Seurat (version 2.3.4)

FitGeneK: Build mixture models of gene expression

Description

Models the imputed gene expression values as a mixture of gaussian distributions. For a two-state model, estimates the probability that a given cell is in the 'on' or 'off' state for any gene. Followed by a greedy k-means step where cells are allowed to flip states based on the overall structure of the data (see Manuscript for details)

Usage

FitGeneK(object, gene, do.k = 2, num.iter = 1, do.plot = FALSE,
  genes.use = NULL, start.pct = NULL)

Arguments

object

Seurat object

gene

Gene to fit

do.k

Number of modes for the mixture model (default is 2)

num.iter

Number of 'greedy k-means' iterations (default is 1)

do.plot

Plot mixture model results

genes.use

Genes to use in the greedy k-means step (See manuscript for details)

start.pct

Initial estimates of the percentage of cells in the 'on' state (usually estimated from the in situ map)

Value

A Seurat object, where the posterior of each cell being in the 'on' or 'off' state for each gene is stored in object@[e-mail]

Examples

# NOT RUN {
# Note that the PBMC test example object does not contain spatially restricted
# examples below are only demonstrate code
pmbc_small <- FitGeneK(object = pbmc_small, gene = "MS4A1")
# }
# NOT RUN {
# }

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