wrapperAutoencoder

This function executes the whole autoencoder pipeline

clusteringWADB_Wrapper(
  group = c("sudo", "docker"),
  scratch.folder,
  file,
  separator,
  nCluster,
  permutation,
  nEpochs,
  patiencePercentage = 5,
  seed = 1111,
  projectName,
  lr = 0.01,
  loss = "mean_squared_error",
  clusterMethod = c("GRIPH", "SIMLR", "SEURAT", "SHARP"),
  pcaDimensions = 5,
  Sp = 0.8
)

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

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.

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

lr,

learning rate, the speed of learning. Higher value may increase the speed of convergence but may also be not very precise

loss,

loss of function to use, for other loss of function check the keras loss of functions.

clusterMethod,

clustering methods: "GRIPH","SIMLR","SEURAT","SHARP"

pcaDimensions,

number of dimensions to use for Seurat Pca reduction.

Sp,

minimun number of percentage of cells that has to be in common between two permutation to be the same cluster.

Value

folders the complete autoencoder analysis.

Author

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

Examples

if (FALSE) {
 clusteringWADB_Wrapper(group=c("sudo"),scratch.folder="/home/user/scratch",file="/home/user/autoencoderClustering_v4/u/setA.csv",separator=",",nCluster=5,permutation=80,nEpochs=1000,patiencePercentage=5,seed=1111,projectName="yuppy",clusterMethod=c( "SIMLR"),lr=0.001)
}