An implementation of SAUCIE (Sparse Autoencoder for Clustering, Imputing, and Embedding) in Tensorflow.
All tests performed with:
tensorflow 1.4.0
numpy 1.13.3
Download with
git clone https://github.com/KrishnaswamyLab/SAUCIE
install requirements with
pip install -r SAUCIE/requirements.txt
and then add SAUCIE to your Python path (e.g. by running Python in the same directory in which you ran git clone, or by adding that directory with sys.path.append("/path/to/git/").
SAUCIE is a python object that loads data from a numpy matrix and produces numpy matrix output for the reconstruction, visualization, and/or clusters. Standard usage is to train a model from a numpy matrix and get the embedding, reconstruction, or clusters for that data. This can be done with:
data = ...
import SAUCIE
saucie = SAUCIE.SAUCIE(data.shape[1])
loadtrain = SAUCIE.Loader(data, shuffle=True)
saucie.train(loadtrain, steps=1000)
loadeval = SAUCIE.Loader(data, shuffle=False)
embedding = saucie.get_embedding(loadeval)
number_of_clusters, clusters = saucie.get_clusters(loadeval)
reconstruction = saucie.get_reconstruction(loadeval)
... work with numpy results as desired ...
See scripts/example.py for an example of running SAUCIE on data. You can also see a tutorial on Google Colab here.
SAUCIE also comes with the option of running a full cohort of samples if the data is prepared in a specific way under scripts/SAUCIE.py. Namely, for a folder of CSV (or FCS files if the flag --fcs is provided), an example of how to use SAUCIE for both batch correction and clustering is:
python SAUCIE.py --input_dir path/to/input/files
--output_dir path/for/output/files
--batch_correct
--cluster
[--lambda_b .1]
[--lambda_c .1]
[--lambda_d .2];
The input directory must contain the CSV (or FCS if you specify --fcs) you wish to run SAUCIE on. If you do not want to run SAUCIE on all columns in the input file, a file named cols_to_use.txt with the 0-indexed column numbers, one per line can be provided.
In the output directory, if batch correction was done, there will be a folder batch_corrected with a batch-corrected CSV (or FCS) file corresponding to each original file. If clustering was done, there will also be a folder clustered with a clustered file corresponding to each original 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.