hippo: HIPPO's hierarchical clustering

In HIPPO: Heterogeneity-Induced Pre-Processing tOol

Description

HIPPO's hierarchical clustering

Usage

hippo(sce, K = 20, z_threshold = 2, outlier_proportion = 0.001, verbose = TRUE)

Arguments

sce	SingleCellExperiment object
K	number of clusters to ultimately get
z_threshold	numeric > 0 as a z-value threshold for selecting the features
outlier_proportion	numeric between 0 and 1, a cut-off so that when the proportion of important features reach this number, the clustering terminates
verbose	if set to TRUE, it shows progress of the algorithm

Value

a list of clustering result for each level of k=1, 2, ... K.

Examples

data(toydata)
toydata = hippo(toydata,K = 10,z_threshold = 1,outlier_proportion = 0.01)

HIPPO documentation built on Nov. 8, 2020, 5:05 p.m.