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We are analyzing https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-10-110.

Title:
Data-driven normalization strategies for high-throughput quantitative RT-PCR | BMC Bioinformatics | Full Text
Description:
Background High-throughput real-time quantitative reverse transcriptase polymerase chain reaction (qPCR) is a widely used technique in experiments where expression patterns of genes are to be profiled. Current stage technology allows the acquisition of profiles for a moderate number of genes (50 to a few thousand), and this number continues to grow. The use of appropriate normalization algorithms for qPCR-based data is therefore a highly important aspect of the data preprocessing pipeline. Results We present and evaluate two data-driven normalization methods that directly correct for technical variation and represent robust alternatives to standard housekeeping gene-based approaches. We evaluated the performance of these methods against a single gene housekeeping gene method and our results suggest that quantile normalization performs best. These methods are implemented in freely-available software as an R package qpcrNorm distributed through the Bioconductor project. Conclusion The utility of the approaches that we describe can be demonstrated most clearly in situations where standard housekeeping genes are regulated by some experimental condition. For large qPCR-based data sets, our approaches represent robust, data-driven strategies for normalization.
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25 years and 10 months (reg. 1999-08-06).

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Keywords {πŸ”}

normalization, genes, data, expression, quantile, gene, methods, sample, qpcr, method, number, housekeeping, rankinvariant, values, article, gapdh, samples, average, google, scholar, set, approach, distribution, experimental, figure, variance, control, primer, pubmed, single, datadriven, approaches, normalized, experiment, plates, authors, highthroughput, quantitative, conditions, levels, plate, effects, raw, cas, analysis, bmc, experiments, results, factor, original,

Topics {βœ’οΈ}

phorbol myristate acetate phorbol 12-myristate-13-acetate author information authors springer nature quantitative real-time rt-pcr high-throughput quantitative rt-pcr background qpcr collection additional files 1 high-throughput qpcr setup author correspondence helpful discussion high-throughput qpcr analyses authors’ original file high-throughput qpcr experiments high-throughput qpcr dataset high-throughput data sets information authors scientific editing discussion springer conclusion real time pcr high-throughput qpcr high quality results quantitative rt-pcr rank-invariant normalization eliminates dana-farber cancer institute delta-delta ct method data-driven normalization strategies rank-invariant gene method open access article full size image rank-invariant set method empirically-derived scale factor data-driven normalization alternatives data-driven normalization methods rank-invariant set normalization content data-driven methods offer comparing figures 3a single-gene normalization control privacy choices/manage cookies rank-invariant average expression dotted blue line figures 3d linear model-based approach statistical computing language average gene-specific coefficient bmc bioinformatics 10

Schema {πŸ—ΊοΈ}

WebPage:
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         headline:Data-driven normalization strategies for high-throughput quantitative RT-PCR
         description:High-throughput real-time quantitative reverse transcriptase polymerase chain reaction (qPCR) is a widely used technique in experiments where expression patterns of genes are to be profiled. Current stage technology allows the acquisition of profiles for a moderate number of genes (50 to a few thousand), and this number continues to grow. The use of appropriate normalization algorithms for qPCR-based data is therefore a highly important aspect of the data preprocessing pipeline. We present and evaluate two data-driven normalization methods that directly correct for technical variation and represent robust alternatives to standard housekeeping gene-based approaches. We evaluated the performance of these methods against a single gene housekeeping gene method and our results suggest that quantile normalization performs best. These methods are implemented in freely-available software as an R package qpcrNorm distributed through the Bioconductor project. The utility of the approaches that we describe can be demonstrated most clearly in situations where standard housekeeping genes are regulated by some experimental condition. For large qPCR-based data sets, our approaches represent robust, data-driven strategies for normalization.
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      headline:Data-driven normalization strategies for high-throughput quantitative RT-PCR
      description:High-throughput real-time quantitative reverse transcriptase polymerase chain reaction (qPCR) is a widely used technique in experiments where expression patterns of genes are to be profiled. Current stage technology allows the acquisition of profiles for a moderate number of genes (50 to a few thousand), and this number continues to grow. The use of appropriate normalization algorithms for qPCR-based data is therefore a highly important aspect of the data preprocessing pipeline. We present and evaluate two data-driven normalization methods that directly correct for technical variation and represent robust alternatives to standard housekeeping gene-based approaches. We evaluated the performance of these methods against a single gene housekeeping gene method and our results suggest that quantile normalization performs best. These methods are implemented in freely-available software as an R package qpcrNorm distributed through the Bioconductor project. The utility of the approaches that we describe can be demonstrated most clearly in situations where standard housekeeping genes are regulated by some experimental condition. For large qPCR-based data sets, our approaches represent robust, data-driven strategies for normalization.
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         Quantile Normalization
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         Computational Biology/Bioinformatics
         Computer Appl. in Life Sciences
         Algorithms
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            name:Harvard School of Public Health
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               type:PostalAddress
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      email:[email protected]
PostalAddress:
      name:Department of Biostatistics, Harvard School of Public Health, Boston, USA
      name:RIKEN, Omics Science Center, Yokohama Institute, Tsurumi-ku, Yokohama, Japan
      name:Institute for Molecular Biosciences, University of Queensland, St Lucia, Australia
      name:Institute for Molecular Biosciences, University of Queensland, St Lucia, Australia
      name:RIKEN, Omics Science Center, Yokohama Institute, Tsurumi-ku, Yokohama, Japan
      name:RIKEN, Omics Science Center, Yokohama Institute, Tsurumi-ku, Yokohama, Japan
      name:Roslin Institute, University of Edinburgh, Scotland, UK
      name:Department of Biostatistics, Harvard School of Public Health, Boston, USA
      name:Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, USA
      name:Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, USA

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