RunICP {ILoReg} | R Documentation

## 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
    )

### 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`.

### Value

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

[Package ILoReg version 1.0.0 Index]