Description Usage Arguments Details Value Author(s) References See Also Examples
View source: R/randomSigCoxmeSimple.r
Cox proportional hazard model with random and fixed effects using gene signatures
1 2 3 4 
nda 

randsign 
NULL or list of gene signatures (names must match with

sigSize 
If 
mc.cores 
The number of cores to use for parallel executions 
signature.method 
Signature score method, to choose from

needScaling 
If TRUE, the expression matrix in 
nite 
Number of signatures (only if 
GS 
Global signature used as an adjusting variable as specified by
attribute 
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 
executation.info 
If 
perm 
If 
An adjustment for global signature is provided to correct for technical (or global) artefacts that can bias the results of the model (see reference)
list with outcome of cox models for tested gene signatures (contains lHR coefficient, HR coefficient, standard.error, statistic and pvalue)
Adria Caballe Mestres
Caballe Mestres A, Berenguer Llergo A and StephanOtto Attolini C. Adjusting for systematic technical biases in risk assessment of gene signatures in transcriptomic cancer cohorts. bioRxiv (2018).
hrunbiasedTesting
and
hrunbiasedDiagnostic
1 2 3 4 5 6  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)

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