Python implementation of the transcriptional regulation model with Gaussian processes using GPflow and TensorFlow probability.
See requirenments.txt for full reproducibility; for lighter version of the code check simulations_python39 with lighter requirements.
-- contains the transcriptional regulation model (custom implementation of GPR for stacked time series; transcriptional regulation kernel).
-- simulated_examples.py runs an experiment on simulated data (generates the data, fits trcd model and runs MCMC).
-- Same as simulations, but with a few updates from latest version of the libraries (i.e., gpflow);
-- Minimal requirements compared to the main directory;
-- Runs on Mac M1, but tensorflow needs to be installed accordingly (see intructions for tensorflow installation here);
-- simulated_examples.py runs an experiment on simulated data (generates the data, fits trcd model and runs MCMC).
Contains files for the experiments on real data.
-- filter_genes.py/filter_genes_2rbf.py filtering of the genes (identifying differentially expressed genes);
-- fit_model_filtered_genes.py optimization of the parameters in transcriptional regulation model for genes that passed filtering;
-- mcmc_single_gene.py/mcmc_all_genes.py MCMC for uncertainty quantification using MALA on single gene/complete data set.
Contains helper functions for loading the data/fitting the model/running MCMC.
-- source code for clustering time series with GPclust.