Identification of cell types in multiplexed in situ images by combining protein expression and spatial information using CELESTA | Nature Methods
Advances in multiplexed in situ imaging are revealing important insights in spatial biology. However, cell type identification remains a major challenge in imaging analysis, with most existing methods involving substantial manual assessment and subjective decisions for thousands of cells. Researchers developed an unsupervised machine learning algorithm, CELESTA, which identifies the cell type of each cell, individually, using the cell’s marker expression profile and, when needed, its spatial information.
Nezzle: an interactive and programmable visualization of biological networks in Python | Bioinformatics
High-quality visualization of biological networks often requires both manual curation for proper alignment and programming to map external data to the graphical components. Nezzle is a network visualization software written in Python, which provides programmable and interactive interfaces for facilitating both manual and automatic curation of the graphical components of networks to create high-quality figures.
Interpreting the B-cell receptor repertoire with single-cell gene expression using Benisse | Nature Machine Intelligence
B-cell receptors (BCRs) are a crucial player in the development and activation of B cells, and their mature forms are secreted as antibodies, which execute functions such as the neutralization of invading pathogens. Here, researchers investigated 43,938 B cells from 13 scRNA-seq datasets with matched scBCR sequencing, and they observed an association between the BCRs and the B cells’ transcriptomics. Motivated by this, they developed the Benisse model (BCR embedding graphical network informed by scRNA-seq) to provide refined analyses of BCRs guided by single-cell gene expression.