Exprs.survtype {survtype}R Documentation

Sample subtype identification via survival information and gene expression data

Description

For discovery of subtypes of samples that are both clinically relevant and biologically meaningful, the Cox regession model and hierarchical clustering are combined.

Usage

Exprs.survtype(surv.data, time, status, exprs.data, K = 2, num.genes = 100,
               gene.sel = FALSE, gene.sel.opt = list(verbose = FALSE), ...)

Arguments

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"

Value

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

Author(s)

Dongmin Jung

References

Bair, E., & Tibshirani, R. (2004). Semi-supervised methods to predict patient survival from gene expression data. PLoS biology, 2(4), e108.

See Also

survival::Surv, survival::survfit, survival::survdiff, survival::coxph, clustvarsel::clustvarsel, pheatmap::pheatmap

Examples

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)

[Package survtype version 1.8.0 Index]