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Month: November 2017

(Sweet) Curry Sauce

(Sweet) Curry Sauce

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(Sweet) Curry Sauce
The classical sauce with any kind of sausage, fries and mayonnaise!
Course Sauce
Servings
Ingredients
Course Sauce
Servings
Ingredients
Instructions
  1. Peel the apple and chop it into small pieces
  2. Pour the coke into a pot, add the apple pieces and condense it to about 100ml
  3. Use a handheld blender to puree the apples in the coke sirup
  4. Add the ketchup
  5. Season everything with the curry powder, vinegar, chili sauce and salt.
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New publication

New publication

The November issue of Computer Methods and Programs in Biomedicine
contains an article about my R-package ‘GenomicTools’:

http://www.sciencedirect.com/science/article/pii/S0169260716308549

The R-package GenomicTools for multifactor dimensionality reduction and the analysis of (exploratory) Quantitative Trait Loci

Background and objectives

We introduce the R-package GenomicTools to perform, among others, a Multifactor Dimensionality Reduction (MDR) for the identification of SNP-SNP interactions. The package further provides a new class of tests for an (exploratory) Quantitative Trait Loci analysis that overcomes some of the limitations of other popular (e)QTL approaches. Popular (e)QTL approaches that use linear models or ANOVA are often based on over-simplified models that have weak statistical properties and which are not robust against outlying observations.

Method

The algorithm to calculate the MDR is well established. To speed up its calculation in R, we implemented it in C++. Further, our implementation also supports the combination of several MDR results to an MDR ensemble classifier. The (e)QTL test procedure is based on a generalized Mann-Whitney test that is tailored for directional alternatives, as they are present in an (e)QTL analysis.

Results

Our package GenomicTools provides functions to determine SNP combinations that have the highest accuracy for a MDR classification problem. It also provides functions to combine the best MDR results to a joined ensemble classifier for improved classification results. Further, the (e)QTL analysis is based on a solid statistical theory. In addition, informative visualizations of the results are provided.

Conclusion

The here presented new class of tests and methods have an easy to apply syntax, so that also researchers inexperienced in R are able to apply our proposed methods and implementations. The package creates publication ready Figures and hence could be a valuable tool for genomic data analysis.