autoencoder4clusteringGPU.Rd
The present function compress data using autoencoder partially connected
autoencoder4clusteringGPU(
group = c("sudo", "docker"),
scratch.folder,
file,
separator,
bias,
permutation,
nEpochs,
patiencePercentage = 5,
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
)
a character string. Two options: sudo or docker, depending to which group the user belongs
a character string indicating the path of the scratch folder
a character string indicating the path of the file, with file name and extension included
separator used in count file, e.g. '\t', ','
bias method to use : "mirna" , "TF", "CUSTOM", kinasi,immunoSignature, cytoBands,ALL
number of permutations to perform the pValue to evaluate clustering
number of Epochs for neural network training
number of Epochs percentage of not training before to stop.
important value to reproduce the same results with same input
might be different from the matrixname in order to perform different analysis on the same dataset
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.
learning rate, the speed of learning. Higher value may increase the speed of convergence but may also be not very precise
look at keras optimizer parameters
look at keras optimizer parameters
look at keras optimizer parameters
look at keras optimizer parameters
loss of function to use, for other loss of function check the keras loss of functions.
this parameter balances between reconstruction loss and enforcing a normal distribution in the latent space.
if (FALSE) {
}