Single-cell RNA sequencing (scRNA-seq) has provided a high-dimensional catalog of millions of cells across species and diseases. These data have spurred the development of hundreds of computational tools to derive nov...
Single-cell RNA sequencing (scRNA-seq) has provided a high-dimensional catalog of millions of cells across species and diseases. These data have spurred the development of hundreds of computational tools to derive novel biological insights. Here, we outline the components of scRNA-seq analytical pipelines and the computational methods that underlie these steps. We describe available methods, highlight well-executed benchmarking studies, and identify opportunities for additional benchmarking studies and computational methods. As the biochemical approaches for single-cell omics advance, we propose coupled development of robust analytical pipelines suited for the challenges that new data present and principled selection of analytical methods that are suited for the biological questions to be addressed.
Simulation-based methods for statistical inference have evolved dramatically over the past 50 years, keeping pace with technological advancements. The field is undergoing a new revolution as it embraces the representa...
Simulation-based methods for statistical inference have evolved dramatically over the past 50 years, keeping pace with technological advancements. The field is undergoing a new revolution as it embraces the representational capacity of neural networks, optimization libraries, and graphics processing units for learning complex mappings between data and inferential targets. The resulting tools are amortized, in the sense that, after an initial setup cost, they allow rapid inference through fast feed-forward operations. In this article we review recent progress in the context of point estimation, approximate Bayesian inference, summary-statistic construction, and likelihood approximation. We also cover software and include a simple illustration to showcase the wide array of tools available for amortized inference and the benefits they offer over Markov chain Monte Carlo methods. The article concludes with an overview of relevant topics and an outlook on future research directions.
The commercial exploitation of distributed computing technologies is slowly starting to become popular under the general area of cloud computing. These solutions allow selling and buying of resources (i.e., computing,...
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ISBN:
(数字)9783642156816
ISBN:
(纸本)9783642156809
The commercial exploitation of distributed computing technologies is slowly starting to become popular under the general area of cloud computing. These solutions allow selling and buying of resources (i.e., computing, network, software, and data resources) on demand. Existing solutions in this area are diverse, ranging from Infrastructure-- a-Service (IaaS) models via Platform-as-a-Service (PaaS) to Software-as-a-Service (SaaS) models. Although the economics of these services is not yet fully understood and the interoperability between such services is still lacking, a common market for computing services is slowly developing. Such a market would allow buyers and sellers of computing services to trade their excess capacity or make available their capacity at a cost. However, it is still not p- sible for a market participant to act as a resource provider or seller, or trade based on the current level of demand. Another example of a developing open market is the emergence of Web2.0-based services. These enable consumers to create new services by aggregating services from multiple providers. The benefit of these solutions is that “value” can be created by combining services at different prices.
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