Package 'PMAPscore'

Title:	Identify Prognosis-Related Pathways Altered by Somatic Mutation
Description:	We innovatively defined a pathway mutation accumulate perturbation score (PMAPscore) to reflect the position and the cumulative effect of the genetic mutations at the pathway level. Based on the PMAPscore of pathways, identified prognosis-related pathways altered by somatic mutation and predict immunotherapy efficacy by constructing a multiple-pathway-based risk model (Tarca, Adi Laurentiu et al (2008) <doi:10.1093/bioinformatics/btn577>).
Authors:	Junwei Han [aut, cre, cph], Yalan He [aut], Xiangmei Li [aut]
Maintainer:	Junwei Han <[email protected]>
License:	GPL (>= 2)
Version:	0.1.1
Built:	2024-11-02 06:01:56 UTC
Source:	https://github.com/hanjunwei-lab/pmapscore

Help Index

final_signature, the final prognosis-related pathways
gene_symbol_Entrez, the genes' symbol and ENTREZID
gene_Ucox
gene_Ucox_res, the univariate Cox regression result of candidate genes.
Convert gene symbol to Entrez_Gene_ID
Identify the candidate prognosis-related pathways
Plot Kaplan-Meier survival curve.
Perform the multivariate Cox regression
Converts MAF file into mutation matrix
draw an GenePathwayOncoplots
Calculates the pathway-based mutation accumulate perturbation score
Plot the response column diagram
Calculates the risk score for patients
Plot the ROC curve
get_sam_cla
Perform the univariate Cox regression analysis.
km_data
maf_data
maffile
mut_num
mut_sam
mut_sample
mut_status
newspia
path_cox_data
path_gene
path_Ucox_mul
path_Ucox_mul_res
pfs_score
response
roc_data, the data frame use for ploting ROC curve
sig
sur
symbol_Entrez

final_signature, the final prognosis-related pathways

Description

The final prognosis-related pathways identified by our approach.

Usage

final_signature
final_signature

Format

An object of class character of length 7.

gene_symbol_Entrez, the genes' symbol and ENTREZID

Description

The genes' symbol and ENTREZID.

Usage

gene_symbol_Entrez
gene_symbol_Entrez

Format

An object of class data.frame with 54245 rows and 2 columns.

gene_Ucox

Description

gene_Ucox

Usage

gene_Ucox
gene_Ucox

Format

An object of class data.frame with 4287 rows and 5 columns.

gene_Ucox_res, the univariate Cox regression result of candidate genes.

Description

The univariate Cox regression result of candidate genes.

Usage

gene_Ucox_res
gene_Ucox_res

Format

An object of class data.frame with 4287 rows and 5 columns.

Convert gene symbol to Entrez_Gene_ID

Description

The function 'get_Entrez_ID' is used to convert gene symbol to Entrez_Gene_ID

Usage

get_Entrez_ID(mut_status, gene_symbol_Entrez, Entrez_ID = TRUE)
get_Entrez_ID(mut_status, gene_symbol_Entrez, Entrez_ID = TRUE)

Arguments

`mut_status`	A binary matrix that contains the mutation state of genes in each sample and its row name is the gene symbol. Noted the matrix can be generated by the function 'get_mut_status'.
`gene_symbol_Entrez`	A data table containing gene symbol and the corresponding gene Entrez ID.
`Entrez_ID`	Logical,tell whether there are Entrez IDs corresponding to gene symbol in the gene_symbol_Entrez.

Value

A binary matrix that contains the mutation state of genes in each sample and its row name is Entrez_Gene_ID.

Examples

#load the data.
data(mut_status,gene_symbol_Entrez)
#perform function `get_Entrez_ID`.
mut_status<-get_Entrez_ID(mut_status,gene_symbol_Entrez,Entrez_ID=TRUE)
#load the data.
data(mut_status,gene_symbol_Entrez)
#perform function `get_Entrez_ID`.
mut_status<-get_Entrez_ID(mut_status,gene_symbol_Entrez,Entrez_ID=TRUE)

Identify the candidate prognosis-related pathways

Description

The function 'get_final_signature' uses to identify the candidate prognosis-related pathways based on the PMAPscore.

Usage

get_final_signature(pfs_score, sur, wilcox_p = 0.05, uni_cox_p = 0.01)
get_final_signature(pfs_score, sur, wilcox_p = 0.05, uni_cox_p = 0.01)

Arguments

`pfs_score`	A 2 X n matrix that contains the pfs_score in each sample of the signal pathways. Noted the matrix can be generated by the function 'get_pfs_score'.
`sur`	This data contains survival status and survival time of each sample.
`wilcox_p`	The threshold of p value for Wilcoxon rank-sum test.
`uni_cox_p`	The threshold of p value for univariate Cox regression analysis.

Value

Return the candidate prognosis-related pathways

Examples

#load the data.
data(pfs_score,sur)
#perform function `get_final_signature`.
final_signature<-get_final_signature(pfs_score,sur)
#load the data.
data(pfs_score,sur)
#perform function `get_final_signature`.
final_signature<-get_final_signature(pfs_score,sur)

Plot Kaplan-Meier survival curve.

Description

The function 'get_km_survival_curve' uses to draw the Kaplan-Meier survival curve.

Usage

get_km_survival_curve(km_data, cut_point, TRAIN = TRUE, risk.table = TRUE)
get_km_survival_curve(km_data, cut_point, TRAIN = TRUE, risk.table = TRUE)

Arguments

`km_data`	A data frame, including survival status, survival time, and risk score of each sample. The data frame can be generated by the function 'get_risk_score'.
`cut_point`	The threshold uses to classify patients into two subgroups with different OS.
`TRAIN`	Logical,if set to TRUE,the 'cut_point' is generated by the median of the risk score; Otherwise,'cut_point' can be customized.
`risk.table`	Allowed values include:TRUE or FALSE specifying whether to show or not the risk table. Default is FALSE.

Value

No return, plot the Kaplan-Meier survival curve.

Examples

#load the data.
data(km_data)
#perform the function `get_km_survival_curve`.
get_km_survival_curve(km_data,cut_point,TRAIN = TRUE,risk.table=TRUE)
#load the data.
data(km_data)
#perform the function `get_km_survival_curve`.
get_km_survival_curve(km_data,cut_point,TRAIN = TRUE,risk.table=TRUE)

Perform the multivariate Cox regression

Description

The function 'get_MultivariateCox_result' uses to perform multivariate Cox regression analysis on the cancer-specific dysregulated signaling pathways.

Usage

get_MultivariateCox_result(DE_path_sur)
get_MultivariateCox_result(DE_path_sur)

Arguments

DE_path_sur

A binary metadata table containing sample survival status and survival time.Note that the column names of survival time and survival status must be "survival" and "event".

Value

Return the multivariate Cox regression results of cancer-specific dysregulated signaling pathways.

Examples

#Load the data.
data(path_cox_data)
#perform function `get_MultivariateCox_result`.
res<-get_MultivariateCox_result(path_cox_data)
#Load the data.
data(path_cox_data)
#perform function `get_MultivariateCox_result`.
res<-get_MultivariateCox_result(path_cox_data)

Converts MAF file into mutation matrix

Description

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

Usage

get_mut_status(maf_data, nonsynonymous = TRUE)
get_mut_status(maf_data, nonsynonymous = TRUE)

Arguments

`maf_data`	The patients' somatic mutation data, which in MAF format.
`nonsynonymous`	Logical, tell if extract the non-silent 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

#load the data
data(maf_data)
#perform the function `get_mut_status`.
mutmatrix.example<-get_mut_status(maf_data,nonsynonymous = TRUE)
#load the data
data(maf_data)
#perform the function `get_mut_status`.
mutmatrix.example<-get_mut_status(maf_data,nonsynonymous = TRUE)

draw an GenePathwayOncoplots

Description

Load the data in MAF format and draws an GenePathwayOncoplots.

Usage

get_Oncoplots(
  maffile,
  path_gene,
  mut_status,
  risk_score,
  cut_off,
  final_signature,
  pathway_name,
  isTCGA = FALSE,
  top = 20,
  clinicalFeatures = "sample_group",
  annotationColor = c("red", "green"),
  sortByAnnotation = TRUE,
  removeNonMutated = FALSE,
  drawRowBar = TRUE,
  drawColBar = TRUE,
  leftBarData = NULL,
  leftBarLims = NULL,
  rightBarData = NULL,
  rightBarLims = NULL,
  topBarData = NULL,
  logColBar = FALSE,
  draw_titv = FALSE,
  showTumorSampleBarcodes = FALSE,
  fill = TRUE,
  showTitle = TRUE,
  titleText = NULL
)
get_Oncoplots(
  maffile,
  path_gene,
  mut_status,
  risk_score,
  cut_off,
  final_signature,
  pathway_name,
  isTCGA = FALSE,
  top = 20,
  clinicalFeatures = "sample_group",
  annotationColor = c("red", "green"),
  sortByAnnotation = TRUE,
  removeNonMutated = FALSE,
  drawRowBar = TRUE,
  drawColBar = TRUE,
  leftBarData = NULL,
  leftBarLims = NULL,
  rightBarData = NULL,
  rightBarLims = NULL,
  topBarData = NULL,
  logColBar = FALSE,
  draw_titv = FALSE,
  showTumorSampleBarcodes = FALSE,
  fill = TRUE,
  showTitle = TRUE,
  titleText = NULL
)

Arguments

`maffile`	A data of MAF format.
`path_gene`	User input pathways geneset list.
`mut_status`	The mutations matrix,generated by 'get_mut_matrix'.
`risk_score`	Samples' PTMB-related risk score,which could be a biomarker for survival analysis and immunotherapy prediction.
`cut_off`	A threshold value(the median risk score as the default value).Using this value to divide the sample into high and low risk groups with different overall survival.
`final_signature`	The pathway signature,use to map gene in the GenePathwayOncoplots.
`pathway_name`	The name of the pathway that you want to visualize.For example "Gap junction"
`isTCGA`	Is input MAF file from TCGA source. If TRUE uses only first 12 characters from Tumor_Sample_Barcode.
`top`	How many top genes to be drawn,genes are arranged from high to low depending on the frequency of mutations. defaults to 20.
`clinicalFeatures`	Columns names from 'clinical.data' slot of MAF to be drawn in the plot. Dafault "sample_group".
`annotationColor`	Custom colors to use for sample annotation-"sample_group". Must be a named list containing a named vector of colors. Default "red" and "green".
`sortByAnnotation`	Logical sort oncomatrix (samples) by provided 'clinicalFeatures'. Sorts based on first 'clinicalFeatures'. Defaults to TRUE. column-sort.
`removeNonMutated`	Logical. If TRUE removes samples with no mutations in the GenePathwayOncoplots for better visualization. Default FALSE.
`drawRowBar`	Logical. Plots righ barplot for each gene. Default TRUE.
`drawColBar`	Logical plots top barplot for each sample. Default TRUE.
`leftBarData`	Data for leftside barplot. Must be a data.frame with two columns containing gene names and values. Default 'NULL'.
`leftBarLims`	Limits for 'leftBarData'. Default 'NULL'.
`rightBarData`	Data for rightside barplot. Must be a data.frame with two columns containing to gene names and values. Default 'NULL' which draws distibution by variant classification. This option is applicable when only 'drawRowBar' is TRUE.
`rightBarLims`	Limits for 'rightBarData'. Default 'NULL'.
`topBarData`	Default 'NULL' which draws absolute number of mutation load for each sample. Can be overridden by choosing one clinical indicator(Numeric) or by providing a two column data.frame contaning sample names and values for each sample. This option is applicable when only 'drawColBar' is TRUE.
`logColBar`	Plot top bar plot on log10 scale. Default FALSE.
`draw_titv`	Logical Includes TiTv plot. Default FALSE
`showTumorSampleBarcodes`	Logical to include sample names.
`fill`	Logical. If TRUE draws genes and samples as blank grids even when they are not altered.
`showTitle`	Default TRUE.
`titleText`	Custom title. Default 'NULL'.

Value

No return value

Examples

#obtain the risksciore
data(km_data)
risk_score<-km_data$multiple_score
names(risk_score)<-rownames(km_data)
cut_off<-median(risk_score)
#load the dtata
data(final_signature,path_gene,mut_status,maffile)
##draw an GenePathwayOncoplots
get_Oncoplots(maffile,path_gene,mut_status,risk_score,cut_off,final_signature,"Gap junction")

#obtain the risksciore
data(km_data)
risk_score<-km_data$multiple_score
names(risk_score)<-rownames(km_data)
cut_off<-median(risk_score)
#load the dtata
data(final_signature,path_gene,mut_status,maffile)
##draw an GenePathwayOncoplots
get_Oncoplots(maffile,path_gene,mut_status,risk_score,cut_off,final_signature,"Gap junction")

Calculates the pathway-based mutation accumulate perturbation score

Description

The function 'get_pfs_score' uses to calculate the pathway-based mutation accumulate perturbation score using the matrix of gene mutation state and pathway information.

Usage

get_pfs_score(
  mut_status,
  percent,
  gene_Ucox_res,
  gene_symbol_Entrez,
  data.dir = NULL,
  organism = "hsa",
  verbose = TRUE,
  Entrez_ID = TRUE,
  gene_set = NULL
)
get_pfs_score(
  mut_status,
  percent,
  gene_Ucox_res,
  gene_symbol_Entrez,
  data.dir = NULL,
  organism = "hsa",
  verbose = TRUE,
  Entrez_ID = TRUE,
  gene_set = NULL
)

Arguments

`mut_status`	Mutation status of a particular gene in a particular sample. The file can be generated by the function 'get_mut_status'.
`percent`	This parameter is used to control the mutation rate of gene. Genes less than this value will be deleted
`gene_Ucox_res`	Results of gene univariate Cox regression.
`gene_symbol_Entrez`	A data table containing gene symbol and gene Entrez ID.
`data.dir`	Location of the "organism"SPIA.RData file containing the pathways data.If set to NULL will look for this file in the extdata folder of the PFS library.
`organism`	A three letter character designating the organism. See a full list at ftp://ftp.genome.jp/pub/kegg/xml/organisms.
`verbose`	If set to TRUE, displays the number of pathways already analyzed.
`Entrez_ID`	Logical,tell whether there are Entrez IDs corresponding to gene symbol in the gene_symbol_Entrez.
`gene_set`	A group of cancer specific gene symbols obtained from the training set

Value

A binary mutations matrix, which column names is sample and the row name is the pathway.

Examples

#get the path of the mutation annotation file.
data(mut_status,gene_Ucox_res,gene_symbol_Entrez)
#perform the function `get_pfs_score`.
pfs_score<-get_pfs_score(mut_status[,1:2],percent=0.03,gene_Ucox_res,gene_symbol_Entrez)
#get the path of the mutation annotation file.
data(mut_status,gene_Ucox_res,gene_symbol_Entrez)
#perform the function `get_pfs_score`.
pfs_score<-get_pfs_score(mut_status[,1:2],percent=0.03,gene_Ucox_res,gene_symbol_Entrez)

Plot the response column diagram

Description

The function 'get_response_plot' uses to plot the column diagram of drug response.

Usage

get_response_plot(km_data, response, cut_point, TRAIN = TRUE)
get_response_plot(km_data, response, cut_point, TRAIN = TRUE)

Arguments

`km_data`	A data frame, including survival status, survival time, and risk score of each sample. The data frame can be generated by the function 'get_risk_score'.
`response`	Response status of the sample to the drug.
`cut_point`	The threshold uses to classify patients into two subgroups with different OS.
`TRAIN`	Logical,if set to TRUE,the 'cut_point' is generated by the median of the risk score; Otherwise,'cut_point' can be customized.

Value

Comparison of the objective response rate between the high-risk and low-risk groups, plot the bar graph and return the p value.

Examples

#Load the data.
data(km_data,response)
#perform the function `get_response_plot`.
get_response_plot(km_data,response,cut_point,TRAIN=TRUE)
#Load the data.
data(km_data,response)
#perform the function `get_response_plot`.
get_response_plot(km_data,response,cut_point,TRAIN=TRUE)

Calculates the risk score for patients

Description

The function 'get_risk_score' uses to calculate the risk score for patients based on cancer-specific dysregulated signaling pathways.

Usage

get_risk_score(
  final_signature,
  pfs_score,
  path_Ucox_mul_res,
  sur,
  TRAIN = TRUE
)
get_risk_score(
  final_signature,
  pfs_score,
  path_Ucox_mul_res,
  sur,
  TRAIN = TRUE
)

Arguments

`final_signature`	Cancer-specific dysregulated signal pathways. It can be generated by the function 'get_final_signature'.
`pfs_score`	A matrix that contains the pfs_score in each sample of the signal pathways. Noted the matrix can be generated by the function 'get_pfs_score'.
`path_Ucox_mul_res`	Results of multivariate Cox regression of cancer specific pathway in training set.
`sur`	This data contains survival status and survival time of each sample.
`TRAIN`	Logical,if set FLASE,we need to load the result of multivariate Cox regression of cancer specific pathways into the training set.

Value

A data set with the risk score for each sample.

Examples

#Load the data.
data(final_signature,pfs_score,sur,path_Ucox_mul_res)
#perform the function `get_risk_score`.
km_data<-get_risk_score(final_signature,pfs_score,path_Ucox_mul_res,sur,TRAIN=TRUE)
#Load the data.
data(final_signature,pfs_score,sur,path_Ucox_mul_res)
#perform the function `get_risk_score`.
km_data<-get_risk_score(final_signature,pfs_score,path_Ucox_mul_res,sur,TRAIN=TRUE)

Plot the ROC curve

Description

The function 'get_roc_curve' uses to plot the ROC curve for predicting immunotherapy response.

Usage

get_roc_curve(roc_data, print.auc = TRUE, main = "Objective Response")
get_roc_curve(roc_data, print.auc = TRUE, main = "Objective Response")

Arguments

`roc_data`	A 2 X n data fram, which contain the immunotherapy response and risk score (generated by the function 'get_risk_score') for patients.
`print.auc`	Boolean. Should the numeric value of AUC be printed on the plot?
`main`	A main title for the plot.

Value

No return, plot the ROC curve for immunotherapy response prediction.

Examples

#Load the data.
data(roc_data)
#perform the function `get_roc_curve`.
get_roc_curve(roc_data,print.auc=TRUE,main="Objective Response")
#Load the data.
data(roc_data)
#perform the function `get_roc_curve`.
get_roc_curve(roc_data,print.auc=TRUE,main="Objective Response")

get_sam_cla

Description

Function 'get_sample_classification' This function is used to judge the classification of samples.

Usage

get_sam_cla(
  mut_sam,
  gene_Ucox,
  symbol_Entrez,
  path_cox_data,
  sur,
  path_Ucox_mul,
  sig,
  cut_off = -0.986,
  data.dir = NULL,
  organism = "hsa",
  TRAIN = FALSE
)
get_sam_cla(
  mut_sam,
  gene_Ucox,
  symbol_Entrez,
  path_cox_data,
  sur,
  path_Ucox_mul,
  sig,
  cut_off = -0.986,
  data.dir = NULL,
  organism = "hsa",
  TRAIN = FALSE
)

Arguments

`mut_sam`	The sample somatic mutation data.
`gene_Ucox`	Results of gene univariate Cox regression.
`symbol_Entrez`	A data table containing gene symbol and gene Entrez ID.
`path_cox_data`	Pathways of Cancer-specifical obtained from the training set.
`sur`	This data contains survival status and survival time of each sample.
`path_Ucox_mul`	Multivariate Cox regression results of Cancer-specifical pathways.
`sig`	Cancer-specific dysregulated signal pathways. It can be generated by the function 'get_final_signature'.
`cut_off`	Threshold of classification.
`data.dir`	Location of the "organism"SPIA.RData file containing the pathways data. If set to NULL will look for this file in the extdata folder of the PMAPscore library.
`organism`	A three letter character designating the organism. See a full list at ftp://ftp.genome.jp/pub/kegg/xml/organisms.
`TRAIN`	Logical,if set FLASE,we need to load the result of multivariate Cox regression of cancer specific pathways into the training set.

Value

Return a data frame, the sample's risk score and the sample's risk group.

Examples

#Load the data.
data(mut_sam,gene_Ucox,symbol_Entrez,path_cox_data,sur,path_Ucox_mul)
#perform function `get_sample_cla`.
get_sam_cla(mut_sam,gene_Ucox,symbol_Entrez,path_cox_data,sur,path_Ucox_mul,sig,cut_off=-0.986)
#Load the data.
data(mut_sam,gene_Ucox,symbol_Entrez,path_cox_data,sur,path_Ucox_mul)
#perform function `get_sample_cla`.
get_sam_cla(mut_sam,gene_Ucox,symbol_Entrez,path_cox_data,sur,path_Ucox_mul,sig,cut_off=-0.986)

Perform the univariate Cox regression analysis.

Description

The function 'get_univarCox_result' uses to perform the univariate Cox regression analysis.

Usage

get_univarCox_result(DE_path_sur)
get_univarCox_result(DE_path_sur)

Arguments

DE_path_sur

A binary metadata table containing survival status and survival time of each sample.Note that the column names of survival time and survival status must be "survival" and "event"

Value

Return a data frame, the univariate Cox regression analysis results.

Examples

#get path of the mutation annotation file.
data(path_cox_data)
#perform function `get_univarCox_result`.
res<-get_univarCox_result(path_cox_data)
#get path of the mutation annotation file.
data(path_cox_data)
#perform function `get_univarCox_result`.
res<-get_univarCox_result(path_cox_data)

km_data

Description

The data use for drawing K-M survival curve.

Usage

km_data
km_data

Format

An object of class data.frame with 105 rows and 10 columns.

maf_data

Description

The mutation data of patients.

Usage

maf_data
maf_data

Format

An object of class data.frame with 24461 rows and 4 columns.

maffile

Description

The mutation data of patients.

Usage

maffile
maffile

Format

An object of class MAF of length 1.

mut_num

Description

mut_num

Usage

mut_num
mut_num

Format

An object of class matrix (inherits from array) with 13858 rows and 105 columns.

mut_sam

Description

mut_sam.

Usage

mut_sam
mut_sam

Format

An object of class matrix (inherits from array) with 13858 rows and 2 columns.

mut_sample

Description

mut_sample.

Usage

mut_sample
mut_sample

Format

An object of class matrix (inherits from array) with 13858 rows and 2 columns.

mut_status

Description

mut_status.

Usage

mut_status
mut_status

Format

An object of class matrix (inherits from array) with 13858 rows and 105 columns.

newspia

Description

Function 'newspia' This function is based on SPIA algorithm to analyse KEGG signal pathway for single sample..

Usage

newspia(
  de = NULL,
  all = NULL,
  organism = "hsa",
  data.dir = NULL,
  pathids = NULL,
  verbose = TRUE,
  beta = NULL
)
newspia(
  de = NULL,
  all = NULL,
  organism = "hsa",
  data.dir = NULL,
  pathids = NULL,
  verbose = TRUE,
  beta = NULL
)

Arguments

`de`	A named vector containing the statue of particular genes in a particular sample.The names of this numeric vector are Entrez gene IDs.
`all`	A vector with the Entrez IDs in the reference set. If the data was obtained from a microarray experiment,this set will contain all genes present on the specific array used for the experiment.This vector should contain all names of the de argument.
`organism`	A three letter character designating the organism. See a full list at ftp://ftp.genome.jp/pub/kegg/xml/organisms.
`data.dir`	Location of the "organism"SPIA.RData file containing the pathways data .If set to NULL will look for this file in the extdata folder of the PMAPscore library.
`pathids`	A character vector with the names of the pathways to be analyzed.If left NULL all pathways available will be tested.
`verbose`	If set to TRUE, displays the number of pathways already analyzed.
`beta`	Weights to be assigned to each type of gene/protein relation type. It should be a named numeric vector of length 23, whose names must be: c("activation","compound","binding/association","expression", "inhibition","activation_phosphorylation","phosphorylation", "indirect","inhibition_phosphorylation","dephosphorylation_inhibition", "dissociation","dephosphorylation","activation_dephosphorylation", "state","activation_indirect","inhibition_ubiquination","ubiquination", "expression_indirect","indirect_inhibition","repression", "binding/association_phosphorylation","dissociation_phosphorylation","indirect_phosphorylation") If set to null, beta will be by default chosen as: c(1,0,0,1,1,1,0,0,1,1,0,0,1,0,1,1,0,1,1,1,0,0,0).

Value

Get one Data in data frame format,which cotains pathway's id,pathway's name and PFS_score.

path_cox_data

Description

path_cox_data

Usage

path_cox_data
path_cox_data

Format

An object of class data.frame with 105 rows and 9 columns.

path_gene

Description

path_gene

Usage

path_gene
path_gene

Format

An object of class list of length 7.

path_Ucox_mul

Description

path_Ucox_mul

Usage

path_Ucox_mul
path_Ucox_mul

Format

An object of class matrix (inherits from array) with 7 rows and 5 columns.

path_Ucox_mul_res

Description

path_Ucox_mul_res

Usage

path_Ucox_mul_res
path_Ucox_mul_res

Format

An object of class matrix (inherits from array) with 7 rows and 5 columns.

pfs_score

Description

pfs_score.

Usage

pfs_score
pfs_score

Format

An object of class matrix (inherits from array) with 123 rows and 105 columns.

response

Description

response.

Usage

response
response

Format

An object of class data.frame with 110 rows and 2 columns.

roc_data, the data frame use for ploting ROC curve

Description

The roc_data is used to generate ROC curves.

Usage

roc_data
roc_data

Format

An object of class matrix (inherits from array) with 105 rows and 4 columns.

sig

Description

sig

Usage

sig
sig

Format

An object of class character of length 7.

sur

Description

sur

Usage

sur
sur

Format

An object of class data.frame with 110 rows and 2 columns.

symbol_Entrez

Description

symbol_Entrez

Usage

symbol_Entrez
symbol_Entrez

Format

An object of class data.frame with 54245 rows and 2 columns.