Description Usage Arguments Details Value Author(s) References See Also Examples
View source: R/hrunbiasedTesting.r
Cox model for a target signature of genes allowing several correction types and testing procedures
1 2 3 4 5 6 7  hrunbiasedTesting(nda, gene.signature, alternative ="two.sided",
test.type = c("asymptotic", "random.signatures", "random.genes"),
correction.type = c("NCsignatures", "Gsignature", "RUV", "none"),
score = NULL, seqquant = 0.25, nrs = 5000,
comp.RUV = c(1,2), adj.var.GS = "GSadj",
adjusted.var.random = NA, adjusted.var.fixed = NA, mc.cores = 1,
signature.method = "zscore", maxGenes = 5000, executation.info=TRUE, ...)

nda 

gene.signature 
Vector with the names of the gene signature to
be tested (must match with 
alternative 
Character string specifying the alternative
hypothesis, must be one of 
test.type 
Character string specifying the reference
distribution to be used, must include at least one of

correction.type 
Standard Cox model can be used when

score 
Vector of the same length as number of rows in 
seqquant 
Value between zero and one used to set negative controls 
nrs 
Number of random signatures to be used when

comp.RUV 
Factors used in factor analysis signature correction
when 
adj.var.GS 
Only if correction.type is not "none" and siganture.method is "zscore". Transforming signature scores by subtracting the correction variable scores "GScor", residuals of linear model using the correction variable as covariate "GSlmcor", or using the correction variable in the Cox model "GSadj" (default) 
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 
mc.cores 
The number of cores to use for parallel executions 
signature.method 
Signature score method, to choose from

maxGenes 
Number of genes to be used to determine reference
distribution if 
executation.info 
If 
... 
Arguments passed to or from other methods to the low level. 
Testing statistical significance of the relationship between a gene signature and recurrence can be done by considering both asymptotic distributions or random signature based distributions. The interpretation of the two is different: asymptotic testing finds evidence against the null hypothesis HR = 1; random signatures quantifies how special is the relation of a gene signature to recurrence in comparison to any possible random set of genes.
A useful correction we consider is the adjusment by a global signature
which uses the information of all genes and that may point towards
technical / sampling directions. This global signature can be
influenced by both technical and biological effects. In order to
reduce the biological impact, adjusting variables are determined by
negative control signatures based on a score ordered from low to high.
For example, we suggest to use a measure of dispersion so that only
lowvariable genes are used in an adjusting signature. Other scores
based on crossstudy reproducibility or any sort of biological prior
information could also be employed. See
hrunbiasedDiagnostic
for more details in selecting the
correction type.
For seqquant = 0
, then the correction type coincides with
correction.type = "none"
and for seqquant = 1
then the
correction type coincides with correction.type = "Gsignature"
.
An object of class hrunbiasedTesting containing the following items
"hr.ts" 
Cox model outcome of target signature 
"hr.rs" 
Cox model outcome of random signatures 
"hr.tg" 
Cox model outcomes of genes composing the target signature 
"hr.rg" 
Cox model outcomes of random genes 
"pvals.rs" 
Pvalue found using as reference distribution the statistics obtained for random signatures 
"test.type" 
Reference distribution used for hypothesis testing 
"gene.signature" 
Gene signature used for hypothesis testing 
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).
GagnonBartsch J. and Speed T. Using control genes to correct for unwanted variation in microarray data. Biostatistics 13, 539552 (2012).
Hanzelmann S., Castelo R. and Guinney J. GSVA: gene set variation analysis for microarray and RNASeq data. BMC Bioinformatics 7 (2013).
plot.hrunbiasedTesting
,
randomSigCoxmeSimple
and
hrunbiasedDiagnostic
1 2 3 4 5 6 7 8 9 10 11 12  eh < ExperimentHub()
nda.brca < query(eh, "mcsurvdata")[["EH1497"]]
nda.gse1456 < nda.brca[,nda.brca$dataset=="GSE1456"]
data(mammaprint.sig)
mammaprint.sig < intersect(mammaprint.sig, rownames(nda.gse1456))
correction.type < "Gsignature"
tout.GS < hrunbiasedTesting(nda.gse1456,
gene.signature = mammaprint.sig,
test.type = c("asymptotic", "random.signatures", "random.genes"),
correction.type = "Gsignature", mc.cores=1,
nrs = 100, executation.info = FALSE)
tout.GS

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