signatureSurvival-package: Signature Survival Analysis

In signatureSurvival: Signature Survival Analysis

Signature Survival Analysis

Description

When multiple Cox proportional hazard models are performed on clinical data (month or year and status) and a set of differential expressions of genes, the results (Hazard risks, z-scores and p-values) can be used to create gene-expression signatures. Weights are calculated using the survival p-values of genes and are utilized to calculate expression values of the signature across the selected genes in all patients in a cohort. A Single or multiple univariate or multivariate Cox proportional hazard survival analyses of the patients in one cohort can be performed by using the gene-expression signature and visualized using our survival plots.

Details

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This package is used to create up and down signatures,do univariate or multivariate survival analysis and make forest plot for the results of multivariate Cox proportional hazard survival analysis. The steps for screening signature are as following: At step1, users should perform differential expression analysis of genes in one or multiple microarray datasets or RNA-seq count datasets or the other expression datasets and then mark these differentally expressed (DE) genes selected with "up" and "down" using negative or positive t-values. At step2, retrieve survival (or clinical data) using these DE genes and construct a new survival data(age, sex, stages/smoking, month, status, and DE genes in column and patients in row). Note that expression data ofthe DE genes are listed in the right side in the survival data. At step 3, perform musvtest.R (multiple univariate survival tests) or mvstest (multiple multivariate survival tests) with covariates age, sex and/smoking ect. Use p-value to select genes in big difference between low and high-survival probalities and use HR and up and down-regulation to classify genes selected into up and down groups in multiple cohorts. At step 4,use weight.R to calculate weight values of each gene in signature and use signatureExp.R to caculate expression values of signature in all patients and move the expression values to the last column in survival data. At step 5, perform MUKMplot.R or MMKMplot.R on signature in the survival data to plot Kaplan-Meier survival curves.

Author(s)

Yuan-De Tan [aut, cre] (<https://orcid.org/0000-0002-0364-2223>), Yuguang Ban [ctb]

Maintainer: Yuan-De Tan <[e-mail]>

Examples

data(GSE50081)
res<-musvtest(sdata=GSE50081,stn=3500,gn=3506,time="month",status="status",
quant=c("no",-0.2,0.2))

signatureSurvival documentation built on July 26, 2023, 5:35 p.m.