Latest news feature in @NatureBiotech on recent developments in spatial biology to unravel disease mechanisms, with insights from experts in field @aruthak @NeBanovich @AI4Pathology @PDulaiMD Jasmine Plummer, Amanda Orr etc

RT @atvbahajournals: The 9p21 risk locus — the first and most impactful CAD genetic risk factor in humans — drives VSMCs to adopt an osteoc….

RT @valelosardo: Thrilled to share the🥇paper from the lab @atvbahajournals. Here we continue to unravel the mysteries of the 9p21.3 CAD loc….

RT @atheroexpress: 🚀 Thrilled to announce our latest #preprint on intraplaque haemorrhage (IPH) quantification and molecular 🧬characterisat….

RT @jsauce7: Trump's slashing of NIH indirects blocked for now in 22 states but not VA. Will the "peoples protector" Attorney General @Jaso….

Huge congrats to PhD student @John_S_IV for successfully defending his thesis today! Thanks to the committee and everyone for their support. @shefflab @uva_bims @MedicineUVA

New model for multimodal spatial omics analysis, MISO, performs feature extraction, clustering and handles large-scale omics data. Includes extensive benchmarking and application to various cancer types. ⁦@DrMingyaoLi⁩ ⁦@naturemethods⁩

New expression foundation model, GET, predicts gene expression in unseen cell types and context specific TF interaction networks - pretrained on chromatin accessibility data from 213 human adult and fetal cell types. @nature @ericxing @RabadanColumbia

New generative model, IMPA, predicts cell morphology responses to genetic and drug perturbations, enhancing phenotypic screening and drug discovery. Corrects batch effects and models unseen perturbations. @NatureComms @mo_lotfollahi @fabian_theis .

R package: Code: Python implementation:

New single-cell analysis framework, LEMUR, disentangles covariates and latent cell states from multi-condition data to predict counterfactual gene expression and identify cell neighborhoods with similar DEGs without clustering. @NatureGenet @wolfgangkhuber.

Delineating the effective use of self-supervised learning in single-cell genomics ⁦@NatMachIntell⁩ ⁦@fabian_theis⁩

Very grateful and humbled to receive promotion to tenure @MedicineUVA! . Huge thanks to my supportive mentors, colleagues, family, and talented trainees who continue to inspire me along this journey. Here’s to the work ahead!.

RT @shitov_happens: The number of cells in single-cell transcriptomics studies grew exponentially over the years. But what about the number….

For simulation tasks scDesign3 outperformed scFMs in simulating reference based scRNA-seq datasets @SongDongyuan @jsb_ucla.

As expected the performance of scFMs is highly task specific. they perform well in certain tasks after fine tuning, eg cell annotation, but worse in gene regulatory networks.

Nice benchmarking study of 10 different foundation models for single cell data analysis. important best practices, limitations and insights to guide future applications @HongyuZhao2

RT @StephenQuake: Virtual cell models have the potential to transform biological research. Today @ChanZuckerberg released an initial set of….

New method CellANOVA recovers lost biological signals and evaluates global and gene level distortions introduced from common single-cell batch correction algorithms ⁦@NatureBiotech⁩

RT @junedh_amrute: We use Multiome and HiC to build a comprehensive single cell variant to enhancer to gene map for coronary artery disease….