getCls {DrImpute}

get base clustering results using SC3 based clustering methods.

Description

Similarity matrix constructed using "pearson", "spearman" or "euclidean". K-means clustering is performed on first few number of principal components of similarity matrix.

Usage

getCls(X, ks = 10:15, dists = c("spearman", "pearson"),
  dim.reduc.prop = 0.05)

Arguments

X

Log transformed gene expression matrix (Gene by Cell).

ks

Number of cell clustering groups. Default set to ks = 10:15.

dists

Distribution matrices to use. Default is set to c("spearman", "pearson"). "euclidean" can be added as well.

dim.reduc.prop

Proportion of principal components to use for K-means clustering.

Value

A matrix object, Each row represent different clustering results.

Author(s)

Il-Youp Kwak

References

Il-Youp Kwak, Wuming Gong, Kaoko Koyano-Nakagawa and Daniel J. Garry (2017+) DrImpute: Imputing dropout eveents in single cell RNA sequencing data

See Also

DrImpute preprocessSC

Examples

data(exdata)
exdata <- preprocessSC(exdata)
exdata <- exdata[1:3000, 1:80]
logdat <- log(exdata+1)
cls <- getCls(logdat)