sampleImputation: Sample-mean Estimation

In rescue: Bootstrap Imputation for Single-Cell RNA-Seq Data

Description

Cluster cells using SNN and a list of given genes, estimate missing expression values for each cell-gene combination with the within-cluster non-zero expression mean

Usage

sampleImputation(
  expression_matrix,
  subset_genes = NULL,
  scale_data = TRUE,
  number_pcs = 8,
  k_neighbors = 30,
  snn_resolution = 0.9,
  impute_index = NULL,
  pseudo_zero = NULL,
  python_path = NULL,
  verbose = FALSE
)

Arguments

expression_matrix	Row by column log-normalized expression matrix
subset_genes	A vector of informative gene names, defaults to all genes
scale_data	Whether to standardize expression by gene, default TRUE
number_pcs	Number of dimensions to inform SNN clustering
k_neighbors	Number of k neighbors to use for NN network
snn_resolution	Resolution parameter for SNN
impute_index	Index to impute, will default to all zeroes
pseudo_zero	Pseudo-zero expression value
python_path	path to your python binary (default = system path)
verbose	Print progress output to the console

Value

Returns a sparse matrix of class 'dgCMatrix'

Examples

set.seed(0)
requireNamespace("Matrix")

## generate (meaningless) counts
c1 <- stats::rpois(5e3, 1)
c2 <- stats::rpois(5e3, 2)
m <- t(
  rbind(
    matrix(c1, nrow = 20),
    matrix(c2, nrow = 20)
  )
)

## construct an expression matrix m
colnames(m) <- paste0('cell', 1:ncol(m))
rownames(m) <- paste0('gene', 1:nrow(m))
m <- log(m/colSums(m)*1e4 + 1)
m <- methods::as(m, 'dgCMatrix')

## impute

m_imputed <- rescue::sampleImputation(
  expression_matrix = m,
  k_neighbors = 10
)

rescue documentation built on July 18, 2020, 5:06 p.m.