Package 'MoonlightR'

Title: Identify oncogenes and tumor suppressor genes from omics data
Description: Motivation: The understanding of cancer mechanism requires the identification of genes playing a role in the development of the pathology and the characterization of their role (notably oncogenes and tumor suppressors). Results: We present an R/bioconductor package called MoonlightR which returns a list of candidate driver genes for specific cancer types on the basis of TCGA expression data. The method first infers gene regulatory networks and then carries out a functional enrichment analysis (FEA) (implementing an upstream regulator analysis, URA) to score the importance of well-known biological processes with respect to the studied cancer type. Eventually, by means of random forests, MoonlightR predicts two specific roles for the candidate driver genes: i) tumor suppressor genes (TSGs) and ii) oncogenes (OCGs). As a consequence, this methodology does not only identify genes playing a dual role (e.g. TSG in one cancer type and OCG in another) but also helps in elucidating the biological processes underlying their specific roles. In particular, MoonlightR can be used to discover OCGs and TSGs in the same cancer type. This may help in answering the question whether some genes change role between early stages (I, II) and late stages (III, IV) in breast cancer. In the future, this analysis could be useful to determine the causes of different resistances to chemotherapeutic treatments.
Authors: Antonio Colaprico [aut], Catharina Olsen [aut], Matthew H. Bailey [aut], Gabriel J. Odom [aut], Thilde Terkelsen [aut], Mona Nourbakhsh [aut], Astrid Saksager [aut], Tiago C. Silva [aut], André V. Olsen [aut], Laura Cantini [aut], Andrei Zinovyev [aut], Emmanuel Barillot [aut], Houtan Noushmehr [aut], Gloria Bertoli [aut], Isabella Castiglioni [aut], Claudia Cava [aut], Gianluca Bontempi [aut], Xi Steven Chen [aut], Elena Papaleo [aut], Matteo Tiberti [cre, aut]
Maintainer: Matteo Tiberti <[email protected]>
License: GPL (>= 3)
Version: 1.33.0
Built: 2024-11-18 05:44:41 UTC
Source: https://github.com/bioc/MoonlightR

Help Index

Gene Expression (Rnaseqv2) data from TCGA LUAD

Description

A data set containing the following data:

Usage

data(dataFilt)

Format

A 13742x20 matrix

Details

  • dataFilt matrix with 13742 rows (genes) and 20 columns samples with TCGA's barcodes (10TP, 10NT)

Value

a 13742x20 matrix

GRN gene regulatory network output

Description

output from GRN function

Usage

data(dataGRN)

Format

A large list of 2 elements

Details

  • dataGRN list of 2 elements miTFGenes, maxmi from GRN function

Value

a large list of 2 elements

Output example from function Upstram Regulator Analysis

Description

A data set containing the following data:

Usage

data(dataURA)

Format

A data frame with 100 rows and 2 variables

Details

  • dataURA matrix with 100 rows (genes) and 2 columns "apoptosis" "proliferation of cells"

Value

a 100x2 matrix

DEG Differentially expressed genes

Description

A data set containing the following data:

Usage

data(DEGsmatrix)

Format

A 3502x5 matrix

Details

  • DEGsmatrix matrix with 3502 rows (genes) and five columns "logFC" "logCPM" "LR" "PValue" "FDR"

Value

the 3502x5 matrix

Information on 101 biological processes

Description

A data set containing the following data:

Usage

data(DiseaseList)

Format

A list of 101 matrices

Details

  • DiseaseList list for 101 biological processes, each containing a matrix with five columns: ID, Genes.in.dataset, Prediction based on expression direction, Log ratio, Findings

Value

list of 101 matrices

DPA

Description

This function carries out the differential phenotypes analysis

Usage

DPA(
  dataType,
  dataFilt,
  dataConsortium = "TCGA",
  fdr.cut = 0.01,
  logFC.cut = 1,
  diffmean.cut = 0.25,
  samplesType,
  colDescription,
  gset,
  gsetFile = "gsetFile.RData"
)

Arguments

dataType

selected
dataFilt

obtained from getDataTCGA
dataConsortium

is TCGA or GEO, default TCGA
fdr.cut

is a threshold to filter DEGs according their p-value corrected
logFC.cut

is a threshold to filter DEGs according their logFC
diffmean.cut

diffmean.cut for DMR
samplesType

samplesType
colDescription

colDescription
gset

gset
gsetFile

gsetFile

Value

result matrix from differential phenotype analysis

Examples

dataDEGs <- DPA(dataFilt = dataFilt, dataType = "Gene expression")

Information about genes

Description

A data set containing the following data:

Usage

data(EAGenes)

Format

A 20038x5 matrix

Details

  • EAGenes matrix with 20038 rows (genes) and five columns "ID" "Gene" "Description" "Location" "Family"

Value

a 20038x5 matrix

FEA

Description

This function carries out the functional enrichment analysis (FEA)

Usage

FEA(BPname = NULL, DEGsmatrix)

Arguments

BPname

BPname biological process such as "proliferation of cells", "ALL" (default) if FEA should be carried out for all 101 biological processes
DEGsmatrix

DEGsmatrix output from DEA such as dataDEGs"

Value

matrix from FEA

Examples

dataDEGs <- DPA(dataFilt = dataFilt,
dataType = "Gene expression")
dataFEA <- FEA(DEGsmatrix = dataDEGs)

Information on GDC projects

Description

A character vector of GDC projects:

Usage

data(GDCprojects)

Format

A character vector of 39 elements

Details

  • character vector for GDC projects.

Value

character vector of 39 elements

Information about genes for normalization

Description

A data set containing the following data:

Usage

data(geneInfo)

Format

A data frame with 20531 rows and 3 variables

Details

  • geneInfo matrix with 20531 rows (genes) and 3 columns "geneLength" "gcContent" "chr"

Value

a 20531x3 matrix

Information on GEO data (and overlap with TCGA)#' A data set containing the following data:

Description

  • GEO_TCGAtab a 18x12 matrix that provides the GEO data set we matched to one of the 18 given TCGA cancer types

Usage

data(GEO_TCGAtab)

Format

A 101x3 matrix

Value

a 101x3 matrix

getDataGEO

Description

This function retrieves and prepares GEO data

Usage

getDataGEO(GEOobject = "GSE39004", platform = "GPL6244", TCGAtumor = NULL)

Arguments

GEOobject

GEOobject
platform

platform
TCGAtumor

tumor name

Value

return GEO gset

Examples

## Not run: 
dataGEO <-  getDataGEO(GEOobject = "GSE20347",platform = "GPL571")

## End(Not run)

getDataTCGA

Description

This function retrieves and prepares TCGA data

Usage

getDataTCGA(
  cancerType,
  dataType,
  directory,
  cor.cut = 0.6,
  qnt.cut = 0.25,
  nSample,
  stage = "ALL",
  subtype = 0,
  samples = NULL
)

Arguments

cancerType

select cancer type for which analysis should be run. panCancer for all available cancer types in TCGA. Defaults to panCancer
dataType

is dataType such as gene expression, cnv, methylation etc.
directory

Directory/Folder where the data was downloaded. Default: GDCdata
cor.cut

cor.cut
qnt.cut

qnt.cut
nSample

nSample
stage

stage
subtype

subtype
samples

samples

Value

returns filtered TCGA data

Examples

## Not run: 
dataFilt <- getDataTCGA(cancerType = "LUAD",
dataType = "Gene expression", directory = "data", nSample = 4)

## End(Not run)

Generate network

Description

This function carries out the gene regulatory network inference using parmigene

Usage

GRN(
  TFs,
  DEGsmatrix,
  DiffGenes = FALSE,
  normCounts,
  kNearest = 3,
  nGenesPerm = 10,
  nBoot = 10
)

Arguments

TFs

a vector of genes.
DEGsmatrix

DEGsmatrix output from DEA such as dataDEGs
DiffGenes

if TRUE consider only diff.expr genes in GRN
normCounts

is a matrix of gene expression with genes in rows and samples in columns.
kNearest

the number of nearest neighbors to consider to estimate the mutual information. Must be less than the number of columns of normCounts.
nGenesPerm

nGenesPerm
nBoot

nBoot

Value

an adjacent matrix

Examples

dataDEGs <- DEGsmatrix
dataGRN <- GRN(TFs = rownames(dataDEGs)[1:100],
DEGsmatrix = dataDEGs,
DiffGenes = TRUE,
normCounts = dataFilt)

GSEA

Description

This function carries out the GSEA enrichment analysis.

Usage

GSEA(DEGsmatrix, top, plot = FALSE)

Arguments

DEGsmatrix

DEGsmatrix output from DEA such as dataDEGs
top

is the number of top BP to plot
plot

if TRUE return a GSEA's plot

Value

return GSEA result

Examples

dataDEGs <- DEGsmatrix
# dataFEA <- GSEA(DEGsmatrix = dataDEGs)

Information on known cancer driver gene from COSMIC

Description

A data set containing the following data:

Usage

data(knownDriverGenes)

Format

A 101x3 matrix

Details

  • TSG known tumor suppressor genes

  • OCG known oncogenes

Value

a 101x3 matrix

Output list from Moonlight

Description

A list containing the following data:

Usage

data(listMoonlight)

Format

A Large list with 5 elements

Details

  • listMoonlight output from moonlight's pipeline containing dataDEGs, dataURA, listCandidates

Value

output from moonlight pipeline

LPA

Description

This function carries out the literature phenotype analysis (LPA)

Usage

LPA(dataDEGs, BP, BPlist)

Arguments

dataDEGs

is output from DEA
BP

is biological process
BPlist

is list of genes annotated in BP

Value

table with number of pubmed that affects, increase or decrase genes annotated in BP

Examples

data(DEGsmatrix)
BPselected <- c("apoptosis")
BPannotations <- DiseaseList[[match(BPselected, names(DiseaseList))]]$ID

moonlight pipeline

Description

moonlight is a tool for identification of cancer driver genes. This function wraps the different steps of the complete analysis workflow. Providing different solutions:

  1. MoonlighR::FEA

  2. MoonlighR::URA

  3. MoonlighR::PIA

Usage

moonlight(
  cancerType = "panCancer",
  dataType = "Gene expression",
  directory = "GDCdata",
  BPname = NULL,
  cor.cut = 0.6,
  qnt.cut = 0.25,
  Genelist = NULL,
  fdr.cut = 0.01,
  logFC.cut = 1,
  corThreshold = 0.6,
  kNearest = 3,
  nGenesPerm = 10,
  DiffGenes = FALSE,
  nBoot = 100,
  nTF = NULL,
  nSample = NULL,
  thres.role = 0,
  stage = NULL,
  subtype = 0,
  samples = NULL
)

Arguments

cancerType

select cancer type for which analysis should be run. panCancer for all available cancer types in TCGA. Defaults to panCancer
dataType

dataType
directory

directory
BPname

biological processes to use, if NULL: all processes will be used in analysis, RF for candidate; if not NULL the candidates for these processes will be determined (no learning)
cor.cut

cor.cut Threshold
qnt.cut

qnt.cut Threshold
Genelist

Genelist
fdr.cut

fdr.cut Threshold
logFC.cut

logFC.cut Threshold
corThreshold

corThreshold
kNearest

kNearest
nGenesPerm

nGenesPerm
DiffGenes

DiffGenes
nBoot

nBoot
nTF

nTF
nSample

nSample
thres.role

thres.role
stage

stage
subtype

subtype
samples

samples

Value

table with cancer driver genes TSG and OCG.

Examples

dataDEGs <- DPA(dataFilt = dataFilt, dataType = "Gene expression")
# to change with moonlight

MoonlightR

Description

MoonlightR is a package designed for the identification of cancer driver genes. Please see the documentation on our Bioconductor page for more details: https://www.bioconductor.org/packages/release/bioc/html/MoonlightR.html

If you experience issues with the package, please open an Issue on our GitHub repository: https://github.com/ELELAB/MoonlightR

If you use this package in your research, please cite this paper: https://doi.org/10.1038/s41467-019-13803-0

plotCircos

Description

This function visualize the plotCircos

Usage

plotCircos(
  listMoonlight,
  listMutation = NULL,
  additionalFilename = NULL,
  intensityColOCG = 0.5,
  intensityColTSG = 0.5,
  intensityColDual = 0.5,
  fontSize = 1
)

Arguments

listMoonlight

output Moonlight function
listMutation

listMutation
additionalFilename

additionalFilename
intensityColOCG

intensityColOCG
intensityColTSG

intensityColTSG
intensityColDual

intensityColDual
fontSize

fontSize

Value

no return value, plot is saved

Examples

plotCircos(listMoonlight = listMoonlight, additionalFilename = "_ncancer5")

plotFEA

Description

This function visualize the functional enrichment analysis (FEA)'s barplot

Usage

plotFEA(
  dataFEA,
  topBP = 10,
  additionalFilename = NULL,
  height,
  width,
  offsetValue = 5,
  angle = 90,
  xleg = 35,
  yleg = 5,
  titleMain,
  minY = -5,
  maxY = 10,
  mycols = c("#8DD3C7", "#FFFFB3", "#BEBADA")
)

Arguments

dataFEA

dataFEA
topBP

topBP
additionalFilename

additionalFilename
height

Figure height
width

Figure width
offsetValue

offsetValue
angle

angle
xleg

xleg
yleg

yleg
titleMain

title of the plot
minY

minY
maxY

maxY
mycols

colors to use for the plot

Value

no return value, FEA result is plotted

Examples

dataFEA <- FEA(DEGsmatrix = DEGsmatrix)
plotFEA(dataFEA = dataFEA, additionalFilename = "_example",height = 20,width = 10)

plotNetworkHive: Hive network plot

Description

This function visualizes the GRN as a hive plot

Usage

plotNetworkHive(dataGRN, namesGenes, thres, additionalFilename = NULL)

Arguments

dataGRN

output GRN function
namesGenes

list TSG and OCG to define axes
thres

threshold of edges to be included
additionalFilename

additionalFilename

Value

no results Hive plot is executed

Examples

data(knownDriverGenes)
data(dataGRN)
plotNetworkHive(dataGRN = dataGRN, namesGenes = knownDriverGenes, thres = 0.55)

plotURA: Upstream regulatory analysis heatmap plot

Description

This function visualizes the URA in a heatmap

Usage

plotURA(dataURA, additionalFilename = "URAplot")

Arguments

dataURA

output URA function
additionalFilename

figure name

Value

heatmap

Examples

data(dataURA)
dataDual <- PRA(dataURA = dataURA,
BPname = c("apoptosis","proliferation of cells"),
thres.role = 0)
TSGs_genes <- names(dataDual$TSG)
OCGs_genes <- names(dataDual$OCG)
plotURA(dataURA = dataURA[c(TSGs_genes, OCGs_genes),],additionalFilename = "_example")

Pattern Recognition Analysis (PRA)

Description

This function carries out the pattern recognition analysis

Usage

PRA(dataURA, BPname, thres.role = 0)

Arguments

dataURA

output URA function
BPname

BPname
thres.role

thres.role

Value

returns list of TSGs and OCGs when biological processes are provided, otherwise a randomForest based classifier that can be used on new data

Examples

data(dataURA)
dataDual <- PRA(dataURA = dataURA,
BPname = c("apoptosis","proliferation of cells"),
thres.role = 0)

Information growing/blocking characteristics for 101 selected biological processes

Description

A data set containing the following data:

Usage

data(tabGrowBlock)

Format

A 101x3 matrix

Details

  • tabGrowBlock matrix that defines if a process is growing or blocking cancer development, for each 101 biological processing

Value

a 101x3 matrix

URA Upstream Regulator Analysis

Description

This function carries out the upstream regulator analysis

Usage

URA(dataGRN, DEGsmatrix, BPname, nCores = 1)

Arguments

dataGRN

output GNR function
DEGsmatrix

output DPA function
BPname

biological processes
nCores

number of cores to use

Value

an adjacent matrix

Examples

dataDEGs <- DEGsmatrix
dataGRN <- GRN(TFs = rownames(dataDEGs)[1:100],
DEGsmatrix = dataDEGs,
DiffGenes = TRUE,
normCounts = dataFilt)
dataURA <-URA(dataGRN = dataGRN,
DEGsmatrix = dataDEGs,
BPname = c("apoptosis",
"proliferation of cells"))