autoencoderAnalysis: Autoencoder Analysis

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

Autoencoder Analysis

Description

This function Compress data using autoencoder partially connected

Usage

autoencoderAnalysis(
  group = c("sudo", "docker"),
  scratch.folder,
  file,
  separator,
  nCluster,
  projectName,
  seed = 1111,
  Sp,
  bestPerm = 1
)

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
seed	important value to reproduce the same results with same input
Sp	minimun number of percentage of cells that has to be in common between two permutation to be the same cluster.
bestPerm	bestPermutation number, just for visualization

Author(s)

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

Examples

## Not run: 
 autoencoderClustering(group="docker", scratch.folder="/home/user/Riccardo/Riccardo/1_inDocker_2/scratch", file="/home/user/Riccardo/Riccardo/1_inDocker_2/data/Results/testDocker/setA.csv",separator=",", nCluster=5,clusterMethod=c("SEURAT"),seed=1111,projectName="testDocker",13)

## End(Not run)

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