ArticleBMC bioinformatics2020
Application of the common base method to regression and analysis of covariance (ANCOVA) in qPCR experiments and subsequent relative expression calculation.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Influence of Cooperative Learning Intervention on the Intrinsic Motivation of Physical Education Students-A Meta-Analysis within a Limited Range.International journal of environmental research and public health · 2021Pooled it
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- Restoring endogenous Dlg4/PSD95 expression by an artificial transcription factor ameliorates cognitive and motor learning deficits in the R6/2 mouse model of Huntington's disease.Clinical epigenetics · 2025Article
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- Kinin B1 receptor deficiency protects mice fed by cafeteria diet from abnormal glucose homeostasis.PloS one · 2022Article
- A flow cytometric method for measuring and isolating mammary epithelial cells from bovine milk.JDS communications · 2021Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundQuantitative polymerase chain reaction (qPCR) is the technique of choice for quantifying gene expression. While the technique itself is well established, approaches for the analysis of qPCR data continue to improve.
resultsHere we expand on the common base method to develop procedures for testing linear relationships between gene expression and either a measured dependent variable, independent variable, or expression of another gene. We further develop functions relating variables to a relative expression value and develop calculations for determination of associated confidence intervals.
conclusionsTraditional qPCR analysis methods typically rely on paired designs. The common base method does not require such pairing of samples. It is therefore applicable to other designs within the general linear model such as linear regression and analysis of covariance. The methodology presented here is also simple enough to be performed using basic spreadsheet software.
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