Dataset Information

Statistical significance of quantitative PCR.

ABSTRACT:

Background

PCR has the potential to detect and precisely quantify specific DNA sequences, but it is not yet often used as a fully quantitative method. A number of data collection and processing strategies have been described for the implementation of quantitative PCR. However, they can be experimentally cumbersome, their relative performances have not been evaluated systematically, and they often remain poorly validated statistically and/or experimentally. In this study, we evaluated the performance of known methods, and compared them with newly developed data processing strategies in terms of resolution, precision and robustness.

Results

Our results indicate that simple methods that do not rely on the estimation of the efficiency of the PCR amplification may provide reproducible and sensitive data, but that they do not quantify DNA with precision. Other evaluated methods based on sigmoidal or exponential curve fitting were generally of both poor resolution and precision. A statistical analysis of the parameters that influence efficiency indicated that it depends mostly on the selected amplicon and to a lesser extent on the particular biological sample analyzed. Thus, we devised various strategies based on individual or averaged efficiency values, which were used to assess the regulated expression of several genes in response to a growth factor.

Conclusion

Overall, qPCR data analysis methods differ significantly in their performance, and this analysis identifies methods that provide DNA quantification estimates of high precision, robustness and reliability. These methods allow reliable estimations of relative expression ratio of two-fold or higher, and our analysis provides an estimation of the number of biological samples that have to be analyzed to achieve a given precision.

SUBMITTER: Karlen Y

PROVIDER: S-EPMC1868764 | biostudies-literature | 2007 Apr

REPOSITORIES: biostudies-literature

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Publications

Statistical significance of quantitative PCR.

Karlen Yann Y McNair Alan A Perseguers Sébastien S Mazza Christian C Mermod Nicolas N

BMC bioinformatics 20070420

<h4>Background</h4>PCR has the potential to detect and precisely quantify specific DNA sequences, but it is not yet often used as a fully quantitative method. A number of data collection and processing strategies have been described for the implementation of quantitative PCR. However, they can be experimentally cumbersome, their relative performances have not been evaluated systematically, and they often remain poorly validated statistically and/or experimentally. In this study, we evaluated the ...[more]

PMID: 17445280

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Project description:BackgroundMany studies have provided algorithms or methods to assess a statistical significance in quantitative proteomics when multiple replicates for a protein sample and a LC/MS analysis are available. But, confidence is still lacking in using datasets for a biological interpretation without protein sample replicates. Although a fold-change is a conventional threshold that can be used when there are no sample replicates, it does not provide an assessment of statistical significance such as a false discovery rate (FDR) which is an important indicator of the reliability to identify differentially expressed proteins. In this work, we investigate whether differentially expressed proteins can be detected with a statistical significance from a pair of unlabeled protein samples without replicates and with only duplicate LC/MS injections per sample. A FDR is used to gauge the statistical significance of the differentially expressed proteins.ResultsWe have experimented to operate on several parameters to control a FDR, including a fold-change, a statistical test, and a minimum number of permuted significant pairings. Although none of these parameters alone gives a satisfactory control of a FDR, we find that a combination of these parameters provides a very effective means to control a FDR without compromising the sensitivity. The results suggest that it is possible to perform a significance analysis without protein sample replicates. Only duplicate LC/MS injections per sample are needed. We illustrate that differentially expressed proteins can be detected with a FDR between 0 and 15% at a positive rate of 4-16%. The method is evaluated for its sensitivity and specificity by a ROC analysis, and is further validated with a [15N]-labeled internal-standard protein sample and additional unlabeled protein sample replicates.ConclusionWe demonstrate that a statistical significance can be inferred without protein sample replicates in label-free quantitative proteomics. The approach described in this study would be useful in many exploratory experiments where a sample amount or instrument time is limited. Naturally, this method is also suitable for proteomics experiments where multiple sample replicates are available. It is simple, and is complementary to other more sophisticated algorithms that are not designed for dealing with a small number of sample replicates.

Project description:BackgroundCausal graphs are an increasingly popular tool for the analysis of biological datasets. In particular, signed causal graphs--directed graphs whose edges additionally have a sign denoting upregulation or downregulation--can be used to model regulatory networks within a cell. Such models allow prediction of downstream effects of regulation of biological entities; conversely, they also enable inference of causative agents behind observed expression changes. However, due to their complex nature, signed causal graph models present special challenges with respect to assessing statistical significance. In this paper we frame and solve two fundamental computational problems that arise in practice when computing appropriate null distributions for hypothesis testing.ResultsFirst, we show how to compute a p-value for agreement between observed and model-predicted classifications of gene transcripts as upregulated, downregulated, or neither. Specifically, how likely are the classifications to agree to the same extent under the null distribution of the observed classification being randomized? This problem, which we call "Ternary Dot Product Distribution" owing to its mathematical form, can be viewed as a generalization of Fisher's exact test to ternary variables. We present two computationally efficient algorithms for computing the Ternary Dot Product Distribution and investigate its combinatorial structure analytically and numerically to establish computational complexity bounds.Second, we develop an algorithm for efficiently performing random sampling of causal graphs. This enables p-value computation under a different, equally important null distribution obtained by randomizing the graph topology but keeping fixed its basic structure: connectedness and the positive and negative in- and out-degrees of each vertex. We provide an algorithm for sampling a graph from this distribution uniformly at random. We also highlight theoretical challenges unique to signed causal graphs; previous work on graph randomization has studied undirected graphs and directed but unsigned graphs.ConclusionWe present algorithmic solutions to two statistical significance questions necessary to apply the causal graph methodology, a powerful tool for biological network analysis. The algorithms we present are both fast and provably correct. Our work may be of independent interest in non-biological contexts as well, as it generalizes mathematical results that have been studied extensively in other fields.

Dataset Information

Statistical significance of quantitative PCR.

Background

Results

Conclusion

Publications

Statistical significance of quantitative PCR.

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