小柯机器人

美国卡内基梅隆大学Barbara E. Engelhardt等研究人员合作开发出应用于空间基因组学的非负空间分解。该研究于2022年12月31日在线发表于国际一流学术期刊《自然—方法学》。

Title: Nonnegative spatial factorization applied to spatial genomics

Author: Townes, F. William, Engelhardt, Barbara E.

Issue&Volume: 2022-12-31

Abstract: Nonnegative matrix factorization (NMF) is widely used to analyze high-dimensional count data because, in contrast to real-valued alternatives such as factor analysis, it produces an interpretable parts-based representation. However, in applications such as spatial transcriptomics, NMF fails to incorporate known structure between observations. Here, we present nonnegative spatial factorization (NSF), a spatially-aware probabilistic dimension reduction model based on transformed Gaussian processes that naturally encourages sparsity and scales to tens of thousands of observations. NSF recovers ground truth factors more accurately than real-valued alternatives such as MEFISTO in simulations, and has lower out-of-sample prediction error than probabilistic NMF on three spatial transcriptomics datasets from mouse brain and liver. Since not all patterns of gene expression have spatial correlations, we also propose a hybrid extension of NSF that combines spatial and nonspatial components, enabling quantification of spatial importance for both observations and features. A TensorFlow implementation of NSF is available from https://github.com/willtownes/nsf-paper.

DOI: 10.1038/s41592-022-01687-w

Source: https://www.nature.com/articles/s41592-022-01687-w

Nature Methods：《自然—方法学》，创刊于2004年。隶属于施普林格·自然出版集团，最新IF：47.99
官方网址：https://www.nature.com/nmeth/
投稿链接：https://mts-nmeth.nature.com/cgi-bin/main.plex