Lin Chen, PhD

Dr. Chen's overall research interests focus on developing statistical methods for analyzing 'big' integrative genomics data. Her lab has developed statistical methods for ‘omics’ studies that involve multiple types of large-scale high-dimensional data sets, for example, genetic data, gene transcriptional expression data, proteomic data and complex phenotypes. Besides methodology development, her lab has also developed many R packages.

Fred Hutchinson Cancer Research Center
Seattle
Postdoctoral Training - Statistical genomics
2010

University of Washington
Seattle
PhD - Biostatistics
2008

Peking University
Beijing
BS - Economics
2002

Integrated Proteogenomic Characterization of Clear Cell Renal Cell Carcinoma.
Integrated Proteogenomic Characterization of Clear Cell Renal Cell Carcinoma. Cell. 2020 Jan 09; 180(1):207.
PMID: 31923397

Using multivariate mixed-effects selection models for analyzing batch-processed proteomics data with non-ignorable missingness.
Using multivariate mixed-effects selection models for analyzing batch-processed proteomics data with non-ignorable missingness. Biostatistics. 2019 10 01; 20(4):648-665.
PMID: 29939200

Insights into Impact of DNA Copy Number Alteration and Methylation on the Proteogenomic Landscape of Human Ovarian Cancer via a Multi-omics Integrative Analysis.
Insights into Impact of DNA Copy Number Alteration and Methylation on the Proteogenomic Landscape of Human Ovarian Cancer via a Multi-omics Integrative Analysis. Mol Cell Proteomics. 2019 08 09; 18(8 suppl 1):S52-S65.
PMID: 31227599

Using multivariate mixed-effects selection models for analyzing batch-processed proteomics data with non-ignorable missingness.
Wang J, Wang P, Hedeker D, Chen LS. Using multivariate mixed-effects selection models for analyzing batch-processed proteomics data with non-ignorable missingness. Biostatistics. 2018 Jun 24.
PMID: 29939200

Co-occurring expression and methylation QTLs allow detection of common causal variants and shared biological mechanisms.
Co-occurring expression and methylation QTLs allow detection of common causal variants and shared biological mechanisms. Nat Commun. 2018 02 23; 9(1):804.
PMID: 29476079

A meta-analysis approach with filtering for identifying gene-level gene-environment interactions.
A meta-analysis approach with filtering for identifying gene-level gene-environment interactions. Genet Epidemiol. 2018 07; 42(5):434-446.
PMID: 29430690

Identifying cis-mediators for trans-eQTLs across many human tissues using genomic mediation analysis.
Identifying cis-mediators for trans-eQTLs across many human tissues using genomic mediation analysis. Genome Res. 2017 11; 27(11):1859-1871.
PMID: 29021290

Risk-based Breast Cancer Screening: Implications of Breast Density.
Risk-based Breast Cancer Screening: Implications of Breast Density. Med Clin North Am. 2017 Jul; 101(4):725-741.
PMID: 28577623

A MIXED-EFFECTS MODEL FOR INCOMPLETE DATA FROM LABELING-BASED QUANTITATIVE PROTEOMICS EXPERIMENTS.
A MIXED-EFFECTS MODEL FOR INCOMPLETE DATA FROM LABELING-BASED QUANTITATIVE PROTEOMICS EXPERIMENTS. Ann Appl Stat. 2017 Mar; 11(1):114-138.
PMID: 29743963

Imputing Gene Expression in Uncollected Tissues Within and Beyond GTEx.
Imputing Gene Expression in Uncollected Tissues Within and Beyond GTEx. Am J Hum Genet. 2016 Apr 07; 98(4):697-708.
PMID: 27040689

View All Publications

The Best Paper in Genetic Epidemiology Award for the Year 2021
International Genetic Epidemiology Society
2021 - 2022

Reviewer for BMRD study section
NIH
2020

Program Chair-Elect for the Section on Statistics in Genomics and Genetics
American Statistical Association
2020 - 2021

Associate Editor for Biometrics
2018

The Departmental Best Dissertation Award (for Dr. Chen's PhD student)
Department of Public Health Sciences
2017 - 2018