Data integration articles from across Nature Portfolio

Complex digital representations of organs were reconstructed by computationally generating virtual slices from sparsely sampled planar spatial transcriptomic data, exemplified by a 38-million-cell mouse brain atlas that bridges gaps between tissue sections and preserves the continuous three-dimensional (3D) molecular landscape at single-cell resolution.

Gallrein et al. pair functional assays with clocks to compare chronological versus biological age of single neuron types in C. elegans, to probe their vulnerability or resilience to neurodegeneration, and use an in silico screen to identify neuroprotective drugs.

Current human tandem repeat catalogs are derived from short-read sequencing data, thus limiting the size range and sequence complexity of captured genotypes. Here the authors generate a comprehensive catalog de novo using long-read sequencing data.

Computer vision coupled with gene expression shed light on the collective organizational patterning of glioma stem cells, which correlates with glioma stem cell phenotypes

Complex digital representations of organs were reconstructed by computationally generating virtual slices from sparsely sampled planar spatial transcriptomic data, exemplified by a 38-million-cell mouse brain atlas that bridges gaps between tissue sections and preserves the continuous three-dimensional (3D) molecular landscape at single-cell resolution.

Comprehensively resolving the cell state landscape requires integrating single-cell omics data from diverse studies. We developed CONCORD, a contrastive learning framework that leverages principled mini-batch sampling to learn denoised, batch-integrated and high-resolution representations of cells, capturing intricate structures such as differentiation trajectories and cell-cycle loops across numerous biological contexts.

A framework called AUTOENCODIX benchmarks diverse autoencoder architectures in biological molecular profiling data, enabling insights from complex, multi-layered data.

We present STORIES, a computational framework for cell trajectory inference from spatial transcriptomics data profiled at several time points. STORIES learns a spatially informed and interpretable model of differentiation that can be used to predict future cell states and regulators of cell fate decisions.

This commentary discusses health data challenges in Africa, focusing on digitization, standardization, and harmonization as key solutions. It highlights how addressing these foundational issues can enable AI and data science to transform healthcare systems across the continent.