Seurat (version 3.0.1)

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

Description

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

Usage

HTODemux(object, assay = "HTO", positive.quantile = 0.99,
  init = NULL, nstarts = 100, kfunc = "clara", nsamples = 100,
  seed = 42, verbose = TRUE)

Arguments

object

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

assay

Name of the Hashtag assay (HTO by default)

positive.quantile

The quantile of inferred 'negative' distribution for each hashtag - over which the cell is considered 'positive'. Default is 0.99

init

Initial number of clusters for hashtags. Default is the # of hashtag oligo names + 1 (to account for negatives)

nstarts

nstarts value for k-means clustering (for kfunc = "kmeans"). 100 by default

kfunc

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

nsamples

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

seed

Sets the random seed

verbose

Prints the output

Value

The Seurat object with the following demultiplexed information stored in the meta data:

hash.maxID

Name of hashtag with the highest signal

hash.secondID

Name of hashtag with the second highest signal

hash.margin

The difference between signals for hash.maxID and hash.secondID

classification

Classification result, with doublets/multiplets named by the top two highest hashtags

classification.global

Global classification result (singlet, doublet or negative)

hash.ID

Classification result where doublet IDs are collapsed

See Also

HTOHeatmap

Examples

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