Publications

we present LINGER (Lifelong neural network for gene regulation), a machine-learning method to infer GRNs from single-cell paired gene expression and chromatin accessibility data. LINGER incorporates atlas-scale external bulk data across diverse cellular contexts and prior knowledge of transcription factor motifs as a manifold regularization.

Qiuyue Yuan and Zhana Duren

Nature Biotechnology 2024

We propose a method named UnpairReg for the regression analysis on unpaired observations to integrate single-cell multi-omics data. On real and simulated data, UnpairReg provides an accurate estimation of cell gene expression where only chromatin accessibility data is available. The cis-regulatory network inferred from UnpairReg is highly consistent with eQTL mapping. UnpairReg improves cell type identification accuracy by joint analysis of single-cell gene expression and chromatin accessibility data.

Qiuyue Yuan and Zhana Duren

Genome Biology 2022

We develop scREG, a dimension reduction methodology, based on the concept of cis-regulatory potential, for single cell multiome data. This concept is further used for the construction of subpopulation-specific cis-regulatory networks. The R package scREG provides comprehensive functions for single cell multiome data analysis.

Zhana Duren, Fengge Chang, Fnu Naqing, Jingxue Xin, Qiao Liu, and Wing Hung Wong

Genome Biology 2022

We propose sc-compReg as a method for the comparative analysis of gene expression regulatory networks between two conditions using single cell gene expression (scRNA-seq) and single cell chromatin accessibility data (scATAC-seq).

Zhana Duren, Sophia Lu, Joseph G. Arthur, Preyas Shah, Jingxue Xin, Francesca Meschi, Miranda Lin Li, Corey M. Nemec, Yifeng Yin, and Wing Hung Wong

Nature communications 2021

Here we develop a Bayesian framework that integrates GWAS summary statistics with regulatory networks to infer genetic enrichments and associations simultaneously. Our method improves upon existing approaches by explicitly modeling network topology to assess enrichments, and by automatically leveraging enrichments to identify associations.

Xiang Zhu, Zhana Duren, Wing Hung Wong

Nature communications 2021

Here we use the expression and accessibility data from a diverse set of cell types to learn a model for the dependence of the accessibility of a regulatory element on its DNA sequence and TF expression. To interpret a personal genome, we combine the sequence information with context-specific TF expression to prioritize variants and regulatory elements in any genomic region of interest. This approach should be helpful in the study of risk loci previously identified by GWAS.

Wenran Li, Zhana Duren, Rui Jiang, Wing Hung Wong

PNAS 2020

We develop a variant interpretation methodology (vPECA) to identify active selected REs (ASREs) and associated regulatory network. We discover three causal SNPs of EPAS1, the key adaptive gene for Tibetans. These SNPs decrease the accessibility of ASREs with weakened binding strength of relevant TFs, and cooperatively down-regulate EPAS1 expression.

Jingxue Xin, Hui Zhang, Yaoxi He, Zhana Duren, Caijuan Bai, Lang Chen, Xin Luo, Dong-Sheng Yan, Chaoyu Zhang, Xiang Zhu, Qiuyue Yuan, Zhanying Feng, Chaoying Cui, Xuebin Qi, Wing Hung Wong, Yong Wang, Bing Su

Nature communications 2020

We develop a computational tool named TimeReg, which can be used to prioritize regulatory elements, to extract core regulatory modules at each time point, to identify key regulators driving changes of the cellular state, and to causally connect the modules across different time points.

Zhana Duren, Xi Chen, Jingxue Xin, Yong Wang, Wing Hung Wong

Genome Research 2020

The first joint analysis method of bulk 3D chromatin interaction data with single-cell gene expression and chromatin accessibility from the same heterogeneous cell population. In this work, we introduce DC3 (De-Convolution and Coupled-Clustering) as a method for the joint analysis of various bulk and single-cell data such as HiChIP, RNA-seq and ATAC-seq from the same heterogeneous cell population. DC3 can simultaneously identify distinct subpopulations, assign single cells to the subpopulations (i.e., clustering) and de-convolve the bulk data into subpopulation-specific data.

Wanwen Zeng, Xi Chen, Zhana Duren, Yong Wang, Rui Jiang, Wing Hung Wong

Nature Communications 2019

Biological samples are often heterogeneous mixtures of different types of cells. Suppose we have two single-cell datasets, each providing information on a different cellular feature and generated on a different sample from this mixture. Then, the clustering of cells in the two samples should be coupled as both clusterings are reflecting the underlying cell types in the same mixture. This “coupled clustering” problem is a new problem not covered by existing clustering methods. In this paper, we develop an approach for its solution based on the coupling of two nonnegative matrix factorizations. The method should be useful for integrative single-cell genomics analysis tasks such as the joint analysis of single-cell RNA-sequencing and single-cell ATAC-sequencing data.

Zhana Duren, Xi Chen, Mahdi Zamanighomi, Wanwen Zeng, Ansuman T. Satpathy, Howard Y. Chang, Yong Wang, Wing Hung Wong

PNAS 2018

The PECA is a computational mechanistic model for gene regulatory network from paired gene expression and chroamtin accessibility data. Chromatin plays a critical role in the regulation of gene expression. Interactions among chromatin regulators, sequence-specific transcription factors, and cis-regulatory sequence elements are the main driving forces shaping context-specific chromatin structure and gene expression. The purpose of the present work is to show that, by modeling matched expression and accessibility data across diverse cellular contexts, it is possible to recover a significant portion of the information in the missing data on binding locations and chromatin states and to achieve accurate inference of gene regulatory relations.

Zhana Duren, Xi Chen, Rui Jiang, Yong Wang, Wing Hung Wong

PNAS 2017