ClussCluster: Function to perform simultaneous detection of cell types and...

In gabriellajg/ClussCluster: Simultaneous Detection of Clusters and Cluster-Specific Genes in High-throughput Transcriptome Data

Description

ClussCluster takes the single-cell transcriptome data and returns an object containing cell types and type-specific signature gene sets

Usage

ClussCluster(x, nclust = NULL, centers = NULL, ws = NULL,
  nepoch.max = 10, theta = NULL, seed = 1, nstart = 20, iter.max = 50,
  verbose = FALSE, progress = TRUE)

Arguments

x	An nxp data matrix. There are n cells and p genes.
nclust	Number of clusters desired if the cluster centers are not provided. If both are provided, nclust must equal the number of cluster centers.
centers	A set of initial (distinct) cluster centres if the number of clusters (nclust) is null. If both are provided, the number of cluster centres must equal nclust.
ws	One or multiple candidate tuning parameters to be evaluated and compared. Determines the sparsity of the selected genes. Should be greater than 1.
nepoch.max	The maximum number of epochs. In one epoch, each cell will be evaluated to determine if its label needs to be updated.
theta	Optional argument. If provided, theta are used as the initial cluster labels of the ClussCluster algorithm; if not, K-means is performed to produce starting cluster labels.
seed	This seed is used wherever K-means is used.
nstart	Argument passed to kmeans. It is the number of random sets used in kmeans.
iter.max	Argument passed to kmeans. The maximum number of iterations allowed.
verbose	Print the updates inside every epoch? If TRUE, the updates of cluster label and the value of objective function will be printed out.
progress	Print the progress? If multiple tuning parameters are evaluated, then each tuning parameter will be printed when progress=TRUE.

Details

Takes the normalized and log transformed number of reads mapped to genes (e.g., log(RPKM+1) or log(TPM+1) where RPKM stands for Reads Per Kilobase of transcript per Million mapped reads and TPM stands for transcripts per million) but NOT centered.

Examples

## Not run: 
data(Hou)
hou.dat <-Hou$x
run.ft <- filter_gene(hou.dat)
hou.test <- ClussCluster(run.ft$dat.ft, nclust=3, ws=4, verbose = F)

## End(Not run)

gabriellajg/ClussCluster documentation built on May 12, 2019, 5:40 a.m.