Single-cell technologies, particularly single-cell RNA sequencing (scRNA-seq) methods,
together with associated computational tools and the growing availability of public data …

The expanding scale and inherent complexity of biological data have encouraged a growing
use of machine learning in biology to build informative and predictive models of the …

Here we report the generation of a multimodal cell census and atlas of the mammalian
primary motor cortex as the initial product of the BRAIN Initiative Cell Census Network …

Interpretability in machine learning (ML) is crucial for high stakes decisions and
troubleshooting. In this work, we provide fundamental principles for interpretable ML, and …

The amygdala is a brain region primarily associated with emotional response. The use of
genetic markers and single-cell transcriptomics can provide insights into behavior …

Dimension reduction (DR) techniques such as t-SNE, UMAP, and TriMap have
demonstrated impressive visualization performance on many real-world datasets. One …

The mammalian brain develops through a complex interplay of spatial cues generated by
diffusible morphogens, cell–cell interactions and intrinsic genetic programs that result in …

Visualization of the cellular heterogeneity and spatial architecture of the tumor
microenvironment (TME) is becoming increasingly important to understand mechanisms of …

The factors mediating fatal SARS-CoV-2 infections are poorly understood. Here, we show
that cigarette smoke causes a dose-dependent upregulation of angiotensin converting …

Our aim here was not to argue which algorithm, t-SNE or UMAP, is more suitable for single-
cell studies. Once informative initialization is used, the two algorithms appear to preserve the …