psm_integrate: scPSM integration

In eleozzr/scPSM: Propensity Score Matching Enables Batch Effect Corrected Imputation In Single-cell RNA-seq Analysis

scPSM integration

Description

Correct for batch effects, impute dropouts, and denoise data using the propensity score matching method for single-cell RNA-sequencing data.

Usage

psm_integrate(
  batches,
  markers,
  hvg,
  k.self = 10,
  k.mnn = 10,
  correct.all = TRUE,
  merge.order = 1:4
)

Arguments

batches	One or more log-expression matrices where genes correspond to rows and cells correspond to columns. Each matrix should contain the same number of rows, corresponding to the same genes in the same order. Each matrix represents a batch.
markers	A vector specifying which features used as marker genes to compute propensity scores.
hvg	A vector specifying which features used as HVGs for identifying MNN group.
k.self	An integer scalar specifying the number of nearest neighbors in searching KNNs.
k.mnn	An integer scalar specifying the number of nearest neighbors in matching MNN pairs.
correct.all	A logical scalar specifying whether correction should be applied to all genes, even if only a subset is used for the MNN group identification.
merge.order	An integer vector containing the linear merge order of batches.

Examples

#Please see the tutorial for the preprocessing and then run the following code
#psm.data <- psm_integrate(batches = batches, markers = markers, hvg = hvg, merge.order = 1:4)

eleozzr/scPSM documentation built on May 23, 2022, 12:05 a.m.