A Coding Theory Perspective on Multiplexed Molecular Profiling of Biological Tissues

TL;DR

This paper applies coding theory to improve multiplexed RNA detection in biological tissues, proposing a realistic asymmetric channel model and MAP decoding to enhance accuracy and reduce false discoveries.

Contribution

It introduces an asymmetric channel model for mFISH, develops MAP decoders, and demonstrates how codebook permutation improves detection performance.

Findings

MAP decoders significantly reduce false discovery rates

Asymmetric channel modeling outperforms classical symmetric models

Codebook permutation aligned with prior improves detection accuracy

Abstract

High-throughput and quantitative experimental technologies are experiencing rapid advances in the biological sciences. One important recent technique is multiplexed fluorescence in situ hybridization (mFISH), which enables the identification and localization of large numbers of individual strands of RNA within single cells. Core to that technology is a coding problem: with each RNA sequence of interest being a codeword, how to design a codebook of probes, and how to decode the resulting noisy measurements? Published work has relied on assumptions of uniformly distributed codewords and binary symmetric channels for decoding and to a lesser degree for code construction. Here we establish that both of these assumptions are inappropriate in the context of mFISH experiments and substantial decoding performance gains can be obtained by using more appropriate, less classical, assumptions. We propose a more appropriate asymmetric channel model that can be readily parameterized from data and use it to develop a maximum a posteriori (MAP) decoders. We show that false discovery rate for rare RNAs, which is the key experimental metric, is vastly improved with MAP decoders even when employed with the existing sub-optimal codebook. Using an evolutionary optimization methodology, we further show that by permuting the codebook to better align with the prior, which is an experimentally straightforward procedure, significant further improvements are possible.