@unpublished {Park2021-mw,
	title = {Single-cell deconvolution of 3,000 post-mortem brain samples for eQTL and GWAS dissection in mental disorders},
	journal = {Cold Spring Harbor Laboratory},
	year = {2021},
	pages = {2021.01.21.426000},
	abstract = {Thousands of genetic variants acting in multiple cell types underlie complex disorders, yet most gene expression studies profile only bulk tissues, making it hard to resolve where genetic and non-genetic contributors act. This is particularly important for psychiatric and neurodegenerative disorders that impact multiple brain cell types with highly-distinct gene expression patterns and proportions. To address this challenge, we develop a new framework, SPLITR, that integrates single-nucleus and bulk RNA-seq data, enabling phenotype-aware deconvolution and correcting for systematic discrepancies between bulk and single-cell data. We deconvolved 3,387 post-mortem brain samples across 1,127 individuals and in multiple brain regions. We find that cell proportion varies across brain regions, individuals, disease status, and genotype, including genetic variants in TMEM106B that impact inhibitory neuron fraction and 4,757 cell-type-specific eQTLs. Our results demonstrate the power of jointly analyzing bulk and single-cell RNA-seq to provide insights into cell-type-specific mechanisms for complex brain disorders. \#\#\# Competing Interest Statement The authors have declared no competing interest.},
	keywords = {1 CRS 2021, My Papers},
	author = {Park, Yongjin and He, Liang and Davila-Velderrain, Jose and Hou, Lei and Mohammadi, Shahin and Mathys, Hansruedi and Peng, Zhuyu and Bennett, David and Tsai, Li-Huei and Kellis, Manolis}
}
@article {Park2019-hy,
	title = {Causal Mediation Analysis Leveraging Multiple Types of Summary Statistics Data},
	year = {2019},
	month = {jan},
	abstract = {Summary statistics of genome-wide association studies (GWAS) teach causal relationship between millions of genetic markers and tens and thousands of phenotypes. However, underlying biological mechanisms are yet to be elucidated. We can achieve necessary interpretation of GWAS in a causal mediation framework, looking to establish a sparse set of mediators between genetic and downstream variables, but there are several challenges. Unlike existing methods rely on strong and unrealistic assumptions, we tackle practical challenges within a principled summary-based causal inference framework. We analyzed the proposed methods in extensive simulations generated from real-world genetic data. We demonstrated only our approach can accurately redeem causal genes, even without knowing actual individual-level data, despite the presence of competing non-causal trails.},
	keywords = {1 CRS 2021, My Papers},
	author = {Park, Yongjin and Sarkar, Abhishek and Nguyen, Khoi and Kellis, Manolis}
}
@unpublished {Park2017-kl,
	title = {A Bayesian approach to mediation analysis predicts 206 causal target genes in Alzheimer{\textquoteright}s disease},
	journal = {bioRxiv},
	year = {2017},
	pages = {219428},
	publisher = {bioRxiv},
	abstract = {Characterizing the intermediate phenotypes, such as gene expression, that mediate genetic effects on complex diseases is a fundamental problem in human genetics. Existing methods utilize genotypic data and summary statistics to identify putative disease genes, but cannot distinguish pleiotropy from causal mediation and are limited by overly strong assumptions about the data. To overcome these limitations, we develop Causal Multivariate Mediation within Extended Linkage disequilibrium (CaMMEL), a novel Bayesian inference framework to jointly model multiple mediated and unmediated effects relying only on summary statistics. We show in simulation that CaMMEL accurately distinguishes between mediating and pleiotropic genes unlike existing methods. We applied CaMMEL to Alzheimer9s disease (AD) and found 206 causal genes in sub-threshold loci (p},
	keywords = {1 CRS 2021, 1 Deconv Paper, mediation, My Papers},
	author = {Park, Yongjin and Sarkar, Abhishek and He, Liang and Davila-Velderrain, Jose and De Jager, Philip L and Kellis, Manolis}
}
@article {Park2017-vv,
	title = {Fast and reliable inference algorithm for hierarchical stochastic block models},
	year = {2017},
	abstract = {Network clustering reveals the organization of a network or corresponding complex system with elements represented as vertices and interactions as edges in a (directed, weighted) graph. Although the notion of clustering can be somewhat loose, network clusters or groups are generally considered as nodes with enriched interactions and edges sharing common patterns. Statistical inference often treats groups as latent variables, with observed networks generated from latent group structure, termed a stochastic block model. Regardless of the definitions, statistical inference can be either translated to modularity maximization, which is provably an NP-complete problem. Here we present scalable and reliable algorithms that recover hierarchical stochastic block models fast and accurately. Our algorithm scales almost linearly in number of edges, and inferred models were more accurate that other scalable methods.},
	keywords = {1 CRS 2021, My Papers, Network},
	author = {Park, Yongjin and Bader, Joel S}
}
@unpublished {Park2017-ce,
	title = {Temporal expression divergence of network modules},
	journal = {bioRxiv},
	year = {2017},
	month = {jul},
	pages = {167734},
	abstract = {Here we propose new module-based approaches to identify differentially regulated network sub-modules combining temporal trajectories of expression profiles with static network skeletons. Starting from modules identified by network clustering of static networks, our analysis refines pre-defined genesets by partitioning them into smaller homogeneous sets by non-paramettric Bayesian methods. Especially for case-control time series data we developed multi-time point discriminative models and identified each network module as a mixture or admixture of dynamic discriminative functions. Our results shows that our proposed approach outperformed existing geneset enrichment methods in simulation studies. Moreover we applied the methods to neural stem cell differentiation data, and discovered novel modules differentially perturbed in different developmental stages.},
	keywords = {1 CRS 2021, My Papers},
	author = {Park, Yongjin and Kang, Tae-Hyuk and Friedmann, Theodore and Bader, Joel}
}
