PCA: Parallelized PCA Analysis

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

Description

Run PCA analysis with a simulation analysis of shuffled data to determine the appropriate number of PCs.

Usage

PCA(environment, regress = NA, groups = NA, nShuffleRuns = 10,
  threshold = 0.1, maxPCs = 100, label = NA, mem = "2GB",
  time = "0:10:00", rerun = F,
  clear.previously.calculated.clustering = T, local = F)

Arguments

environment	environment object
regress	gene signature activation scores to regress
groups	experimental design annotation to guide dataset-specific regression
nShuffleRuns	number of shuffled analyses
threshold	FDR threshold
maxPCs	maximum number of possible PCs
label	optional analyses label folder
mem	HPC memory
time	HPC time
rerun	whether to rerun the analysis rather than load from cache
clear.previously.calculated.clustering	whether to clear previous clustering analysis
local	whether to run jobs locally on slurm instead of submitting the job

Value

environment parameter containing PC coordinates

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)

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