autoencoder: Autoencoder

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

Autoencoder

Description

The present function compress data using autoencoder partially connected

Usage

autoencoder(
  group = c("sudo", "docker"),
  scratch.folder,
  file,
  separator,
  nCluster,
  bias,
  permutation,
  nEpochs,
  patiencePercentage = 5,
  cl,
  seed = 1111,
  projectName,
  bN = "NULL",
  lr = 0.01,
  beta_1 = 0.9,
  beta_2 = 0.999,
  epsilon = 1e-08,
  decay = 0,
  loss = "mean_squared_error",
  regularization = 10,
  variational = FALSE
)

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
separator	separator used in count file, e.g. '\t', ','
nCluster	number of cluster in which the dataset is divided
bias	bias method to use : "mirna" , "TF", "CUSTOM", kinasi,immunoSignature,ALL
permutation	number of permutations to perform the pValue to evaluate clustering
nEpochs	number of Epochs for neural network training
patiencePercentage	number of Epochs percentage of not training before to stop.
cl	Clustering.output file. Can be the output of every clustering algorithm from rCASC or can be customized with first column cells names, second column cluster they belong. All path needs to be provided.
seed	important value to reproduce the same results with same input
projectName	might be different from the matrixname in order to perform different analysis on the same dataset
bN	name of the custom bias file. This file need header, in the first column has to be the source and in the second column the gene symbol. All path needs to be provided.
lr	learning rate, the speed of learning. Higher value may increase the speed of convergence but may also be not very precise
beta_1	look at keras optimizer parameters
beta_2	look at keras optimizer parameters
epsilon	look at keras optimizer parameters
decay	look at keras optimizer parameters
loss	loss of function to use, for other loss of function check the keras loss of functions.
regularization	this parameter balances between reconstruction loss and enforcing a normal distribution in the latent space.
variational	TRUE or FALSE if use the variational autoencoder or just the standard autoencoder.

Author(s)

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

Examples

## Not run: 
 autoencoder(group="docker",scratch.folder="/home/user/Riccardo/Riccardo/1_inDocker/scratch",file="/home/user/Riccardo/Riccardo/1_inDocker/data/setA.csv",separator=",",nCluster=5,bias="TF",permutation=10,nEpochs=10,cl="/home/user/Riccardo/Riccardo/1_inDocker/data/setA_clustering.output.csv",projectName="testDocker")

## End(Not run)

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