InterOpt: Improved gene expression quantification in qPCR experiments using weighted aggregation of reference genes.
Biocomputational method
Data processing in systems biology
Expression study
Journal
iScience
ISSN: 2589-0042
Titre abrégé: iScience
Pays: United States
ID NLM: 101724038
Informations de publication
Date de publication:
20 Oct 2023
20 Oct 2023
Historique:
received:
11
08
2023
revised:
30
08
2023
accepted:
13
09
2023
medline:
13
10
2023
pubmed:
13
10
2023
entrez:
13
10
2023
Statut:
epublish
Résumé
qPCR is still the gold standard for gene expression quantification. However, its accuracy is highly dependent on the normalization procedure. The conventional method involves using the geometric mean of multiple study-specific reference genes (RGs) expression for cross-sample normalization. While research on selecting stably expressed RGs is extensive, scant literature exists regarding the optimal approach for aggregating multiple RGs into a unified RG. In this paper, we introduce a family of scale-invariant functions as an alternative to the geometric mean aggregation. Our candidate method (weighted geometric mean minimizing standard deviation) demonstrated significantly better results compared to other proposed methods. We provide theoretical and experimental support for this finding using real data from solid tumors and liquid biopsies. Moreover, the closed form and regression-based solution enable efficient computation and straightforward adoption on various platforms. All the proposed methods have been implemented within an easy-to-use R package with graphics processing unit (GPU) acceleration.
Identifiants
pubmed: 37829204
doi: 10.1016/j.isci.2023.107945
pii: S2589-0042(23)02022-9
pmc: PMC10565776
doi:
Types de publication
Journal Article
Langues
eng
Pagination
107945Informations de copyright
© 2023 The Authors.
Déclaration de conflit d'intérêts
The authors declare no competing interests.
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