randomSigCoxmeSimple: Cox model using gene random signatures

In adricaba/hrunbiased: Survival analysis for gene signatures

Description

Cox proportional hazard model with random and fixed effects using gene signatures

Usage

randomSigCoxmeSimple(nda, randsign,  sigSize = NULL, mc.cores = 1,
    signature.method = "zscore", needScaling = FALSE, nite = 500, GS = NULL,
    adjusted.var.random = NA, adjusted.var.fixed = NA, adj.var.GS = "GSadj",
    executation.info = TRUE, perm = FALSE)

Arguments

nda	ExpressionSet object. pData(nda) must contain survival information in attributes evn (numeric vector with event description - 0 no event 1 event) and tev (time to event)
randsign	NULL or list of gene signatures (names must match with rownames(nda))
sigSize	If nda = NULL, random signatures of size sigSize are generated
mc.cores	The number of cores to use for parallel executions
signature.method	Signature score method, to choose from "zscore", "gsva" or "plage"
needScaling	If TRUE, the expression matrix in nda is scaled
nite	Number of signatures (only if randsign = NULL)
GS	Global signature used as an adjusting variable as specified by attribute adj.var.GS. If NULL, no correction is applied
adjusted.var.random	Variables that enter in the model as random effect
adjusted.var.fixed	Confounding variables that enter in the model as fixed effect
adj.var.GS	To choose among 'GSadj' (default), where GS enters in the Cox model as confounding variable, 'GScor', where GS is deducted a priori from the expression matrix, and 'GSlmcor', where gene signatures are adjusted a priori by GS using a linear model
executation.info	If TRUE information regarding executation stages is shown
perm	If TRUE, a permutation of the sample labels in the expression matrix is applied

Details

An adjustment for global signature is provided to correct for technical (or global) artefacts that can bias the results of the model (see reference)

Value

list with outcome of cox models for tested gene signatures (contains lHR coefficient, HR coefficient, standard.error, statistic and p-value)

Author(s)

Adria Caballe Mestres

References

Caballe Mestres A, Berenguer Llergo A and Stephan-Otto Attolini C. Adjusting for systematic technical biases in risk assessment of gene signatures in transcriptomic cancer cohorts. bioRxiv (2018).

See Also

hrunbiasedTesting and hrunbiasedDiagnostic

Examples

eh <- ExperimentHub()
nda.brca <- query(eh, "mcsurvdata")[["EH1497"]]
nda.gse1456 <- nda.brca[,nda.brca$dataset=="GSE1456"]
rs.s <- randomSigCoxmeSimple(nda.gse1456, randsign = NULL,
    sigSize = 50, mc.cores = 1, signature.method = "zscore", needScaling = TRUE,
    nite = 100, GS = NULL, executation.info = FALSE)

adricaba/hrunbiased documentation built on May 24, 2019, 7:48 a.m.