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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