HarmonizR: blocking and singular feature data adjustment improve runtime efficiency and data preservation

Background Data adjustment is an essential tool for increasing statistical power during analysis, for example in case of complex multi-experiment data from (single-cell) RNA, proteomics and other omics data. Despite its benefits, data integration introduces internal biases—so-called batch effects. D...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	BMC bioinformatics Jg. 26; H. 1; S. 47 - 16
Hauptverfasser:	Schlumbohm, Simon, Neumann, Julia E., Neumann, Philipp
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 11.02.2025 BioMed Central Ltd BMC
Schlagworte:	Algorithms Analysis Batch effects Big data Bioinformatics Biomedical and Life Sciences Computational Biology - methods Computational Biology/Bioinformatics Computational efficiency Computer Appl. in Life Sciences Dataset integration Electronic data processing Genomics Life Sciences Methods Microarrays Proteomics RNA sequencing Software Germany Big data Computational efficiency Dataset integration Batch effects Proteomics
ISSN:	1471-2105, 1471-2105
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!