cluster.analysis: Distributed Clustering Analysis

In robustSingleCell: Robust Clustering of Single Cell RNA-Seq Data

Description

Perform clustering analysis for a range of hyperparameter (KNN Ratios) values and assess clustering quality relative to simulation analysis of shuffled data.

Usage

cluster.analysis(environment, knn.ratios = c(0.01, 0.05, 0.1),
  nShuffleRuns = 10, shuffledKNN = 10, loadPreviousKnn = T,
  rerun = F, deleteCache = F, mem = "4GB", time = "0:15:00",
  plot = T, local = F)

Arguments

environment	environment object
knn.ratios	range of KNN parameters to scan (corresponding to different resolutions)
nShuffleRuns	number of shuffled clustering analyses to perform per KNN threshold
shuffledKNN	number of closest KNN shuffled analyses to include in background clustering quality computation
loadPreviousKnn	whether to load previous analysis results
rerun	whether to rerun the analysis rather than load from cache
deleteCache	whether to delete cache files
mem	HPC memory
time	HPC time
plot	whether to plot the clustering qualities compared to shuffled
local	whether to run jobs locally rather than using distributed slurm system

Value

environment parameter containing clustering assignment and provisional cluster names

Examples

LCMV1 <- setup_LCMV_example()
LCMV1 <- get.variable.genes(LCMV1, min.mean = 0.1, min.frac.cells = 0,
min.dispersion.scaled = 0.1)
LCMV1 <- PCA(LCMV1)
LCMV1 <- cluster.analysis(LCMV1)

robustSingleCell documentation built on May 2, 2019, 2:11 p.m.