A Bayesian model for single cell transcript expression analysis on MERFISH data

Multiplexed error-robust fluorescence in-situ hybridization (MERFISH) is a new technology to obtain spatially resolved gene or transcript expression profiles in single cells for hundreds to thousands of genes in parallel. We present a Bayesian model for expression analysis on MERFISH data. We show that the model successfully captures the uncertainty in MERFISH data and eliminates systematic biases that can occur in raw RNA molecule counts obtained with MERFISH. Our model accurately estimates transcript expression, fold-change and coefficient of variation along with credible intervals and control of the expected false discovery rate.This enables MERFISH to scale towards the whole genome while being able to control the uncertainty in the obtained results.