A Maximum Entropy Approach to Defining Cell State

TL;DR

This paper introduces a maximum entropy method that integrates sequence and count data from RNA-seq to identify cell states, enhancing the analysis of molecular composition in single cells.

Contribution

It develops a novel maximum entropy framework that utilizes both sequence and count information for more comprehensive cellular state analysis.

Findings

Successfully mapped RNA sequences to spin vectors and defined an energy function.

Identified specific biological states using mean energies and Boltzmann probabilities.

Provides a new quantitative tool for analyzing -omics data and biological functions.

Abstract

The past few decades have seen great leaps in technologies to analyze cells and tissues. Omics methods in particular now allow us unprecedented access to their the molecular composition where the base-level resolution of transcripts and their numbers can be determined at a single cell level. Existing methods to analyze the resulting data make use of the count data while discarding the information present in the sequences themselves. In this paper we used a maximum entropy approach to develop a method to analyze RNA-seq data using both the sequence and count information. By mapping sequences to vectors of spins and defining an energy function on them, we were able to identify specific states in a biological process using mean energies and their associated Boltzmann-probabilities. This approach opens up new avenues in the quantitative analysis of -omics data and analysis of biological function.