Package 'ProgModule' reference manual

Title:	Identification of Prognosis-Related Mutually Exclusive Modules
Description:	A novel tool to identify candidate driver modules for predicting the prognosis of patients by integrating exclusive coverage of mutations with clinical characteristics in cancer.
Authors:	Junwei Han [aut, cre, cph], Xiangmei Li [aut], Bingyue Pan [aut]
Maintainer:	Junwei Han <[email protected]>
License:	GPL (>= 2)
Version:	0.1.0
Built:	2024-11-13 04:47:13 UTC
Source:	https://github.com/cran/ProgModule

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
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
final_candidate_module

Format

An object of class list of length 4.

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

Arguments

`local_network`	The local networks,generated by 'get_local_network'.
`network`	The maximum connected subnet,extracted by mapping all mutated genes to the PPI network.
`freq_matrix`	The mutations matrix,generated by 'get_mut_status'.
`sur`	A nx2 data frame of samples' survival data,the first line is samples' survival event and the second line is samples' overall survival.
`seed`	The canonical drivers from NCG database, which use as the starting node of the greedy algorithm.
`max.size`	The maximum size of the module,default is 200.
`rate`	The rate of increase in score,default is 0.05.

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)
#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
)
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

`index`	A index file of PPI networks,downloaded from http://compbio-research.cs.brown.edu/pancancer/hotnet2/.
`edge`	The edge lists of PPI networks,downloaded from http://compbio-research.cs.brown.edu/pancancer/hotnet2/.
`mut_status`	The mutations matrix,generated by 'get_mut_status'.
`sur`	A nx2 data frame of samples' survival data,the first line is samples' survival event and the second line is samples' overall survival.
`seed`	The canonical drivers from NCG database, which use as the starting node of the greedy algorithm.
`cutoff`	The perturbed p-value cutoff point,default is 0.05.
`max.size.local`	The size of maximum connected local network,default is 500.
`max.size.candidate`	The maximum size of the candidate module,default is 200.
`rate`	The rate of increase in score,default is 0.05.
`perm`	The perturbation number,default is 1000.

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

Arguments

`network`	The PPI network.
`freq_matrix`	The mutations matrix,generated by 'get_mut_status'.
`max.size`	The size of maximum connected local network,default is 500.

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)
#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
)
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

`maf`	The patients' somatic mutation data, which in MAF format.
`gene`	Modular gene from final_candidate_module,generated by 'get_final_candidate_module'.
`AACol`, `labelPos`, `labPosSize`, `showMutationRate`, `showDomainLabel`, `cBioPortal`, `refSeqID`, `proteinID`, `roundedRect`, `repel`, `collapsePosLabel`, `showLegend`, `legendTxtSize`, `labPosAngle`, `domainLabelSize`, `axisTextSize`, `printCount`, `colors`, `domainAlpha`, `domainBorderCol`, `bgBorderCol`, `labelOnlyUniqueDoamins`, `defaultYaxis`, `titleSize`, `pointSize`	see `lollipopPlot`

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")
#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)
get_mut_status(mutvariant, nonsynonymous = TRUE)

Arguments

`mutvariant`	A nx3 data frame of patients' somatic mutation data,the first line is gene symbol,the second line is sample ID and the third line is mutation classification.
`nonsynonymous`	Logical, tell if extract the non-synonymous somatic mutations (nonsense mutation, missense mutation, frame-shif indels, splice site, nonstop mutation, translation start site, inframe indels).

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)
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)
get_mut_survivalresult(module, freq_matrix, sur)

Arguments

`module`	The gene module,generated by 'get_final_candidate_module'.
`freq_matrix`	The mutations matrix,generated by 'get_mut_status'.
`sur`	A nx2 data frame of samples' survival data,the first line is samples' survival event and the second line is samples' overall survival.

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

Arguments

`module`	The Original modular gene set.
`net`	The local network extracted from PPI network.
`freq_matrix`	The mutations matrix,generated by 'get_mut_status'.
`sur`	A nx2 data frame of samples' survival data,the first line is samples' survival event and the second line is samples' overall survival.
`module_sig`	A label for whether the module is a risk factor or a protective factor for survival.
`univarCox_result`	The result of Cox univariate analysis,generated by 'get_univarCox_result'.
`rate`	The rate of increase in score,default is 0.05.

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)
#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
)
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

`maf`	The patients' somatic mutation data, which in MAF format.
`genes`	Modular gene set from final_candidate_module,generated by 'get_final_candidate_module'.
`removeNonMutated`, `top`, `minMut`, `altered`, `drawRowBar`, `drawColBar`, `leftBarData`, `leftBarLims`, `rightBarData`, `rightBarLims`, `topBarData`, `logColBar`, `includeColBarCN`, `clinicalFeatures`, `annotationColor`, `annotationDat`, `pathways`, `path_order`, `selectedPathways`, `pwLineCol`, `pwLineWd`, `draw_titv`, `titv_col`, `showTumorSampleBarcodes`, `barcode_mar`, `barcodeSrt`, `gene_mar`, `anno_height`, `legend_height`, `sortByAnnotation`, `groupAnnotationBySize`, `annotationOrder`, `sortByMutation`, `keepGeneOrder`, `GeneOrderSort`, `sampleOrder`, `additionalFeature`, `additionalFeaturePch`, `additionalFeatureCol`, `additionalFeatureCex`, `genesToIgnore`, `fill`, `cohortSize`, `colors`, `cBioPortal`, `bgCol`, `borderCol`, `annoBorderCol`, `numericAnnoCol`, `drawBox`, `fontSize`, `SampleNamefontSize`, `titleFontSize`, `legendFontSize`, `annotationFontSize`, `sepwd_genes`, `sepwd_samples`, `writeMatrix`, `colbar_pathway`, `showTitle`, `titleText`, `showPct`	see `oncoplot`

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]])
#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
)
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

`module`	The gene module,generated by 'get_final_candidate_module'.
`genes`	The modular gene,generated by 'get_final_candidate_module'.
`freq_matrix`	The mutations matrix,generated by 'get_mut_status'.
`pvalue`, `returnAll`, `fontSize`, `showSigSymbols`, `showCounts`, `countStats`, `countType`, `countsFontSize`, `countsFontColor`, `colPal`, `nShiftSymbols`, `sigSymbolsSize`, `sigSymbolsFontSize`, `pvSymbols`, `limitColorBreaks`	see `plotMutInteract`

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

Arguments

`module`	The candidate module,generated by 'get_candidate_module'.
`freq_matrix`	The mutations matrix,generated by 'get_mut_status'.
`sur`	A nx2 data frame of samples' survival data,the first line is samples' survival event and the second line is samples' overall survival.
`perms`	The perturbation number,default is 1000.
`local_network`	The local network gene sets,generated by 'get_local_network'.

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)
#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 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)
get_univarCox_result(freq_matrix, sur)

Arguments

`freq_matrix`	The mutations matrix,generated by 'get_mut_status'.
`sur`	A nx2 data frame of samples' survival data,the first line is samples' survival event and the second line is samples' overall survival.

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)
#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
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
maf_data

Format

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

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)
MI(mylist1, mylist2)

Arguments

`mylist1`	Is input a list of samples' survival status.
`mylist2`	Is input a list of samples' mutation status.

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])
#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])

module, gene set

Description

module, Original modular gene set.

Usage

module
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
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
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
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
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
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
univarCox_result

Format

An object of class numeric of length 8103.

Package 'ProgModule'

Help Index

candidate_module, candidate module

Description

Usage

Format

final_candidate_module, Final candidate modules

Description

Usage

Format

Get candidate module.

Description

Usage

Arguments

Value

Examples

Get final module.

Description

Usage

Arguments

Value

Examples

Extract the local networks from the PPI network.

Description

Usage

Arguments

Value

Examples

Draw an lollipopPlot for module genes

Description

Usage

Arguments

Value

Examples

Converts MAF file into mutation matrix.

Description

Usage

Arguments

Value

Examples

Plot Kaplan-Meier survival curve.

Description

Usage

Arguments

Value

Examples

Extract the mutually exclusive module.

Description

Usage

Arguments

Value

Examples

Draw a waterfall plot of mutated genes involved in the module

Description

Usage

Arguments

Value

Examples

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

Description

Usage

Arguments

Value

Examples

Get perturbed p-value.

Description

Usage

Arguments

Value

Examples

Get univarCox result.

Description

Usage

Arguments

Value

Examples

local_network, local network gene set

Description

Usage

Format