View source: R/autoencoder4Pseudobulk.R
autoencoder4pseudoBulk | R Documentation |
The present function compress data using autoencoder partially connected creating pseudoBulk matrix
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
)
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 |
Luca Alessandri, alessandri [dot] luca1991 [at] gmail [dot] com, University of Torino
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