FastEMC

Fast Exponential Monte Carlo

FastEMC is a method for dimensionality reduction. FastEMC was designed for datasets with a small number of samples, and a large number of features. This version of FastEMC can only handle numerical features, and binary classification of samples. FastEMC can be installed using pip

$ pip install fastemc

If pip fails on windows try installing scikit-learn manually using conda, then install fastemc using pip. You can interact with FastEMC directly using the python module

>>> import fastemc
>>> scores, clusters = fastemc.run(features, labels, **kwargs)

or through the command line

$ python -m fastemc --features features.csv --labels labels.csv

The features.csv and labels.csv files can be generated using pandas, e.g.,

>>> labels.to_csv("labels.csv")
>>> features.to_csv("features.csv")

where labels and features are pandas dataframes with the same index.

FastEMC outputs a list of feature clusters. The size of each cluster and the number of clusters to collect are optional parameters. Each cluster is also given a score. The score is based on k-fold cross-validation of a logistic regression classifier using only features in the cluster.

When using FastEMC in published works, please cite the original manuscript and the author of the software:

[1] Stackhouse, C.T.; Rowland, J.R.; Shevin, R.S.; Singh, R.; Gillespie, G.Y.; Willey, C.D. A Novel Assay for Profiling GBM Cancer Model Heterogeneity and Drug Screening. Cells 2019, 8, 702. (https://www.ncbi.nlm.nih.gov/pubmed/31336733)

[2] Rowland, J.R. FastEMC. 2019. (https://github.com/rowland-208/fastemc)