Package 'MOSClip'

Title: Multi Omics Survival Clip
Description: Topological pathway analysis tool able to integrate multi-omics data. It finds survival-associated modules or significant modules for two-class analysis. This tool have two main methods: pathway tests and module tests. The latter method allows the user to dig inside the pathways itself.
Authors: Paolo Martini [aut, cre] , Anna Bortolato [aut] , Anna Tanada [aut] , Enrica Calura [aut] , Stefania Pirrotta [aut] , Federico Agostinis [aut]
Maintainer: Paolo Martini <[email protected]>
License: AGPL-3
Version: 1.1.0
Built: 2024-10-30 08:23:52 UTC
Source: https://github.com/bioc/MOSClip

Help Index


Find Pathway Fathers

Description

Given the hierarchy of the pathways, this formula finds the fathers of the respective pathway (e.g. pathway: 'PI3K Cascade'; father: 'Signaling Pathways'). This function is necessary for calculating the contribution of different omics to survival prediction in different biological processes, grouping the pathways by hierarchy.

Usage

annotePathwayToFather(pathways, graphiteDB, hierarchy)

Arguments

pathways

vector of pathway names

graphiteDB

graphite DB object (e.g. an object containing all reactome pathways)

hierarchy

a graph object with the pathway hierarchy

Value

a vector of the pathway fathers' names

Examples

data(multiOmics)
data(reactSmall)

genesToUse <- row.names(multiOmics[[1]])

MOM_list <- lapply(reactSmall[1:2], function(g) {
    fcl <- multiOmicsSurvivalModuleTest(multiOmics, g,
        survFormula = "Surv(days, status) ~",
        autoCompleteFormula = TRUE,
        useTheseGenes = genesToUse
    )
    fcl
})

moduleSummary <- multiPathwayModuleReport(MOM_list)

pathHierarchy <- downloadPathwayRelationFromReactome()
pathHierarchyGraph <- igraph::graph.data.frame(
    d = pathHierarchy,
    directed = TRUE
)

omicsClasses2pathways <- computeOmicsIntersections(
    moduleSummary,
    pvalueThr = 1, zscoreThr = 1,
    excludeColumns = c("pathway", "module")
)

omicsClasses2pathways <- lapply(
    omicsClasses2pathways,
    stripModulesFromPathways
)

# This step requires to download the whole reactome graph, which usually
# takes a lot of time.
# reactome <- graphite::pathways('hsapiens', 'reactome')
# reactome <- graphite::convertIdentifiers(reactome, 'entrez')
# omicsClasses2fathers <- lapply(omicsClasses2pathways, 
#                                annotePathwayToFather, 
#                                graphiteDB = reactome,
#                                hierarchy = pathHierarchyGraph)

Get available Omics Summarizing Methods

Description

Gives a vector of the available methods to summarize omics.

Usage

availableOmicMethods()

Value

character vector with the implemented methods.

Examples

availableOmicMethods()

Check if all the list object have the same order of pathway module

Description

For internal use only

For internal use only

For internal use only

For internal use only

For internal use only

Prepare subset of patients for permutations

Usage

checkOrder(li)

resolveAndOrder(li)

mergeCol(li, col = "PC1", resolve = FALSE)

filterExpr(exp, samples)

filterMultiOmicsForSamples(MO, samples)

preparePerms(fullMultiOmics, nperm = 100, nPatients = 3)

Arguments

li

a list of summaries

col

the column to merge

resolve

weather to resolve the issues

exp

a matrix

samples

the vector of samples to select

MO

a multiOmic object

fullMultiOmics

a multiOmic object

nperm

number of permutations

nPatients

number of patients to remove for resampling

Value

a matrix

a filtered matrix

a filtered MultiOmics objects

list of sampled patients for each permutation


Regular PCA

Description

Regular PCA

Usage

compPCs(exp, shrink, k)

Arguments

exp

a matrix

shrink

logical, whether to shrink or not.

k

the number of components to use

Value

a list with the following elements:

x

the computed PCs

sdev

the standard deviation captured by the PCs

loadings

the loadings


Compute Frequencies in a Named List

Description

Compute frequencies in a named list. This function is necessary for plotFrequencies, in which it will calculate the frequency of each pathway father for every omics intersection.

Usage

computeFreqs(elementsIntersections)

Arguments

elementsIntersections

a named list

Value

a data.frame of the frequencies

Examples

omicsIntersection <- list(
    "exp;met" = c("PathwayA", "PathwayB", "PathwayC"),
    "exp;mut" = c("PathwayA", "PathwayC"),
    "cnv;mut" = c("PathwayB")
)
freqDf <- computeFreqs(omicsIntersection)

Compute Omics Intersections

Description

Finds the modules that have any intersection among the available omics

Usage

computeOmicsIntersections(
  multiPathwayReportData,
  pvalueThr = 0.05,
  zscoreThr = 0.05,
  resampligThr = NULL,
  excludeColumns = NULL
)

Arguments

multiPathwayReportData

data.frame, the output of the multiPathwayReport or multiPathwayModuleReport functions.

pvalueThr

numeric value. Overall pvalue cut-off to be used

zscoreThr

numeric value. Covariates coefficient cut-off to be used.

resampligThr

numeric value. Filters the modules according to the number of success in the resampling procedure, takes only the modules above this threshold.

excludeColumns

a vector of characters listing the columns of multiPathwayReportData object to be excluded by the analysis. In the case multiPathwayReportData derives from multiPathwayModuleReport you should set excludeColumns = c('pathway','module').

Value

a list of pathway modules present for every intersection of omics present

Examples

df <- data.frame(
    pvalue = c(0.06, 0.04, 0.04, 0.03, 0.02),
    cnv = c(0.07, 0.03, 0.02, 0.04, 0.01),
    mut = c(0.08, 0.02, 0.01, 0.04, 0.04),
    row.names = c(
        "PathwayA", "PathwayB", "PathwayC",
        "PathwayD", "PathwayE"
    )
)

omicsClasses2Pathways <- computeOmicsIntersections(df,
    pvalueThr = 0.1,
    zscoreThr = 0.1
)

compute PCs.

Description

For internal use only. Performs Principal Componenent analysis.

Usage

computePCs(
  exp,
  shrink = FALSE,
  method = c("regular", "topological", "sparse"),
  cliques = NULL,
  maxPCs = 3
)

Arguments

exp

a matrix

shrink

logical, whether to shrink or not.

method

one of 'regular', 'topological' and 'sparse'

cliques

the pathway topology summarized in a list of cliques

maxPCs

the maximum number of PCs to consider

Details

Three methods are implemented:

  • regular: a regular PCA ('prcomp')

  • topological: PCA using a pathway topology.

  • sparse: sparse PCA analysis implemented by 'elasticnet'

Value

a list with the following elements:

x

the computed PCs

sdev

the standard deviation captured by the PCs

loadings

the loadings


A generic function to convert pathway

Description

A generic function to convert pathway

Usage

convertPathway(graph, useTheseGenes)

Arguments

graph

a graphNEL object

useTheseGenes

list of genes to be used

Value

NULL. No value is returned


Create Cox Object

Description

Create the coxObj from the covariates used in the test

Usage

createCoxObj(colData, moView)

Arguments

colData

colData from multiOmic object

moView

modulesView or pathView from multiOmicsModules or multiOmicsPathway object

Value

data.frame, samples in the rows, covariates in the columns


Create Data Module

Description

Extract sub-matrix for the genes of a module or pathway from data matrix of a specific omic

Usage

createDataModule(omic, multiOmicObj)

Arguments

omic

modulesView or pathView object

multiOmicObj

object of class 'Omics'

Value

matrix, genes in the rows, samples in the columns


Create the list of covariates that are going to be tested

Description

Create the list of covariates that are going to be tested

Usage

createMOMView(omicsObj, genes)

Arguments

omicsObj

Omics class object

genes

genes of the clique

Value

list with 1 reduced representation of the omics 2 sdev 3 loadings or eigenvector 4 usedGenes 5 method 6 namesCov 7 omicName


Download Reactome Pathway Relations

Description

Download Pathway Relations from Reactome. The file is retrieved from the url

Usage

downloadPathwayRelationFromReactome(url = NULL, speciesAbbr = "HSA")

Arguments

url

the location of the file. Can be local. If NULL pick the package reactome file.

speciesAbbr

species acronim

Value

A data frame with 2 columns:

parent

The Reactome pathway ID of the parent pathway.

child

The Reactome pathway ID of the child pathway.

Examples

downloadPathwayRelationFromReactome()

Estimate Single Covariance Matrix

Description

For internal use only. Estimate Covariance from one matrix

Usage

estimateExprCov(expr, shrink)

Arguments

expr

a numeric matrix

shrink

logical wheter to shrink the matrix

Value

a covariance matrix


Extract the maximal cliques

Description

For internal use only. Extract the cliques.

For internal use only. Force Moralization

Usage

extractCliquesFromDag(dag, root = NULL)

mmmoralize(graph)

Arguments

dag

a Directed Aciclic Graph

root

a node to use as root

graph

a graphNEL object

Value

list of nodes cliques


Extract Summary Binary from MultiOmics Objects

Description

Given an omic summarized by 'summarizeToBinaryEvents' extract the most important features.

Usage

extractSummaryFromBinary(omic, multiOmicObj, n = 3)

Arguments

omic

a summarized omic

multiOmicObj

Omics object

n

maximum number of features to retrieve

Value

Meant for internal use only. The summary for omic summarized using binary events.

sigModule

the original data for significant features

discrete

the discrete version of the significant covariates converted (when needed) into the discrete version

subset

data.frame(row.names=names(topGenes), cov=sum binary events)

covsConsidered

the name of the considered omic


Extract Summary Cluster from MultiOmics Objects

Description

Given an omic summarized by 'summarizeInCluster' extract the most important features.

Usage

extractSummaryFromCluster(omic, multiOmicObj, n = 3)

Arguments

omic

a summarized omic

multiOmicObj

Omics object

n

maximum number of features to retrieve

Value

summary for omic summarized using clusters

sigModule

the original data for significant features

discrete

the discrete version of the significant covariates converted (when needed) into the discrete version

subset

data.frame(row.names=names(topGenes), metClust=topGenes)

pvalues

Kruskal Wallis pvalues of the selected features

covsConsidered

the name of the considered omic


Extract Summary Binary from MultiOmics Objects

Description

Given an omic summarized by 'summarizeToNumberOfEvents' extract the most important features.

Usage

extractSummaryFromNumberOfEvents(
  omic,
  multiOmicObj,
  moduleCox,
  analysis,
  n = 3,
  minprop = 0.1,
  labels = c("few", "many")
)

Arguments

omic

a summarized omic

multiOmicObj

Omics object

moduleCox

the coxObj of the interesting module

analysis

two-class or survival type

n

maximum number of features to retrieve

minprop

the minimal proportion of cutp

labels

the category labels

Value

Meant for internal use only. The summary for omic summarized using counting of events.

sigModule

the original data for significant features

discrete

the discrete version of the significant covariates converted (when needed) into the discrete version

subset

data.frame(row.names=names(topGenes), covariates=covariate)

covsConsidered

the name of the considered omic


Extract Summary PCA from MultiOmics Objects

Description

Given an omic summarized by 'summarizeWithPca' extract the most important features.

Usage

extractSummaryFromPCA(
  omic,
  multiOmicObj,
  moduleCox,
  analysis,
  loadThr = 0.6,
  atleast = 1,
  minprop = 0.1
)

Arguments

omic

a summarized omic

multiOmicObj

Omics object

moduleCox

the coxObj of the interesting module

analysis

two-class or survival type

loadThr

the thr value to select the most influent features according to the loading

atleast

the minimum number of gene to retrieve

minprop

the minimal proportion of cutp

Value

summary for omic summarized using pca

sigModule

the original data for significant features

discrete

the discrete version of the significant covariates converted (when needed) into the discrete version

subset

data.frame(row.names=names(topGenes), covariate)

covsConsidered

the name of the considered omic


Retrieves pathways relatives

Description

For internal use only. Retrieves relatives given a pathway id.

Usage

getPathFathers(pathway, hierarchyGraph, ord = 3, plot = FALSE)

Arguments

pathway

a pathway id

hierarchyGraph

a igraph with pathway hierarchy

ord

how far you need to go backward

plot

plot relatives. For checking purpose

Details

Pathway Hierarchy is needed as igraph object.

Value

a character vector with the relatives


Two-classes glm test.

Description

Two-classes glm test.

Usage

glmTest(data, fullModelFormula, nullModelFormula)

Arguments

data

data

fullModelFormula

complete model

nullModelFormula

null model formula

Value

Two-classes glm test results


Guess the most influent features from MultiOmics Survival or Two-class results.

Description

Given a pathway analyzed by multiOmicsModuleSurvivalTest or multiOmicsTwoClassModuleTest, it retrieves for each omic the most influent features.

Usage

guessInvolvement(
  pathway,
  moduleNumber,
  loadThr = 0.6,
  n = 3,
  atleast = 1,
  min_prop_pca = 0.1,
  min_prop_events = 0.1,
  ...
)

Arguments

pathway

MultiOmicsModules object from a pathway

moduleNumber

the module number

loadThr

the loading threshold to select genes (PCA only)

n

the maximum number of genes to retrieve (cluster and binary only)

atleast

the minimum number of features to select (PCA only)

min_prop_pca

the minimal proportion to compute the PCA classes

min_prop_events

the minimal proportion to compute the event classes

...

additional arguments passed to get function

Value

a list. Each item of the list corresponds to an omic that is summarized with the specific 'extractSummary' functions. Each item is the summary for an omic summarized using the setted method: pvalues are present only for cluster method.


Guess the most influent features from MultiOmics Survival or Two-class results.

Description

Given a pathway analyzed by multiOmicsSurvivalPathwayTest or multiOmicsTwoClassPathwayTest, it retrieves for each omic the most influent features.

Usage

guessInvolvementPathway(
  pathway,
  loadThr = 0.6,
  n = 3,
  atleast = 1,
  min_prop_pca = 0.1,
  min_prop_events = 0.1,
  ...
)

Arguments

pathway

MultiOmicsModules object from a pathway

loadThr

the loading threshold to select genes (PCA only)

n

the maximum number of genes to retrieve (cluster and binary only)

atleast

the minimum number of features to select (PCA only)

min_prop_pca

the minimal proportion to compute the PCA classes

min_prop_events

the minimal proportion to compute the event classes

...

additional arguments passed to get function

Value

a list. Each item of the list corresponds to an omic that is summarized with the specific 'extractSummary' functions. Each item is the summary for an omic summarized using the setted method: pvalues are present only for cluster method.


Convert id to pathway name

Description

For internal use only. Retrieves name from pathway id.

Usage

id2name(idList, namedVect)

Arguments

idList

a list of pathway id

namedVect

a named vector

Details

You must provide a namedVect to be used as translator.

Value

a character vector with the names


Omics class initializer function

Description

makeOmics creates the Omics object necessary to perform most of the analyses of this package. It contains all the omics data in the format of a ExperimentList, the clinical data, and all the information necessary for the dimensionality reduction step.

Usage

makeOmics(
  experiments = ExperimentList(),
  colData = S4Vectors::DataFrame(),
  sampleMap = S4Vectors::DataFrame(assay = factor(), primary = character(), colname =
    character()),
  metadata = list(),
  drops = list(),
  modelInfo = character(),
  specificArgs = list()
)

Arguments

experiments

A list or ExperimentList of all combined experiments

colData

A DataFrame or data.frame of characteristics for all biological units

sampleMap

A DataFrame or data.frame of assay names, sample identifiers, and colname samples

metadata

An optional argument of 'ANY' class (usually list) for content describing the experiments

drops

A list of unmatched information (included after subsetting)

modelInfo

A list with length equal to length(data) that are modelInfo to process each dataset

specificArgs

a list with length equal to length(data) to set additional parameters specific of the modelInfo

Value

an Omics class object

Examples

data(ovarianDataset)

myColData <- data.frame(
    status = sample(c(0, 1), 50, replace = TRUE),
    days = sample(c(0, 500), 50, replace = TRUE),
    row.names = colnames(ovarianDataset$exp)
)

myOmicsObj <- makeOmics(
    experiments = ovarianDataset,
    colData = myColData,
    modelInfo = c(
        "summarizeWithPca",
        "summarizeInCluster",
        "summarizeToNumberOfEvents",
        "summarizeToNumberOfDirectionalEvents"
    ),
    specificArgs = list(
        pcaArgs = list(
            name = "exp", shrink = "FALSE",
            method = "sparse", maxPCs = 3
        ),
        clusterArgs = list(
            name = "met",
            max_cluster_number = 3
        ),
        countEvent = list(name = "mut", min_prop = 0.05),
        cnvAgv = list(name = "cnv", min_prop = 0.05)
    )
)

Make positive and definite covariance matrix

Description

Make positive and definite covariance matrix

Usage

makePositiveDefinite(m1, m2 = NULL, m3 = NULL, threshold = 0.1)

Arguments

m1

matrix 1

m2

matrix 2

m3

matrix 3

threshold

threshold of difference

Value

list with

m1

the matrix m1 positive and definite

m2

the matrix m2 positive and definite

m3

the matrix m3 positive and definite

correction

the magneturde of the correction

value

the value


Map Pathways ID from Graphite

Description

For internal use only. Retrieve pathway id and names from Pathways object.

Usage

mapPathwaysIDfromGraphite(pathways, pathwayNames = NULL)

Arguments

pathways

a PathwayList object

pathwayNames

in not NULL, a subset of pathway to extract

Value

a data frame, id and pathway name


Minimum or NA

Description

For internal use only. Get back minimum or NA.

Usage

minOrNA(x)

Arguments

x

a numeric

Value

a numeric. The minimum or NA

Examples

# minOrNA(c(1,5,0.1,NA))
# minOrNA(c(NA,NA,NA))

MOSClip: Multi-Omics Survival Clip

Description

MOSClip R package implements a statistical approach able to integrate multi-omic data and look for survival associated gene modules. It integrates multiple omics - trascriptomics, methylomics, genomic mutations, and genomic copy number variations - using various data dimensionality reduction strategies and multivariate models. Exploiting graph theory, pathways can be decomposed into their connected components, that we call modules. The analysis can then be performed at the level of entire pathways or pathway modules. MOSClip pathway analysis serves two primary purposes: testing the survival association of pathways or modules using the Cox proportional hazard model, and conducting a two-class analysis with a generalized linear model. Additionally, the package offers valuable graphical tools to visualize and interpret the results.

Details

To conduct a multi-omic survival analysis on pathways or modules use:

To perform a two-class comparison enrichment analysis on pathways or modules use:

Author(s)

Maintainer: Paolo Martini [email protected] (ORCID)

Authors:

References

Paolo Martini, Monica Chiogna, Enrica Calura, and Chiara Romualdi. 2019. “MOSClip: Multi-Omic and Survival Pathway Analysis for the Identification of Survival Associated Gene and Modules.” Nucleic Acids Research 47 (14): e80. https://doi.org/10.1093/nar/gkz324

See Also

Useful links:


Omics class object with TCGA ovarian data

Description

An Omics class object containing data from TCGA ovarian cancer. The TCGA data was manually selected and preprocessed. It contains 4 omics: expression, methylation, mutation, and copy number variation. Additionally, it contains specific arguments to perform the dimensionality reduction. The datasets were downloaded from TCGA using TCGABiolink R package, selecting only patients with primary solid tumors. Expression matrix was processed first, converting gene identifiers into Entrez IDs. The profiles of genes present more than once were averaged. Genes with at least 100 counts in at least one patients were selected, to avoid data sparsity. Mutation matrix was filtered, keeping only genes with expression data available. We chose to consider only missense and nonsense mutations and mutation impact was also considered following Mutect2 pipeline. CNV values were transformed into numeric values. Methylation data were processed with Methyl Mix R package. Patients that had both normal and primary tumors samples were selected. With the help of a dictionary array probes were connected to CpG clusters, and finally CpG clusters were mapped to genes (Entrez ID). Survival annotation curated by Liu et al. (2018) was used to extract PFS information. Only patients with matched data across the four omics were considered. After the selection of patients and genes, we performed expression normalization and log2 of the counts+1 transformation. This will ensure us to work with expression data approximately close to a normal distribution, the most suitable distribution for the subsequent MOSClip tests. Genes and samples were manually selected to create this small example dataset for demonstration purposes.

Usage

data('multiOmics')

Format

multiOmics

An Omics with 4 omics:

exp

Matrix with 151 rows and 50 columns of RNA expression values

met

A matrix with 178 rows and 50 columns of methylation data with probes clustered

mut

A matrix with 107 rows and 50 columns of mutation counts

cnv

A matrix with matrix with 145 rows and 50 columns of copy number

...


Multi Omics Modules.

Description

This class organizes the results of the Multi Omics Module Test analysis, in which corresponds to one pathway decomposed into modules.

Usage

## S4 method for signature 'MultiOmicsModules'
showModule(object)

Arguments

object

an object of class MultiOmicsModules

Methods (by generic)

  • showModule(MultiOmicsModules): shows module info

Slots

alphas

a numeric vector of the pvalues of all the modules.

zlists

a list of numeric vectors with the zs of the covariates for each module.

modulesView

a list of module information: for each omic, the name of the omic, the genes used, the method, the name of the covariates analyzed and other specific information based on the omic.

modules

a list with the genes that belong to the module.

title

the name of the pathway.

analysis

the type of analysis done: survival or two-class.


Multi Omics Pathway.

Description

This class organize the results of the Multi-Omics Pathway Survival Test analysis.

Usage

## S4 method for signature 'MultiOmicsPathway'
showPathway(object)

Arguments

object

an object of class MultiOmicsPathway

Methods (by generic)

  • showPathway(MultiOmicsPathway): shows module info

Slots

pvalue

a numeric vector of the pvalues of the pathways.

zlist

a numeric vector with the zs of all the covariates.

pathView

a list of pathway information: for each omic, the name of the omic, the genes used, the method, the name of the covariates analyzed and other specific information based on the omic.

title

the name of the pathway.

analysis

the type of analysis done: survival or two-class.


Compute Multi Omics Survival in Pathway Modules

Description

Performs survival analysis using an Omics object. The pathway (graph) used is decomposed in modules (cliques) using graph theory.

Usage

multiOmicsSurvivalModuleTest(
  omicsObj,
  graph,
  survFormula = "Surv(days, status) ~",
  autoCompleteFormula = TRUE,
  useTheseGenes = NULL,
  pathName = NULL,
  robust = FALSE,
  include_from_annot = FALSE
)

Arguments

omicsObj

Object of class Omics

graph

a pathway in graphNEL, Pathway or geneset format

survFormula

a character with the formula to compute survival

autoCompleteFormula

logical. If TRUE autocomplete the survFormula using all the available covariates

useTheseGenes

vector of genes used to filter pathways

pathName

title of the pathway. If NULL and graph is Pathway, graph@title is used as title

robust

logical, whether the robust mode should be used for cox model analysis

include_from_annot

logical. If TRUE compute cox model analysis using additional covariates from colData

Value

MultiOmicsModules object

Examples

data(multiOmics)
data(reactSmall)

genesToUse <- row.names(multiOmics[[1]])

MOM_survival <- multiOmicsSurvivalModuleTest(multiOmics, reactSmall[[1]],
    survFormula = "Surv(days, status) ~", autoCompleteFormula = TRUE,
    useTheseGenes = genesToUse
)

Compute Multi Omics Survival in Pathways

Description

Performs topological survival analysis using an Omics object.

Usage

multiOmicsSurvivalPathwayTest(
  omicsObj,
  graph,
  survFormula = "Surv(days, status) ~",
  autoCompleteFormula = TRUE,
  useTheseGenes = NULL,
  pathName = NULL,
  robust = FALSE,
  include_from_annot = FALSE
)

Arguments

omicsObj

Object of class Omics

graph

a pathway in graphNEL, Pathway or geneset format

survFormula

a character with the formula to compute survival

autoCompleteFormula

logical. If TRUE autocomplete the survFormula using all the available covariates

useTheseGenes

vector of genes used to filter pathways

pathName

title of the pathway. If NULL and graph is Pathway, graph@title is used as title

robust

logical, whether the robust mode should be used for cox model analysis

include_from_annot

logical. If TRUE compute cox model analysis using additional covariates from colData

Value

MultiOmicsPathway object

Examples

data(multiOmics)
data(reactSmall)

genesToUse <- row.names(multiOmics[[1]])

MOP_survival <- multiOmicsSurvivalPathwayTest(multiOmics, reactSmall[[1]],
    survFormula = "Surv(days, status) ~", autoCompleteFormula = TRUE,
    useTheseGenes = genesToUse
)

Omics class object with TCGA ovarian data for topological analysis

Description

An Omics class object containing data from TCGA ovarian cancer. The data are the same as in multiOmics object. Arguments in specificArgs slot have been set to efficiently run a topological pathway analysis, i.e., the topological method is used for PCA and shrink parameter is set to TRUE. This method can't be used for analyses on modules.

Usage

data('multiOmicsTopo')

Format

multiOmicsTopo

An Omics with 4 omics:

exp

Matrix with 151 rows and 50 columns of RNA expression values

met

A matrix with 178 rows and 50 columns of methylation data with probes clustered

mut

A matrix with 107 rows and 50 columns of mutation counts

cnv

A matrix with matrix with 145 rows and 50 columns of copy number

...


Computes Multi Omics Two-Class in Pathway Modules

Description

Performs topological two-class analysis using an Omics object. It decomposes graphs (pathways) into modules.

Usage

multiOmicsTwoClassModuleTest(
  omicsObj,
  graph,
  classAnnot,
  baseFormula = "classes ~",
  autoCompleteFormula = TRUE,
  useTheseGenes = NULL,
  nullModel = "classes ~ 1",
  pathName = NULL
)

Arguments

omicsObj

object of class Omics

graph

a pathway as a graphNEL object.

classAnnot

a data.frame with the class annotation. It is necessary at least a column with the classes labels, and the row.names as the samples labels

baseFormula

model formula to be used for the test. It should be written as 'classes ~ ', while 'classes' being the column name for the class labels

autoCompleteFormula

a logical value. If TRUE. It autocompletes the formula used to fit generalized lienar models function using all the available covariates (omics)

useTheseGenes

(optional) vector of specific genes to be used

nullModel

the null model formula. It should be written the same as the baseFormula, followed by ' 1'. (e.g. 'classes ~ 1')

pathName

(optional) title of the pathway. If NULL, graph@title is used as title

Value

MultiOmicsModule object

Examples

data("multiOmics")
data("reactSmall")

genesToUse <- row.names(multiOmics[[1]])

classAnnot <- data.frame(
    "treatment" = c(rep("A", 25), rep("B", 25)),
    row.names = colnames(multiOmics[[1]])
)

MOM_twoclasses <- multiOmicsTwoClassModuleTest(
    multiOmics, reactSmall[[1]], classAnnot,
    baseFormula = "treatment ~ ", nullModel = "treatment ~ 1",
    useTheseGenes = genesToUse
)

Compute Multi Omics Two-Class in Pathways

Description

Performs topological two-class analysis using an Omics object.

Usage

multiOmicsTwoClassPathwayTest(
  omicsObj,
  graph,
  classAnnot,
  baseFormula = "classes ~ ",
  autoCompleteFormula = TRUE,
  useTheseGenes = NULL,
  nullModel = "classes ~ 1",
  pathName = NULL
)

Arguments

omicsObj

object of class Omics

graph

a pathway as a graphNEL object.

classAnnot

a data.frame with the class annotation. It is necessary at least a column with the classes labels, and the row.names as the samples labels

baseFormula

model formula to be used for the test. It should be written as 'classes ~ ', while 'classes' being the column name for the class labels

autoCompleteFormula

a logical value. If TRUE. It autocompletes the formula used to fit generalized lienar models function using all the available covariates (omics)

useTheseGenes

(optional) vector of specific genes to be used

nullModel

the null model formula. It should be written the same as the baseFormula, followed by ' 1'. (e.g. 'classes ~ 1')

pathName

(optional) title of the pathway. If NULL, graph@title is used as title

Value

MultiOmicsPathway object

Examples

data("multiOmics")
data("reactSmall")

genesToUse <- row.names(multiOmics[[1]])

classAnnot <- data.frame(
    "treatment" = c(rep("A", 25), rep("B", 25)),
    row.names = colnames(multiOmics[[1]])
)

MOP_twoClasses <- multiOmicsTwoClassPathwayTest(
    multiOmics, reactSmall[[1]], classAnnot,
    baseFormula = "treatment ~ ", nullModel = "treatment ~ 1",
    useTheseGenes = genesToUse
)

Provides a Table of the Modules Test Results

Description

Summarizes the results of a multi omics module test given a list of MultiOmicsModules objects

Usage

multiPathwayModuleReport(multiPathwayModuleList, priority_to = NULL)

Arguments

multiPathwayModuleList

a list of MultiOmicsModules objects resulting from a multi-omics module test.

priority_to

a vector with the covariates (the omics names) that should appear first in the dataframe columns

Value

a data.frame class object. Rows correspond to the modules, and the columns to the overall and covariates pvalues of the test.

Examples

data(multiOmics)
data(reactSmall)

genesToUse <- row.names(multiOmics[[1]])

MOM_list <- lapply(reactSmall[1:2], function(g) {
    fcl <- multiOmicsSurvivalModuleTest(multiOmics, g,
        survFormula = "Surv(days, status) ~",
        autoCompleteFormula = TRUE,
        useTheseGenes = genesToUse
    )
    fcl
})

moduleSummary <- multiPathwayModuleReport(MOM_list)

Summarize pathways' info from a list of MultiOmicsPathway objects (MOP)

Description

Given the list of MOPs, it creates the table.

Usage

multiPathwayReport(multiPathwayList, priority_to = NULL)

Arguments

multiPathwayList

a list of MultiOmicsPathway objects resulting from a multi-omics pathway test.

priority_to

a vector with the covariates (omic name) that should go first.

Value

a data.frame, pathways in rows, overall pvalue of the coxph, followed by covariates pvalues, in columns.

Examples

data(multiOmics)
data(reactSmall)

genesToUse <- row.names(multiOmics[[1]])

MOP_list <- lapply(reactSmall, function(g) {
    fcl <- multiOmicsSurvivalPathwayTest(multiOmics, g,
        survFormula = "Surv(days, status) ~",
        autoCompleteFormula = TRUE,
        useTheseGenes = genesToUse
    )
    fcl
})

pathwaysSummary <- multiPathwayReport(MOP_list)

Omics.

Description

This class is the storage for the different omic datasets that we need to analyze. It is based on MultiAssayExperiment.

Usage

## S4 method for signature 'Omics'
showOmics(object)

Arguments

object

an object of class Omics

Methods (by generic)

  • showOmics(Omics): shows model parameters

Slots

modelInfo

a list with length equal to length(data) that are modelInfo to process each dataset.

specificArgs

a list with length equal to length(data) to set additional parameters specific of the modelInfo.


ExperimentList class object with TCGA ovarian data

Description

An ExperimentList class object containing data from TCGA ovarian cancer. The TCGA data was manually selected and preprocessed. It contains 4 omics: expression, methylation, mutation, and copy number variation.

Usage

data('ovarianDataset')

Format

ExperimentList

An ExperimentList with 4 omics:

exp

Matrix with 101 rows and 50 columns of RNA expression values

met

A matrix with 97 rows and 50 columnsof methylation data with probes clustered

mut

A matrix with 55 rows and 50 columns of mutation counts

cnv

A matrix with matrix with 101 rows and 50 columns of copy number

...


Plot Frequencies of Pathway Fathers for Omics intersection

Description

Plots the frequencies of the pathway fathers by every omics intersection from a data.frame of the frequencies returned with the function computeFreqs.

Usage

plotFrequencies(
  frequencies,
  manualColors = NULL,
  minSize = 4,
  maxSize = 20,
  width = 20,
  relMagnificationOfLegend = 0.5,
  lineSize = 1
)

Arguments

frequencies

a data.frame created from 'computeFreqs'

manualColors

optional vector of colors to be used

minSize

the minimal font size. Maximal frequencies will be added for each class

maxSize

the maximal font size dimension, all values above are clipped

width

the number of character to wrap the labels

relMagnificationOfLegend

the relative magnification of the text of the legend

lineSize

the thickness of the lines

Value

a circular plot of the frequencies of pathway fathers

Examples

df <- data.frame(
    category = c("PathwayA", "PathwayB", "PathwayC", "PathwayD"),
    frequencies = c(1, 2, 1, 3),
    class = rep("Mut", 4), stringsAsFactors = FALSE
)
plotFrequencies(df)

Plot a Heatmap of a Module by Omics

Description

It creates a heatmap of the most involved genes of each omic of a specific module from a MultiOmicsModule object.

Usage

plotModuleHeat(
  moduleobj,
  moduleNumber,
  sortBy = NULL,
  paletteNames = NULL,
  additionalAnnotations = NULL,
  additionalPaletteNames = NULL,
  withSampleNames = TRUE,
  fontsize_row = 10,
  fontsize_col = 1,
  nrowsHeatmaps = 3,
  orgDbi = "org.Hs.eg.db",
  discr_prop_pca = 0.15,
  discr_prop_events = 0.05,
  ...
)

Arguments

moduleobj

MultiOmicsModule class object

moduleNumber

module number of interest

sortBy

a covariate (omic) to sort by

paletteNames

a palette containing three colors

additionalAnnotations

optional additional sample annotations

additionalPaletteNames

optional additional colors for annotations

withSampleNames

show sample names

fontsize_row

font size for row labels

fontsize_col

font size for column labels

nrowsHeatmaps

magnification respect to annotation of sample (annotations take 1 row)

orgDbi

a Dbi organism to be used. Default is org.Hs.eg.db

discr_prop_pca

the minimal proportion to compute the PCA classes

discr_prop_events

the minimal proportion to compute the event classes

...

additional arguments passed to guessInvolvement function

Value

A heatmap of a pathway module (results of the module test)

Examples

data(multiOmics)
data(reactSmall)

survAnnot <- data.frame(
    status = multiOmics$status,
    days = multiOmics$days,
    row.names = colnames(multiOmics[[1]])
)

genesToUse <- row.names(multiOmics[[1]])

MOM_survival <- multiOmicsSurvivalModuleTest(multiOmics, reactSmall[[1]],
    survFormula = "Surv(days, status) ~", autoCompleteFormula = TRUE,
    useTheseGenes = genesToUse
)

plotModuleHeat(MOM_survival, 1,
    sortBy = c("mut", "expPC1", "status", "days"),
    additionalAnnotations = survAnnot,
    additionalPaletteNames = list(status = "teal", days = "violet"),
    withSampleNames = F
)

Plot a Directed Graph of the MultiOmicsModules Object

Description

From a MultiOmicsModules object, it plots the position of a given module in the pathway. The omics are also represented in the graph.

Usage

plotModuleInGraph(
  modulesobj,
  pathList,
  moduleNumber,
  orgDbi = "org.Hs.eg.db",
  paletteNames = NULL,
  legendLabels = NULL,
  fileName = NULL,
  discr_prop_pca = 0.15,
  discr_prop_events = 0.05,
  pathTitle = NULL,
  ...
)

Arguments

modulesobj

a MultiOmicsModule class object

pathList

a PathwayList from graphite package that contains the pathways to be used

moduleNumber

a module number

orgDbi

if needed, indicates an organism Dbi to translate the vectors

paletteNames

named vector of MOSpalettes, names replace makeLegend arguments

legendLabels

set up your favourite names for the omics

fileName

optional filenames to save the plot

discr_prop_pca

the minimal proportion to compute the PCA classes

discr_prop_events

the minimal proportion to compute the event classes

pathTitle

title of the graph, to be searched in pathList

...

additional arguments passed to guessInvolvement function

Value

a MOSClip plot in form of a list class object

Examples

data(multiOmics)
data(reactSmall)

genesToUse <- row.names(multiOmics[[1]])

MOM_survival <- multiOmicsSurvivalModuleTest(multiOmics, reactSmall[[1]],
    survFormula = "Surv(days, status) ~", autoCompleteFormula = TRUE,
    useTheseGenes = genesToUse
)

plotModuleInGraph(MOM_survival, reactSmall,
    moduleNumber = 1,
    paletteNames = c(exp = "red", met = "green", 
                     mut = "blue", cnv = "yellow")
)

Plot Kaplan-Meier survival curves of a specific module

Description

Given a MultiOmicsModule class object and a specific module number, it plots Kaplan-Meier curves, in which the strata corresponds to the omics

Usage

plotModuleKM(
  MOM,
  moduleNumber,
  formula = "Surv(days, status) ~ PC1",
  fileName = NULL,
  paletteNames = NULL,
  h = 9,
  w = 7,
  risk.table = TRUE,
  pval = TRUE,
  size = 1,
  inYears = FALSE,
  discr_prop_pca = 0.15,
  discr_prop_events = 0.05,
  additional_discrete = NULL,
  additional_continuous = NULL,
  ...
)

Arguments

MOM

a MultiOmicsModule class object

moduleNumber

numeric value. The module number of interest

formula

a formula for the survival analysis. It should be written as 'Surv(days, status) ~ omic'. To plot more than one omic, write them separated by a '+' character after the separator (~)

fileName

optional filenames to save the plot

paletteNames

a palette name to be used

h

the height of the plot

w

the width of the plot

risk.table

logical value. If TRUE, shows the risk.table. Default is TRUE.

pval

logical value. If TRUE, shows the p-value of the curves. Default is TRUE.

size

line width of the KM curves

inYears

set time in years

discr_prop_pca

the minimal proportion to compute the PCA classes

discr_prop_events

the minimal proportion to compute the event classes

additional_discrete

names of the additional discrete variables to include

additional_continuous

names of the additional continous variables to include

...

additional arguments passed to guessInvolvement and get function

Value

a ggsurvplot class object

Examples

data(multiOmics)
data(reactSmall)

genesToUse <- row.names(multiOmics[[1]])

MOM_survival <- multiOmicsSurvivalModuleTest(multiOmics, reactSmall[[1]],
    survFormula = "Surv(days, status) ~", autoCompleteFormula = TRUE,
    useTheseGenes = genesToUse
)

plotModuleKM(MOM_survival, 1,
    formula = "Surv(days, status) ~ mut + expPC2",
    paletteNames = "Paired", inYears = TRUE
)

Plot a table of a MultiOmicsModules (MOM) object

Description

Given a MultiOmicsModules object, it plots its results in a tabular fashion

Usage

plotModuleReport(
  modulesObj,
  MOcolors = NULL,
  priority_to = NULL,
  fontsize = 12,
  ...
)

Arguments

modulesObj

MultiOmicsModules class object

MOcolors

character vector with the omic colors. The colors should be among the colors in showMOSpalette

priority_to

a vector with the covariates (omic names) that should go first

fontsize

Size of the font to be used in the plot

...

additional argument to be passed to pheatmap

Value

a Heatmap list object from ComplexHeatmap package of the results contained in the MultiOmicsModules object provided

Examples

data(multiOmics)
data(reactSmall)

genesToUse <- row.names(multiOmics[[1]])

MOM_survival <- multiOmicsSurvivalModuleTest(multiOmics, reactSmall[[1]],
    survFormula = "Surv(days, status) ~", autoCompleteFormula = TRUE,
    useTheseGenes = genesToUse
)

plotModuleReport(MOM_survival,
    MOcolors = c(
        exp = "red", met = "green", mut = "blue",
        cnv = "yellow"
    )
)

Summarize and plot pathways' info from a list of MultiOmicsPathway (MOP)

Description

Given the list of MOPs, it plots a table of its results.

Usage

plotMultiPathwayReport(
  multiPathwayList,
  top = 25,
  MOcolors = NULL,
  priority_to = NULL,
  fontsize = 6,
  ...
)

Arguments

multiPathwayList

a list of MultiOmicsPathway class objects

top

numeric value. Plot only the top number of pathways

MOcolors

character vector with the omic colors. The colors should be among the colors in showMOSpalette

priority_to

a vector with the covariates (omic names) that should go first

fontsize

the font size to be used. Default is 12.

...

additional argument to be passed to pheatmap

Value

a Heatmap list object from ComplexHeatmap package of the results contained in the MultiOmicsPathway object provided

Examples

data(multiOmics)
data(reactSmall)

genesToUse <- row.names(multiOmics[[1]])

MOP_list <- lapply(reactSmall, function(g) {
    fcl <- multiOmicsSurvivalPathwayTest(multiOmics, g,
        survFormula = "Surv(days, status) ~",
        autoCompleteFormula = TRUE,
        useTheseGenes = genesToUse
    )
    fcl
})

plotMultiPathwayReport(MOP_list,
    MOcolors = c(
        exp = "red", met = "green", mut = "blue",
        cnv = "yellow"
    ),
    fontsize = 12
)

Plot heatmaps of the pathway by omics

Description

Given the pathway, it creates the heatmaps of the mostly involved genes for each omic.

Usage

plotPathwayHeat(
  pathway,
  sortBy = NULL,
  paletteNames = NULL,
  additionalAnnotations = NULL,
  additionalPaletteNames = NULL,
  discr_prop_pca = 0.15,
  discr_prop_events = 0.05,
  withSampleNames = TRUE,
  nrowsHeatmaps = 3,
  orgDbi = "org.Hs.eg.db",
  ...
)

Arguments

pathway

MultiOmicsPathway class object

sortBy

one or more covariates to sort the samples

paletteNames

name of the colors for each omic

additionalAnnotations

optional additional sample annotations (e.g. survival annotation)

additionalPaletteNames

colors for additional annotations. The colors available are the ones in showMOSpalette

discr_prop_pca

the minimal proportion to compute the PCA classes

discr_prop_events

the minimal proportion to compute the event classes

withSampleNames

show the sample names in the plot

nrowsHeatmaps

magnification respect to annotation of sample (annotations take 1 row)

orgDbi

a Dbi organism to be used. Default is org.Hs.eg.db

...

additional arguments passed to guessInvolvementPathway function (internal use)

Value

An object of class ggplot plotted with ComplexHeatMap package.

Examples

data(multiOmics)
data(reactSmall)

genesToUse <- row.names(multiOmics[[1]])

survAnnot <- data.frame(
    status = multiOmics$status,
    days = multiOmics$days,
    row.names = colnames(multiOmics[[1]])
)

# Creating the MultiOmicsPathway object
MOP_survival <- multiOmicsSurvivalPathwayTest(multiOmics, reactSmall[[1]],
    survFormula = "Surv(days, status) ~", autoCompleteFormula = TRUE,
    useTheseGenes = genesToUse
)

# Plotting
plotPathwayHeat(MOP_survival,
    sortBy = c("expPC2", "mut", "status", "days"),
    paletteNames = c(exp = "red", met = "green", 
                     mut = "blue", cnv = "yellow"),
    additionalAnnotations = survAnnot,
    additionalPaletteNames = list(status = "teal", days = "violet"),
    nrowsHeatmaps = 2, withSampleNames = F
)

Plot Kaplan-Meier survival curves of a specific pathway

Description

Given a MultiOmicsPathway class object, it plots Kaplan-Meier curves, in which the strata corresponds to the chosen omics

Usage

plotPathwayKM(
  pathway,
  formula = "Surv(days, status) ~ PC1",
  fileName = NULL,
  paletteNames = NULL,
  h = 9,
  w = 7,
  risk.table = TRUE,
  pval = TRUE,
  size = 1,
  inYears = FALSE,
  discr_prop_pca = 0.15,
  discr_prop_events = 0.05,
  additional_discrete = NULL,
  additional_continuous = NULL,
  ...
)

Arguments

pathway

MultiOmicsPathway class object

formula

a formula to compute the plot

fileName

optional filenames to save the plot

paletteNames

a palette containing three colors

h

the height of the plot

w

the width of the plot

risk.table

logical value. If TRUE, shows the risk.table. Default is TRUE.

pval

logical value. Shows p-value of the curves

size

line width of the KM curves

inYears

logical value. If TRUE, converts days to years

discr_prop_pca

the minimal proportion to compute the PCA classes

discr_prop_events

the minimal proportion to compute the event classes

additional_discrete

names of the additional discrete variables to include

additional_continuous

names of the additional continuous variables to include

...

additional arguments passed to guessInvolvementPathway and get function (internal use)

Value

a ggsurvplot class object

Examples

data(multiOmics)
data(reactSmall)

genesToUse <- row.names(multiOmics[[1]])

# Creating the MultiOmicsPathway object
MOP_survival <- multiOmicsSurvivalPathwayTest(multiOmics, reactSmall[[1]],
    survFormula = "Surv(days, status) ~", autoCompleteFormula = TRUE,
    useTheseGenes = genesToUse
)

plotPathwayKM(MOP_survival,
    formula = "Surv(days, status) ~ mut + expPC2",
    paletteNames = "Paired", inYears = TRUE
)

Compute pvalue Summary

Description

Compute pvalue Summary

Usage

pvalueSummary(multiPathwayReportData, excludeColumns = NULL, as.list = FALSE)

Arguments

multiPathwayReportData

data.frame, the output of the multiPathwayReport or multiPathwayModuleReport functions.

excludeColumns

a vector of characters listing the columns of multiPathwayReportData object to be excluded by the analysis. In the case multiPathwayReportData derives from multiPathwayModuleReport you should set excludeColumns = c('pathway','module').

as.list

return a list rather than a data.frame

Value

a list


PathwayList of pathways from Reactome

Description

A PathwayList with three pathways necessary for the analysis: 'Activation of Matrix Metalloproteinases', 'FGFR1 mutant receptor activation', and 'VEGFA-VEGFR2 Pathway'. Pathways were downloaded using graphite package and the names of the nodes were converted into Entrez IDs.

Usage

data('reactSmall')

Format

reactSmall

A PathwayList with Reactome pathways for hsapiens

entries

Three Reactome pathways with their nodes


Remove self loops from a graphNEL

Description

Remove the self loops that a present in the graph graphNEL object

Usage

removeSelfLoops(graph)

Arguments

graph

a graphNEL object

Value

a graphNEL object

#' @rdname graph-processing


Resampling function for survival analysis on modules

Description

Resampling function for survival analysis on modules

Resampling function for pathways (survival analysis)

Usage

resamplingModulesSurvival(
  fullMultiOmics,
  pathdb,
  nperm = 100,
  pathwaySubset = NULL,
  nPatients = 3,
  genesToConsider = NULL
)

resamplingPathwaySurvival(
  fullMultiOmics,
  pathdb,
  nperm = 100,
  pathwaySubset = NULL,
  nPatients = 3,
  genesToConsider = NULL
)

Arguments

fullMultiOmics

a multiOmic object

pathdb

pathway database

nperm

number of permutations

pathwaySubset

a list of pathways to resample

nPatients

number of patients to remove for resampling

genesToConsider

vector of genes used to filter pathways; if NULL, genes found in the first experiment of the multiOmic object are used

Value

list of the resampling tables of results

list of the resampling tables of results

Examples

data(multiOmics)
data(reactSmall)

perms <- resamplingModulesSurvival(
    fullMultiOmics = multiOmics, reactSmall,
    nperm = 10,
    pathwaySubset =
        "FGFR1 mutant receptor activation"
)

Resampling function for two-class analysis on modules

Description

Resampling function for two-class analysis on modules

Resampling function for pathways (two-class analysis)

Usage

resamplingModulesTwoClass(
  fullMultiOmics,
  classAnnot,
  pathdb,
  nperm = 100,
  pathwaySubset = NULL,
  nPatients = 3,
  genesToConsider = NULL
)

resamplingPathwayTwoClass(
  fullMultiOmics,
  classAnnot,
  pathdb,
  nperm = 100,
  pathwaySubset = NULL,
  nPatients = 3,
  genesToConsider = NULL
)

Arguments

fullMultiOmics

a multiOmic object

classAnnot

patients class annotations

pathdb

pathway database

nperm

number of permutations

pathwaySubset

a list of pathways to resample

nPatients

number of patients to remove for resampling

genesToConsider

vector of genes used to filter pathways; if NULL, genes found in the first experiment of the multiOmic object are used

Value

list of the resampling tables of results

list of the resampling tables of results

Examples

data(multiOmics)
data(reactSmall)

classAnnot <- data.frame(
    "treatment" = c(rep("A", 25), rep("B", 25)),
    row.names = colnames(multiOmics[[1]])
)

perms <- resamplingModulesTwoClass(
    fullMultiOmics = multiOmics,
    classAnnot, reactSmall,
    nperm = 10,
    pathwaySubset =
        "FGFR1 mutant receptor activation"
)

Performs a Exact test - analysis of omics intersection

Description

This function performs a exact test implementing a theorical framework using the SuperExactTest package. It calculates the statistical distributions of multi omics set intersections. It can be used with both a MultiOmicsModules or MultiOmicsPathway class objects.

Usage

runSupertest(
  multiPathwayReportData,
  pvalueThr = 0.05,
  zscoreThr = 0.05,
  resampligThr = NULL,
  plot = c("circular", "landscape", "noplot"),
  sort.by = c("set", "size", "degree", "p-value"),
  excludeColumns = NULL,
  color.on = "#f6bb42",
  color.off = "#D3D3D3"
)

Arguments

multiPathwayReportData

data.frame, the output of the multiPathwayReport or multiPathwayModuleReport functions.

pvalueThr

numeric value. Overall pvalue cut-off to be used

zscoreThr

numeric value. Covariates coefficient cut-off to be used.

resampligThr

numeric value. Filters the modules according to the number of success in the resampling procedure, takes only the modules above this threshold.

plot

character indicating the layout for plotting. It is one of circular, landscape or noplot. By default, plot='circular', if plot='noplot' no plot will be provided.

sort.by

character indicating how to sort the intersections in the plot. It is one of 'set' (by omics), 'size' (by intersection size), 'degree' (by number of intersected omics), and 'p-value'.

excludeColumns

a vector of characters listing the columns of multiPathwayReportData object to be excluded by the analysis. In the case multiPathwayReportData derives from multiPathwayModuleReport you should set excludeColumns = c('pathway','module').

color.on

color that represent the active omics in the sector

color.off

color that represent the omics mnot considered in the sector

Details

This function calculates intersection sizes between multiple set of pathways or modules and performs statistical test of the intersections using the total amout of analyzed pathways or modules as background. The super exact test of this function was described in Wang et al 2015.

Value

a data.frame containing all the numeric information of the plot included the pathways shared by different omics.

References

Minghui Wang, Yongzhong Zhao, and Bin Zhang (2015). Efficient Test and Visualization of Multi-Set Intersections. Scientific Reports 5: 16923.

Examples

df <- data.frame(
    pvalue = c(0.06, 0.04, 0.04, 0.03, 0.02),
    cnv = c(0.07, 0.03, 0.02, 0.04, 0.01),
    mut = c(0.08, 0.02, 0.01, 0.04, 0.04),
    row.names = c(
        "PathwayA", "PathwayB", "PathwayC",
        "PathwayD", "PathwayE"
    )
)

runSupertest(df, pvalueThr = 0.05, zscoreThr = 0.05)

Select stable pathway modules

Description

Select stable pathway modules

Count the resampling success

Add resampling counts to module summary

Usage

selectStablePathwaysModules(perms, moduleSummary, success = 90, col = "pvalue")

getPathwaysModulesSuccess(perms, moduleSummary, col = "pvalue", thr = 0.05)

addResamplingCounts(moduleSummary, resamplingCounts)

Arguments

perms

a list. Result of resampling function

moduleSummary

summary of modules or pathways obtained from multiPathwayModuleReport or multiPathwayReport

success

number of success to consider the pathway or module stable

col

the name of the column in the summary to be used to evaluate resampling success

thr

the threshold for significance

resamplingCounts

the counts of success obtained with getPathwaysModulesSuccess

Value

the subset of stable modules

the counts of success for each pathway or module

a module or pathway summary with resampling counts column appended

Examples

data("multiOmics")
data("reactSmall")

perms <- resamplingPathwaySurvival(multiOmics, reactSmall, nperm = 5)
res <- lapply(reactSmall, function(g) {
    multiOmicsSurvivalPathwayTest(multiOmics, g,
        useTheseGenes = row.names(multiOmics[[1]])
    )
})
pathSummary <- multiPathwayReport(res)
getPathwaysModulesSuccess(perms, pathSummary)

A generic function showing pathway's module info

Description

A generic function showing pathway's module info

Usage

showModule(object)

Arguments

object

an object of class MultiOmicsModules

Value

NULL. No value is returned

Examples

data(multiOmics)
data(reactSmall)

genesToUse <- row.names(multiOmics[[1]])

MOM_survival <- multiOmicsSurvivalModuleTest(multiOmics, reactSmall[[1]],
    survFormula = "Surv(days, status) ~", autoCompleteFormula = TRUE,
    useTheseGenes = genesToUse
)

showModule(MOM_survival)

Shows the MOSClip palette.

Description

This function shows the MOSClip palette. Each omic should be coupled to a color panel, this match will be preserved in plots.

Usage

showMOSpalette()

Value

NULL. No value is returned

Examples

showMOSpalette()

A generic functions showing parameter associated with each omics

Description

A generic functions showing parameter associated with each omics

Usage

showOmics(object)

Arguments

object

an object of class Omics

Value

NULL. No value is returned

Examples

data(multiOmics)

showOmics(multiOmics)

A generic function showing pathway info

Description

A generic function showing pathway info

Usage

showPathway(object)

Arguments

object

an object of class MultiOmicsPathway

Value

NULL. No value is returned

Examples

data(multiOmics)
data(reactSmall)

genesToUse <- row.names(multiOmics[[1]])

MOP_survival <- multiOmicsSurvivalPathwayTest(multiOmics, reactSmall[[1]],
    survFormula = "Surv(days, status) ~", autoCompleteFormula = TRUE,
    useTheseGenes = genesToUse
)

showPathway(MOP_survival)

Sparse PCA

Description

Sparse PCA

Usage

sparseCompPCs(exp, shrink, k)

Arguments

exp

a matrix

shrink

logical, whether to shrink or not.

k

the number of components to use

Value

a list with the following elements:

x

the computed PCs

sdev

the standard deviation captured by the PCs

loadings

the loadings


Remove Module Number From Pathway Name

Description

Function to remove the suffix corresponding to the module number of the pathway name. Necessary step for annotePathwayToFather and plotFrequencies

Usage

stripModulesFromPathways(pathways)

Arguments

pathways

vector of pathway names

Value

list of pathway names without the module number

Examples

pathwaysModules <- list(
    "Intrinsic Pathway for Apoptosis.1",
    "Intrinsic Pathway for Apoptosis.2",
    "Opioid Signalling.1", "Opioid Signalling.2"
)

resPathwayNames <- stripModulesFromPathways(pathwaysModules)

Summarize Using Cluster Analysis

Description

Given a matrix it summarize in classes

Usage

summarizeInCluster(
  data,
  features,
  name = "clu",
  dictionary = NULL,
  max_cluster_number = 3,
  cliques = NULL
)

Arguments

data

a data matrix

features

a vector with the features to analyze

name

prefix of the covariates

dictionary

translate features (genes) into sets (row.names of the data)

max_cluster_number

the maximum number of cluster to evaluate

cliques

the features organized in cliques. Only use for topology

Details

The user can define a maximum of classes. The function guess the optimal number of clusters using NbClust methods.

Value

a list with summary of the omic:

x

summary of the omic for each sample

usedGenes

genes list of genes used to calculate the summary

namesCov

names of the covariates

cls

the genes in clusters

method

method used for the analysis

omicName

name of the omic


Summarize Omics Covaraites By Min Pvalue

Description

For internal use only. for each line extrac 'col' and get the minimum.

Usage

summarizeOmicsResByMinPvalue(col, mat)

Arguments

col

columns to extract from the line

mat

the matrix to be summarized (were to extract lines and 'col')

Value

a summarized version of the matrix.

Examples

# summarizeOmicsResByMinPvalue(2:3, mat=matrix(c(1,2,4,1,2,5), nrow=2))

Summarize To Binary Directional Events

Description

Given a matrix it summarize the positive and negative to 0 or 1 in two vectors

Usage

summarizeToBinaryDirectionalEvents(
  data,
  features,
  name = "dirBin",
  binaryClassMin = 10,
  eventThr = 2,
  cliques = NULL
)

Arguments

data

a data matrix

features

a vector with the features to analyze

name

prefix of the covariates

binaryClassMin

the minimum number of event to include the covariate

eventThr

the absolute value to threshold an event

cliques

the features organized in cliques. Only use for topology

Value

a list with summary of the omic:

x

summary of the omic for each sample

usedGenes

genes list of genes used to calculate the summary

namesCov

names of the covariates

method

method used for the analysis

omicName

name of the omic

evenThr

threshold fot event counting


Summarize To Binary Events

Description

Given a matrix it summarize to a 0 or 1

Usage

summarizeToBinaryEvents(
  data,
  features,
  name = "bin",
  binaryClassMin = 10,
  cliques = NULL
)

Arguments

data

a data matrix

features

a vector with the features to analyze

name

prefix of the covariates

binaryClassMin

the minimum number of event to include the covariate

cliques

the features organized in cliques. Only use for topology

Value

a list with summary of the omic:

x

summary of the omic for each sample

usedGenes

genes list of genes used to calculate the summary

namesCov

names of the covariates

method

method used for the analysis

omicName

name of the omic

evenThr

threshold fot event counting


Summarize With Directed Sum

Description

Given a matrix it summarize the positive and negative in two vectors, with counts of the events

Usage

summarizeToNumberOfDirectionalEvents(
  data,
  features,
  name = "dCount",
  eventThr = 2,
  min_prop = 0.1,
  cliques = NULL
)

Arguments

data

a data matrix

features

a vector with the features to analyze

name

prefix of the covariates

eventThr

the absolute value to threshold an event

min_prop

minimal proportion in classes

cliques

the features organized in cliques. Only use for topology

Value

a list with summary of the omic:

x

summary of the omic for each sample

usedGenes

genes list of genes used to calculate the summary

namesCov

names of the covariates

method

method used for the analysis

omicName

name of the omic

evenThr

threshold fot event counting

min_prop

minimum proportion of samples to exclude to check the variability of values


Summarize To Number of Binary Events

Description

Given a matrix it summarize to a 0 or 1

Usage

summarizeToNumberOfEvents(
  data,
  features,
  name = "event",
  min_prop = 0.1,
  cliques = NULL
)

Arguments

data

a data matrix

features

a vector with the features to analyze

name

prefix of the covariates

min_prop

minimal proportion in classes

cliques

the features organized in cliques. Only use for topology

Value

a list with summary of the omic:

x

summary of the omic for each sample

usedGenes

genes list of genes used to calculate the summary

namesCov

names of the covariates

method

method used for the analysis

omicName

name of the omic

evenThr

threshold fot event counting

min_prop

minimum proportion of samples to exclude to check the variability of values


Summarize Using PCA

Description

Given a matrix it summarize to principal components. The user can specify the number of principal components. Default 3.

Usage

summarizeWithPca(
  data,
  features,
  name = "pca",
  shrink = FALSE,
  method = "regular",
  cliques = NULL,
  maxPCs = 3,
  loadThr = 0.6
)

Arguments

data

a data matrix

features

a vector with the features to analyze

name

prefix of the covariates

shrink

shirnk or not the covariance matrix.

method

either 'regular', 'sparse' or 'topological'

cliques

the features organized in cliques. Only use for topology.

maxPCs

maximum number of pcs to consider

loadThr

loading threshold

Value

a list with summary of the omic:

x

summary of the omic for each sample (principal components)

sdev

standard deviation of the principal components

loadings

loadings of PCA

usedGenes

genes list of genes used to calculate the summary

namesCov

names of the covariates

method

method used for the analysis

omicName

name of the omic


Cox Model Analysis

Description

Cox Analysis

Usage

survivalcox(coxObj, formula)

Arguments

coxObj

data.frame: patients x covariates

formula

formula to use

Details

For internal use only

Value

A list with

pvalue

pvalue of the model

zlist

pvalues of single covariates

coxObj

the original coxObj passed to the function


Cox Robust Model Analysis

Description

Cox Robust Analysis

Usage

survivalcoxr(coxObj, formula)

coxrsummary(x)

Arguments

coxObj

data.frame: patients x covariates

formula

formula to use

x

a coxr.obj

Details

For internal use only

Value

A list with

pvalue

pvalue of the model

zlist

pvalues of single covariates

coxObj

the original coxObj passed to the function

a list with wald test and robust and partial coefficients


Topological PCA

Description

Topological PCA

Usage

topoCompPCs(exp, shrink, cliques, k)

Arguments

exp

a matrix

shrink

logical, whether to shrink or not.

cliques

the pathway topology summarized in a list of cliques

k

the number of components to use

Value

a list with the following elements:

x

the computed PCs

sdev

the standard deviation captured by the PCs

loadings

the loadings