clustering_bagging: 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

subsamples cells for each bagging run and performs 40 clustering runs or more depending on windows.

Usage

clustering_bagging(object = NULL, ngenes = 1500, bagging_run = 20,
  subsample_proportion = 0.8, windows = seq(from = 0.025, to = 1, by =
  0.025), remove_outlier = c(0), nRounds = 1, PCA = FALSE,
  nPCs = 20)

Arguments

object	is a SingleCellExperiment object from the train mixed population.
ngenes	number of genes used for clustering calculations.
bagging_run	an integer specifying the number of bagging runs to be computed.
subsample_proportion	a numeric specifying the proportion of the tree to be chosen in subsampling.
windows	a numeric vector specifying the rages of each window.
remove_outlier	a vector containing IDs for clusters to be removed the default vector contains 0, as 0 is the cluster with singletons.
nRounds	a integer specifying the number rounds to attempt to remove outliers.
PCA	logical specifying if PCA is used before calculating distance matrix.
nPCs	an integer specifying the number of principal components to use.

Value

a list of clustering results containing each bagging run as well as the clustering of the original tree and the tree itself.

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_bagging(mixedpop2, remove_outlier = c(0),
    bagging_run = 2, subsample_proportion = .7)

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