Batch effect detection and correction in RNA-seq data using machine-learning-based automated assessment of quality.

ABSTRACT:

Background

The constant evolving and development of next-generation sequencing techniques lead to high throughput data composed of datasets that include a large number of biological samples. Although a large number of samples are usually experimentally processed by batches, scientific publications are often elusive about this information, which can greatly impact the quality of the samples and confound further statistical analyzes. Because dedicated bioinformatics methods developed to detect unwanted sources of variance in the data can wrongly detect real biological signals, such methods could benefit from using a quality-aware approach.

Results

We recently developed statistical guidelines and a machine learning tool to automatically evaluate the quality of a next-generation

SUBMITTER: Sprang M 

PROVIDER: S-EPMC9284682 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature