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Abstract MotivationEmerging omics technologies have introduced a two-way grouping structure in multiple testing, as seen in single-cell omics data, where the features can be grouped by either genes or cell types. Traditional multiple testing methods have limited ability to exploit such two-way grouping structure, leading to potential power loss. ResultsWe propose a new 2D Group Benjamini–Hochberg (2dGBH) procedure to harness the two-way grouping structure in omics data, extending the traditional one-way adaptive GBH procedure. Using both simulated and real datasets, we show that 2dGBH effectively controls the false discovery rate across biologically relevant settings, and it is more powerful than the BH or q-value procedure and more robust than the one-way adaptive GBH procedure. Availability and implementation2dGBH is available as an R package at: https://github.com/chloelulu/tdGBH. The analysis code and data are available at: https://github.com/chloelulu/tdGBH-paper. 

Obtaining solutions to optimal transportation (OT) problems is typically intractable when marginal spaces are continuous. Recent research has focused on approximating continuous solutions with discretization methods based on i.i.d. sampling, and this has shown convergence as the sample size increases. However, obtaining OT solutions with large sample sizes requires intensive computation effort, which can be prohibitive in practice. In this paper, we propose an algorithm for calculating discretizations with a given number of weighted points for marginal distributions by minimizing the (entropy-regularized) Wasserstein distance and providing bounds on the performance. The results suggest that our plans are comparable to those obtained with much larger numbers of i.i.d. samples and are more efficient than existing alternatives. Moreover, we propose a local, parallelizable version of such discretizations for applications, which we demonstrate by approximating adorable images. 

Abstract High-sensitivity interstellar scintillation and polarization observations of PSR B0656+14 made at three epochs over a year using the Five-hundred-meter Aperture Spherical radio Telescope (FAST) show that the scattering is dominated by two different compact regions. We identify the one nearer to the pulsar with the shell of the Monogem Ring, thereby confirming the association. The other is probably associated with the Local Bubble. We find that the observed position angles of the pulsar spin axis and the spatial velocity are significantly different, with a separation of 19.°3 ± 0.°8, inconsistent with a previously published near-perfect alignment of 1° ± 2°. The two independent scattering regions are clearly defined in the secondary spectra, which show two strong forward parabolic arcs. The arc curvatures imply that the scattering screens corresponding to the outer and inner arcs are located approximately 28 pc from PSR B0656+14 and 185 pc from the Earth, respectively. Comparison of the observed Doppler profiles with electromagnetic simulations shows that both scattering regions are mildly anisotropic. For the outer arc, we estimate the anisotropy A R to be approximately 1.3, with the scattering irregularities aligned parallel to the pulsar velocity. For the outer arc, we compare the observed delay profiles with delay profiles computed from a theoretical strong-scattering model. Our results suggest that the spatial spectrum of the scattering irregularities in the Monogem Ring is flatter than Kolmogorov, but further observations are required to confirm this. 

Abstract Background Applying directed acyclic graph (DAG) models to proteogenomic data has been shown effective for detecting causal biomarkers of complex diseases. However, there remain unsolved challenges in DAG learning to jointly model binary clinical outcome variables and continuous biomarker measurements. Results In this paper, we propose a new tool, DAGBagM, to learn DAGs with both continuous and binary nodes. By using appropriate models, DAGBagM allows for either continuous or binary nodes to be parent or child nodes. It employs a bootstrap aggregating strategy to reduce false positives in edge inference. At the same time, the aggregation procedure provides a flexible framework to robustly incorporate prior information on edges. Conclusions Through extensive simulation experiments, we demonstrate that DAGBagM has superior performance compared to alternative strategies for modeling mixed types of nodes. In addition, DAGBagM is computationally more efficient than two competing methods. When applying DAGBagM to proteogenomic datasets from ovarian cancer studies, we identify potential protein biomarkers for platinum refractory/resistant response in ovarian cancer. DAGBagM is made available as a github repository at https://github.com/jie108/dagbagM .