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Communication between cells is crucial for coordinated cellular functions in multicellular organisms. We present an optimal transport theory-based tool to infer cell–cell communication networks, spatial signaling directions and downstream targets in multicellular systems from spatial gene expression data.
Alignment of single-cell proteomics data across platforms is difficult when different data sets contain limited shared features, as is typical in single-cell assays with antibody readouts. Therefore, we developed matching with partial overlap (MARIO) to enable confident and accurate matching for multimodal data integration and cross-species analysis.
Dimension reduction is a cornerstone of exploratory data analysis; however, traditional methods fail to preserve the spatial context of spatial genomics data. In this work, we develop a nonnegative spatial factorization (NSF) model that allows interpretable, parts-based decomposition of spatial single-cell count data. NSF allows label-free annotation of regions of interest in spatial genomics data and identifies genes and cells that can be used to define those regions.
We developed an advanced deep learning approach called local shape descriptors (LSDs) to enable analysis of large electron microscopy datasets with increased efficiency. This technique will speed processing of future petabyte-sized datasets and democratize connectomics research by enabling these analyses using modest computational infrastructure available to most laboratories.
