Search for: All records

Abstract The field of spatially resolved transcriptomics (SRT) has greatly advanced our understanding of cellular microenvironments by integrating spatial information with molecular data collected from multiple tissue sections or individuals. However, methods for multi-sample spatial clustering are lacking, and existing methods primarily rely on molecular information alone. This paper introduces BayeSMART, a Bayesian statistical method designed to identify spatial domains across multiple samples. BayeSMART leverages artificial intelligence (AI)-reconstructed single-cell level information from the paired histology images of multi-sample SRT datasets while simultaneously considering the spatial context of gene expression. The AI integration enables BayeSMART to effectively interpret the spatial domains. We conducted case studies using four datasets from various tissue types and SRT platforms, and compared BayeSMART with alternative multi-sample spatial clustering approaches and a number of state-of-the-art methods for single-sample SRT analysis, demonstrating that it surpasses existing methods in terms of clustering accuracy, interpretability, and computational efficiency. BayeSMART offers new insights into the spatial organization of cells in multi-sample SRT data. 

Abstract Researchers have investigated whether machine learning (ML) may be able to resolve one of the most fundamental concerns in personnel selection, which is by helping reduce the subgroup differences (and resulting adverse impact) by race and gender in selection procedure scores. This article presents three such investigations. The findings show that the growing practice of making statistical adjustments to (nonlinear) ML algorithms to reduce subgroup differences must create predictive bias (differential prediction) as a mathematical certainty. This may reduce validity and inadvertently penalize high‐scoring racial minorities. Similarly, one approach that adjusts the ML input data only slightly reduces the subgroup differences but at the cost of slightly reduced model accuracy. Other emerging tactics involve weighting predictors to balance or find a compromise between the competing goals of reducing subgroup differences while maintaining validity, but they have been limited to two outcomes. The third investigation extends this to three outcomes (e.g., validity, subgroup differences, and cost) and presents an online tool. Collectively, the studies in this article illustrate that ML is unlikely to be able to resolve the issue of adverse impact, but it may assist in finding incremental improvements.