FEATS is a new Python tool for performing the following downstream analysis on single-cell RNA-seq datasets:

1. Clustering
2. Estimating the number of clusters
3. Outlier detection
4. Batch correction and integration of data from multiple experiments

FEATS depends on the following packages

1. numpy
2. pandas
3. scikit-learn
4. scipy
5. singlecelldata

To install FEATS run the following command:

pip install feats

The functional reference manual for FEATS is available here.

To use FEATS, please refer to the following example code presented in notebook sytle environment.

1. Clustering using FEATS
2. Performing outlier analysis
3. Performing batch correction

The data for the examples in this section is available here. The data is contained in subfolders in the datasets folder. The subfolders are named according to the dataset name. To load the data for the examples above, provide the path to the datasets folder.

The paper is available here

Contact the author on vans.edw@gmail.com to give feedback/suggestions for further improvements and to report issues.