Exprs.survtype {survtype} | R Documentation |
For discovery of subtypes of samples that are both clinically relevant and biologically meaningful, the Cox regession model and hierarchical clustering are combined.
Exprs.survtype(surv.data, time, status, exprs.data, K = 2, num.genes = 100, gene.sel = FALSE, gene.sel.opt = list(verbose = FALSE), ...)
surv.data |
survival data |
time |
survival time |
status |
status indicator |
exprs.data |
expression data |
K |
the number of clusters (default: 2) |
num.genes |
the number of top genes based on the Cox score, before variable selection (default: 100) |
gene.sel |
a logical value indicating whether or not gene selection for clustring is applied (default: FALSE) |
gene.sel.opt |
a list of options for the gene selection function "clustvarsel". "verbose" is set to FALSE as default. |
... |
additional parameters for the "pheatmap" |
n |
the number of subjects in each group |
obs |
the weighted observed number of events in each group |
exp |
the weighted expected number of events in each group |
chisq |
the chi-squared statistic for a test of equality |
call |
the matched call |
fit |
fitted survival curves |
cluster |
a vector of integers indicating the cluster to which each sample is assigned |
time |
survival time |
status |
status indicator |
surv.data |
survival data |
exprs.data |
expression data |
Dongmin Jung
Bair, E., & Tibshirani, R. (2004). Semi-supervised methods to predict patient survival from gene expression data. PLoS biology, 2(4), e108.
survival::Surv, survival::survfit, survival::survdiff, survival::coxph, clustvarsel::clustvarsel, pheatmap::pheatmap
set.seed(1) nrows <- 5 ncols <- nrow(ovarian) counts <- matrix(runif(nrows * ncols, 1, 1e4), nrows) colnames(counts) <- paste("X", 1:ncols, sep = "") rownames(ovarian) <- paste("X", 1:ncols, sep = "") SE <- SummarizedExperiment(assays = SimpleList(counts = counts)) ovarian.survtype <- Exprs.survtype(ovarian, time = "futime", status = "fustat", assay(SE), num.genes = 2, scale = "row", clustering_method = "ward.D2") plot(ovarian.survtype, pval = TRUE)