Limits...
Mixture models for analysis of melting temperature data.

Nellåker C, Uhrzander F, Tyrcha J, Karlsson H - BMC Bioinformatics (2008)

Bottom Line: Mixture model analysis correctly identified categories in Tm data obtained for known plasmid targets.Mixture model analysis of Tm data allows the minimum number of different sequences in a set of amplicons and their relative frequencies to be determined.This approach allows Tm data to be analyzed, classified, and compared in an unbiased manner.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Neuroscience, Karolinska Institutet, Retzius Väg, Stockholm, Sweden. christoffer.nellaker@ki.se

ABSTRACT

Background: In addition to their use in detecting undesired real-time PCR products, melting temperatures are useful for detecting variations in the desired target sequences. Methodological improvements in recent years allow the generation of high-resolution melting-temperature (Tm) data. However, there is currently no convention on how to statistically analyze such high-resolution Tm data.

Results: Mixture model analysis was applied to Tm data. Models were selected based on Akaike's information criterion. Mixture model analysis correctly identified categories in Tm data obtained for known plasmid targets. Using simulated data, we investigated the number of observations required for model construction. The precision of the reported mixing proportions from data fitted to a preconstructed model was also evaluated.

Conclusion: Mixture model analysis of Tm data allows the minimum number of different sequences in a set of amplicons and their relative frequencies to be determined. This approach allows Tm data to be analyzed, classified, and compared in an unbiased manner.

Show MeSH

Related in: MedlinePlus

Plot of P values determined with χ2 tests against the number of data points fitted to the mixture model.χ2 tests between four equally proportioned Tm categories were compared with the fitted mixing proportions determined from data points when one of the four categories was not represented. The different lines represent the various separations of "temperatures" used to generate the data points, where each line is denoted by a multiplier of σ.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2567994&req=5

Figure 4: Plot of P values determined with χ2 tests against the number of data points fitted to the mixture model.χ2 tests between four equally proportioned Tm categories were compared with the fitted mixing proportions determined from data points when one of the four categories was not represented. The different lines represent the various separations of "temperatures" used to generate the data points, where each line is denoted by a multiplier of σ.

Mentions: Next, we evaluated the fit of the data points to preestablished models. For this purpose, we generated data points corresponding to a sample containing three of four possible Tm represented in a model. We compared the mixing proportions reported by the mixture model analysis with the mixing proportions in which all four Tm were present at equal frequencies. In Figure 4, the P values obtained from χ2 analyses for various separations of the Tm are plotted against the numbers of data points used. The P values for the χ2 test drop rapidly with increasing sample numbers for any Tm separation of more than 1 × σ. With smaller separations of the Tm categories, the mixture model analysis is unable to reliably establish the differences in the mixing proportions.


Mixture models for analysis of melting temperature data.

Nellåker C, Uhrzander F, Tyrcha J, Karlsson H - BMC Bioinformatics (2008)

Plot of P values determined with χ2 tests against the number of data points fitted to the mixture model.χ2 tests between four equally proportioned Tm categories were compared with the fitted mixing proportions determined from data points when one of the four categories was not represented. The different lines represent the various separations of "temperatures" used to generate the data points, where each line is denoted by a multiplier of σ.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2567994&req=5

Figure 4: Plot of P values determined with χ2 tests against the number of data points fitted to the mixture model.χ2 tests between four equally proportioned Tm categories were compared with the fitted mixing proportions determined from data points when one of the four categories was not represented. The different lines represent the various separations of "temperatures" used to generate the data points, where each line is denoted by a multiplier of σ.
Mentions: Next, we evaluated the fit of the data points to preestablished models. For this purpose, we generated data points corresponding to a sample containing three of four possible Tm represented in a model. We compared the mixing proportions reported by the mixture model analysis with the mixing proportions in which all four Tm were present at equal frequencies. In Figure 4, the P values obtained from χ2 analyses for various separations of the Tm are plotted against the numbers of data points used. The P values for the χ2 test drop rapidly with increasing sample numbers for any Tm separation of more than 1 × σ. With smaller separations of the Tm categories, the mixture model analysis is unable to reliably establish the differences in the mixing proportions.

Bottom Line: Mixture model analysis correctly identified categories in Tm data obtained for known plasmid targets.Mixture model analysis of Tm data allows the minimum number of different sequences in a set of amplicons and their relative frequencies to be determined.This approach allows Tm data to be analyzed, classified, and compared in an unbiased manner.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Neuroscience, Karolinska Institutet, Retzius Väg, Stockholm, Sweden. christoffer.nellaker@ki.se

ABSTRACT

Background: In addition to their use in detecting undesired real-time PCR products, melting temperatures are useful for detecting variations in the desired target sequences. Methodological improvements in recent years allow the generation of high-resolution melting-temperature (Tm) data. However, there is currently no convention on how to statistically analyze such high-resolution Tm data.

Results: Mixture model analysis was applied to Tm data. Models were selected based on Akaike's information criterion. Mixture model analysis correctly identified categories in Tm data obtained for known plasmid targets. Using simulated data, we investigated the number of observations required for model construction. The precision of the reported mixing proportions from data fitted to a preconstructed model was also evaluated.

Conclusion: Mixture model analysis of Tm data allows the minimum number of different sequences in a set of amplicons and their relative frequencies to be determined. This approach allows Tm data to be analyzed, classified, and compared in an unbiased manner.

Show MeSH
Related in: MedlinePlus