Biology subjects

Claassen, T.

Publications and source records attributed to Claassen, T..

2 recordsLinked to original sources

bioRxiv2020

Quantitative Genetic Scoring, or how to put a number on an arbitrary genetic region

MotivationWith the increasing availability of genome-wide genetic data, methods to combine genetic variables with other sources of data in statistical models are required. This paper introduces quantitative genetic scoring (QGS), a dimensionality reduction method to create quantitative genetic variables representing arbitrary genetic regions. MethodsQGS is defined as the sum of absolute differences in the genetic sequence between a subject and a reference population. QGS properties such as distribution and sensitivity to region size were examined, and QGS was tested in six different existing genomic data sets of various sizes and various phenotypes. ResultsQGS can reduce genetic information by >98% yet explain phenotypic variance at low, medium, and high level of granularity. Associations based on QGS are independent of both size and linkage disequilibrium structure of the underlying region. In combination with stability selection, QGS finds significant results where a traditional genome-wide association approaches struggle. In conclusion, QGS preserves phenotypically significant genetic variance while reducing dimensionality, allowing researchers to include quantitative genetic information in any type of statistical analysis. Availabilityhttps://github.com/machine2learn/QGS Contactgido.schoenmacker@radboudumc.nl Supplemental informationSupplemental data are available online.

bioinformatics↗

bioRxiv2019

Quantifying free behavior in an open field using k-motif approach

Quantification and parametrization of movement in animal models is widely used in behavioral paradigms. In particular, free movement of an animal in controlled conditions (e.g., the open field paradigm) is used as a proxy for indices of baseline and drug-induced behavioural changes. However, the analysis of this is often time- and labour-intensive and existing algorithms do not always classify the behaviour correctly.\n\nHere, we propose a new approach to quantify behaviour in an unconstrained environment: searching for frequent patterns (k-motifs) in the time series representing position of the subject over time. Validation of this method was performed using subchronic quinpirole-induced changes in open field experiment behaviors in rodents. Analysis of this data was performed using k-motifs as features to better classify subjects into experimental groups on the basis of behavior in the open field. Our classifier using k-motifs gives as high as 94% accuracy in classifying repetitive behaviour versus controls which is a substantial improvement compared to currently available methods including using standard feature definitions (depending on the choice of feature set and classification strategy, accuracy up to 88%). Furthermore, vizualization of the movement / time patterns is highly predictive of these behaviours. By using machine learning to create features in a data driven fashion, this can be applied to general behavioural analysis across experimental paradigms beyond the open field.

animal behavior and cognition↗