Publications

2025

1. CVPR 2025

   Embodied Scene Understanding for Vision Language Models via MetaVQA

   Weizhen Wang, Chenda Duan, Zhenghao Peng, and 2 more authors

   In arXiv Preprint 2025

   PDF Website

2024

1. ICLR 2025 Spotlight

   MetaUrban: An Embodied AI Simulation Platform for Urban Micromobility

   Wayne Wu, Honglin He, Jack He, and 5 more authors

   In Preprint 2024

   PDF Website

2023

1. NeurIPS 2023 Spotlight

   Learning from Active Human Involvement through Proxy Value Propagation

   Zhenghao Peng, Wenjie Mo, Chenda Duan, and 2 more authors

   In Advances in Neural Information Processing Systems 2023

   PDF Code Website
2. NeurIPS 2023

   ScenarioNet: Open-Source Platform for Large-Scale Traffic Scenario Simulation and Modeling

   Quanyi Li*, Zhenghao Peng*, Lan Feng*, and 4 more authors

   In Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track 2023

   PDF Code Website

2022

1. European Neuropsychopharmacology

   Cell type deconvolution of bulk blood RNA-Seq to reveal biological insights of neuropsychiatric disorders

   Toni Boltz, Tommer Schwarz, Merel Bot, and 9 more authors

   European Neuropsychopharmacology 2022

   Abs Bib HTML

   Summary Genome-wide association studies (GWAS) have uncovered susceptibility loci associated with psychiatric disorders like bipolar disorder (BP) and schizophrenia (SCZ). However, most of these loci are in non-coding regions of the genome with unknown causal mechanisms of the link between genetic variation and disease risk. Expression quantitative trait loci (eQTL) analysis of bulk tissue is a common approach to decipher underlying mechanisms, though this can obscure cell-type specific signals thus masking trait-relevant mechanisms. While single-cell sequencing can be prohibitively expensive in large cohorts, computationally inferred cell type proportions and cell type gene expression estimates have the potential to overcome these problems and advance mechanistic studies. Using bulk RNA-Seq from 1,730 samples derived from whole blood in a cohort ascertained for individuals with BP and SCZ this study estimated cell type proportions and their relation with disease status and medication. We found between 2,875 and 4,629 eGenes for each cell type, including 1,211 eGenes that are not found using bulk expression alone. We performed a colocalization test between cell type eQTLs and various traits and identified hundreds of associations between cell type eQTLs and GWAS loci that are not detected in bulk eQTLs. Finally, we investigated the effects of lithium use on cell type expression regulation and found examples of genes that are differentially regulated dependent on lithium use. Our study suggests that computational methods can be applied to large bulk RNA-Seq datasets of non-brain tissue to identify disease-relevant, cell type specific biology of psychiatric disorders and psychiatric medication.

   @article{chenda_bioinfo2,
     teaser = {cover_celltype.png},
     title = {Cell type deconvolution of bulk blood RNA-Seq to reveal biological insights of neuropsychiatric disorders},
     journal = {European Neuropsychopharmacology},
     year = {2022},
     author = {Boltz, Toni and Schwarz, Tommer and Bot, Merel and Hou, Kangcheng and Caggiano, Christa and Lapinska, Sandra and Duan, Chenda and Boks, Marco P. and Kahn, Rene S. and Zaitlen, Noah and Pasaniuc, Bogdan and Ophoff, Roel},
     keywords = {RNA-seq, eQTL mapping, association testing, low coverage}
   }

2. HGG Advance

   Powerful eQTL mapping through low-coverage RNA sequencing

   Tommer Schwarz, Toni Boltz, Kangcheng Hou, and 7 more authors

   Human Genetics and Genomics Advances 2022

   Abs Bib HTML

   Summary Mapping genetic variants that regulate gene expression (eQTL mapping) in large-scale RNA sequencing (RNA-seq) studies is often employed to understand functional consequences of regulatory variants. However, the high cost of RNA-seq limits sample size, sequencing depth, and, therefore, discovery power in eQTL studies. In this work, we demonstrate that, given a fixed budget, eQTL discovery power can be increased by lowering the sequencing depth per sample and increasing the number of individuals sequenced in the assay. We perform RNA-seq of whole-blood tissue across 1,490 individuals at low coverage (5.9 million reads/sample) and show that the effective power is higher than that of an RNA-seq study of 570 individuals at moderate coverage (13.9 million reads/sample). Next, we leverage synthetic datasets derived from real RNA-seq data (50 million reads/sample) to explore the interplay of coverage and number individuals in eQTL studies, and show that a 10-fold reduction in coverage leads to only a 2.5-fold reduction in statistical power to identify eQTLs. Our work suggests that lowering coverage while increasing the number of individuals in RNA-seq is an effective approach to increase discovery power in eQTL studies.

   @article{chenda_bioinfo1,
     teaser = {cover_eqtl.jpg},
     title = {Powerful eQTL mapping through low-coverage RNA sequencing},
     journal = {Human Genetics and Genomics Advances},
     volume = {3},
     number = {3},
     pages = {100103},
     year = {2022},
     issn = {2666-2477},
     doi = {https://doi.org/10.1016/j.xhgg.2022.100103},
     author = {Schwarz, Tommer and Boltz, Toni and Hou, Kangcheng and Bot, Merel and Duan, Chenda and Loohuis, Loes Olde and Boks, Marco P. and Kahn, René S. and Ophoff, Roel A. and Pasaniuc, Bogdan},
     keywords = {RNA-seq, eQTL mapping, association testing, low coverage}
   }