This tutorial describes an R-package for finding active metabolic modules based on high throughput data. The pipeline takes as input transcriptional and/or metabolic data and finds a metabolic subnetwork (module) most regulated between the two conditions of interest.
The package relies on the active module analysis framework developed in BioNet package, but extends it to work with metabolic reaction networks. Further, it illustrates the usage of mwcsr package which provides a number of solvers for Maximum Weight Connected Subgraph problem and its variants.
Example of using the pipeline include:
More details on the pipeline are available in Sergushichev et al, 2016 and Emelianova et al, 2022.
You can install gatom via BiocManager:
# if (!require("BiocManager", quietly = TRUE))
# install.packages("BiocManager")
#
# BiocManager::install("gatom")In this example we will find an active metabolic module based on macrophage activation gene expression and metabolomics data (Jha et al, 2015). For improved performance here we will consider a simplified version of the data. See Example on full data and full network section for the real-scale analysis.
library(gatom)
library(data.table)
library(igraph)
library(mwcsr)First let’s load the example tables with input differential gene expression and metabolite abundance data for LPS-IFNg stimulated macrophages compared to controls:
data("gene.de.rawEx")
print(head(gene.de.rawEx))## # A tibble: 6 × 4
## ID pval log2FC baseMean
## <chr> <dbl> <dbl> <dbl>
## 1 NM_008392 8.83e-16 10.3 16589.
## 2 NM_001110517 1.12e-12 5.97 153.
## 3 NM_001253809 3.22e-11 -5.87 43.6
## 4 NM_013693 1.88e-10 4.92 326.
## 5 NM_009114 8.59e-10 -3.21 473.
## 6 NM_021491 2.36e- 9 -6.69 50.9data("met.de.rawEx")
print(head(met.de.rawEx))## # A tibble: 6 × 4
## ID pval log2FC baseMean
## <chr> <dbl> <dbl> <dbl>
## 1 HMDB02092 8.83e-34 3.12 17.1
## 2 HMDB00749 8.83e-34 3.12 17.1
## 3 HMDB00263 3.16e-19 1.41 12.8
## 4 HMDB01316 2.88e-13 -1.23 13.2
## 5 HMDB00191 4.39e-13 0.735 16.4
## 6 HMDB01112 6.60e-13 0.653 15.2Next we will load example network related objects: global reaction network (networkEx object),
metabolite annotations (met.kegg.dbEx), and organism-specific enzyme annotations for mouse (org.Mm.eg.gatom.annoEx).
data("networkEx")
data("met.kegg.dbEx")
data("org.Mm.eg.gatom.annoEx")Here networkEx object contain information about 204 KEGG reactions, their atom mappings and relation to enzymes:
str(networkEx, max.level=1, give.attr = FALSE)## List of 5
## $ reactions :Classes 'data.table' and 'data.frame': 204 obs. of 4 variables:
## $ enzyme2reaction:Classes 'data.table' and 'data.frame': 243 obs. of 2 variables:
## $ reaction2align :Classes 'data.table' and 'data.frame': 3919 obs. of 3 variables:
## $ atoms :Classes 'data.table' and 'data.frame': 1689 obs. of 3 variables:
## $ metabolite2atom:Classes 'data.table' and 'data.frame': 1689 obs. of 2 variables:Object met.kegg.dbEx contains information about 138 KEGG metabolites, including mappings from HMDB and ChEBI:
str(met.kegg.dbEx, max.level=2, give.attr = FALSE)## List of 4
## $ baseId : chr "KEGG"
## $ metabolites:Classes 'data.table' and 'data.frame': 138 obs. of 3 variables:
## ..$ metabolite : chr [1:138] "C00002" "C00003" "C00004" "C00005" ...
## ..$ metabolite_name: chr [1:138] "ATP" "NAD+" "NADH" "NADPH" ...
## ..$ metabolite_url : chr [1:138] "http://www.kegg.jp/entry/C00002" "http://www.kegg.jp/entry/C00003" "http://www.kegg.jp/entry/C00004" "http://www.kegg.jp/entry/C00005" ...
## $ anomers :List of 2
## ..$ metabolite2base_metabolite:Classes 'data.table' and 'data.frame': 238 obs. of 2 variables:
## ..$ base_metabolite2metabolite:Classes 'data.table' and 'data.frame': 238 obs. of 2 variables:
## $ mapFrom :List of 2
## ..$ HMDB :Classes 'data.table' and 'data.frame': 212 obs. of 2 variables:
## ..$ ChEBI:Classes 'data.table' and 'data.frame': 161 obs. of 2 variables:Object org.Mm.eg.gatom.annoEx contains mouse-specific mapping between enzyme classes
and genes, as well as mapping between different types of gene identifiers:
str(org.Mm.eg.gatom.annoEx, max.level=2, give.attr = FALSE)## List of 4
## $ baseId : chr "Entrez"
## $ gene2enzyme:Classes 'data.table' and 'data.frame': 150 obs. of 2 variables:
## ..$ gene : chr [1:150] "100042025" "100198" "100198" "100678" ...
## ..$ enzyme: chr [1:150] "1.2.1.12" "3.1.1.31" "1.1.1.47" "3.1.3.3" ...
## $ genes :Classes 'data.table' and 'data.frame': 135 obs. of 2 variables:
## ..$ gene : chr [1:135] "100042025" "100198" "100678" "100705" ...
## ..$ symbol: chr [1:135] "Gapdh-ps15" "H6pd" "Psph" "Acacb" ...
## $ mapFrom :List of 3
## ..$ RefSeq :Classes 'data.table' and 'data.frame': 724 obs. of 2 variables:
## ..$ Ensembl:Classes 'data.table' and 'data.frame': 135 obs. of 2 variables:
## ..$ Symbol :Classes 'data.table' and 'data.frame': 135 obs. of 2 variables:Then we create a metabolic graph with atom topology from the loaded data.
Depending on topology parameter, the graph vertices can correspond either
to atoms or metabolites. For metabolite topology,
see Using metabolite-level network section.
g <- makeMetabolicGraph(network=networkEx,
topology="atoms",
org.gatom.anno=org.Mm.eg.gatom.annoEx,
gene.de=gene.de.rawEx,
met.db=met.kegg.dbEx,
met.de=met.de.rawEx)## Found DE table for genes with RefSeq IDs## Found DE table for metabolites with HMDB IDsprint(g)## IGRAPH bd3aa47 UN-- 176 190 --
## + attr: name (v/c), metabolite (v/c), element (v/c), label (v/c), url
## | (v/c), pval (v/n), origin (v/n), HMDB (v/c), log2FC (v/n), baseMean
## | (v/n), logPval (v/n), signal (v/c), label (e/c), pval (e/n), origin
## | (e/n), RefSeq (e/c), gene (e/c), enzyme (e/c), reaction_name (e/c),
## | reaction_equation (e/c), url (e/c), reaction (e/c), log2FC (e/n),
## | baseMean (e/n), logPval (e/n), signal (e/c), signalRank (e/n)
## + edges from bd3aa47 (vertex names):
## [1] C00025_-0.3248_2.8125 --C00026_-0.3248_2.8125
## [2] C00025_-1.6238_3.5625 --C00026_-1.6238_3.5625
## [3] C00025_-2.9228_2.8125 --C00026_-2.9228_2.8125
## + ... omitted several edgesAfter creating the metabolic graph, we then score it, obtaining an instance of Signal Generalized Maximum Weight Subgraph (SGMWCS) problem.
The size of the module can be controlled by changing scoring parameters k.gene
and k.met. The higher the values of scoring parameters are, the bigger the
module is going to be.
gs <- scoreGraph(g, k.gene = 25, k.met = 25)Then we initialize an SMGWCS solver. Here, we use a heuristic relax-and-cut
solver rnc_solver for simplicity.
See mwcsr package documentation for more solver options, or
Using exact solver section for the recommended way.
solver <- rnc_solver()Then we find an active metabolic module with chosen solver and scored graph:
set.seed(42)
res <- solve_mwcsp(solver, gs)
m <- res$graphThe result module is an igraph object that captures the most regulated
reactions:
print(m)## IGRAPH a3b1ebd UN-- 33 32 --
## + attr: signals (g/n), name (v/c), metabolite (v/c), element (v/c),
## | label (v/c), url (v/c), pval (v/n), origin (v/n), HMDB (v/c), log2FC
## | (v/n), baseMean (v/n), logPval (v/n), signal (v/c), score (v/n),
## | label (e/c), pval (e/n), origin (e/n), RefSeq (e/c), gene (e/c),
## | enzyme (e/c), reaction_name (e/c), reaction_equation (e/c), url
## | (e/c), reaction (e/c), log2FC (e/n), baseMean (e/n), logPval (e/n),
## | signal (e/c), signalRank (e/n), score (e/n)
## + edges from a3b1ebd (vertex names):
## [1] C00025_-1.6238_3.5625--C00026_-1.6238_3.5625
## [2] C00022_-1.299_2.25 --C00041_-1.299_2.25
## + ... omitted several edgeshead(E(m)$label)## [1] "Psat1" "Gpt2" "Got2" "Shmt1" "Pkm" "Tpi1"head(V(m)$label)## [1] "Pyruvate" "L-Glutamate" "2-Oxoglutarate" "Oxaloacetate"
## [5] "Glycine" "L-Alanine"The module can be plotted in R Viewer with createShinyCyJSWidget().
Here, red color corresponds to up-regulation (positive log-2 fold change) and
green to down-regulation (negative log-2 fold change). Blue nodes and edges
come from data with absent log-2 fold change values. Bigger size of nodes and
width of edges reflect lower p-values.
createShinyCyJSWidget(m)We can save the module to graphml format with write_graph() function from igraph:
write_graph(m, file = file.path(tempdir(), "M0.vs.M1.graphml"), format = "graphml")Or it can be saved to an interactive html widget:
saveModuleToHtml(module = m, file = file.path(tempdir(), "M0.vs.M1.html"),
name="M0.vs.M1")We can also save the module to dot format:
saveModuleToDot(m, file = file.path(tempdir(), "M0.vs.M1.dot"), name = "M0.vs.M1")Such dot file can be further used to generate svg file using neato tool
from graphviz suite if it is installed on the system:
system(paste0("neato -Tsvg ", file.path(tempdir(), "M0.vs.M1.dot"),
" > ", file.path(tempdir(), "M0.vs.M1.svg")),
ignore.stderr=TRUE)Alternatively, the module can be saved to pdf format with a nice layout.
You may vary the meaning of repel force and the number of iterations of repel algorithm for label layout. Note, that the larger your graph is the softer force you should use.
You may also set different seed for different variants of edge layout with set.seed().
set.seed(42)
saveModuleToPdf(m, file = file.path(tempdir(), "M0.vs.M1.pdf"), name = "M0.vs.M1",
n_iter=100, force=1e-5)Let’s now look at how the analysis will work with the full dataset and the full network. For this case we will be using the combined network instead of KEGG network (see Networks for details on the network types).
library(R.utils)
library(data.table)The full macrophage LPS+IFNG-activation dataset can be downloaded from http://artyomovlab.wustl.edu/publications/supp_materials/GAM/:
met.de.raw <- fread("http://artyomovlab.wustl.edu/publications/supp_materials/GAM/Ctrl.vs.MandLPSandIFNg.met.de.tsv.gz")
gene.de.raw <- fread("http://artyomovlab.wustl.edu/publications/supp_materials/GAM/Ctrl.vs.MandLPSandIFNg.gene.de.tsv.gz")Full pre-generated combined network, corresponding metabolite annotation, and enzyme annotation can be downloaded from http://artyomovlab.wustl.edu/publications/supp_materials/GATOM/:
network.combined <- readRDS(url("http://artyomovlab.wustl.edu/publications/supp_materials/GATOM/network.combined.rds"))
met.combined.db <- readRDS(url("http://artyomovlab.wustl.edu/publications/supp_materials/GATOM/met.combined.db.rds"))
org.Mm.eg.gatom.anno <- readRDS(url("http://artyomovlab.wustl.edu/publications/supp_materials/GATOM/org.Mm.eg.gatom.anno.rds"))For better work of the combined network we highly recommend using additional supplementary gene files (see Supplementary Genes).
gene2reaction.extra <- fread("http://artyomovlab.wustl.edu/publications/supp_materials/GATOM/gene2reaction.combined.mmu.eg.tsv", colClasses="character")Running gatom on the combined network and the full dataset:
cg <- makeMetabolicGraph(network=network.combined,
topology="atoms",
org.gatom.anno=org.Mm.eg.gatom.anno,
gene.de=gene.de.raw,
met.db=met.combined.db,
met.de=met.de.raw,
gene2reaction.extra=gene2reaction.extra)## Found DE table for genes with RefSeq IDs## Found DE table for metabolites with HMDB IDscgs <- scoreGraph(cg, k.gene = 50, k.met = 50)## Metabolite p-value threshold: 0.038971## Metabolite BU alpha: 0.078854## FDR for metabolites: 0.054345## Gene p-value threshold: 0.001204## Gene BU alpha: 0.153709## FDR for genes: 0.006234solver <- rnc_solver()
set.seed(42)
sol <- solve_mwcsp(solver, cgs)
cm <- sol$graph
cm## IGRAPH d95db7f UN-- 75 77 --
## + attr: signals (g/n), name (v/c), metabolite (v/c), element (v/c),
## | label (v/c), url (v/c), pval (v/n), origin (v/n), HMDB (v/c), log2FC
## | (v/n), baseMean (v/n), logPval (v/n), signal (v/c), score (v/n),
## | label (e/c), pval (e/n), origin (e/n), RefSeq (e/c), gene (e/c),
## | enzyme (e/c), reaction_name (e/c), reaction_equation (e/c), url
## | (e/c), reaction (e/c), log2FC (e/n), baseMean (e/n), logPval (e/n),
## | signal (e/c), signalRank (e/n), score (e/n)
## + edges from d95db7f (vertex names):
## [1] C00025_1.299_0.75--C00025_1.299_0.75 C00022_1.299_0.75--C00026_-1.299_0.75
## [3] C00025_1.299_0.75--C00026_-1.299_0.75 C00025_0_0 --C00026_1.299_0.75
## + ... omitted several edgesThe result module for combined network:
createShinyCyJSWidget(cm)We provide four types of networks that can be used for analysis:
KEGG network consists of network.kegg.rds & met.kegg.db.rds files and is
based on KEGG database.
Both metabolites and reactions in KEGG network have KEGG IDs.
This network was generated with the pipeline available here. For extra details on KEGG network you can also reference shinyGatom and GAM articles.
network <- readRDS(url("http://artyomovlab.wustl.edu/publications/supp_materials/GATOM/network.kegg.rds"))
met.db <- readRDS(url("http://artyomovlab.wustl.edu/publications/supp_materials/GATOM/met.kegg.db.rds"))Running gatom with KEGG network on full dataset:
kg <- makeMetabolicGraph(network=network,
topology="atoms",
org.gatom.anno=org.Mm.eg.gatom.anno,
gene.de=gene.de.raw,
met.db=met.db,
met.de=met.de.raw)## Found DE table for genes with RefSeq IDs## Found DE table for metabolites with HMDB IDskgs <- scoreGraph(kg, k.gene = 50, k.met = 50)## Metabolite p-value threshold: 0.055133## Metabolite BU alpha: 0.087937## FDR for metabolites: 0.070765## Gene p-value threshold: 0.002471## Gene BU alpha: 0.164266## FDR for genes: 0.010231solver <- rnc_solver()
set.seed(42)
sol <- solve_mwcsp(solver, kgs)
km <- sol$graph
km## IGRAPH ef23d0b UN-- 67 67 --
## + attr: signals (g/n), name (v/c), metabolite (v/c), element (v/c),
## | label (v/c), url (v/c), pval (v/n), origin (v/n), HMDB (v/c), log2FC
## | (v/n), baseMean (v/n), logPval (v/n), signal (v/c), score (v/n),
## | label (e/c), pval (e/n), origin (e/n), RefSeq (e/c), gene (e/c),
## | enzyme (e/c), reaction_name (e/c), reaction_equation (e/c), url
## | (e/c), reaction (e/c), log2FC (e/n), baseMean (e/n), logPval (e/n),
## | signal (e/c), signalRank (e/n), score (e/n)
## + edges from ef23d0b (vertex names):
## [1] C00025_-4.2219_3.5625--C00026_-4.2219_3.5625
## [2] C00025_0.9743_3.5625 --C00026_-4.2219_3.5625
## + ... omitted several edgescreateShinyCyJSWidget(km)Rhea network consists of network.rhea.rds & met.rhea.db.rds files and
is based on Rhea database.
Reactions in Rhea have their own IDs, but unlike KEGG, metabolite IDs come from a separate database – ChEBI database.
This network was generated with the pipeline available here. For extra details on Rhea network you can also reference shinyGatom article.
To use Rhea network download the following files:
network.rhea <- readRDS(url("http://artyomovlab.wustl.edu/publications/supp_materials/GATOM/network.rhea.rds"))
met.rhea.db <- readRDS(url("http://artyomovlab.wustl.edu/publications/supp_materials/GATOM/met.rhea.db.rds"))For proper work of the Rhea network we also need a corresponding supplementary gene file (ref. Supplementary Genes).
gene2reaction.extra <- fread("http://artyomovlab.wustl.edu/publications/supp_materials/GATOM/gene2reaction.rhea.mmu.eg.tsv", colClasses="character")And run gatom on Rhea network:
rg <- makeMetabolicGraph(network=network.rhea,
topology="atoms",
org.gatom.anno=org.Mm.eg.gatom.anno,
gene.de=gene.de.raw,
met.db=met.rhea.db,
met.de=met.de.raw,
gene2reaction.extra=gene2reaction.extra)## Found DE table for genes with RefSeq IDs## Found DE table for metabolites with HMDB IDsrgs <- scoreGraph(rg, k.gene = 50, k.met = 50)## Metabolite p-value threshold: 0.046206## Metabolite BU alpha: 0.082053## FDR for metabolites: 0.092911## Gene p-value threshold: 0.000695## Gene BU alpha: 0.161730## FDR for genes: 0.004909solver <- rnc_solver()
set.seed(42)
sol <- solve_mwcsp(solver, rgs)
rm <- sol$graph
rm## IGRAPH 945f392 UN-- 46 46 --
## + attr: signals (g/n), name (v/c), metabolite (v/c), element (v/c),
## | label (v/c), url (v/c), pval (v/n), origin (v/n), HMDB (v/c), log2FC
## | (v/n), baseMean (v/n), logPval (v/n), signal (v/c), score (v/n),
## | label (e/c), pval (e/n), origin (e/n), RefSeq (e/c), gene (e/c),
## | enzyme (e/c), url (e/c), reaction (e/c), log2FC (e/n), baseMean
## | (e/n), logPval (e/n), signal (e/c), signalRank (e/n), score (e/n)
## + edges from 945f392 (vertex names):
## [1] CHEBI:15562_-2.9228_2.8125--CHEBI:16383_-0.3248_2.8125
## [2] CHEBI:15361_-1.299_3.75 --CHEBI:16452_-1.9486_3.375
## [3] CHEBI:15562_-2.9228_2.8125--CHEBI:16810_-0.3248_2.8125
## + ... omitted several edgesResult Rhea network module:
createShinyCyJSWidget(rm)Combined network comprises not only KEGG and Rhea reactions, but also transport reactions from BIGG database.
This means that reactions in such network have either KEGG or Rhea or BIGG IDs, and metabolite IDs are KEGGs and ChEBIs.
Rhea lipid subnetwork is subset of Rhea reactions that involve lipids,
and it consists of network.rhea.lipids.rds & met.rhea.lipids.db.rds files.
This network was generated with the pipeline available here. For extra details on Rhea lipid subnetwork you can also reference shinyGatom article.
network.lipids <- readRDS(url("http://artyomovlab.wustl.edu/publications/supp_materials/GATOM/network.rhea.lipids.rds"))
met.lipids.db <- readRDS(url("http://artyomovlab.wustl.edu/publications/supp_materials/GATOM/met.lipids.db.rds"))For proper work of the lipid network we will also need a corresponding supplementary gene file (ref. Supplementary Genes)
gene2reaction.extra <- fread("http://artyomovlab.wustl.edu/publications/supp_materials/GATOM/gene2reaction.rhea.mmu.eg.tsv", colClasses="character")To test lipid network we will use example lipidomics data for WT mice control vs high fat diet comparison from ST001289 dataset.
met.de.lipids <- fread("https://artyomovlab.wustl.edu/publications/supp_materials/GATOM/Ctrl.vs.HighFat.lipid.de.csv")For lipid network we recommend setting topology to metabolites
(ref. Using metabolite-level network):
lg <- makeMetabolicGraph(network=network.lipids,
topology="metabolites",
org.gatom.anno=org.Mm.eg.gatom.anno,
gene.de=NULL,
met.db=met.lipids.db,
met.de=met.de.lipids,
gene2reaction.extra=gene2reaction.extra)## Found DE table for metabolites with Species IDslgs <- scoreGraph(lg, k.gene = NULL, k.met = 50)## Metabolite p-value threshold: 0.009204## Metabolite BU alpha: 0.145591## FDR for metabolites: 0.006065solver <- rnc_solver()
set.seed(42)
sol <- solve_mwcsp(solver, lgs)
lm <- sol$graph
lm## IGRAPH 79db9cb UN-- 47 46 --
## + attr: signals (g/n), name (v/c), metabolite (v/c), element (v/c),
## | label (v/c), url (v/c), LipidMaps_ID (v/c), SwissLipids_ID (v/c),
## | Class_LipidMaps (v/c), Abbreviation_LipidMaps (v/c),
## | Synonyms_LipidMaps (v/c), Class_SwissLipids (v/c),
## | Abbreviation_SwissLipids (v/c), pval (v/n), origin (v/n), Species
## | (v/c), Mean(ctrl) (v/n), Mean(exp) (v/n), FC (v/n), log2FC (v/n),
## | -Log10(p-value) (v/n), -Log10(padj) (v/n), adj.P.Val (v/n),
## | Significance(padj) (v/c), Significance(p-value) (v/c), logPval (v/n),
## | signal (v/c), score (v/n), label (e/c), pval (e/l), gene (e/c),
## | enzyme (e/c), url (e/c), reaction (e/c), score (e/n), signal (e/c)
## + edges from 79db9cb (vertex names):Result lipid subnetwork module:
createShinyCyJSWidget(lm)If IDs for metabolite differential abundance data are of type Species we can
process metabolite labels into more readable ones:
lm1 <- abbreviateLabels(lm, orig.names = TRUE, abbrev.names = TRUE)
createShinyCyJSWidget(lm1)For combined, Rhea and lipid networks we provide supplementary files with genes that either come from proteome or are not linked to a specific enzyme. These files are organism-specific and are also available at http://artyomovlab.wustl.edu/publications/supp_materials/GATOM/.
network.combined <- readRDS(url("http://artyomovlab.wustl.edu/publications/supp_materials/GATOM/network.combined.rds"))
met.combined.db <- readRDS(url("http://artyomovlab.wustl.edu/publications/supp_materials/GATOM/met.combined.db.rds"))
gene2reaction.extra <- fread("http://artyomovlab.wustl.edu/publications/supp_materials/GATOM/gene2reaction.combined.mmu.eg.tsv", colClasses="character")
gg <- makeMetabolicGraph(network=network.combined,
topology="atoms",
org.gatom.anno=org.Mm.eg.gatom.anno,
gene.de=gene.de.raw,
met.db=met.combined.db,
met.de=met.de.raw,
gene2reaction.extra=gene2reaction.extra)## Found DE table for genes with RefSeq IDs## Found DE table for metabolites with HMDB IDsgg## IGRAPH c31076b UN-- 5047 6374 --
## + attr: name (v/c), metabolite (v/c), element (v/c), label (v/c), url
## | (v/c), pval (v/n), origin (v/n), HMDB (v/c), log2FC (v/n), baseMean
## | (v/n), logPval (v/n), signal (v/c), label (e/c), pval (e/n), origin
## | (e/n), RefSeq (e/c), gene (e/c), enzyme (e/c), reaction_name (e/c),
## | reaction_equation (e/c), url (e/c), reaction (e/c), log2FC (e/n),
## | baseMean (e/n), logPval (e/n), signal (e/c), signalRank (e/n)
## + edges from c31076b (vertex names):
## [1] C00019_-0.75_-1.299--C00019_-0.75_-1.299
## [2] C00019_-1.5_0 --C00019_-1.5_0
## [3] C00019_0.75_-1.299 --C00019_0.75_-1.299
## + ... omitted several edgesOptionally, we can also preserve non-enzymatic reactions that are found in the network.
This can be done by setting keepReactionsWithoutEnzymes to TRUE:
ge <- makeMetabolicGraph(network=network.combined,
topology="atoms",
org.gatom.anno=org.Mm.eg.gatom.anno,
gene.de=gene.de.raw,
met.db=met.combined.db,
met.de=met.de.raw,
gene2reaction.extra=gene2reaction.extra,
keepReactionsWithoutEnzymes=TRUE)## Found DE table for genes with RefSeq IDs## Found DE table for metabolites with HMDB IDsge## IGRAPH 3105d5b UN-- 5171 6519 --
## + attr: name (v/c), metabolite (v/c), element (v/c), label (v/c), url
## | (v/c), pval (v/n), origin (v/n), HMDB (v/c), log2FC (v/n), baseMean
## | (v/n), logPval (v/n), signal (v/c), label (e/c), pval (e/n), origin
## | (e/n), RefSeq (e/c), gene (e/c), enzyme (e/c), reaction_name (e/c),
## | reaction_equation (e/c), url (e/c), reaction (e/c), log2FC (e/n),
## | baseMean (e/n), logPval (e/n), signal (e/c), signalRank (e/n)
## + edges from 3105d5b (vertex names):
## [1] C00019_-0.75_-1.299--C00019_-0.75_-1.299
## [2] C00019_-1.5_0 --C00019_-1.5_0
## [3] C00019_0.75_-1.299 --C00019_0.75_-1.299
## + ... omitted several edgesFor proper analysis quality we recommend to use exact SGMWCS solver virgo_solver(),
which requires Java (version >= 11) and CPLEX (version >= 12.7) to be installed.
If the requirements are met you can then find a module as following:
vsolver <- virgo_solver(cplex_dir=Sys.getenv("CPLEX_HOME"),
threads=4, penalty=0.001, log=1)
sol <- solve_mwcsp(vsolver, gs)
m <- sol$graphEdge penalty option there is used to remove excessive redundancy in genes.
If there is no metabolite data in your experiment assign met.de and k.met to NULL:
g <- makeMetabolicGraph(network=networkEx,
topology="atoms",
org.gatom.anno=org.Mm.eg.gatom.annoEx,
gene.de=gene.de.rawEx,
met.db=met.kegg.dbEx,
met.de=NULL)## Found DE table for genes with RefSeq IDsgs <- scoreGraph(g, k.gene = 50, k.met = NULL)## Warning in bumOptim(x = x, starts): One or both parameters are on the limit of
## the defined parameter space## Gene p-value threshold: 0.130626## Gene BU alpha: 0.364527## FDR for genes: 0.100000If there is no gene data in your experiment assign gene.de and k.gene to NULL:
g <- makeMetabolicGraph(network=networkEx,
topology="atoms",
org.gatom.anno=org.Mm.eg.gatom.annoEx,
gene.de=NULL,
met.db=met.kegg.dbEx,
met.de=met.de.rawEx)## Found DE table for metabolites with HMDB IDsgs <- scoreGraph(g, k.gene = NULL, k.met = 50)## Metabolite p-value threshold: 0.260385## Metabolite BU alpha: 0.080774## FDR for metabolites: 0.100000Sometimes it could make sense to work with metabolite-metabolite topology of the network, not atom-atom one. Such network is less structured, but contains more genes.
gm <- makeMetabolicGraph(network=network,
topology="metabolite",
org.gatom.anno=org.Mm.eg.gatom.anno,
gene.de=gene.de.raw,
met.db=met.db,
met.de=met.de.raw)## Found DE table for genes with RefSeq IDs## Found DE table for metabolites with HMDB IDsgms <- scoreGraph(gm, k.gene = 50, k.met = 50)## Metabolite p-value threshold: 0.037216## Metabolite BU alpha: 0.078983## FDR for metabolites: 0.062353## Gene p-value threshold: 0.001241## Gene BU alpha: 0.163383## FDR for genes: 0.006075solver <- rnc_solver()
set.seed(42)
sol <- solve_mwcsp(solver, gms)
mm <- sol$graph
mm## IGRAPH 23ba94b UN-- 63 63 --
## + attr: signals (g/n), name (v/c), metabolite (v/c), element (v/c),
## | label (v/c), url (v/c), pval (v/n), origin (v/n), HMDB (v/c), log2FC
## | (v/n), baseMean (v/n), logPval (v/n), signal (v/c), score (v/n),
## | label (e/c), pval (e/n), origin (e/n), RefSeq (e/c), gene (e/c),
## | enzyme (e/c), reaction_name (e/c), reaction_equation (e/c), url
## | (e/c), reaction (e/c), log2FC (e/n), baseMean (e/n), logPval (e/n),
## | signal (e/c), signalRank (e/n), score (e/n)
## + edges from 23ba94b (vertex names):
## [1] C00219--C05956 C00062--C05933 C00327--C05933 C00417--C00490 C00062--C00086
## [6] C00157--C00162 C00157--C00219 C00162--C00350 C00550--C00588 C00669--C01879
## + ... omitted several edgesTo get functional annotation of obtained modules by KEGG and Reactome
metabolic pathways, we can use hypergeometric test with fora() function from fgsea package.
foraRes <- fgsea::fora(pathways=org.Mm.eg.gatom.anno$pathways,
genes=E(km)$gene,
universe=unique(E(kg)$gene),
minSize=5)
foraRes[padj < 0.05]## pathway
## <char>
## 1: mmu_M00002: Glycolysis, core module involving three-carbon compounds
## 2: mmu_M00003: Gluconeogenesis, oxaloacetate => fructose-6P
## 3: mmu_M00001: Glycolysis (Embden-Meyerhof pathway), glucose => pyruvate
## 4: mmu00565: Ether lipid metabolism
## 5: mmu00010: Glycolysis / Gluconeogenesis
## 6: mmu00030: Pentose phosphate pathway
## 7: mmu00020: Citrate cycle (TCA cycle)
## 8: mmu00564: Glycerophospholipid metabolism
## 9: mmu_M00004: Pentose phosphate pathway (Pentose phosphate cycle)
## pval padj overlap size overlapGenes
## <num> <num> <int> <int> <list>
## 1: 5.046444e-05 0.003532511 5 5 21991, 1....
## 2: 2.695203e-04 0.005915625 5 6 74551, 1....
## 3: 3.925355e-04 0.005915625 6 9 212032, ....
## 4: 3.925355e-04 0.005915625 6 9 18783, 2....
## 5: 4.225447e-04 0.005915625 10 23 13806, 1....
## 6: 2.154500e-03 0.025135833 7 15 110208, ....
## 7: 3.034903e-03 0.026789277 6 12 11428, 1....
## 8: 3.061632e-03 0.026789277 9 24 14571, 1....
## 9: 3.996354e-03 0.031082754 5 9 110208, ....Optionally, redundancy in pathways can be decreased with collapsePathwaysORA() function:
mainPathways <- fgsea::collapsePathwaysORA(
foraRes[padj < 0.05],
pathways=org.Mm.eg.gatom.anno$pathways,
genes=E(km)$gene,
universe=unique(E(kg)$gene))
foraRes[pathway %in% mainPathways$mainPathways]## pathway
## <char>
## 1: mmu_M00002: Glycolysis, core module involving three-carbon compounds
## 2: mmu00565: Ether lipid metabolism
## 3: mmu00030: Pentose phosphate pathway
## 4: mmu00020: Citrate cycle (TCA cycle)
## pval padj overlap size overlapGenes
## <num> <num> <int> <int> <list>
## 1: 5.046444e-05 0.003532511 5 5 21991, 1....
## 2: 3.925355e-04 0.005915625 6 9 18783, 2....
## 3: 2.154500e-03 0.025135833 7 15 110208, ....
## 4: 3.034903e-03 0.026789277 6 12 11428, 1....sessionInfo()## R version 4.4.0 RC (2024-04-16 r86468)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.20-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] R.utils_2.12.3 R.oo_1.26.0 R.methodsS3_1.8.2 mwcsr_0.1.8
## [5] igraph_2.0.3 data.table_1.15.4 gatom_1.3.0 BiocStyle_2.33.0
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.2.1 farver_2.1.1 dplyr_1.1.4
## [4] blob_1.2.4 Biostrings_2.73.0 fastmap_1.1.1
## [7] GGally_2.2.1 XML_3.99-0.16.1 digest_0.6.35
## [10] lifecycle_1.0.4 KEGGREST_1.45.0 RSQLite_2.3.6
## [13] magrittr_2.0.3 compiler_4.4.0 rlang_1.1.3
## [16] sass_0.4.9 tools_4.4.0 utf8_1.2.4
## [19] yaml_2.3.8 sna_2.7-2 knitr_1.46
## [22] htmlwidgets_1.6.4 bit_4.0.5 plyr_1.8.9
## [25] RColorBrewer_1.1-3 BiocParallel_1.39.0 withr_3.0.0
## [28] purrr_1.0.2 BiocGenerics_0.51.0 shinyCyJS_1.0.0
## [31] grid_4.4.0 stats4_4.4.0 fansi_1.0.6
## [34] colorspace_2.1-0 ggplot2_3.5.1 scales_1.3.0
## [37] cli_3.6.2 rmarkdown_2.26 crayon_1.5.2
## [40] generics_0.1.3 httr_1.4.7 DBI_1.2.2
## [43] cachem_1.0.8 stringr_1.5.1 zlibbioc_1.51.0
## [46] network_1.18.2 parallel_4.4.0 AnnotationDbi_1.67.0
## [49] BiocManager_1.30.22 XVector_0.45.0 vctrs_0.6.5
## [52] Matrix_1.7-0 jsonlite_1.8.8 bookdown_0.39
## [55] IRanges_2.39.0 S4Vectors_0.43.0 bit64_4.0.5
## [58] BioNet_1.65.0 jquerylib_0.1.4 tidyr_1.3.1
## [61] intergraph_2.0-4 glue_1.7.0 statnet.common_4.9.0
## [64] ggstats_0.6.0 codetools_0.2-20 cowplot_1.1.3
## [67] stringi_1.8.3 gtable_0.3.5 GenomeInfoDb_1.41.0
## [70] UCSC.utils_1.1.0 munsell_0.5.1 tibble_3.2.1
## [73] pillar_1.9.0 htmltools_0.5.8.1 fgsea_1.31.0
## [76] GenomeInfoDbData_1.2.12 R6_2.5.1 evaluate_0.23
## [79] Biobase_2.65.0 lattice_0.22-6 png_0.1-8
## [82] memoise_2.0.1 pryr_0.1.6 bslib_0.7.0
## [85] fastmatch_1.1-4 Rcpp_1.0.12 coda_0.19-4.1
## [88] xfun_0.43 pkgconfig_2.0.3