robustSingleCell (version 0.1.1)

PCA: Parallelized PCA Analysis

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

# NOT RUN {
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)
# }