Stars
CellLENS: Cross-domain information fusion for enhanced cell population delineation in single-cell spatial-omics data
Unpaired single-cell multi-omics data integration
Notebooks for reproducing figures in the SEACells manuscript
Spatial Transcriptomic Analysis using Reference-Free auxiliarY deep generative modeling and Shared Histology
Multi-omics data analysis for rare population inference using single-cell graph transformer
a collection of AWESOME things about Optimal Transport in Deep Learning
SpatialGlue is a novel deep learning methods for spatial multi-omics data integration.
Multimodal Prompting with Missing Modalities for Visual Recognition, CVPR'23
Awesome graph anomaly detection techniques built based on deep learning frameworks. Collections of commonly used datasets, papers as well as implementations are listed in this github repository. We…
Official implementation of CVPR'24 paper 'Anomaly Heterogeneity Learning for Open-set Supervised Anomaly Detection'.
Official PyTorch implementation for the paper ADA-GAD: Anomaly-Denoised Autoencoders for Graph Anomaly Detection (AAAI 2024).
A list of awesome papers and cool resources on optimal transport and its applications in general! As you will notice, this list is currently mostly focused on optimal transport for machine learning…
Book on Spatial MultiOmics Analysis with Bioconductor
Methods to discover gene programs on single-cell data
Graph-linked unified embedding for single-cell multi-omics data integration
Collection of awesome medical dataset resources.
Enables cellxgene to generate violin, stacked violin, stacked bar, heatmap, volcano, embedding, dot, track, density, 2D density, sankey and dual-gene plot in high-resolution SVG/PNG format. It also…
Identification and targeting of treatment resistant progenitor populations in T-cell Acute Lymphoblastic Leukemia
Cluster decomposition-based Anomaly Detection method (scCAD) is used to effectively identify rare cell types in scRNA-seq data.
Classified DNA microarray gene expression data used different classifiers on 22 datasets.
Google Research
scAce: an adaptive embedding and clustering method for scRNA-seq data