CORE_clustering {scGPS} | R Documentation

## Main clustering CORE V2.0 updated

### 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

    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,
      log_transform = 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
`log_transform` | boolean whether log transform should be computed

### Value

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

### Author(s)

Quan Nguyen, 2017-11-25

### Examples

    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)

[Package scGPS version 1.8.0 Index]