autocluster4clustering: Autoencoder clustering

In kendomaniac/rCASC: rCASC reproducible Classification Analysis of Single Cell Sequencing Data

Autoencoder clustering

Description

This function Compress data using autoencoder partially connected

Usage

autocluster4clustering(
  group = c("sudo", "docker"),
  scratch.folder,
  file,
  separator,
  nCluster,
  projectName,
  clusterMethod = c("GRIPH", "SIMLR", "SEURAT", "SHARP", "KMEANS", "HCLUST", "FLOW"),
  seed = 1111,
  pcaDimensions = 20,
  perplexity = 20
)

Arguments

group	a character string. Two options: sudo or docker, depending to which group the user belongs
scratch.folder	a character string indicating the path of the scratch folder
file	a character string indicating the path of the file, with file name and extension included.Has to be the one in the projectName folder.Different so from the previous one.
separator	separator used in count file, e.g. '\t', ','
nCluster	number of cluster in which the dataset is divided
projectName	might be different from the matrixname in order to perform different analysis on the same dataset
clusterMethod	clustering methods: "GRIPH","SIMLR","SEURAT","SHARP","KMEANS","HCLUST","FLOW"
seed	important value to reproduce the same results with same input
pcaDimensions	number of dimensions to use for Seurat Pca reduction.
perplexity	value for cluster aggregation, required for KMEANS

Author(s)

Luca Alessandri, alessandri [dot] luca1991 [at] gmail [dot] com, University of Torino

Examples

## Not run: 
 autocluster4clustering(group=c("sudo"), scratch.folder=scratch, file=file, separator=",", nCluster=3, projectName=projectName, clusterMethod=i,seed=1111,pcaDimensions=20)

## End(Not run)

kendomaniac/rCASC documentation built on July 3, 2024, 6:05 a.m.