SAUCIE

An implementation of SAUCIE (Sparse Autoencoder for Clustering, Imputing, and Embedding) in Tensorflow.

Requirements

All tests performed with:

tensorflow 1.4.0
numpy 1.13.3

Running

An example of how to use it for both batch correction and clustering, and optionally specifying the values of the regularization parameters:

python SAUCIE.py --input_dir path/to/input/fcs_files
                 --output_dir path/for/output/fcs_files
                 --batch_correct
                 --cluster
                 [--lambda_b .1]
                 [--lambda_c .1]
                 [--lambda_d .2];

The input directory must contain the FCS you wish to run SAUCIE on and a file named cols_to_use.txt with the 0-indexed column numbers, one per line, from the FCS files you want to run (don't include column 0 if the first column in the FCS file is Event#, for example).

In the output directory, if batch correction was done, there will be a folder batch_corrected with a batch-corrected FCS corresponding to each original FCS file. If clustering was done, there will also be a folder clustered with a clustered FCS file corresponding to each original FCS file. In each clustered file, there is either the original or batch-corrected data with additional columns giving the cluster number and the X and Y coordinate for the visualization.