robustSingleCell (version 0.1.1)

summarize: Differential Expression and Figure Generation

Description

Summarize the clustering results by conducting differential expression analysis and plotting figures.

Usage

summarize(environment, perplexity = seq(10, 30, 10), max_iter = 10000,
  rerun = F, order = NA, contrast = "all", min.fold = 1.5,
  quantile = 0.95, local = F, mem = "4GB", time = "0:15:00")

Arguments

environment

environment object

perplexity

perplexity parameters for tSNE analyses

max_iter

maximum iterations for tSNE

rerun

whether to rerun

order

order in which to plot the clusters

contrast

either 'all' indicating differential expression between one cluster against all others or 'datasets' indicating differential expression analysis comparing one cluster to all other within each dataset separately ('datasets' should be used in pooled analysis for optimal results)

min.fold

minimum fold change for filtering final differentially expressed gene lists

quantile

q-value cutoff for differential expression analysis

local

Whether to run tSNE locally on SLURM

mem

Memory for each job; default 4 GB

time

Time for each job; default 15 minutes

Examples

# NOT RUN {
# after running cluster.analysis()
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)
summarize(LCMV1)
# }