CORE_clustering: Main clustering CORE V2.0 updated

Description Usage Arguments Value Author(s) Examples

View source: R/CORE_clustering.R

Description

CORE is an algorithm to generate reproduciable clustering, CORE is first implemented in ascend R package. Here, CORE V2.0 introduces several new functionalities, including three key features: fast (and more memory efficient) implementation with C++ and paralellisation options allowing clustering of hundreds of thousands of cells (ongoing development), outlier revomal important if singletons exist (done), a number of dimensionality reduction methods including the imputation implementation (CIDR) for confirming clustering results (done), and an option to select the number of optimisation tree height windows for increasing resolution

Usage

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

Arguments

mixedpop

is a SingleCellExperiment object from the train mixed population

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

an 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.

ngenes

number of genes used for clustering calculations.

verbose

a logical whether to display additional messages

Value

a list with clustering results of all iterations, and a selected optimal resolution

Author(s)

Quan Nguyen, 2017-11-25

Examples

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day5 <- day_5_cardio_cell_sample
#day5$dat5_counts needs to be in a matrix format
cellnames <- colnames(day5$dat5_counts)
cluster <-day5$dat5_clusters
cellnames <-data.frame('Cluster'=cluster, 'cellBarcodes' = cellnames)
mixedpop2 <-new_summarized_scGPS_object(ExpressionMatrix = day5$dat5_counts, 
    GeneMetadata = day5$dat5geneInfo, CellMetadata = day5$dat5_clusters)
test <- CORE_clustering(mixedpop2, remove_outlier = c(0), PCA=FALSE, nPCs=20,
    ngenes=1500)

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