dim_reduce: Dimension Reduction

In kgellatl/SignallingSingleCell: Network Analysis for single cell RNA sequencing Data (sc-RNASeq)

Description

This function will do dimensionality reduction.

Usage

dim_reduce(
  input,
  genelist,
  pre_reduce = "iPCA",
  nComp = 15,
  tSNE_perp = 30,
  iterations = 1000,
  print_progress = TRUE,
  nVar = NA,
  log = F,
  scale = T
)

Arguments

input	the input ex_sc
genelist	the subset of genes to perform dimensionality reduction on
pre_reduce	the algorithm choice for reduction before tSNE (either "ICA", "PCA", "iPCA").
nComp	the number of components to reduce too before tSNE, 5-20 recommended.
tSNE_perp	number of cells expressed above threshold for a given gene, 10-100 recommended.
iterations	The number of iterations for tSNE to perform.
print_progress	will print progress if TRUE
nVar	cutoff for percent of variance explained from PCs

Details

If the method is ICA, independent component analysis will be performed, and then tSNE will do the final dimension reduction. If PCA is selected, PCA will be performed before on the expression matrix transpose before tSNE. This PCA will use the cells positions on the principal components. If iPCA is selected, PCA will be be performed but without transposing the data. This will create "meta cells" instead of meta genes created in the typical PCA. Then tSNE will be performed on each cells contribution (loading) to the meta cell. We find that iPCA is much more robust and leads to cleaner clusters than traditional PCA.

Examples

ex_sc_example <- dim_reduce(input = ex_sc_example, genelist = gene_subset, pre_reduce = "iPCA", nComp = 15, tSNE_perp = 30, iterations = 500, print_progress=TRUE)

kgellatl/SignallingSingleCell documentation built on Dec. 29, 2021, 4:12 p.m.