yuhanH

seurat

R toolkit for single cell genomics

C-Tran

General Multi-label Image Classification with Transformers

cellphonedb

cobolt

A Python package for jointly analyzing multimodal single-cell sequencing data

CPA

The Compositional Perturbation Autoencoder (CPA) is a deep generative framework to learn effects of perturbations at the single-cell level. CPA performs OOD predictions of unseen combinations of drugs, learns interpretable embeddings, estimates dose-response curves, and provides uncertainty estimates.

crosslink

Deep-learning-application

HPViewer

sns

illumina sequencing data analysis pipelines

uwot

An R package implementing the UMAP dimensionality reduction method.

yuhanH/HPViewer

yuhanH/cobolt

A Python package for jointly analyzing multimodal single-cell sequencing data

yuhanH/sns

illumina sequencing data analysis pipelines

yuhanH/uwot

An R package implementing the UMAP dimensionality reduction method.

yuhanH/C-Tran

General Multi-label Image Classification with Transformers

yuhanH/cellphonedb

yuhanH/CPA

The Compositional Perturbation Autoencoder (CPA) is a deep generative framework to learn effects of perturbations at the single-cell level. CPA performs OOD predictions of unseen combinations of drugs, learns interpretable embeddings, estimates dose-response curves, and provides uncertainty estimates.

yuhanH/crosslink

yuhanH/Deep-learning-application

yuhanH/harmony

Fast, sensitive and accurate integration of single-cell data with Harmony

yuhanH/KLcolor

yuhanH/sctransform

R package for modeling single cell UMI expression data using regularized negative binomial regression

yuhanH/latch

a python bioinformatics framework

yuhanH/seurat

R toolkit for single cell genomics

yuhanH/SpadesContig_ReadCoverage

yuhanH/yuhanH.github.io