Package 'gatom'

Title: Finding an Active Metabolic Module in Atom Transition Network
Description: This package implements a metabolic network analysis pipeline to identify an active metabolic module 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 further provides functions for module post-processing, annotation and visualization.
Authors: Anastasiia Gainullina [aut], Mariia Emelianova [aut], Alexey Sergushichev [aut, cre]
Maintainer: Alexey Sergushichev <[email protected]>
License: MIT + file LICENCE
Version: 1.3.0
Built: 2024-05-31 05:51:23 UTC
Source: https://github.com/bioc/gatom

Help Index

Abbreviate lipid labels for lipid module

Description

Abbreviate lipid labels for lipid module

Usage

abbreviateLabels(module, orig.names, abbrev.names)

Arguments

module

Module to prepare
orig.names

whether to use original names from the dataset
abbrev.names

whether to use abbreviated names for all lipids

Value

module object with abbreviated labels

Add reactions without highly changing genes but with high average expression

Description

Add reactions without highly changing genes but with high average expression

Usage

addHighlyExpressedEdges(m, g, top = 3000)

Arguments

m

Metabolic module
g

Scored graph
top

Maximum rank value for the gene to be considered highly expressed

Value

module with added edges that correspond to high average expression

Examples

data(mEx)
data(gEx)
m <- addHighlyExpressedEdges(m = mEx, g = gEx)

Collapse atoms belonging to the same metabolite into one vertex

Description

Collapse atoms belonging to the same metabolite into one vertex

Usage

collapseAtomsIntoMetabolites(m)

Arguments

m

Metabolic module

Value

module in which atoms of the same metabolite are collapsed into one

Examples

data(mEx)
m <- collapseAtomsIntoMetabolites(m = mEx)

Connect atoms belonging to the same metabolite with edges

Description

Connect atoms belonging to the same metabolite with edges

Usage

connectAtomsInsideMetabolite(m)

Arguments

m

Metabolic module

Value

module in which atoms of the same metabolite are connected

Examples

data(mEx)
m <- connectAtomsInsideMetabolite(m = mEx)

Creates shinyCyJS widget from module

Description

Creates shinyCyJS widget from module

Usage

createShinyCyJSWidget(
  module,
  layout = list(name = "cose-bilkent", animate = FALSE, randomize = FALSE,
    nodeDimensionsIncludeLabels = TRUE),
  ...
)

Arguments

module

Module
layout

Layout for the module
...

Other parameters

Value

html widget of input module

Examples

data(mEx)
hw <- createShinyCyJSWidget(module = mEx)

gatom: a package for finding an active metabolic module in atom transition network

Description

This package implements a metabolic network analysis pipeline to identify an active metabolic module 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 further provides functions for module post-processing, annotation and visualization.

Functions

Data preprocessing: prepareDE, getMetDEMeta, getGeneDEMeta

Graph creation: makeMetabolicGraph

Graph scoring: scoreGraph

Module postprocessing: collapseAtomsIntoMetabolites, connectAtomsInsideMetabolite, addHighlyExpressedEdges, abbreviateLabels

Plotting module: createShinyCyJSWidget

Exporting module: saveModuleToHtml, saveModuleToDot, saveModuleToPdf, saveModuleToXgmml

For detailed pipeline analysis, see gatom vignette: vignette("gatom-tutorial", package = "gatom")

Example Data

Example data provided by gatom consists of: metabolite differential abundance data (met.de.rawEx), gene differential expression data (gene.de.rawEx), KEGG-based network object (networkEx), KEGG-based metabolite database object (met.kegg.dbEx), Example organism annotation object (org.Mm.eg.gatom.annoEx), metabolic graph with atom topology (gEx), scored metabolic graph with atom topology (gsEx), and metabolic module (mEx).

Example gene differential expression data.

Description

See file https://github.com/ctlab/gatom/blob/master/inst/scripts/example.R for details.

Format

tibble/data.frame object

Finds columns in gene differential expression table required for gatom analysis

Description

Default values for all columns are NULL which mean they are determined automatically.

Usage

getGeneDEMeta(
  gene.de.raw,
  org.gatom.anno,
  idColumn = NULL,
  idType = NULL,
  pvalColumn = NULL,
  logPvalColumn = NULL,
  log2FCColumn = NULL,
  baseMeanColumn = NULL,
  signalColumn = NULL,
  signalRankColumn = NULL
)

Arguments

gene.de.raw

A table with differential expression results, an object convertable to data.frame.
org.gatom.anno

Organsim-specific annotation obtained from makeOrgGatomAnnotation function.
idColumn

Specifies column name with gene identifiers.
idType

Specifies type of gene IDs (one of the supported by annotation).
pvalColumn

Specifies column with p-values.
logPvalColumn

Specifies column with log p-values, if there is no such column one will be generated automatically.
log2FCColumn

Specifies column with log2-fold changes.
baseMeanColumn

Specifies column with average expression across samples.
signalColumn

Specifies column with identifier of the measured entity (such as gene ID for RNA-seq and probe ID for microarrays). Could be NULL (automatic, set from based on pval and log2FC columns), character (column name), or function (evaluated in a scope of original data frame)
signalRankColumn

Specifies how the genes are ranked from highly to lowly expressed, used in 'addHighlyExpressedEdgues' function. Could be NULL (automatic), character (column name) function (evaluated in a scope of original data frame).

Value

object with prepared columns for the analysis for gene data

Examples

data("org.Mm.eg.gatom.annoEx")
data("gene.de.rawEx")
de.meta <- getGeneDEMeta(gene.de.rawEx, org.gatom.anno = org.Mm.eg.gatom.annoEx)

Generate list of metabolic pathways from Reactome and KEGG databases

Description

Generate list of metabolic pathways from Reactome and KEGG databases

Usage

getMetabolicPathways(
  universe,
  metGenes,
  keggOrgCode,
  threshold = 0.01,
  includeReactome = TRUE,
  includeKEGG = TRUE
)

Arguments

universe

list of genes
metGenes

list of metabolic genes
keggOrgCode

KEGG organism code, like mmu or hsa
threshold

threshold for Fisher test to filter out non-metabolic pathways
includeReactome

whether to include Reactome pathways (only works for Entrez ID universe)
includeKEGG

whether to include KEGG pathways and modules

Value

list of metabolic pathways for given organism and list of genes

Finds columns in differential expression table for metabolites required for gatom analysis

Description

Finds columns in differential expression table for metabolites required for gatom analysis

Usage

getMetDEMeta(
  met.de.raw,
  met.db,
  idColumn = NULL,
  idType = NULL,
  pvalColumn = NULL,
  logPvalColumn = NULL,
  log2FCColumn = NULL,
  signalColumn = NULL
)

Arguments

met.de.raw

A table with differential expression results, an object convertable to data.frame.
met.db

Metabolite database
idColumn

Specifies column name with metabolite identifiers.
idType

Specifies type of metabolite IDs (one of the supported by annotation).
pvalColumn

Specifies column with p-values.
logPvalColumn

Specifies column with log p-values, if there is no such column one will be generated automatically.
log2FCColumn

Specifies column with log2-fold changes.
signalColumn

Specifies column with identifier of the measured entity Could be NULL (automatic, set from based on pval and log2FC columns), character (column name), or function (evaluated in a scope of original data frame)

Value

object with prepared columns for the analysis for metabolite data

Examples

data("met.kegg.dbEx")
data("met.de.rawEx")
de.meta <- getMetDEMeta(met.de.rawEx, met.db = met.kegg.dbEx)

Example metabolic graph with atom topology.

Description

See file https://github.com/ctlab/gatom/blob/master/inst/scripts/example.R for details.

Format

igraph object

Example scored metabolic graph with atom topology.

Description

See file https://github.com/ctlab/gatom/blob/master/inst/scripts/example.R for details.

Format

igraph object

Creates metabolic graph based on specified data

Description

Creates metabolic graph based on specified data

Usage

makeMetabolicGraph(
  network,
  topology = c("atoms", "metabolites"),
  org.gatom.anno,
  gene.de,
  gene.de.meta = getGeneDEMeta(gene.de, org.gatom.anno),
  gene.keep.top = 12000,
  met.db,
  met.de,
  met.de.meta = getMetDEMeta(met.de, met.db),
  met.to.filter = fread(system.file("extdata", "mets2mask.lst", package = "gatom"))$ID,
  gene2reaction.extra = NULL,
  keepReactionsWithoutEnzymes = FALSE,
  largest.component = TRUE
)

Arguments

network

Network object
topology

Way to determine network vertices
org.gatom.anno

Organism annotation object
gene.de

Table with the differential gene expression, set to NULL if absent
gene.de.meta

Annotation of 'gene.de' table
gene.keep.top

Only the 'gene.keep.top' of the most expressed genes will be kept for the network
met.db

Metabolite database
met.de

Table with the differential expression for metabolites, set to NULL if absent
met.de.meta

Annotation of 'met.de' table
met.to.filter

List of metabolites to filter from the network
gene2reaction.extra

Additional gene to reaction mappings. Should be a data.table with 'gene' and 'reaction' columns
keepReactionsWithoutEnzymes

If TRUE, keep reactions that have no annotated enzymes, thus expanding the network but including some reactions which are not possible in the considered species.
largest.component

If TRUE, only the largest connected component is returned

Value

igraph object created from input data

Examples

data("gene.de.rawEx")
data("met.de.rawEx")
data("met.kegg.dbEx")
data("networkEx")
data("org.Mm.eg.gatom.annoEx")
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)

Create an organism annotation object for network analysis

Description

Create an organism annotation object for network analysis

Usage

makeOrgGatomAnnotation(
  org.db,
  idColumns = c(Entrez = "ENTREZID", RefSeq = "REFSEQ", Ensembl = "ENSEMBL", Symbol =
    "SYMBOL"),
  nameColumn = "SYMBOL",
  enzymeColumn = "ENZYME",
  appendEnzymesFromKegg = TRUE,
  appendOrthologiesFromKegg = TRUE,
  filterNonSpecificEnzymes = TRUE,
  keggOrgCode = NULL
)

Arguments

org.db

Bioconductor org.db object, e.g. org.Mm.eg.db
idColumns

vector of column names from 'org.db' object to creat ID mappings. First ID will be used as a base identifier, should be compatible with KEGG and Reactome databases.
nameColumn

column with a human readable gene symbol. Default to "SYMBOL".
enzymeColumn

column with an Enzyme Commission ID. Default to "ENZYME".
appendEnzymesFromKegg

if TRUE, KEGG databases will be sued to extend gene to enzyme mappings obtained from org.db package.
appendOrthologiesFromKegg

if TRUE, KEGG database will be sued to extend gene to orthology mappings obtained from org.db package
filterNonSpecificEnzymes

if TRUE, will filter out non-specific enzymes from gene to enzyme mappings obtained from org.db package
keggOrgCode

KEGG organism code, e.g. "mmu". If set to NULL, the code is determined automatically.

Value

organism annotation object that will be used for network analysis

Examples

library(org.Mm.eg.db)
org.Mm.eg.gatom.anno <- makeOrgGatomAnnotation(org.db = org.Mm.eg.db)

Example metabolite differential abundance data.

Description

See file https://github.com/ctlab/gatom/blob/master/inst/scripts/example.R for details.

Format

tibble/data.frame object

Example KEGG-based metabolite database object

Description

See file https://github.com/ctlab/gatom/blob/master/inst/scripts/example.R for details.

Format

list object

Example metabolic module.

Description

See file https://github.com/ctlab/gatom/blob/master/inst/scripts/example.R for details.

Format

igraph object

Example KEGG-based network object

Description

See file https://github.com/ctlab/gatom/blob/master/inst/scripts/example.R for details.

Format

list object

Example organism annotation object

Description

See file https://github.com/ctlab/gatom/blob/master/inst/scripts/example.R for details.

Format

list object

Makes data.table with differential expression results containing all columns required for gatom and in the expected format based on metadata object

Description

Makes data.table with differential expression results containing all columns required for gatom and in the expected format based on metadata object

Usage

prepareDE(de.raw, de.meta)

Arguments

de.raw

Table with differential expression results, an object convertable to data.frame
de.meta

Object with differential expression table metadata acquired with getGeneDEMeta or getMetDEMeta functions

Value

data.table object with converted differential expression table

Examples

data("org.Mm.eg.gatom.annoEx")
data("gene.de.rawEx")
de.meta <- getGeneDEMeta(gene.de.rawEx, org.gatom.anno = org.Mm.eg.gatom.annoEx)
de <- prepareDE(gene.de.rawEx, de.meta)

Save module to a graphviz dot file

Description

Save module to a graphviz dot file

Usage

saveModuleToDot(
  module,
  file,
  name = NULL,
  extra.node.attrs = NULL,
  extra.edge.attrs = NULL
)

Arguments

module

Module to save
file

File to save to
name

Name of the module
extra.node.attrs

Table with additional node attributes to be written to the dot file as is
extra.edge.attrs

Table with additional edge attributes to be written to the dot file as is

Value

Returns NULL

Examples

data(mEx)
saveModuleToDot(module = mEx, file = "module.dot")

Save module to a html widget

Description

Save module to a html widget

Usage

saveModuleToHtml(
  module,
  file,
  name = "",
  sizingPolicy = htmlwidgets::sizingPolicy(defaultWidth = "100%", defaultHeight =
    "90vh", padding = 10),
  ...
)

Arguments

module

Module to save
file

File to save to
name

Name of the module
sizingPolicy

A widget sizing policy
...

Other parameters

Value

Returns NULL

Examples

data(mEx)
saveModuleToHtml(module = mEx, file = "module.html")

Save module to a nice pdf file

Description

Save module to a nice pdf file

Usage

saveModuleToPdf(module, file, name = NULL, n_iter = 100, force = 1e-05)

Arguments

module

Module to save
file

File to save to
name

Name of the module
n_iter

Number of repel algorithm iterations
force

Value of repel force

Value

Returns NULL

Examples

data(mEx)
saveModuleToPdf(module = mEx, file = "module.pdf")

Save module to an XGMML file

Description

Save module to an XGMML file

Usage

saveModuleToXgmml(module, file, name = NULL)

Arguments

module

Module to save
file

File to save to
name

Name of the module

Value

Returns NULL

Examples

data(mEx)
saveModuleToXgmml(module = mEx, file = "module.xgmml")

Score metabolic graph

Description

Score metabolic graph

Usage

scoreGraph(
  g,
  k.gene,
  k.met,
  vertex.threshold.min = 0.1,
  edge.threshold.min = 0.1,
  met.score.coef = 1,
  show.warnings = TRUE,
  raw = FALSE
)

Arguments

g

Metabolic graph obtained with makeMetabolic graph function
k.gene

Number of gene signals to be scored positively, the higher is the number, the larger will be the resulting module. If set to NULL, genes will not be used for scoring.
k.met

Number of metabolite signals to be scored positively, the higher is the number, the larger will be the resulting module. If set to NULL, metabolites will not be used for scoring.
vertex.threshold.min

The worst acceptable estimated FDR for vertices. If necessary number of positive metabolite signals will be decreased from 'k.met' to reach this threshold. Default value is 0.1.
edge.threshold.min

The worst acceptable estimated FDR for vertices. If necessary number of positive metabolite signals will be decreased from 'k.gene' to reach this threshold. Default value is 0.1.
met.score.coef

Coefficient on which all vertex weights are multiplied. Can be used to balance vertex and edge weights. Default values is 1.
show.warnings

whether to show warnings
raw

whether to return raw scored graph, not a SGMWCS instance. Default to FALSE.

Value

SGMWCS instance or scored igraph object

Examples

data("gEx")
gs <- scoreGraph(g = gEx, k.gene = 25, k.met = 25)