Bayesian inference of transcription dynamics from population snapshots of smFISH
BayFish is a computational pipeline to infer kinetic parameters of gene expression from sparse single-molecule RNA fluorescence in situ hybridization (smFISH) data at multiple time points after induction. Given an underlying model of gene expression, BayFish uses a Markov Chanin Monte Carlo method to estimate the posterior probability of the model parameters and quantify the parameter uncertainty given the observed smFISH data.
The CODE_MATLAB directory contains MATLAB code used to process the data (DATA_*.m), run the pipeline (SIM_*.m), and analyze (FIG_*.m) the BayFish results.
The CODE_CPP directory contains C++ code used to run the BayFish pipeline (main.cpp), as well as the required function files (*.h).
The DATA directory contains an example of data to be processed: smFISH measurements of the neuronal activity inducible gene Npas4 in primary neurons (see Gómez-Schiavon et al., 2017; https://doi.org/10.1186/s13059-017-1297-9).
To use the MATLAB version, please refer to CODE_MATLAB/README.md To use the C++ version, please refer to CODE_CPP/README.md
If you use this code or the data associated with it please cite:
Gómez-Schiavon et al. (2017); https://doi.org/10.1186/s13059-017-1297-9.
Latest release:
(C) Copyright 2017 Mariana Gómez-Schiavon
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