Thoresen, M. | bioRxiv Science

bioRxiv2020

Integrative analyses of multi-omics data improves model predictions: an application to lung cancer

BackgroundCancer genomic studies often include data collected from several omics platforms. Each omics data source contributes to the understanding of the underlying biological process via source specific ("individual") patterns of variability. At the same time, statistical associations and potential interactions among the different data sources can reveal signals from common biological processes that might not be identified by single source analyses. These common patterns of variability are referred to as "shared" or "joint". In this work, we show how the use of joint and individual components can lead to better predictive models, and to a deeper understanding of the biological process at hand. We identify joint and individual contributions of DNA methylation, miRNA and mRNA expression collected from blood samples in a lung cancer case-control study nested within the Norwegian Women and Cancer (NOWAC) cohort study, and we use such components to build prediction models for case-control and metastatic status. To assess the quality of predictions, we compare models based on simultaneous, integrative analysis of multi-source omics data to a standard non-integrative analysis of each single omics dataset, and to penalized regression models. Additionally, we apply the proposed approach to a breast cancer dataset from The Cancer Genome Atlas. ResultsOur results show how an integrative analysis that preserves both components of variation is more appropriate than standard multi-omics analyses that are not based on such a distinction. Both joint and individual components are shown to contribute to a better quality of model predictions, and facilitate the interpretation of the underlying biological processes in lung cancer development. ConclusionIn the presence of multiple omics data sources, we recommend the use of data integration techniques that preserve the joint and individual components across the omics sources. We show how the inclusion of such components increases the quality of model predictions of clinical outcomes.

genomics↗

bioRxiv2020

An Age-Specific Atlas for Delineation of White Matter Pathways in Children Aged 6-8 Years

Diffusion MRI allows non-invasive assessment of white matter maturation in typical development and of white matter damage due to brain injury or pathology. Probabilistic white matter atlases provide delineation of white matter tracts, allowing diffusion metrics to be measured in specific white matter pathways. However, given the known age-dependency of developmental change in white matter it may not be optimal to use an adult template when assessing data acquired from children. This study develops an age-specific probabilistic white matter atlas for delineation of 12 major white matter tracts in children aged 6-8 years. By comparing to subject-specific tract tracing in two validation cohorts, we demonstrate that this age-specific atlas gives better overall performance than simply registering to the Johns Hopkins University adult white matter template. Specifically, when normalising diffusion data acquired from children to an adult template, estimates of fractional anisotropy (FA) values for corticospinal tract, uncinate fasciculus, forceps minor, cingulate gyrus part of the cingulum and anterior thalamic radiation were all less accurate than those obtained when using an age-specific atlas, potentially leading to false negatives when performing group comparisons. We then applied the newly developed atlas to compare FA between children treated with therapeutic hypothermia for neonatal encephalopathy and age-matched controls, which revealed significant reductions in the fornix, the left superior longitudinal fasciculus, and both the hippocampal and cingulum parts of the left cingulate gyrus. To our knowledge, this is the first publicly available probabilistic atlas of white matter tracts for this age group.

neuroscience↗