Stars
STAgent is a multimodal LLM-based AI agent that enables deep research about spatial transcriptomics data
QuST: QuPath Extension for Integrative Whole Slide Image and Spatial Transcriptomics Analysis
R-based Xenium Spatial Analysis Toolkit to assess gene expression gradients
Permutation analysis and normalization strategy for the colocatome paper.
GraphCompass: Graph Comparison Tools for Differential Analyses in Spatial Systems
Supervised Pathway DEConvolution of InTerpretable Gene ProgRAms
marker-based purification of cell types from single-cell RNA-seq datasets
https://www.sc-best-practices.org
Python tool for alignment of spatial transcriptomics (ST) data using diffeomorphic metric mapping
Probabilistic cell segmentation for in situ spatial transcriptomics
Integrative and Reference-Informed Spatial Domain Detection for Spatial Transcriptomics
An R framework for choosing clustering parameters in scRNA-seq analysis pipelines
an extension that wraps a Cellpose environment such that WSI can be analyzed using Cellpose through QuPath.
Friends don't let friends make certain types of data visualization - What are they and why are they bad.
A spatially aware Bayesian clustering approach that allows for the incorporation of prior biological knowledge.
Sphinx theme from Read the Docs
MAGIC (Markov Affinity-based Graph Imputation of Cells), is a method for imputing missing values restoring structure of large biological datasets.
Data available at: https://www.dropbox.com/sh/jh4s57kgbb0rnxe/AABoMK466cGc1OKsOuFN10wca?dl=0 and https://nyulangone-my.sharepoint.com/:f:/g/personal/dalia_barkley_nyulangone_org/EuHetdAOc2pLjm9XUwq…
Code for reproducing the analysis in Gavish et al. "The transcriptional hallmarks of intra-tumor heterogeneity across a thousand tumors".
Code associated to the publication: Scaling self-supervised learning for histopathology with masked image modeling, A. Filiot et al., MedRxiv (2023). We publicly release Phikon 🚀
A standardized Python API with necessary preprocessing, machine learning and explainability tools to facilitate graph-analytics in computational pathology.
Implementation of Attention-based Deep Multiple Instance Learning in PyTorch
Visualizer for neural network, deep learning and machine learning models