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FEATS is a 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


The latest version of FEATS can be installed from PyPI:

pip install feats


The functional reference manual for FEATS is available here.


To use FEATS, please refer to the latest version of example code presented in the Jupyter notebook.

  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 on your local machine.


The FEATS paper is published in the journal Briefings in Bioinformatics. It is available here


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