clustering {scGPS} | R Documentation

## HC clustering for a number of resolutions

### Description

performs 40 clustering runs or more depending on windows

### Usage

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

### Arguments

`object` | is a SingleCellExperiment object from the train mixed population
`ngenes` | number of top variable genes to be used
`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` | number of iterations to remove a selected clusters
`PCA` | logical specifying if PCA is used before calculating distance matrix
`nPCs` | number of principal components from PCA dimensional reduction to be used
`verbose` | a logical whether to display additional messages
`log_transform` | boolean whether log transform should be computed

### Value

clustering results

### 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(mixedpop2, remove_outlier = c(0))

[Package scGPS version 1.8.0 Index]