The data science ecosystem encompasses data fairness, statistical, ML and AI methods and tools, interpretable data analysis and results, and trustworthy decision-making. Rapid advancements in AI have revolutionized data utilization and enabled machines to learn from data more effectively. Statistics, as the science of learning from data while accounting for uncertainty, plays a pivotal role in addressing complex real-world problems and facilitating trustworthy decision-making. In this talk, I will discuss the challenges and opportunities involved in building an end-to-end scalable and interpretable data science ecosystem using the analysis of whole genome sequencing studies and biobanks that integrates statistics, ML/AI, and genomic and health science as an example. Biobanks collect whole genome data, electronic health records and epidemiological data. I will illustrate key points using the analysis of multi-ancestry whole genome sequencing studies and biobanks by discussing a few scalable and interpretable statistical and ML/AI methods, tools and data science resources.

SUMMARY: We developed the variant-Set Test for Association using Annotation infoRmation (STAAR) workflow description language (WDL) workflow to facilitate the analysis of rare variants in whole genome sequencing association studies. The open-access STAAR workflow written in the WDL allows a user to perform rare variant testing for both gene-centric and genetic region approaches, enabling genome-wide, candidate and conditional analyses. It incorporates functional annotations into the workflow as introduced in the STAAR method in order to boost the rare variant analysis power. This tool was specifically developed and optimized to be implemented on cloud-based platforms such as BioData Catalyst Powered by Terra. It provides easy-to-use functionality for rare variant analysis that can be incorporated into an exhaustive whole genome sequencing analysis pipeline. AVAILABILITY AND IMPLEMENTATION: The workflow is freely available from https://dockstore.org/workflows/github.com/sheilagaynor/STAAR_workflow. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: Amidst the continuing spread of coronavirus disease-19 (COVID-19), real-time data analysis and visualization remain critical the general public to track the pandemic's impact and to inform policy making by officials. Multiple metrics permit the evaluation of the spread, infection and mortality of infectious diseases. For example, numbers of new cases and deaths provide easily interpretable measures of absolute impact within a given population and time frame, while the effective reproduction rate provides an epidemiological measure of the rate of spread. By evaluating multiple metrics concurrently, users can leverage complementary insights into the impact and current state of the pandemic when formulating prevention and safety plans for oneself and others. We describe COVID-19 Spread Mapper, a unified framework for estimating and quantifying the uncertainty in the smoothed daily effective reproduction number, case rate and death rate in a region using log-linear models. We apply this framework to characterize COVID-19 impact at multiple geographic resolutions, including by US county and state as well as by country, demonstrating the variation across resolutions and the need for harmonized efforts to control the pandemic. We provide an open-source online dashboard for real-time analysis and visualization of multiple key metrics, which are critical to evaluate the impact of COVID-19 and make informed policy decisions. AVAILABILITY AND IMPLEMENTATION: Our model and tool are publicly available as implemented in R and hosted at https://metrics.covid19-analysis.org/. The source code is freely available from https://github.com/lin-lab/COVID19-Rt and https://github.com/lin-lab/COVID19-Viz. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Cancer genomics studies frequently aim to identify genes that are differentially expressed between clinically distinct patient subgroups, generally by testing single genes one at a time. However, the results of any individual transcriptomic study are often not fully reproducible. A particular challenge impeding statistical analysis is the difficulty of distinguishing between differential expression comprising part of the genomic disease etiology and that induced by downstream effects. More robust analytical approaches that are well-powered to detect potentially causative genes, are less prone to discovering spurious associations, and can deliver reproducible findings across different studies are needed. RESULTS: We propose a set-based procedure for testing of differential expression and show that this set-based approach can produce more robust results by aggregating information across multiple, correlated genomic markers. Specifically, we adapt the Generalized Berk-Jones statistic to test for the transcription factors that may contribute to the progression of estrogen receptor positive breast cancer. We demonstrate the ability of our method to produce reproducible findings by applying the same analysis to 21 publicly available datasets, producing a similar list of significant transcription factors across most studies. Our Generalized Berk-Jones approach produces results that show improved consistency over three set-based testing algorithms: Generalized Higher Criticism, Gene Set Analysis and Gene Set Enrichment Analysis. AVAILABILITY AND IMPLEMENTATION: Data are in the MetaGxBreast R package. Code is available at github.com/ryanrsun/gaynor_sun_GBJ_breast_cancer. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The objective of this article is to introduce valid and robust methods for the analysis of rare variants for family-based exome chips, whole-exome sequencing or whole-genome sequencing data. Family-based designs provide unique opportunities to detect genetic variants that complement studies of unrelated individuals. Currently, limited methods and software tools have been developed to assist family-based association studies with rare variants, especially for analyzing binary traits. In this article, we address this gap by extending existing burden and kernel-based gene set association tests for population data to related samples, with a particular emphasis on binary phenotypes. The proposed approach blends the strengths of kernel machine methods and generalized estimating equations. Importantly, the efficient generalized kernel score test can be applied as a mega-analysis framework to combine studies with different designs. We illustrate the application of the proposed method using data from an exome sequencing study of autism. Methods discussed in this article are implemented in an R package 'gskat', which is available on CRAN and GitHub.

SUMMARY: The development of the Infinium HumanMethylation450 BeadChip enables epigenome-wide association studies at a reduced cost. One observation of the 450K data is that many CpG sites the beadchip interrogates have very large measurement errors. Including these noisy CpGs will decrease the statistical power of detecting relevant associations due to multiple testing correction. We propose to use intra-class correlation coefficient (ICC), which characterizes the relative contribution of the biological variability to the total variability, to filter CpGs when technical replicates are available. We estimate the ICC based on a linear mixed effects model by pooling all the samples instead of using the technical replicates only. An ultra-fast algorithm has been developed to address the computational complexity and CpG filtering can be completed in minutes on a desktop computer for a 450K data set of over 1000 samples. Our method is very flexible and can accommodate any replicate design. Simulations and a real data application demonstrate that our whole-sample ICC method performs better than replicate-sample ICC or variance-based method. AVAILABILITY AND IMPLEMENTATION: CpGFilter is implemented in R and publicly available under CRAN via the R package 'CpGFilter'. CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Gene set analyses have become increasingly important in genomic research, as many complex diseases are contributed jointly by alterations of numerous genes. Genes often coordinate together as a functional repertoire, e.g., a biological pathway/network and are highly correlated. However, most of the existing gene set analysis methods do not fully account for the correlation among the genes. Here we propose to tackle this important feature of a gene set to improve statistical power in gene set analyses. RESULTS: We propose to model the effects of an independent variable, e.g., exposure/biological status (yes/no), on multiple gene expression values in a gene set using a multivariate linear regression model, where the correlation among the genes is explicitly modeled using a working covariance matrix. We develop TEGS (Test for the Effect of a Gene Set), a variance component test for the gene set effects by assuming a common distribution for regression coefficients in multivariate linear regression models, and calculate the p-values using permutation and a scaled chi-square approximation. We show using simulations that type I error is protected under different choices of working covariance matrices and power is improved as the working covariance approaches the true covariance. The global test is a special case of TEGS when correlation among genes in a gene set is ignored. Using both simulation data and a published diabetes dataset, we show that our test outperforms the commonly used approaches, the global test and gene set enrichment analysis (GSEA). CONCLUSION: We develop a gene set analyses method (TEGS) under the multivariate regression framework, which directly models the interdependence of the expression values in a gene set using a working covariance. TEGS outperforms two widely used methods, GSEA and global test in both simulation and a diabetes microarray data.

MOTIVATION: Pathway and gene set-based approaches for the analysis of gene expression profiling experiments have become increasingly popular for addressing problems associated with individual gene analysis. Since most genes are not differently expressed, existing gene set tests, which consider all the genes within a gene set, are subject to considerable noise and power loss, a concern exacerbated in studies in which the degree of differential expression is moderate for truly differentially expressed genes. For a significantly differentially expressed pathway, it is also of substantial interest to select important genes that drive the differential expression of the pathway. METHODS: We develop a unified framework to jointly test the significance of a pathway and to select a subset of genes that drive the significant pathway effect. To achieve dimension reduction and gene selection, we decompose each gene pathway into a single score by using a regularized form of linear discriminant analysis, called sparse linear discriminant analysis (sLDA). Testing for the significance of the pathway effect proceeds via permutation of the sLDA score. The sLDA-based test is compared with competing approaches with simulations and two applications: a study on the effect of metal fume exposure on immune response and a study of gene expression profiles among Type II Diabetes patients. RESULTS: Our results show that sLDA-based testing provides a powerful approach to test for the significance of a differentially expressed pathway and gene selection. AVAILABILITY: An implementation of the proposed sLDA-based pathway test in the R statistical computing environment is available at http://www.hsph.harvard.edu/~mwu/software/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Growing interest on biological pathways has called for new statistical methods for modeling and testing a genetic pathway effect on a health outcome. The fact that genes within a pathway tend to interact with each other and relate to the outcome in a complicated way makes nonparametric methods more desirable. The kernel machine method provides a convenient, powerful and unified method for multi-dimensional parametric and nonparametric modeling of the pathway effect. RESULTS: In this paper we propose a logistic kernel machine regression model for binary outcomes. This model relates the disease risk to covariates parametrically, and to genes within a genetic pathway parametrically or nonparametrically using kernel machines. The nonparametric genetic pathway effect allows for possible interactions among the genes within the same pathway and a complicated relationship of the genetic pathway and the outcome. We show that kernel machine estimation of the model components can be formulated using a logistic mixed model. Estimation hence can proceed within a mixed model framework using standard statistical software. A score test based on a Gaussian process approximation is developed to test for the genetic pathway effect. The methods are illustrated using a prostate cancer data set and evaluated using simulations. An extension to continuous and discrete outcomes using generalized kernel machine models and its connection with generalized linear mixed models is discussed. CONCLUSION: Logistic kernel machine regression and its extension generalized kernel machine regression provide a novel and flexible statistical tool for modeling pathway effects on discrete and continuous outcomes. Their close connection to mixed models and attractive performance make them have promising wide applications in bioinformatics and other biomedical areas.

MOTIVATION: Mass spectrometry (MS), such as the surface-enhanced laser desorption and ionization time-of-flight (SELDI-TOF) MS, provides a potentially promising proteomic technology for biomarker discovery. An important matter for such a technology to be used routinely is its reproducibility. It is of significant interest to develop quantitative measures to evaluate the quality and reliability of different experimental methods. RESULTS: We compare the quality of SELDI-TOF MS data using unfractionated, fractionated plasma samples and abundant protein depletion methods in terms of the numbers of detected peaks and reliability. Several statistical quality-control and quality-assessment techniques are proposed, including the Graeco-Latin square design for the sample allocation on a Protein chip, the use of the pairwise Pearson correlation coefficient as the similarity measure between the spectra in conjunction with multi-dimensional scaling (MDS) for graphically evaluating similarity of replicates and assessing outlier samples; and the use of the reliability ratio for evaluating reproducibility. Our results show that the number of peaks detected is similar among the three sample preparation technologies, and the use of the Sigma multi-removal kit does not improve peak detection. Fractionation of plasma samples introduces more experimental variability. The peaks detected using the unfractionated plasma samples have the highest reproducibility as determined by the reliability ratio. AVAILABILITY: Our algorithm for assessment of SELDI-TOF experiment quality is available at http://www.biostat.harvard.edu/~xlin. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.