Description Usage Arguments Details Value Author(s) See Also Examples
View source: R/pathwaySurvivalTest.R
This function performs survival analysis on pathways. The analysis can be either topological or classical. The analysis is based on data reduction based on Principal Component Analysis and a Cox proportional hazard model on the most influent PCs.
1 2 3 | pathwaySurvivalTest(expr, survAnnot, graph,
pcsSurvCoxMethod = c("regular", "topological", "sparse"),
alwaysShrink = FALSE, maxPCs = 10, survFormula = "Surv(days, status) ~")
|
expr |
expression matrix. |
survAnnot |
a data frame for survival annotations specified according to the survFormula. The data frame must contain days and status. |
graph |
a graphNEL object for a graph |
pcsSurvCoxMethod |
a method to perform PCA. Can be "regular", "topological", "sparse" for regular PCA, topological based PCA and sparse PCA, respectively. The latter one (sparse) is particularly suited for cliques only. |
alwaysShrink |
if TRUE, always shrink the covariance matrix. Deafult=FALSE |
maxPCs |
maximum number of PCs used in the cox formula "Surv(days, status) ~ PC1.." |
survFormula |
the formula used in Coxph analysis. Defaut="Surv(days, status) ~". Please note that the formula end with '~' meaning that PCs will be added. |
With 'regular' method, a regular PCA analysis is used to compute PCs. With 'topological' method, the covariance matrix is estimated using the topology of the pathway with IPS algorithm. With 'sparse' method, a penalized regression is used for the estimation of PCs (as implemented in elasticnet). The max number of PCs used by the model is estimated by "estim_ncp" in FactoMineR. A maximum number of PCs can be fixed by the user. The minimum ot the two is chosen.
A survPath object.
Paolo Martini
1 2 3 4 5 6 7 8 9 10 | if (require(graphite)) {
data(exp)
data(survAnnot)
data(graph)
row.names(exp) <- paste0("ENTREZID:", row.names(exp))
genes <- intersect(graph::nodes(graph), row.names(exp))
graph <- graph::subGraph(genes, graph)
expr <- exp[genes, , drop=FALSE]
pathwaySurvivalTest(expr, survAnnot, graph, maxPCs=2)
}
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