#> 18: Pathway26 0.40404040 0.9782609 0.10473282 -0.3597180 -1.0151497 8
#> 19: Pathway24 0.40759494 0.9782609 0.10434395 -0.3571429 -1.0584396 9
#> 20: Pathway47 0.41362530 0.9782609 0.10099059 -0.4023854 -1.0347550 6
#> 21: Pathway12 0.41666667 0.9782609 0.10672988 -0.3333333 -0.9975776 14
#> 22: Pathway3 0.41666667 0.9782609 0.10672988 -0.3333333 -0.9975776 14
#> 23: Pathway33 0.41666667 0.9782609 0.10672988 -0.3333333 -0.9975776 14
#> 24: Pathway6 0.56334232 0.9974747 0.08889453 -0.3000000 -0.9097001 13
#> 25: Pathway101 0.56521739 0.9974747 0.08916471 -0.3076923 -0.9175704 10
#> 26: Pathway49 0.57301587 0.9974747 0.06077195 0.3480110 0.9327974 14
#> 27: Pathway11 0.61012658 0.9974747 0.08108021 -0.2992943 -0.8869979 9
#> 28: Pathway13 0.62521008 0.9974747 0.05934877 0.3385562 0.8506094 7
#> 29: Pathway31 0.63072776 0.9974747 0.08266464 -0.2913365 -0.8834296 13
#> 30: Pathway37 0.68720379 0.9974747 0.05205700 0.3000000 0.8215613 13
#> 31: Pathway20 0.69400631 0.9974747 0.05153091 0.3076923 0.8264479 10
#> 32: Pathway21 0.71505376 0.9974747 0.07588869 -0.2683539 -0.7848126 15
#> 33: Pathway50 0.71766562 0.9974747 0.04999139 0.3001043 0.8060668 10
#> 34: Pathway39 0.73146623 0.9974747 0.05111480 0.3019127 0.7805432 8
#> 35: Pathway48 0.80808081 0.9974747 0.06658921 -0.2666667 -0.7525522 8
#> 36: Pathway32 0.84408602 0.9974747 0.06751890 -0.2500000 -0.7311359 15
#> 37: Pathway23 0.86075949 0.9974747 0.06364241 -0.2277685 -0.6750217 9
#> 38: Pathway1 0.88440860 0.9974747 0.06523531 -0.2222222 -0.6650517 14
#> 39: Pathway29 0.88440860 0.9974747 0.06523531 -0.2222222 -0.6650517 14
#> 40: Pathway22 0.88860759 0.9974747 0.06211242 -0.2172001 -0.6437008 9
#> 41: Pathway15 0.89915966 0.9974747 0.04258778 0.2585972 0.6497156 7
#> 42: Pathway9 0.90151515 0.9974747 0.06130261 -0.2394046 -0.6756165 8
#> 43: Pathway41 0.90378549 0.9974747 0.03943665 0.2307692 0.6198359 10
#> 44: Pathway2 0.90909091 0.9974747 0.06090393 -0.2349751 -0.6631162 8
#> 45: Pathway19 0.92268908 0.9974747 0.04140443 0.2500000 0.6281155 7
#> 46: Pathway8 0.92271293 0.9974747 0.03847869 0.2261323 0.6073812 10
#> 47: Pathway38 0.93103448 0.9974747 0.06211242 -0.2012729 -0.6215045 11
#> 48: Pathway17 0.95090016 0.9974747 0.03879622 0.2142857 0.5706585 9
#> 49: Pathway42 0.95755968 0.9974747 0.06077195 -0.1800970 -0.5561160 11
#> 50: Pathway25 0.96134454 0.9974747 0.03951722 0.2278119 0.5478896 6
#> 51: Pathway102 0.97364086 0.9974747 0.03800562 0.2099911 0.5428958 8
#> 52: Pathway43 0.98223801 0.9974747 0.04107133 0.2222222 0.4979655 5
#> 53: Pathway30 0.98296837 0.9974747 0.05547933 -0.2065311 -0.5311055 6
#> 54: Pathway28 0.99747475 0.9974747 0.05652995 -0.1537743 -0.4339619 8
#> pathway pval padj log2err ES NES size
#> leadingEdge input_count significance
#> <list> <int> <char>
#> 1: C00004, .... 14 NS
#> 2: C00010, .... 8 NS
#> 3: C00004, .... 7 NS
#> 4: C00014, .... 5 NS
#> 5: C00010, .... 11 NS
#> 6: C00011, .... 10 NS
#> 7: C00004, .... 6 NS
#> 8: C00014, .... 11 NS
#> 9: C00008, .... 15 NS
#> 10: C00014, .... 14 NS
#> 11: C00008, .... 7 NS
#> 12: C00021, .... 4 NS
#> 13: C00004, .... 10 NS
#> 14: C00010, .... 12 NS
#> 15: C00004, .... 4 NS
#> 16: C00014, .... 14 NS
#> 17: C00010, .... 10 NS
#> 18: C00014, .... 5 NS
#> 19: C00004, .... 9 NS
#> 20: C00004, .... 3 NS
#> 21: C00004, .... 14 NS
#> 22: C00004, .... 14 NS
#> 23: C00004, .... 14 NS
#> 24: C00004, .... 13 NS
#> 25: C00004, .... 10 NS
#> 26: C00010, .... 13 NS
#> 27: C00014, .... 8 NS
#> 28: C00005, .... 6 NS
#> 29: C00004, .... 5 NS
#> 30: C00010, .... 13 NS
#> 31: C00008, .... 10 NS
#> 32: C00004, .... 14 NS
#> 33: C00010, .... 5 NS
#> 34: C00010, .... 7 NS
#> 35: C00014, .... 8 NS
#> 36: C00004, .... 15 NS
#> 37: C00014, .... 8 NS
#> 38: C00004, .... 14 NS
#> 39: C00004, .... 14 NS
#> 40: C00004, .... 4 NS
#> 41: C00008, .... 4 NS
#> 42: C00004, .... 5 NS
#> 43: C00010, .... 10 NS
#> 44: C00004, .... 2 NS
#> 45: C00010, .... 7 NS
#> 46: C00010, .... 4 NS
#> 47: C00004, .... 4 NS
#> 48: C00010, .... 9 NS
#> 49: C00014, .... 4 NS
#> 50: C00010, .... 2 NS
#> 51: C00010, .... 4 NS
#> 52: C00010, .... 5 NS
#> 53: C00004, .... 2 NS
#> 54: C00014, .... 5 NS
#> leadingEdge input_count significance
#>
#> $gsea_plot
#>
#> $metabolite_centrality
#> Metabolite RBC_Metabolite Display_Name
#> 1 C00005 0.15717641 Valine
#> 2 C00015 0.05152146 Asparagine
#> 3 C00013 0.04726846 Aspartate
#> 4 C00004 0.04691644 Alanine
#> 5 C00017 0.04620232 Lysine
#> 6 C00014 0.04410009 Glutamate
#> 7 C00006 0.04113730 Leucine
#> 8 C00018 0.04088794 Arginine
#> 9 C00016 0.03934820 Glutamine
#> 10 C00020 0.03808712 Phenylalanine
#> 11 C00010 0.03763882 Threonine
#> 12 C00007 0.03556122 Isoleucine
#> 13 C00003 0.03494654 Pyruvate
#> 14 C00011 0.03094167 Cysteine
#> 15 C00019 0.02768143 Histidine
#> 16 C00008 0.02736923 Proline
#> 17 C00012 0.02371414 Methionine
#> 18 C00009 0.02220371 Serine
#> 19 C00001 0.02077305 Glucose
#> 20 C00002 0.01867062 Lactate
#>
#> $rbc_plot
#>
#> $network_plot
#>
#> $heatmap_plot
#>
#> $membership_plot
#>
#> $interaction_plot
