⚡️🔬📣Excited to share our two new Nature Medicine articles, we develop computational pathology foundation models, 1. UNI, a self-supervised computational pathology model trained on 100 million pathology images from 100k+ slides. 2. CONCH, a vision-language model for

A distinct tumor microenvironment makes anaplastic thyroid cancer more lethal but immunotherapy sensitive than papillary thyroid cancer: buff.ly/4bcNNA1 #Oncology

#StayWellinResearch 10. Motivation The way motivation really works is that action has to come first. As you act and start to make progress, then you feel motivated, and then you take more action. You have to take action first. From: 52 Ways to Stay Well. buff.ly/2RkaHPn

In need of an educational weekend read? Check out our new perspective Cell with Rahul Rahul Satija. Led by Mo Lotfollahi & YUHAN HAO, we outline how to automate single-cell data analysis via reference mapping. cell.com/cell/fulltext/…

Nature research paper: Obesity induces PD-1 on macrophages to suppress anti-tumour immunity go.nature.com/3KCsk8x

CellRank 2 is a comprehensive framework for cell fate analysis using large-scale multiview single-cell data. Philipp Weiler Marius Lange dana_peer Fabian Theis nature.com/articles/s4159…

Your spatial cancer-associated macrophage cheat sheet from Magdalena Matusiak Stanford Pathology is formally published in Cancer Discovery Spatially Segregated Macrophage Populations Predict Distinct Outcomes in Colon Cancer aacrjournals.org/cancerdiscover… ICYMI, original tweetorial from Dr. Matusiak is

Systematic comparison of sequencing-based spatial transcriptomic methods | Nature Methods nature.com/articles/s4159…

Vivier and colleagues integrated scRNA-seq and CITE-seq data to provide a resource of human NK cell heterogeneity in healthy tissues and in the tumor setting. Read it here: rdcu.be/dM61D nature.com/articles/s4159…

SpatialQC: automated quality control for spatial transcriptome data academic.oup.com/bioinformatics…

A systematic overview of single-cell transcriptomics databases, their use cases, and limitations frontiersin.org/journals/bioin…

The last manuscript from the lab is out! scMaSigPro to identify genes responsible for cell type fate decisions in #single-cell trajectory analyses. In collaboration with BioBam and under the Marie Skłodowska-Curie Actions INTERCEPT-MDS ITN project. tinyurl.com/2aksah72

How is chronic inflammation within cancers being dissected to identify immune vulnerabilities for therapeutic targeting? Watch #NASmember Lisa Coussens explain her groundbreaking research at the 161st NAS annual meeting: ow.ly/5VeE50SMkc5 #NAS161 OHSU News

🤩Excited to share our Nature Methods comment on “Unlocking the Power of Spatial Omics with AI” nature.com/articles/s4159… co-authored with Kyle Coleman and Amelia Schroeder Dept. of Biostatistics, Epidemiology & Informatics. Big thanks to Rita Strack for inviting us to contribute.

Benchmarking clustering, alignment, and integration methods for spatial transcriptomics👍 genomebiology.biomedcentral.com/articles/10.11…

Many #SpatialTranscriptomics tools are out there! Which performs best? Which runs fastest? A nice resource here😆👇 16 clustering methods SpatialPCA DR.SC SpaGCN BANKSY GraphST STAGATE SEDR PRECAST conST DeepST CCST BASS ADEPT SpaceFlow BayesSpace ConGI 10

Now out - WebAtlas pipeline for visualizing and processing integrated single cell and spatial transcriptomics data ⁦Nature Methods⁩ ⁦Omer Ali Bayraktar⁩ ⁦Haniffa Lab⁩ ⁦Shila Ghazanfar⁩ ⁦Human Cell Atlas⁩ github.com/haniffalab/web… nature.com/articles/s4159…

A unified model for interpretable latent embedding of multi-sample, multi-condition single-cell data nature.com/articles/s4146…

Mapping cellular interactions from spatially resolved transcriptomics data nature.com/articles/s4159… (read free: rdcu.be/dSTC5) Spacia software: github.com/yunguan-wang/S… 🧬🖥️

SpatialOne: end-to-end analysis of visium data at scale academic.oup.com/bioinformatics… 🧬🖥️ github.com/Sanofi-Public/…