clustering_bagging {scGPS} | R Documentation

## HC clustering for a number of resolutions

### 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)

[Package scGPS version 1.2.0 Index]