MOTIVATION: Learning the joint distributions of measurements, and in particular identification of an appropriate low-dimensional manifold, has been found to be a powerful ingredient of deep leaning approaches. Yet, such approaches have hardly been applied to single nucleotide polymorphism (SNP) data, probably due to the high number of features typically exceeding the number of studied individuals. RESULTS: After a brief overview of how deep Boltzmann machines (DBMs), a deep learning approach, can be adapted to SNP data in principle, we specifically present a way to alleviate the dimensionality problem by partitioned learning. We propose a sparse regression approach to coarsely screen the joint distribution of SNPs, followed by training several DBMs on SNP partitions that were identified by the screening. Aggregate features representing SNP patterns and the corresponding SNPs are extracted from the DBMs by a combination of statistical tests and sparse regression. In simulated case-control data, we show how this can uncover complex SNP patterns and augment results from univariate approaches, while maintaining type 1 error control. Time-to-event endpoints are considered in an application with acute myeloid leukemia patients, where SNP patterns are modeled after a pre-screening based on gene expression data. The proposed approach identified three SNPs that seem to jointly influence survival in a validation dataset. This indicates the added value of jointly investigating SNPs compared to standard univariate analyses and makes partitioned learning of DBMs an interesting complementary approach when analyzing SNP data. AVAILABILITY AND IMPLEMENTATION: A Julia package is provided at 'http://github.com/binderh/BoltzmannMachines.jl'. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: When processing gene expression profiles or other biological data, it is often required to assign measurements to distinct categories (e.g. 'high' and 'low' and possibly 'intermediate'). Subsequent analyses strongly depend on the results of this quantization. Poor quantization will have potentially misleading effects on further investigations. We propose the BiTrinA package that integrates different multiscale algorithms for binarization and for trinarization of one-dimensional data with methods for quality assessment and visualization of the results. By identifying measurements that show large variations over different time points or conditions, this quality assessment can determine candidates that are related to the specific experimental setting. AVAILABILITY AND IMPLEMENTATION: BiTrinA is freely available on CRAN. CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.