autoencoder4pseudoBulk: Autoencoder for pseudoBulk

View source: R/autoencoder4Pseudobulk.R

autoencoder4pseudoBulkR Documentation

Autoencoder for pseudoBulk

Description

The present function compress data using autoencoder partially connected creating pseudoBulk matrix

Usage

autoencoder4pseudoBulk(
  group = c("sudo", "docker"),
  scratch.folder,
  file,
  separator,
  permutation,
  nEpochs,
  patiencePercentage = 5,
  seed = 1111,
  projectName,
  bN,
  lr = 0.01,
  beta_1 = 0.9,
  beta_2 = 0.999,
  epsilon = 1e-08,
  decay = 0,
  loss = "mean_squared_error",
  regularization = 10,
  version = 2
)

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', ','

permutation

number of permutations to perform the pValue to evaluate clustering. Suggested minimal number of permutations 10

nEpochs

number of Epochs for neural network training

patiencePercentage

number of Epochs percentage of not training before to stop.

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

path to the clustering.output file

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.

version

version 1 implements static batchsize, version 2 implements adaptive batchsize

Author(s)

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


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