Combining denoising of RNA-seq data and flux balance analysis for cluster analysis of single cells.

ABSTRACT:

Background

Sophisticated methods to properly pre-process and analyze the increasing collection of single-cell RNA sequencing (scRNA-seq) data are increasingly being developed. On the contrary, the best practices to integrate these data into metabolic networks, aiming at describing metabolic phenotypes within a heterogeneous cell population, have been poorly investigated. In this regard, a critical factor is the presence of false zero values in reactions essential for a fundamental metabolic function, such as biomass or energy production. Here, we investigate the role of denoising strategies in mitigating this problem.

Methods

We applied state-of-the-art denoising strategies - namely MAGIC, ENHANCE, and SAVER - on three public scRNA-seq datasets. We then associated a metaboli

SUBMITTER: Galuzzi BG 

PROVIDER: S-EPMC9597960 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature