scRNA Seq

Single-cell RNA sequencing (scRNA-seq) allows researchers to profile gene expression in individual cells, revealing cellular heterogeneity within tissues. Current research focuses on developing advanced computational methods, including deep learning architectures like graph neural networks, transformers, and diffusion models, to overcome challenges posed by the high dimensionality, sparsity, and noise inherent in scRNA-seq data. These improvements aim to enhance accuracy in tasks such as cell type identification, gene regulatory network inference, and the prediction of spatial gene expression. The resulting insights are transforming our understanding of biological processes and disease mechanisms, with applications ranging from cancer research to developmental biology.

Papers

November 11, 2024

October 30, 2024

eDOC: Explainable Decoding Out-of-domain Cell Types with Evidential Learning
Chaochen Wu, Meiyun Zuo, Lei Xie
LeArning Abstract Single Cell RNA scRNA Seq Cell Annotation scRNA Seq Data Evidential Reasoning

October 21, 2024

LLM4GRN: Discovering Causal Gene Regulatory Networks with LLMs -- Evaluation through Synthetic Data Generation
Tejumade Afonja, Ivaxi Sheth, Ruta Binkyte, Waqar Hanif, Thomas Ulas, Matthias Becker, Mario Fritz
Large Language Model Global Evaluation Causal Discovery Synthetic Data Generation Regulatory Network Single Cell RNA scRNA Seq

August 20, 2024

Single-cell Curriculum Learning-based Deep Graph Embedding Clustering
Huifa Li, Jie Fu, Xinpeng Ling, Zhiyu Sun, Kuncan Wang, Zhili Chen
Single Cell RNA Deep Graph scRNA Seq Cell Annotation scRNA Seq Data

August 13, 2024

Pan-cancer gene set discovery via scRNA-seq for optimal deep learning based downstream tasks
Jong Hyun Kim, Jongseong Jang
Scientific Discovery Downstream Task scRNA Seq Cancer Dataset

August 9, 2024

July 29, 2024

Making Multi-Axis Gaussian Graphical Models Scalable to Millions of Samples and Features
Bailey Andrew, David R. Westhead, Luisa Cutillo
Feature Wise External Sample Large Datasets scRNA Seq Gaussian Graphical Model

July 25, 2024

Gene Regulatory Network Inference from Pre-trained Single-Cell Transcriptomics Transformer with Joint Graph Learning
Sindhura Kommu, Yizhi Wang, Yue Wang, Xuan Wang
Transformer Language Model Pre Trained BERT Regulatory Network scRNA Seq Gene Regulatory Neural Network

July 18, 2024

SpaDiT: Diffusion Transformer for Spatial Gene Expression Prediction using scRNA-seq
Xiaoyu Li, Fangfang Zhu, Wenwen Min
Diffusion Transformer Spatial Transcriptomics scRNA Seq Unseen Bird Specie Spatial Gene Expression Prediction

July 13, 2024

CellAgent: An LLM-driven Multi-Agent Framework for Automated Single-cell Data Analysis
Yihang Xiao, Jinyi Liu, Yan Zheng, Xiaohan Xie, Jianye Hao, Mingzhi Li, Ruitao Wang, Fei Ni, Yuxiao Li, Jintian Luo, Shaoqing Jiao, Jiajie Peng
Agent System Single Cell Single Cell RNA scRNA Seq scRNA Seq Data

June 27, 2024

scTree: Discovering Cellular Hierarchies in the Presence of Batch Effects in scRNA-seq Data
Moritz Vandenhirtz, Florian Barkmann, Laura Manduchi, Julia E. Vogt, Valentina Boeva
Speech Presence scRNA Seq Tree Representation Cell Population Tree Variational

May 6, 2024

sc-OTGM: Single-Cell Perturbation Modeling by Solving Optimal Mass Transport on the Manifold of Gaussian Mixtures
Andac Demir, Elizaveta Solovyeva, James Boylan, Mei Xiao, Fabrizio Serluca, Sebastian Hoersch, Jeremy Jenkins, Murthy Devarakonda, Bulent Kiziltan
Gaussian Mixture Dimensional Manifold scRNA Seq Single Cell Data Perturbation Analysis Genetic Perturbation Cell Nucleus Optimal Mass Transport

April 9, 2024

February 18, 2024

scInterpreter: Training Large Language Models to Interpret scRNA-seq Data for Cell Type Annotation
Cong Li, Meng Xiao, Pengfei Wang, Guihai Feng, Xin Li, Yuanchun Zhou
Large Language Model Single Cell RNA scRNA Seq Cell Annotation Cell Type Single Cell Multi Omics

January 8, 2024

scDiffusion: conditional generation of high-quality single-cell data using diffusion model
Erpai Luo, Minsheng Hao, Lei Wei, Xuegong Zhang
Diffusion Model Single Cell Conditional Generation Single Cell RNA scRNA Seq

December 27, 2023

scRNA-seq Data Clustering by Cluster-aware Iterative Contrastive Learning
Weikang Jiang, Jinxian Wang, Jihong Guan, Shuigeng Zhou
scRNA Seq scRNA Seq Data

December 21, 2023

Single-Cell RNA-seq Synthesis with Latent Diffusion Model
Yixuan Wang, Shuangyin Li, Shimin DI, Lei Chen
Latent Diffusion Model Latent Diffusion Single Cell RNA scRNA Seq

scRNA Seq

Papers

White-Box Diffusion Transformer for single-cell RNA-seq generation

Mixed Effects Deep Learning Autoencoder for interpretable analysis of single cell RNA Sequencing data

eDOC: Explainable Decoding Out-of-domain Cell Types with Evidential Learning

LLM4GRN: Discovering Causal Gene Regulatory Networks with LLMs -- Evaluation through Synthetic Data Generation

Single-cell Curriculum Learning-based Deep Graph Embedding Clustering

Pan-cancer gene set discovery via scRNA-seq for optimal deep learning based downstream tasks

Masked adversarial neural network for cell type deconvolution in spatial transcriptomics

scASDC: Attention Enhanced Structural Deep Clustering for Single-cell RNA-seq Data

Making Multi-Axis Gaussian Graphical Models Scalable to Millions of Samples and Features

Gene Regulatory Network Inference from Pre-trained Single-Cell Transcriptomics Transformer with Joint Graph Learning

SpaDiT: Diffusion Transformer for Spatial Gene Expression Prediction using scRNA-seq

CellAgent: An LLM-driven Multi-Agent Framework for Automated Single-cell Data Analysis

scTree: Discovering Cellular Hierarchies in the Presence of Batch Effects in scRNA-seq Data

sc-OTGM: Single-Cell Perturbation Modeling by Solving Optimal Mass Transport on the Manifold of Gaussian Mixtures

scCDCG: Efficient Deep Structural Clustering for single-cell RNA-seq via Deep Cut-informed Graph Embedding

scRDiT: Generating single-cell RNA-seq data by diffusion transformers and accelerating sampling

scInterpreter: Training Large Language Models to Interpret scRNA-seq Data for Cell Type Annotation

scDiffusion: conditional generation of high-quality single-cell data using diffusion model

scRNA-seq Data Clustering by Cluster-aware Iterative Contrastive Learning

Single-Cell RNA-seq Synthesis with Latent Diffusion Model