clustering: HC clustering for a number of resolutions

In scGPS: A complete analysis of single cell subpopulations, from identifying subpopulations to analysing their relationship (scGPS = single cell Global Predictions of Subpopulation)

Description

performs 40 clustering runs or more depending on windows

Usage

clustering(object = NULL, ngenes = 1500, windows = seq(from = 0.025,
  to = 1, by = 0.025), remove_outlier = c(0), nRounds = 1,
  PCA = FALSE, nPCs = 20, verbose = FALSE)

Arguments

object	is a SingleCellExperiment object from the train mixed population
ngenes	number of top variable genes to be used
windows	a numeric specifying the number of windows to test
remove_outlier	a vector containing IDs for clusters to be removed the default vector contains 0, as 0 is the cluster with singletons
nRounds	number of iterations to remove a selected clusters
PCA	logical specifying if PCA is used before calculating distance matrix
nPCs	number of principal components from PCA dimensional reduction to be used
verbose	a logical whether to display additional messages

Value

clustering results

Author(s)

Quan Nguyen, 2017-11-25

Examples

day5 <- day_5_cardio_cell_sample
mixedpop2 <-new_summarized_scGPS_object(ExpressionMatrix = day5$dat5_counts, 
    GeneMetadata = day5$dat5geneInfo, CellMetadata = day5$dat5_clusters)
test <-clustering(mixedpop2, remove_outlier = c(0))

scGPS documentation built on Nov. 8, 2020, 5:22 p.m.