Calculate mutual information.

Description

The function ‘MI' is used to calculate the mutual information score between samples’ survival status and mutation status.

Usage

MI(mylist1, mylist2)

Arguments

Value

The mutual information score

Examples

#load the data
data(mut_status)
sur<-system.file("extdata","sur.csv",package = "ProgModule")
sur<-read.delim(sur,sep=",",header=TRUE,row.names = 1)
#perform the function 'MI'
mut_matrix<-MI(mylist1 = as.numeric(mut_status[1,]),mylist2 = sur[,1])

candidate_module, candidate module

Description

candidate_module, Is the gene set from each local network by greedy algorithm,generated by 'get_candidate_module'.

Usage

candidate_module

Format

An object of class list of length 4.

final_candidate_module, Final candidate modules

Description

final_candidate_module, Is the final candidate module by intersecting modules from three protein networks in pairs,generated by 'get_final_candidate_module'.

Usage

final_candidate_module

Format

An object of class list of length 4.

Get perturbed p-value.

Description

The function 'get_Pvalue_S' is used to calculate the perturbed p-value.

Usage

get_Pvalue_S(module, freq_matrix, sur, perms = 1000, local_network)

Arguments

Value

Perturbed p-value.

Examples

#load the data
data(local_network)
data(mut_status)
data(candidate_module)
sur<-system.file("extdata","sur.csv",package = "ProgModule")
sur<-read.delim(sur,sep=",",header=TRUE,row.names = 1)
#perform the function `get_Pvalue_S`.
turbulence.example<-get_Pvalue_S(module=candidate_module,freq_matrix=mut_status,
sur=sur,perms=100,local_network)

Get candidate module.

Description

The function 'get_candidate_module' is used to search candidate module of each local network using greedy algorithm.

Usage

get_candidate_module(
  local_network,
  network,
  freq_matrix,
  sur,
  seed,
  max.size,
  rate
)

Arguments

Value

candidate module.

Examples

#load the data
data(local_network)
data(mut_status)
data(subnet)
canonical_drivers<-system.file("extdata","canonical_drivers.txt",package = "ProgModule")
seed_gene<-read.table(canonical_drivers,header=FALSE)
sur<-system.file("extdata","sur.csv",package = "ProgModule")
sur<-read.delim(sur,sep=",",header=TRUE,row.names=1)
#perform the function `get_candidate_module`.
candidatemodule.example<-get_candidate_module(local_network=local_network,network=subnet,
freq_matrix=mut_status,sur=sur,seed=seed_gene[,1],max.size=200,rate=0.05)

Get final module.

Description

The function 'get_final_module' is used to identify the final module.

Usage

get_final_module(
  index,
  edge,
  mut_status,
  sur,
  seed,
  cutoff = 0.05,
  max.size.local = 500,
  max.size.candidate = 200,
  rate = 0.05,
  perm = 1000
)

Arguments

Value

The final module.

Examples

#load the data
indexdata<-system.file("extdata","hint+hi2012_index_file.txt",package="ProgModule")
index<-read.table(indexdata,sep="\t",header=FALSE)
edgedata<-system.file("extdata","hint+hi2012_edge_file.txt",package="ProgModule")
edge<-read.table(edgedata,sep="\t",header=FALSE)
data(mut_status)
sur<-system.file("extdata","sur.csv",package ="ProgModule")
sur<-read.delim(sur,sep=",",header=TRUE,row.names=1)
canonical_drivers<-system.file("extdata","canonical_drivers.txt",package="ProgModule")
seed_gene<-read.table(canonical_drivers,header=FALSE)
#perform the function `get_final_module`.
finalmodule.example<-get_final_module(index,edge,mut_status,sur,seed=seed_gene,
cutoff=0.05,max.size.local=500,max.size.candidate=200,rate=0.05,perm=100)

Extract the local networks from the PPI network.

Description

The function 'get_local_network' is used to search local network of each gene by breadth-first algorithm.

Usage

get_local_network(network, freq_matrix, max.size = 500)

Arguments

Value

local nerwork.

Examples

#load the data
data(mut_status)
data(subnet)
#perform the function `get_local_network`.
localnetwork.example<-get_local_network(network=subnet,freq_matrix=mut_status,max.size=500)

Draw an lollipopPlot for module genes

Description

Load the data in MAF format and draws an lollipopPlot.

Usage

get_lollipopPlot(
  maf,
  gene,
  AACol = NULL,
  labelPos = NULL,
  labPosSize = 0.9,
  showMutationRate = TRUE,
  showDomainLabel = TRUE,
  cBioPortal = FALSE,
  refSeqID = NULL,
  proteinID = NULL,
  roundedRect = TRUE,
  repel = FALSE,
  collapsePosLabel = TRUE,
  showLegend = TRUE,
  legendTxtSize = 0.8,
  labPosAngle = 0,
  domainLabelSize = 0.8,
  axisTextSize = c(1, 1),
  printCount = FALSE,
  colors = NULL,
  domainAlpha = 1,
  domainBorderCol = "black",
  bgBorderCol = "black",
  labelOnlyUniqueDoamins = TRUE,
  defaultYaxis = FALSE,
  titleSize = c(1.2, 1),
  pointSize = 1.5
)

Arguments

Value

No return value

Examples

#load the data.
maffile<-system.file("extdata","maffile.maf",package="ProgModule")
#draw an lollipopPlot
get_lollipopPlot(maf=maffile,gene="TP53")

Converts MAF file into mutation matrix.

Description

The function 'get_mut_status' uses to convert MAF file into mutation matrix.

Usage

get_mut_status(mutvariant, nonsynonymous = TRUE)

Arguments

Value

A binary mutations matrix, in which 1 represents that a particular gene has mutated in a particular sample, and 0 represents that gene has no mutation in a particular sample.

Examples

maf<-system.file("extdata","maffile.maf",package = "ProgModule")
maf_data<-read.delim(maf)
mutvariant<-maf_data[,c("Hugo_Symbol","Tumor_Sample_Barcode","Variant_Classification")]
#perform the function `get_mut_status`.
mut_status.example<-get_mut_status(mutvariant,nonsynonymous = TRUE)

Plot Kaplan-Meier survival curve.

Description

The function 'get_mut_survivalresult' uses to draw the Kaplan-Meier survival curve based on the mutated status of candidate module.

Usage

get_mut_survivalresult(module, freq_matrix, sur)

Arguments

Value

No return value

Examples

#load the data.
data(mut_status)
sur<-system.file("extdata","sur.csv",package="ProgModule")
sur<-read.delim(sur,sep=",",header=TRUE,row.names=1)
data(final_candidate_module)
#perform the function `get_mut_survivalresult`.
get_mut_survivalresult(module=final_candidate_module,freq_matrix=mut_status,sur)

Extract the mutually exclusive module.

Description

The function 'get_mutual_module' is used to determine if neighbor genes should be added to the module by calculating the score.

Usage

get_mutual_module(
  module,
  net,
  freq_matrix,
  sur,
  module_sig,
  univarCox_result,
  rate
)

Arguments

Value

The mutually exclusive module.

Examples

#load the data
data(mut_status)
sur<-system.file("extdata","sur.csv",package = "ProgModule")
sur<-read.delim(sur,sep=",",header=TRUE,row.names = 1)
data(net)
data(module)
data(univarCox_result)
#perform the function `get_mutual_module`.
mutuallyexclusivemodule.example<-get_mutual_module(module,net,freq_matrix=mut_status,sur,
module_sig="risk",univarCox_result,rate=0.05)

Draw a waterfall plot of mutated genes involved in the module

Description

Load the data in MAF format and draws a waterfall plot of mutated genes involved in the module.

Usage

get_oncoplots(
  maf,
  genes,
  removeNonMutated = TRUE,
  top = 20,
  minMut = NULL,
  altered = FALSE,
  drawRowBar = TRUE,
  drawColBar = TRUE,
  leftBarData = NULL,
  leftBarLims = NULL,
  rightBarData = NULL,
  rightBarLims = NULL,
  topBarData = NULL,
  logColBar = FALSE,
  includeColBarCN = TRUE,
  clinicalFeatures = NULL,
  annotationColor = NULL,
  annotationDat = NULL,
  pathways = NULL,
  path_order = NULL,
  selectedPathways = NULL,
  pwLineCol = "#535c68",
  pwLineWd = 1,
  draw_titv = FALSE,
  titv_col = NULL,
  showTumorSampleBarcodes = FALSE,
  barcode_mar = 4,
  barcodeSrt = 90,
  gene_mar = 5,
  anno_height = 1,
  legend_height = 4,
  sortByAnnotation = FALSE,
  groupAnnotationBySize = TRUE,
  annotationOrder = NULL,
  sortByMutation = FALSE,
  keepGeneOrder = FALSE,
  GeneOrderSort = TRUE,
  sampleOrder = NULL,
  additionalFeature = NULL,
  additionalFeaturePch = 20,
  additionalFeatureCol = "gray70",
  additionalFeatureCex = 0.9,
  genesToIgnore = NULL,
  fill = TRUE,
  cohortSize = NULL,
  colors = NULL,
  cBioPortal = FALSE,
  bgCol = "#CCCCCC",
  borderCol = "white",
  annoBorderCol = NA,
  numericAnnoCol = NULL,
  drawBox = FALSE,
  fontSize = 0.8,
  SampleNamefontSize = 1,
  titleFontSize = 1.5,
  legendFontSize = 1.2,
  annotationFontSize = 1.2,
  sepwd_genes = 0.5,
  sepwd_samples = 0.25,
  writeMatrix = FALSE,
  colbar_pathway = FALSE,
  showTitle = TRUE,
  titleText = NULL,
  showPct = TRUE
)

Arguments

Value

No return value

Examples

#load the data.
maffile<-system.file("extdata","maffile.maf",package="ProgModule")
data(final_candidate_module)
#draw an oncoplot
get_oncoplots(maf=maffile,genes=final_candidate_module[[1]])

Exact tests to detect mutually exclusive, co-occuring and altered genesets or pathways.

Description

Performs Pair-wise Fisher's Exact test to detect mutually exclusive or co-occuring events.

Usage

get_plotMutInteract(
  module = NULL,
  genes = NULL,
  freq_matrix,
  pvalue = c(0.05, 0.01),
  returnAll = TRUE,
  fontSize = 0.8,
  showSigSymbols = TRUE,
  showCounts = FALSE,
  countStats = "all",
  countType = "all",
  countsFontSize = 0.8,
  countsFontColor = "black",
  colPal = "BrBG",
  nShiftSymbols = 5,
  sigSymbolsSize = 2,
  sigSymbolsFontSize = 0.9,
  pvSymbols = c(46, 42),
  limitColorBreaks = TRUE
)

Arguments

Value

No return value

Examples

#load the data.
data(plotMutInteract_moduledata,plotMutInteract_mutdata)
#draw an plotMutInteract of genes
get_plotMutInteract(genes=unique(unlist(plotMutInteract_moduledata)),
freq_matrix=plotMutInteract_mutdata)
#draw an plotMutInteract of modules
get_plotMutInteract(module=plotMutInteract_moduledata,
freq_matrix=plotMutInteract_mutdata)

Get univarCox result.

Description

The function 'get_univarCox_result' is used to calculate the result of Cox univariate analysis.

Usage

get_univarCox_result(freq_matrix, sur)

Arguments

Value

The result of Cox univariate analysis.

Examples

#load the data
data(mut_status)
sur<-system.file("extdata","sur.csv",package ="ProgModule")
sur<-read.delim(sur,sep=",",header=TRUE,row.names=1)
#perform the function `get_univarCox_result`.
univarCoxresult.example<-get_univarCox_result(freq_matrix=mut_status,sur)

local_network, local network gene set

Description

local_network, the local network gene set of each gene by breadth-first algorithm,,generated by 'get_local_network'.

Usage

local_network

Format

An object of class list of length 4.

maf_data, MAF file

Description

maf_data, The patients' somatic mutation data, which in MAF format.

Usage

maf_data

Format

An object of class data.frame with 3745 rows and 10 columns.

module, gene set

Description

module, Original modular gene set.

Usage

module

Format

An object of class character of length 1.

mut_status, mutations matrix

Description

mut_status, the mutations matrix,generated by 'get_mut_status'.

Usage

mut_status

Format

An object of class matrix (inherits from array) with 338 rows and 331 columns.

net, network

Description

net, Is a local network extracted from the ppi network.

Usage

net

Format

An object of class igraph of length 76.

plotMutInteract_moduledata

Description

The data use for drawing mutually exclusive and co-occurrence plots.

Usage

plotMutInteract_moduledata

Format

An object of class list of length 7.

plotMutInteract_mutdata

Description

The data use for drawing mutually exclusive and co-occurrence plots.

Usage

plotMutInteract_mutdata

Format

An object of class matrix (inherits from array) with 89 rows and 430 columns.

subnet, network

Description

subnet, Is a maximum connected subnet,extracted by mapping all genes to the ppi network.

Usage

subnet

Format

An object of class igraph of length 1624.

univarCox_result

Description

The result of Cox univariate analysis,generated by 'get_univarCox_result'.

Usage

univarCox_result

Format

An object of class numeric of length 8103.