RunICP: Iterative Clustering Projection (ICP) clustering

In elolab/iloreg: ILoReg: a tool for high-resolution cell population identification from scRNA-Seq data

Iterative Clustering Projection (ICP) clustering

Description

The function implements Iterative Clustering Projection (ICP): a supervised learning -based clustering, which maximizes clustering similarity between the clustering and its projection by logistic regression.

Usage

RunICP(
  normalized.data = NULL,
  k = 15,
  d = 0.3,
  r = 5,
  C = 5,
  reg.type = "L1",
  max.iter = 200,
  icp.batch.size = Inf
)

Arguments

normalized.data	A sparse matrix (dgCMatrix) containing normalized gene expression data with genes in rows and cells in columns. Default is NULL.
k	A positive integer greater or equal to 2, denoting the number of clusters in ICP. Default is 15.
d	A numeric that defines how many cells per cluster should be down- and oversampled (d in ceiling(N/k*d)), when stratified.downsampling=FALSE, or what fraction should be downsampled in the stratified approach ,stratified.downsampling=TRUE. Default is 0.3.
r	A positive integer that denotes the number of reiterations performed until the algorithm stops. Default is 5.
C	Cost of constraints violation (C) for L1-regulatization. Default is 0.3.
reg.type	"L1" for LASSO and "L2" for Ridge. Default is "L1".
max.iter	A positive integer that denotes the maximum number of iterations performed until the algorithm ends. Default is 200.
icp.batch.size	A positive integer that specifies how many cells to randomly select for each ICP run from the complete data set. This is a new feature intended to speed up the process with larger data sets. Default is Inf, which means using all cells.

Value

A list that includes the probability matrix and the clustering similarity measures: ARI, NMI, etc.

elolab/iloreg documentation built on March 27, 2022, 4:19 a.m.