hidimsvc: hidimsvc Survival analysis on high dimensional by creating...

In SurvHiDim: High Dimensional Survival Data Analysis

Description

Survival analysis on high dimensional data by creating batches of covariates

Usage

hidimsvc(m, n, batchsize, siglevel, ID, OS, Death, PFS, Prog, data)

Arguments

m	Starting column number form where study variables of high dimensional data will get selected.
n	Ending column number till where study variables of high dimensional data will get selected.
batchsize	Number of variables to be consider at time while running function (maximum batch size should not be greater than one third of the total number of high dimensional variables)
siglevel	Level of significance pre-determined by the user
ID	Column name of subject ID, a string value. i.e. "id"
OS	Column name of survival duration event, a string value. i.e. "os"
Death	Column name of survival event, a string value. i.e "death"
PFS	Column name of progression free survival duration, a string value. i.e "pfs"
Prog	Column name of progression event, a string value. i.e "prog"
data	High dimensional data containing the survival, progression and genomic observations.

Details

hidimsvc function fits Univarate Cox Proportinal Hazard models by considering each variables at a time. Then it filters out study variables having P-value < siglevel(significance level taken as input from user). Once by survival and survival eevent and another by progression and progression events. Secondly, it merges the commom sigificant variables from both OS and PFS analysis and creates a new data frame which contains columns, 'ID','OS','Death','PFS','Prog','Status' and observations of common significant variables (which are supposed to be leading to death given they leads to progression of cancer as well as accounts for competing risks) Further, it lists the common variables names and outs corresponding results in .csv format by default in user's current working directory.

It works similary to HiDimSurv unlike it creates batches of decided study vriables by user to make the analysis less time consuming.

hidimsvc(m1,m2,batchsize,siglevel,data),

1) Subject ID column should be named as 'ID'.

2) OS column must be named as 'OS'.

3) Death status/event column should be named as 'Death'.

4) Progression Fress Survival column should be named as 'PFS'.

5) Progression event column should be named as 'Prog'.

OSDeathcoeff - A data frame containing HR estimates and p-values for study variables on fitting univariate CoxPh on OS and Survival event.

PFSProgcoeff - A data frame containg HR estimates and p-values for study variables on fitting univariate CoxPh on PFS and Progression event.

namevect - List of all the study variable names.

significantOSDeathgenes - A data frame containing HR estimates and p-values for significant study variables.

significantPFSProggenes - A data frame containing HR estimates and p-values for significant study variables

commongenes - A data frame containing estimated values of significant study variables found common from significant study variables on fitting CoxPh on survival and progression times and events.

odnames - List of significant variables on fitting CoxPh using survival and survival event.

ppnames - List of significant variables on fitting CoxPh using progression and progression event.

cvar - List of common significant study variables on fitting CoxPh on survival and progression, times and events.

commondata - A data out which contains the clinical observations and observations on commongenes variables.

Value

Estimate values of significant variables/DEGs on considering Death with Progression

Estimate values of significant variables/DEGs on considering Death without Progression

List of variable/DEGs considering Death with Progression

List of variable/DEGs considering Death without Progression

Estimates data for the DEGs/Variables found common between significant DEGs from data having death due to progression and data showing death without progression"

List of variable/DEGs found common between significant DEGs from data having death due to progression and data showing death without progression"

Author(s)

Atanu Bhattacharjee and Akash Pawar

References

Bhattacharjee, A. (2020). Bayesian Approaches in Oncology Using R and OpenBUGS. CRC Press.

Congdon, P. (2014). Applied bayesian modelling (Vol. 595). John Wiley & Sons.

Banerjee, S., Vishwakarma, G. K., & Bhattacharjee, A. (2019). Classification Algorithm for High Dimensional Protein Markers in Time-course Data. arXiv preprint arXiv:1907.12853.

Examples

##
data(hnscc)
hidimsvc(7,105,5,0.05,ID="id",OS="os",Death="death",PFS="pfs",Prog="prog",hnscc)
##

SurvHiDim documentation built on June 26, 2021, 5:06 p.m.