| signatureSurvival-package {signatureSurvival} | R Documentation |
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
The DESCRIPTION file:
| Package: | signatureSurvival |
| Type: | Package |
| Title: | Signature Survival Analysis |
| Version: | 1.0.0 |
| Date: | 2023-07-17 |
| Authors@R: | c(person(given = "Yuan-De", family = "Tan", role = c("aut", "cre"), email = "tanyuande@gmail.com", comment = c(ORCID = "0000-0002-0364-2223")), person(given = "Yuguang", family = "Ban", role = "ctb")) |
| Maintainer: | Yuan-De Tan <tanyuande@gmail.com> |
| 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. |
| License: | GPL(>=3) |
| Depends: | R(>= 3.5.0) |
| Imports: | stats, utils,graphics,grDevices,dplyr,forestplot, gplots, gtools, survival,survminer,ggplot2 |
| Suggests: | Rmisc |
| LazyLoad: | yes |
| NeedsCompilation: | no |
| Encoding: | UTF-8 |
| LazyData: | true |
| Packaged: | 2023-07-17 13:44:39 UTC; yxt477 |
| Author: | Yuan-De Tan [aut, cre] (<https://orcid.org/0000-0002-0364-2223>), Yuguang Ban [ctb] |
Index of help topics:
GSE50081 Survival data from cohort GSE50081
MKMplot Multivariate Kaplan-Meier survival curve plot
MMKMplot Multiple multivariate Kaplan-Meier survival
curve plots
MUKMplot Function multiple univariate Kaplan-Meier
survival curve plots
MVKMresult Multivariate survival analysis with multiple
specified independent variables
SKMCresult Univariate Cox proportional hazard survival
analysis with a specified independent variable
TCGA_forestplt Data for forestplot
TCGA_survivalData TCGA data for survival analysis
TS_signature A signature constructed with a set of tumor
suppressor genes
UKMplot Univariate Kaplan-Meier survival curve plot
musvtest Multiple univariate suvival tests with a set of
genes
mvstest Multivariate Cox proportional hazard survival
analyses with multiple genes
results results of univariate Cox proportional hazard
analysis of patients with ADC in three cohorts.
signatureExp Signature expression or signature score
signatureSurvival-package
Signature Survival Analysis
signature_weight Weights of genes in a signature
survivalForest Forestplot for result of multivariate Cox
proportional hazard survival analysis
weight Caculation of Weights for signature genes
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 <tanyuande@gmail.com>
Examples
data(GSE50081)
res<-musvtest(sdata=GSE50081,stn=3500,gn=3506,time="month",status="status",
quant=c("no",-0.2,0.2))