learnSignatures: Method to Learn Signatures from SummarizedExperiment

In kevinrue/hancock: Learn and Apply Cell Type Signatures

Description

These method signatures learn gene set signatures optionally augmented with (semi-)quantitative information for the prediction of sample and cell identities in SummarizedExperiment objects.

Usage

learnSignatures(se, assay.type = "counts",
  method = c("PositiveProportionDifference", "PPD"), ...)

Arguments

se	An object of class inheriting from "SummarizedExperiment".
assay.type	A string specifying which assay values to use, e.g., "counts" or "logcounts".
method	Learning method. See section "Learning methods".
...	Additional arguments affecting the learning method.

Value

A Sets object.

Learning methods

PositiveProportionDifference, PPD

Requires prior cluster membership information. This method computes the proportion of samples positive for each feature in each cluster, and subsequently identifies for each cluster the features showing the maximal difference between the detection rate in the cluster of interest and the detection rate in all other clusters.

Author(s)

Kevin Rue-Albrecht

See Also

learnMarkersByPositiveProportionDifference()

Examples

# Example data ----
library(SummarizedExperiment)
nsamples <- 100
u <- matrix(rpois(20000, 2), ncol=nsamples)
rownames(u) <- paste0("Gene", sprintf("%03d", seq_len(nrow(u))))
colnames(u) <- paste0("Cell", sprintf("%03d", seq_len(ncol(u))))
se <- SummarizedExperiment(assays=list(counts=u))

# Example usage ----
se1 <- se
colData(se1)[, "cluster"] <- factor(sample(head(LETTERS, 3), ncol(se1), replace=TRUE))
gs <- learnSignatures(se1, method="PositiveProportionDifference", cluster.col="cluster")

relations(gs)

kevinrue/hancock documentation built on May 17, 2020, 7:55 a.m.