run_consensus_clust: Wrapper function to repeatively run clustering on subsampled...

Description Usage Arguments

View source: R/run_consensus.R

Description

Wrapper function to repeatively run clustering on subsampled cells and infer consensus clusters

Usage

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run_consensus_clust(norm.dat, select.cells = colnames(norm.dat),
  niter = 100, sample.frac = 0.8, de.param = de_param(),
  output_dir = "subsample_result", mc.cores = 1, override = FALSE,
  init.result = NULL, cut.method = "auto", ...)

Arguments

norm.dat

normalized expression data matrix in log transform, using genes as rows, and cells and columns. Users can use log2(FPKM+1) or log2(CPM+1).

select.cells

The cells to be clustered. Default: columns of norm.dat

niter

The number of iteractions to run. Default 100.

sample.frac

The fraction of of cells sampled per run. Default: 0.8.

de.param

The differential gene expression threshold. See de_param() function for details.

output_dir

The output directory to store clutering results for each iteraction.

mc.cores

The number of cores to be used for parallel processing.

override

binary variable determine if the clustering results already stored in output_dir should be overriden.

init.result

The pre-set high level clusters. If set, the function will only find finer splits of the current clusters.

...

Other parameters passed to iter_clust


AllenInstitute/scrattch.hicat documentation built on May 5, 2019, 1:32 a.m.