`tsneBootstrap.Rd`

This function executes a ubuntu docker that produces a specific number of permutation using tSne as clustering tool.

```
tsneBootstrap(
group = c("sudo", "docker"),
scratch.folder,
file,
nPerm,
permAtTime,
percent,
range1,
range2,
separator,
logTen = 0,
seed = 111,
sp = 0.8,
clusterPermErr = 0.05,
perplexity = 10
)
```

- 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 randomly selected cells removed in each permutation

- range1,
beginning of the range of clusters to be investigated

- range2,
end of the range of clusters to be investigated

- 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, default is 111

- 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

- perplexity,
number of close neighbors for each point. This parameter is specific for tSne. Default value is 10. the performance of t-SNE is fairly robust under different settings of the perplexity. The most appropriate value depends on the density of your data. A larger/denser dataset requires a larger perplexity. Typical values for the perplexity range between 5 and 50

A folder Results containing a folder with the name of the experiment, which contains: VioPlot of silhouette cells value for each number of cluster used, a folder with the number of clusters used for SIMLR clustering, which contains: 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

```
if (FALSE) {
system("wget http://130.192.119.59/public/section4.1_examples.zip")
unzip("section4.1_examples.zip")
setwd("section4.1_examples")
tsneBootstrap(group="docker",scratch.folder="/data/scratch/",file=paste(getwd(), "bmsnkn_5x100cells.txt", sep="/"), nPerm=160, permAtTime=8, percent=10, range1=4, range2=6, separator="\t",logTen=0, seed=111, sp=0.8, clusterPermErr=0.05, perplexity=10)
}
```