Privacy preserving validation for multiomic prediction models.

Reproducibility of results obtained using ribonucleic acid (RNA) data across labs remains a major hurdle in cancer research. Often, molecular predictors trained on one dataset cannot be applied to another due to differences in RNA library preparation and quantification, which inhibits the validation of predictors across labs. While current RNA correction algorithms reduce these differences, they require simultaneous access to patient-level data from all datasets, which necessitates the sharing of training data for predictors when sharing predictors. Here, we describe SpinAdapt, an unsupervised RNA correction algorithm that enables the transfer of molecular models without requiring access to patient-level data. It computes data corrections only via aggregate statistics of each dataset, thereby maintaining patient data privacy. Despite an inherent trade-off between privacy and performance, SpinAdapt outperforms current correction methods, like Seurat and ComBat, on publicly available cancer studies, including TCGA and ICGC. Furthermore, SpinAdapt can correct new samples, thereby enabling unbiased evaluation on validation cohorts. We expect this novel correction paradigm to enhance research reproducibility and to preserve patient privacy.

Practical Analysis of Genome Contact Interaction Experiments.

The three dimensional (3D) architecture of chromosomes is not random but instead tightly organized due to chromatin folding and chromatin interactions between genomically distant loci. By bringing genomically distant functional elements such as enhancers and promoters into close proximity, these interactions play a key role in regulating gene expression. Some of these interactions are dynamic, that is, they differ between cell types, conditions and can be induced by specific stimuli or differentiation events. Other interactions are more structural and stable, that is they are constitutionally present across several cell types. Genome contact interactions can occur via recruitment and physical interaction between chromatin-binding proteins and correlate with epigenetic marks such as histone modifications. Absence of a contact can occur due to presence of insulators, that is, chromatin-bound complexes that physically separate genomic loci. Understanding which contacts occur or do not occur in a given cell type is important since it can help explain how genes are regulated and which functional elements are involved in such regulation. The analysis of genome contact interactions has been greatly facilitated by the relatively recent development of chromosome conformation capture (3C). In an even more recent development, 3C was combined with next generation sequencing and led to Hi-C, a technique that in theory queries all possible pairwise interactions both within the same chromosome (intra) and between chromosomes (inter). Hi-C has now been used to study genome contact interactions in several human and mouse cell types as well as in animal models such as Drosophila and yeast. While it is fair to say that Hi-C has revolutionized the study of chromatin interactions, the computational analysis of Hi-C data is extremely challenging due to the presence of biases, artifacts, random polymer ligation and the huge number of potential pairwise interactions. In this chapter, we outline a strategy for analysis of genome contact experiments based on Hi-C using R and Bioconductor.