Spark feature selection library for bigdata multiomics

The spark feature selection library for bigdata multiomics in an evolution of a previous R-package developed by [Enrique et. al.](https://pubmed.ncbi.nlm.nih.gov/29261781/). Major steps to finalize the library are:

- [ ] Create a README file in the repository where the dataset format and structure are described.
- [ ] Add to the README dataset file, the link to the Single-cell example we have been using for the benchmark of the algorithms.
- [ ] Benchmark the single-cell dataset again with the Feature selection R-package previously developed.
- [ ] Benchmark the single-cell dataset in the following infrastructures:
- [ ] Single machine benchmark (preferably in a user laptop).
- [ ] Spark cluster of a single node with multiple processors, benchmark with multiple processor sizes 10, 20, 50, 100?
- [ ] Spark cluster with multiple nodes.
- [x] Contact CPTAC team to get the list of phospho-sites with spectral counting with the different cancer and tumor types. @ypriverol #3
- [ ] Perform the same benchmarks previously done for single-cell dataset.
- [ ] Create a readthedocs for the project.
- [ ] Implement the framework of algorithms:
- [ ] Implement the independent feature selection algorithms: RF, correlation analysis, PCA.
- [ ] Implement different workflows combining multiple FS algorithms. Name them.
- [ ] Provide as group of command line tools that enable access to some of the workflow for a given dataset file.
- [ ] Discuss the given results and write a publication.