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

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 publi...

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Bibliographic Details
Main Authors: Maximilian Sprang, Miguel A. Andrade-Navarro, Jean-Fred Fontaine
Format: Article
Language:English
Published: BMC 2022-07-01
Series:BMC Bioinformatics
Subjects:
Batch effect
Quality control
NGS
RNA-seq
Next-generation sequencing
Machine learning
Online Access:https://doi.org/10.1186/s12859-022-04775-y

Internet

https://doi.org/10.1186/s12859-022-04775-y

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