SIMLR: SIMLR

In YTLogos/SIMLR: SIMLR: Single-cell Interpretation via Multi-kernel LeaRning

Description

perform the SIMLR clustering algorithm

Usage

SIMLR(X, c, no.dim = NA, k = 10, if.impute = FALSE, normalize = FALSE,
  cores.ratio = 1)

Arguments

X	an (m x n) data matrix of gene expression measurements of individual cells or and object of class SCESet
c	number of clusters to be estimated over X
no.dim	number of dimensions
k	tuning parameter
if.impute	should I traspose the input data?
normalize	should I normalize the input data?
cores.ratio	ratio of the number of cores to be used when computing the multi-kernel

Value

clusters the cells based on SIMLR and their similarities

list of 8 elements describing the clusters obtained by SIMLR, of which y are the resulting clusters: y = results of k-means clusterings, S = similarities ccomputed by SIMLR, F = results from network diffiusion, ydata = data referring the the results by k-means, alphaK = clustering coefficients, execution.time = execution time of the present run, converge = iterative convergence values by T-SNE, LF = parameters of the clustering

Examples

SIMLR(X = BuettnerFlorian$in_X, c = BuettnerFlorian$n_clust, cores.ratio = 0)

library(scran)
ncells = 50
ngenes = 25
mu <- 2^runif(ngenes, 3, 10)
gene.counts <- matrix(rnbinom(ngenes*ncells, mu=mu, size=2), nrow=ngenes)
rownames(gene.counts) = paste0("X", seq_len(ngenes))
sce = newSCESet(countData=data.frame(gene.counts))
output = SIMLR(X = sce, c = 8, cores.ratio = 0)

YTLogos/SIMLR documentation built on May 9, 2019, 11:06 p.m.