Calculating Kolmogorov Complexity from the Transcriptome Data

TL;DR

This paper introduces a method to estimate Kolmogorov complexity from transcriptome data, including zero counts, improving the ability to distinguish similar transcriptomes compared to traditional entropy-based methods.

Contribution

It presents a novel approach to calculate Kolmogorov complexity from transcriptome data that accounts for zero counts, enhancing data differentiation.

Findings

Kolmogorov complexity estimation improves transcriptome differentiation.

Including zero counts preserves more information than entropy-based methods.

Method enables better comparison of similar transcriptomes.

Abstract

Information entropy is used to summarize transcriptome data, but ignoring zero count data contained them. Ignoring zero count data causes loss of information and sometimes it was difficult to distinguish between multiple transcriptomes. Here, we estimate Kolmogorov complexity of transcriptome treating zero count data and distinguish similar transcriptome data.