Autoencoder

The present function compress data using autoencoder partially connected

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

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

Examples

if (FALSE) {
 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")
}