View source: R/preprocessing.R
HTODemux | R Documentation |
Assign sample-of-origin for each cell, annotate doublets.
HTODemux(
object,
assay = "HTO",
positive.quantile = 0.99,
init = NULL,
nstarts = 100,
kfunc = "clara",
nsamples = 100,
seed = 42,
verbose = TRUE
)
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. If NULL, seed is not set |
verbose |
Prints the output |
The Seurat object with the following demultiplexed information stored in the meta data:
Name of hashtag with the highest signal
Name of hashtag with the second highest signal
The difference between signals for hash.maxID and hash.secondID
Classification result, with doublets/multiplets named by the top two highest hashtags
Global classification result (singlet, doublet or negative)
Classification result where doublet IDs are collapsed
HTOHeatmap
## Not run:
object <- HTODemux(object)
## End(Not run)
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