This function executes a ubuntu docker that produces a specific number of permutation to evaluate clustering and identify the genes that play the major role in clustering.

```
genesPrioritization(
group = c("sudo", "docker"),
scratch.folder,
file,
nPerm,
permAtTime,
percent,
nCluster,
separator,
logTen = 0,
seed = 111,
sp = 0.8,
clusterPermErr = 0.05
)
```

## 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

- nPerm,
number of permutations to be executed

- permAtTime,
number of permutations computed in parallel

- percent,
percentage of random cells removed in each permutation

- nCluster,
the number of clusters, where to run prioritization

- separator,
separator used in count file, e.g. '\t', ','

- logTen,
1 if the count matrix is already in log10, 0 otherwise

- seed,
important value to reproduce the same results with same input

- sp,
minimun number of percentage of cells that has to be in common in a cluster, between two permutations, default 0.8

- clusterPermErr,
probability error in depicting the number of clusters in each permutation, default = 0.05

## Value

VioPlot of silhouette cells value for each number of cluster used,clusterP file with clustering results for each permutation, killedCell file with removed cells in each permutation, clustering.output a sommarize file with general information for each cells.

## Author

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

## Examples

```
if (FALSE) {
system("wget http://130.192.119.59/public/section4.1_examples.zip")
unzip("section4.1_examples.zip")
setwd("section4.1_examples")
genesPrioritization(group="docker",scratch.folder="/data/scratch/",file=paste(getwd(), "bmsnkn_5x100cells.txt", sep="/"), nPerm=160, permAtTime=8, percent=10, nCluster=5, separator="\t", logTen=0, seed=111, sp=0.8, clusterPermErr=0.05)
}
```