doubletFinder: Doublet detection in single-cell RNA sequencing data

In AllenInstitute/scrattch.hicat: Hierarchical Iterative Clustering Analysis for Transcriptomic data

Doublet detection in single-cell RNA sequencing data

Usage

doubletFinder(
  data,
  select.genes,
  proportion.artificial = 0.2,
  k = NULL,
  plot = FALSE
)

Arguments

data	gene x sample matrix with counts (non-normalized)
select.genes	list of genes with highest variance between samples
proportion.artificial	The proportion (from 0-1) of the merged real-artificial dataset that is artificial. In other words, this argument defines the total number of artificial doublets. Default is set to 20%
k	The number of nearest neighbours of the merged real-artificial dataset used to define each cell's neighborhood in PC space. Value is the minimum of 1 \item plot

An list of doublet.scores per samples and plots depicting the doublet scores for cells and artificial doublets. Adopted from https://www.cell.com/cell-systems/fulltext/S2405-4712(19)30073-0 (https://github.com/chris-mcginnis-ucsf/DoubletFinder) This function generates artificial nearest neighbors from existing single-cell RNA sequencing data. First, real and artificial data are merged. Second, dimension reduction is performed on the merged real-artificial dataset using PCA. Third, the proportion of artificial nearest neighbors is defined for each real cell. Finally, real cells are rank- ordered and predicted doublets are defined via thresholding based on the expected number of doublets.

AllenInstitute/scrattch.hicat documentation built on June 6, 2024, 5:31 a.m.