summarize: Differential Expression and Figure Generation

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

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

# 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)

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