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...

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Vydané v:	BMC bioinformatics Ročník 26; číslo 1; s. 47 - 16
Hlavní autori:	Schlumbohm, Simon, Neumann, Julia E., Neumann, Philipp
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London BioMed Central 11.02.2025 BioMed Central Ltd BMC
Predmet:	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
On-line prístup:	Získať plný text
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