A common base method for analysis of qPCR data and the application of simple blocking in qPCR experiments

Background qPCR has established itself as the technique of choice for the quantification of gene expression. Procedures for conducting qPCR have received significant attention; however, more rigorous approaches to the statistical analysis of qPCR data are needed. Results Here we develop a mathematic...

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Vydáno v:BMC bioinformatics Ročník 18; číslo 1; s. 534 - 11
Hlavní autoři: Ganger, Michael T., Dietz, Geoffrey D., Ewing, Sarah J.
Médium: Journal Article
Jazyk:angličtina
Vydáno: London BioMed Central 01.12.2017
BioMed Central Ltd
BMC
Témata:
Algorithms
Analysis of variance (ANOVA)
Bioinformatics
Biomedical and Life Sciences
Blocking
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Confidence intervals
Gene expression
Genes
Life Sciences
Methodology
Methodology Article
Methods
Microarrays
Paired and unpaired tests
qPCR analysis
Results and data
Spreadsheets
Statistics
Confidence intervals
Paired and unpaired tests
Analysis of variance (ANOVA)
Blocking
Statistics
qPCR analysis
ISSN:1471-2105, 1471-2105
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Shrnutí:Background qPCR has established itself as the technique of choice for the quantification of gene expression. Procedures for conducting qPCR have received significant attention; however, more rigorous approaches to the statistical analysis of qPCR data are needed. Results Here we develop a mathematical model, termed the Common Base Method, for analysis of qPCR data based on threshold cycle values ( C q ) and efficiencies of reactions ( E ). The Common Base Method keeps all calculations in the logscale as long as possible by working with log 10 ( E ) ∙  C q , which we call the efficiency-weighted C q value; subsequent statistical analyses are then applied in the logscale. We show how efficiency-weighted C q values may be analyzed using a simple paired or unpaired experimental design and develop blocking methods to help reduce unexplained variation. Conclusions The Common Base Method has several advantages. It allows for the incorporation of well-specific efficiencies and multiple reference genes. The method does not necessitate the pairing of samples that must be performed using traditional analysis methods in order to calculate relative expression ratios. Our method is also simple enough to be implemented in any spreadsheet or statistical software without additional scripts or proprietary components.
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ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-017-1949-5