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Abstract Motivation In the training of predictive models using high-dimensional genomic data, multiple studies’ worth of data are often combined to increase sample size and improve generalizability. A drawback of this approach is that there may be different sets of features measured in each study due to variations in expression measurement platform or technology. It is often common practice to work only with the intersection of features measured in common across all studies, which results in the blind discarding of potentially useful feature information that is measured in individual or subsets of studies. Results We characterize the loss in predictive performance incurred by using only the intersection of feature information available across all studies when training predictors using gene expression data from microarray and sequencing datasets. We study the properties of linear and polynomial regression for imputing discarded features and demonstrate improvements in the external performance of prediction functions through simulation and in gene expression data collected on breast cancer patients. To improve this process, we propose a pairwise strategy that applies any imputation algorithm to two studies at a time and averages imputed features across pairs. We demonstrate that the pairwise strategy is preferable to first merging all datasets together and imputing any resulting missing features. Finally, we provide insights on which subsets of intersected and study-specific features should be used so that missing-feature imputation best promotes cross-study replicability. Availability and implementation The code is available at https://github.com/YujieWuu/Pairwise_imputation. Supplementary information Supplementary information is available at Bioinformatics online. 

Jointly using data from multiple similar sources for the training of prediction models is increasingly becoming an important task in many fields of science. In this paper, we propose a framework for {\it generalist and specialist} predictions that leverages multiple datasets, with potential heterogenity in the relationships between predictors and outcomes. Our framework uses ensembling with stacking, and includes three major components: 1) training of the ensemble members using one or more datasets, 2) a no-data-reuse technique for stacking weights estimation and 3) task-specific utility functions. We prove that under certain regularity conditions, our framework produces a stacked prediction function with oracle property. We also provide analytically the conditions under which the proposed no-data-reuse technique will increase the prediction accuracy of the stacked prediction function compared to using the full data. We perform a simulation study to numerically verify and illustrate these results and apply our framework to predicting mortality based on a collection of variables including long-term exposure to common air pollutants. 

It is challenging to associate features such as human health outcomes, diet, environmental conditions, or other metadata to microbial community measurements, due in part to their quantitative properties. Microbiome multi-omics are typically noisy, sparse (zero-inflated), high-dimensional, extremely non-normal, and often in the form of count or compositional measurements. Here we introduce an optimized combination of novel and established methodology to assess multivariable association of microbial community features with complex metadata in population-scale observational studies. Our approach, MaAsLin 2 (Microbiome Multivariable Associations with Linear Models), uses generalized linear and mixed models to accommodate a wide variety of modern epidemiological studies, including cross-sectional and longitudinal designs, as well as a variety of data types (e.g., counts and relative abundances) with or without covariates and repeated measurements. To construct this method, we conducted a large-scale evaluation of a broad range of scenarios under which straightforward identification of meta-omics associations can be challenging. These simulation studies reveal that MaAsLin 2’s linear model preserves statistical power in the presence of repeated measures and multiple covariates, while accounting for the nuances of meta-omics features and controlling false discovery. We also applied MaAsLin 2 to a microbial multi-omics dataset from the Integrative Human Microbiome (HMP2) project which, in addition to reproducing established results, revealed a unique, integrated landscape of inflammatory bowel diseases (IBD) across multiple time points and omics profiles.