Exprs.survtype: Sample subtype identification via survival information and...

Description Usage Arguments Value Author(s) References See Also Examples

View source: R/Exprs.survtype.R

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

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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

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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)

survtype documentation built on Nov. 8, 2020, 7:24 p.m.