CORE_bagging {scGPS} | R Documentation

## Main clustering SCORE (CORE V2.0) Stable Clustering at Optimal REsolution with bagging and bootstrapping

### Description

CORE is an algorithm to generate reproduciable clustering, CORE is first implemented in ascend R package. Here, CORE V2.0 uses bagging analysis to find a stable clustering result and detect rare clusters mixed population.

### Usage

    CORE_bagging(
      mixedpop = NULL,
      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,
      ngenes = 1500,
      log_transform = FALSE
    )

### Arguments

`mixedpop` | is a SingleCellExperiment object from the train mixed population.
`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 ranges 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` | 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.
`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, 2018-05-11

### Examples

    day5 <- day_5_cardio_cell_sample
    cellnames<-colnames(day5$dat5_counts)
    cluster <-day5$dat5_clusters
    cellnames <- data.frame('cluster' = cluster, 'cellBarcodes' = cellnames)
    #day5$dat5_counts needs to be in a matrix format
    mixedpop2 <-new_summarized_scGPS_object(ExpressionMatrix = day5$dat5_counts,
        GeneMetadata = day5$dat5geneInfo, CellMetadata = day5$dat5_clusters)
    test <- CORE_bagging(mixedpop2, remove_outlier = c(0), PCA=FALSE,
        bagging_run = 2, subsample_proportion = .7)

[Package scGPS version 1.8.0 Index]