cluster_cell_net: Dimension Reduction

Description Usage Arguments Details Examples

View source: R/cluster_cell_net.R

Description

This function will do dimensionality reduction.

Usage

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cluster_cell_net(
  input_ex_sc,
  input_cell_net,
  verbose = T,
  cluster_method = "eigen",
  crossing_edge_weight = 1,
  internal_edge_weight = 10
)

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

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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.