Seurat (version 2.3.1)

HTODemux: Demultiplex samples based on data from cell 'hashing'

Description

Assign sample-of-origin for each cell, annotate doublets.

Usage

HTODemux(object, percent_cutoff = 0.999, init_centers = NULL,
  cluster_nstarts = 100, k_function = "kmeans", nsamples = 100,
  print.output = TRUE, assay.type = "HTO", confidence_threshold = 0.05)

Arguments

object

Seurat object. Assumes that the hash tag oligo (HTO) data has been added and normalized in the HTO slot.

percent_cutoff

The quantile of inferred 'negative' distribution for each HTO - over which the cell is considered 'positive'. Default is 0.999

init_centers

Initial number of clusters for kmeans of the HTO oligos. Default is the # of samples + 1 (to account for negatives)

cluster_nstarts

nstarts value for the initial k-means clustering

k_function

Clustering function for initial HTO grouping. Default is "kmeans", also support "clara" for fast k-medoids clustering on large applications

nsamples

Number of samples to be drawn from the dataset used for clustering, for k_function = "clara"

print.output

Prints the output

assay.type

Naming of HTO assay

confidence_threshold

The quantile of the inferred 'positive' distribution for each HTO. Cells that have lower counts than this threshold are labeled as uncertain in the confidence field. Default is 0.05

Value

Seurat object. Demultiplexed information is stored in the object meta data.

Examples

# NOT RUN {
object <- HTODemux(object)
# }