With the explosion in available technologies for measuring many biological phenomena on a large scale, there have been concerted efforts in a variety of biological and medical settings to perform systems biology analyses. A crucial question then becomes how to combine data across the various large-scale data types. This article reviews the data types that can be considered and treats socalled horizontal and vertical integration analyses. This article focuses on the use of multiple testing approaches in order to perform integrative analyses. Two questions help to clarify the class of procedures that should be used. The first deals with whether a horizontal or vertical integration is being performed. The second is if there is a priority for a given platform. Based on the answers to these questions, we review various methodologies that could be applied. This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Knowledge Discovery Statistical and Graphical Methods of Data Analysis > Nonparametric Methods Applications of Computational Statistics > Genomics/Proteomics/Genetics
The discussion paper "Statistical Modeling: the Two Cultures" (Statistical Science, Vol 16, 2001) by the late Leo Breiman sent shockwaves throughout the statistical community and subsequently redirected the efforts of much of the field towards machine learning, highdimensional analysis and data mining approaches. In this discussion, we discuss some of the implications of this work in the sphere of causal inference. In particular, we define the concept of comparability, which is fundamental to the ability to draw causal inferences and reinterpret some concepts in high-dimensional data analysis from this viewpoint. One of the points we highlight in this discussion is the need to consider data-adaptive estimands for causal effects with high-dimensional confounders. We also revisit matching and develop some mathematical formalism for matching algorithms.
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