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Kang K, Seidlitz J, Bethlehem RA, Xiong J, Jones MT, Mehta K, Keller AS, Tao R, Randolph A, Larsen B, Tervo-Clemmens B, Feczko E, Miranda Dominguez O, Nelson S, Schildcrout J, Fair D, Satterthwaite TD, Alexander-Bloch A, Vandekar S. Study design features increase replicability in cross-sectional and longitudinal brain-wide association studies. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.05.29.542742. [PMID: 37398345 PMCID: PMC10312450 DOI: 10.1101/2023.05.29.542742] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Brain-wide association studies (BWAS) are a fundamental tool in discovering brain-behavior associations. Several recent studies showed that thousands of study participants are required for good replicability of BWAS because the standardized effect sizes (ESs) are much smaller than the reported standardized ESs in smaller studies. Here, we perform analyses and meta-analyses of a robust effect size index using 63 longitudinal and cross-sectional magnetic resonance imaging studies from the Lifespan Brain Chart Consortium (77,695 total scans) to demonstrate that optimizing study design is critical for increasing standardized ESs and replicability in BWAS. A meta-analysis of brain volume associations with age indicates that BWAS with larger variability in covariate have larger reported standardized ES. In addition, the longitudinal studies we examined reported systematically larger standardized ES than cross-sectional studies. Analyzing age effects on global and regional brain measures from the United Kingdom Biobank and the Alzheimer's Disease Neuroimaging Initiative, we show that modifying longitudinal study design through sampling schemes improves the standardized ESs and replicability. Sampling schemes that improve standardized ESs and replicability include increasing between-subject age variability in the sample and adding a single additional longitudinal measurement per subject. To ensure that our results are generalizable, we further evaluate these longitudinal sampling schemes on cognitive, psychopathology, and demographic associations with structural and functional brain outcome measures in the Adolescent Brain and Cognitive Development dataset. We demonstrate that commonly used longitudinal models can, counterintuitively, reduce standardized ESs and replicability. The benefit of conducting longitudinal studies depends on the strengths of the between- versus within-subject associations of the brain and non-brain measures. Explicitly modeling between- versus within-subject effects avoids averaging the effects and allows optimizing the standardized ESs for each separately. Together, these results provide guidance for study designs that improve the replicability of BWAS.
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Affiliation(s)
- Kaidi Kang
- Department of Biostatistics, Vanderbilt University Medical Center
| | - Jakob Seidlitz
- Department of Child and Adolescent Psychiatry and Behavioral Sciences, The Children’s Hospital of Philadelphia
- Department of Psychiatry, University of Pennsylvania
- Lifespan Brain Institute of The Children’s Hospital of Philadelphia and Penn Medicine
| | | | - Jiangmei Xiong
- Department of Biostatistics, Vanderbilt University Medical Center
| | - Megan T. Jones
- Department of Biostatistics, Vanderbilt University Medical Center
| | - Kahini Mehta
- Department of Psychiatry, University of Pennsylvania
- Lifespan Brain Institute of The Children’s Hospital of Philadelphia and Penn Medicine
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania
| | - Arielle S. Keller
- Department of Psychiatry, University of Pennsylvania
- Lifespan Brain Institute of The Children’s Hospital of Philadelphia and Penn Medicine
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania
| | - Ran Tao
- Department of Biostatistics, Vanderbilt University Medical Center
| | - Anita Randolph
- Department of Pediatrics, University of Minnesota Medical School
| | - Bart Larsen
- Department of Pediatrics, University of Minnesota Medical School
| | - Brenden Tervo-Clemmens
- Department of Department of Psychiatry & Behavioral Sciences, University of Minnesota Medical School
| | - Eric Feczko
- Department of Pediatrics, University of Minnesota Medical School
| | | | - Steve Nelson
- Department of Pediatrics, University of Minnesota Medical School
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Damien Fair
- Department of Pediatrics, University of Minnesota Medical School
| | - Theodore D. Satterthwaite
- Department of Psychiatry, University of Pennsylvania
- Lifespan Brain Institute of The Children’s Hospital of Philadelphia and Penn Medicine
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania
| | - Aaron Alexander-Bloch
- Department of Child and Adolescent Psychiatry and Behavioral Sciences, The Children’s Hospital of Philadelphia
- Department of Psychiatry, University of Pennsylvania
- Lifespan Brain Institute of The Children’s Hospital of Philadelphia and Penn Medicine
| | - Simon Vandekar
- Department of Biostatistics, Vanderbilt University Medical Center
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2
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Horng H, Scott C, Winham S, Jensen M, Pantalone L, Mankowski W, Kerlikowske K, Vachon CM, Kontos D, Shinohara RT. Multivariate testing and effect size measures for batch effect evaluation in radiomic features. Sci Rep 2024; 14:13923. [PMID: 38886407 PMCID: PMC11183083 DOI: 10.1038/s41598-024-64208-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Accepted: 06/06/2024] [Indexed: 06/20/2024] Open
Abstract
While precision medicine applications of radiomics analysis are promising, differences in image acquisition can cause "batch effects" that reduce reproducibility and affect downstream predictive analyses. Harmonization methods such as ComBat have been developed to correct these effects, but evaluation methods for quantifying batch effects are inconsistent. In this study, we propose the use of the multivariate statistical test PERMANOVA and the Robust Effect Size Index (RESI) to better quantify and characterize batch effects in radiomics data. We evaluate these methods in both simulated and real radiomics features extracted from full-field digital mammography (FFDM) data. PERMANOVA demonstrated higher power than standard univariate statistical testing, and RESI was able to interpretably quantify the effect size of site at extremely large sample sizes. These methods show promise as more powerful and interpretable methods for the detection and quantification of batch effects in radiomics studies.
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Affiliation(s)
- Hannah Horng
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Radiology, Center for Biomedical Image Computing and Analysis (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Penn Statistics in Imaging Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, 19104, USA.
| | | | | | | | - Lauren Pantalone
- Department of Radiology, Center for Biomedical Image Computing and Analysis (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Walter Mankowski
- Department of Radiology, Center for Biomedical Image Computing and Analysis (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA
| | | | | | - Despina Kontos
- Department of Radiology, Center for Biomedical Image Computing and Analysis (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA
- Center for Innovation in Imaging Biomarkers and Integrated Diagnostics (CIMBID), Columbia University, New York, NY, 10027, USA
| | - Russell T Shinohara
- Department of Radiology, Center for Biomedical Image Computing and Analysis (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA
- Penn Statistics in Imaging Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, 19104, USA
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3
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Xiong J, Kaur H, Heiser CN, McKinley ET, Roland JT, Coffey RJ, Shrubsole MJ, Wrobel J, Ma S, Lau KS, Vandekar S. GammaGateR: semi-automated marker gating for single-cell multiplexed imaging. Bioinformatics 2024; 40:btae356. [PMID: 38833684 PMCID: PMC11193056 DOI: 10.1093/bioinformatics/btae356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 04/20/2024] [Accepted: 06/03/2024] [Indexed: 06/06/2024] Open
Abstract
MOTIVATION Multiplexed immunofluorescence (mIF) is an emerging assay for multichannel protein imaging that can decipher cell-level spatial features in tissues. However, existing automated cell phenotyping methods, such as clustering, face challenges in achieving consistency across experiments and often require subjective evaluation. As a result, mIF analyses often revert to marker gating based on manual thresholding of raw imaging data. RESULTS To address the need for an evaluable semi-automated algorithm, we developed GammaGateR, an R package for interactive marker gating designed specifically for segmented cell-level data from mIF images. Based on a novel closed-form gamma mixture model, GammaGateR provides estimates of marker-positive cell proportions and soft clustering of marker-positive cells. The model incorporates user-specified constraints that provide a consistent but slide-specific model fit. We compared GammaGateR against the newest unsupervised approach for annotating mIF data, employing two colon datasets and one ovarian cancer dataset for the evaluation. We showed that GammaGateR produces highly similar results to a silver standard established through manual annotation. Furthermore, we demonstrated its effectiveness in identifying biological signals, achieved by mapping known spatial interactions between CD68 and MUC5AC cells in the colon and by accurately predicting survival in ovarian cancer patients using the phenotype probabilities as input for machine learning methods. GammaGateR is a highly efficient tool that can improve the replicability of marker gating results, while reducing the time of manual segmentation. AVAILABILITY AND IMPLEMENTATION The R package is available at https://github.com/JiangmeiRubyXiong/GammaGateR.
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Affiliation(s)
- Jiangmei Xiong
- Department of Biostatistics, Vanderbilt University, 2525 West End Avenue, Suite 1100, Nashville, TN 37203-1741, United States
| | - Harsimran Kaur
- Program of Chemical and Physical Biology, Vanderbilt University School of Medicine, 340 Light Hall, 2215 Garland Ave, Nashville, TN 37232, United States
- Epithelial Biology Center, Vanderbilt University Medical Center, MRBIV 10415-E, 2213 Garland Avenue, Nashville, TN 37232, United States
| | - Cody N Heiser
- Program of Chemical and Physical Biology, Vanderbilt University School of Medicine, 340 Light Hall, 2215 Garland Ave, Nashville, TN 37232, United States
- Epithelial Biology Center, Vanderbilt University Medical Center, MRBIV 10415-E, 2213 Garland Avenue, Nashville, TN 37232, United States
- Regeneron Pharmaceuticals, 777 Old Saw Mill River Road, Tarrytown, NY 10591, United States
| | - Eliot T McKinley
- Epithelial Biology Center, Vanderbilt University Medical Center, MRBIV 10415-E, 2213 Garland Avenue, Nashville, TN 37232, United States
- GlaxoSmithKline, 410 Blackwell St, Durham, NC 27701, United States
| | - Joseph T Roland
- Epithelial Biology Center, Vanderbilt University Medical Center, MRBIV 10415-E, 2213 Garland Avenue, Nashville, TN 37232, United States
- Department of Surgery, Vanderbilt University Medical Center, 2215 Garland Ave Medical Research Building IV, Nashville, TN 37232, United States
| | - Robert J Coffey
- Epithelial Biology Center, Vanderbilt University Medical Center, MRBIV 10415-E, 2213 Garland Avenue, Nashville, TN 37232, United States
- Department of Medicine, Vanderbilt University Medical Center, 1161 21st Ave S, Nashville, TN 37232, United States
| | - Martha J Shrubsole
- Department of Medicine, Vanderbilt University Medical Center, 1161 21st Ave S, Nashville, TN 37232, United States
| | - Julia Wrobel
- Department of Biostatistics and Bioinformatics, Emory University, 1518 Clifton Rd, Atlanta, GA 30322, United States
| | - Siyuan Ma
- Department of Biostatistics, Vanderbilt University, 2525 West End Avenue, Suite 1100, Nashville, TN 37203-1741, United States
| | - Ken S Lau
- Program of Chemical and Physical Biology, Vanderbilt University School of Medicine, 340 Light Hall, 2215 Garland Ave, Nashville, TN 37232, United States
- Epithelial Biology Center, Vanderbilt University Medical Center, MRBIV 10415-E, 2213 Garland Avenue, Nashville, TN 37232, United States
- Regeneron Pharmaceuticals, 777 Old Saw Mill River Road, Tarrytown, NY 10591, United States
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, 10475 Medical Research Building IV, 2215 Garland Avenue, Nashville, TN 37232, United States
| | - Simon Vandekar
- Department of Biostatistics, Vanderbilt University, 2525 West End Avenue, Suite 1100, Nashville, TN 37203-1741, United States
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Long HL, Ramsay G, Bene ER, Su PL, Yoo H, Klaiman C, Pulver SL, Richardson S, Pileggi ML, Brane N, Oller DK. Canonical babbling trajectories across the first year of life in autism and typical development. AUTISM : THE INTERNATIONAL JOURNAL OF RESEARCH AND PRACTICE 2024:13623613241253908. [PMID: 38757642 DOI: 10.1177/13623613241253908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/18/2024]
Abstract
LAY ABSTRACT Our study examined how babies develop their ability to talk to help identify early signs of autism. We looked at babies' production of babbling with mature syllables across the first year of life. Babies usually start producing mature babbling at 7 months of age before they say their first words. Some studies have suggested that babies who are later diagnosed with autism produce this kind of babbling less frequently in their first year of life, but other studies have shown complicated outcomes. In this new study, we followed 44 autistic babies and compared them to 127 typically developing babies. We recorded the babies once every month, all day long, from the time that they were born until they were around 13 months old. Then, we studied their mature babbling from segments of these recordings. We found that the rate at which babies used mature babbling was lower in boys with autism, and higher in girls with autism, compared to babies without autism. This research helps us understand how babies with autism learn to talk. It also raises important questions about differences between boys and girls with autism. Our study can help us improve how scientists and clinicians can identify autism earlier, which could lead to better communication supports for autistic children and their families.
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Affiliation(s)
| | - Gordon Ramsay
- Emory University School of Medicine, USA
- Children's Healthcare of Atlanta, USA
| | | | | | | | - Cheryl Klaiman
- Emory University School of Medicine, USA
- Children's Healthcare of Atlanta, USA
| | - Stormi L Pulver
- Emory University School of Medicine, USA
- Children's Healthcare of Atlanta, USA
| | | | | | | | - D Kimbrough Oller
- The University of Memphis, USA
- Konrad Lorenz Institute for Evolution and Cognition Research, Austria
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5
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Zhang X, Kang K, Yan C, Feng Y, Vandekar S, Yu D, Rosenbloom ST, Samuels J, Srivastava G, Williams B, Albaugh VL, English WJ, Flynn CR, Chen Y. Enhanced Patient Portal Engagement Associated with Improved Weight Loss Outcomes in Post-Bariatric Surgery Patients. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.20.24301550. [PMID: 38293039 PMCID: PMC10827275 DOI: 10.1101/2024.01.20.24301550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Background Bariatric surgery is an effective intervention for obesity, but it requires comprehensive postoperative self-management to achieve optimal outcomes. While patient portals are generally seen as beneficial in engaging patients in health management, the link between their use and post-bariatric surgery weight loss remains unclear. Objective This study investigated the association between patient portal engagement and postoperative body mass index (BMI) reduction among bariatric surgery patients. Methods This retrospective longitudinal study included patients who underwent Roux-en-Y gastric bypass (RYGB) or sleeve gastrectomy (SG) at Vanderbilt University Medical Center (VUMC) between January 2018 and March 2021. Using generalized estimating equations, we estimated the association between active days of postoperative patient portal use and the reduction of BMI percentage (%BMI) at 3, 6, and 12 months post-surgery. Covariates included duration since surgery, the patient's age at the time of surgery, gender, race and ethnicity, type of bariatric surgery, severity of comorbid conditions, and socioeconomic disadvantage. Results The study included 1,415 patients, mostly female (80.9%), with diverse racial and ethnic backgrounds. 805 (56.9%) patients underwent RYGB and 610 (43.1%) underwent SG. By one-year post-surgery, the mean (SD) %BMI reduction was 31.1% (8.3%), and the mean (SD) number of patient portal active days was 61.0 (41.2). A significantly positive association was observed between patient portal engagement and %BMI reduction, with variations revealed over time. Each 10-day increment of active portal use was associated with a 0.57% ([95% CI: 0.42- 0.72], P < .001) and 0.35% ([95% CI: 0.22- 0.49], P < .001) %BMI reduction at 3 and 6 months postoperatively. The association was not statistically significant at 12 months postoperatively (β=-0.07, [95% CI: -0.24- 0.09], P = .54). Various portal functions, including messaging, visits, my record, medical tools, billing, resources, and others, were positively associated with %BMI reduction at 3- and 6-months follow-ups. Conclusions Greater patient portal engagement, which may represent stronger adherence to postoperative instructions, better self-management of health, and enhanced communication with care teams, was associated with improved postoperative weight loss. Future investigations are needed to identify important portal features that contribute to the long-term success of weight loss management.
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Affiliation(s)
- Xinmeng Zhang
- Department of Computer Science, Vanderbilt University, Nashville, TN
| | - Kaidi Kang
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN
| | - Chao Yan
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
| | - Yubo Feng
- Department of Computer Science, Vanderbilt University, Nashville, TN
| | - Simon Vandekar
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN
| | - Danxia Yu
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - S. Trent Rosenbloom
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
- Department of Pediatrics, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Jason Samuels
- Department of Surgery, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Gitanjali Srivastava
- Department of Pediatrics, Vanderbilt University School of Medicine, Nashville, TN, USA
- Department of Surgery, Vanderbilt University School of Medicine, Nashville, TN, USA
- Division of Diabetes, Endocrinology & Metabolism, Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA
- Vanderbilt Weight Loss Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Brandon Williams
- Department of Surgery, Vanderbilt University School of Medicine, Nashville, TN, USA
- Vanderbilt Weight Loss Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Vance L. Albaugh
- Metamor Institute, Pennington Biomedical Research Center, Baton Rouge, LA, USA
| | - Wayne J. English
- Department of Surgery, Vanderbilt University School of Medicine, Nashville, TN, USA
- Vanderbilt Weight Loss Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Charles R. Flynn
- Department of Surgery, Vanderbilt University School of Medicine, Nashville, TN, USA
- Vanderbilt Weight Loss Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - You Chen
- Department of Computer Science, Vanderbilt University, Nashville, TN
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
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6
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Xiong J, Kaur H, Heiser CN, McKinley ET, Roland JT, Coffey RJ, Shrubsole MJ, Wrobel J, Ma S, Lau KS, Vandekar S. GammaGateR: semi-automated marker gating for single-cell multiplexed imaging. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.20.558645. [PMID: 37781604 PMCID: PMC10541135 DOI: 10.1101/2023.09.20.558645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/03/2023]
Abstract
Motivation Multiplexed immunofluorescence (mIF) is an emerging assay for multichannel protein imaging that can decipher cell-level spatial features in tissues. However, existing automated cell phenotyping methods, such as clustering, face challenges in achieving consistency across experiments and often require subjective evaluation. As a result, mIF analyses often revert to marker gating based on manual thresholding of raw imaging data. Results To address the need for an evaluable semi-automated algorithm, we developed GammaGateR, an R package for interactive marker gating designed specifically for segmented cell-level data from mIF images. Based on a novel closed-form gamma mixture model, GammaGateR provides estimates of marker-positive cell proportions and soft clustering of marker-positive cells. The model incorporates user-specified constraints that provide a consistent but slide-specific model fit. We compared GammaGateR against the newest unsupervised approach for annotating mIF data, employing two colon datasets and one ovarian cancer dataset for the evaluation. We showed that GammaGateR produces highly similar results to a silver standard established through manual annotation. Furthermore, we demonstrated its effectiveness in identifying biological signals, achieved by mapping known spatial interactions between CD68 and MUC5AC cells in the colon and by accurately predicting survival in ovarian cancer patients using the phenotype probabilities as input for machine learning methods. GammaGateR is a highly efficient tool that can improve the replicability of marker gating results, while reducing the time of manual segmentation. Availability and Implementation The R package is available at https://github.com/JiangmeiRubyXiong/GammaGateR.
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Affiliation(s)
| | - Harsimran Kaur
- Program of Chemical and Physical Biology, Vanderbilt University School of Medicine, USA
- Epithelial Biology Center, Vanderbilt University Medical Center, USA
| | - Cody N Heiser
- Program of Chemical and Physical Biology, Vanderbilt University School of Medicine, USA
- Epithelial Biology Center, Vanderbilt University Medical Center, USA
- Regeneron Pharmaceuticals, USA
| | - Eliot T McKinley
- Epithelial Biology Center, Vanderbilt University Medical Center, USA
- GlaxoSmithKline, USA
| | - Joseph T Roland
- Epithelial Biology Center, Vanderbilt University Medical Center, USA
- Department of Surgery, Vanderbilt University Medical Center, USA
| | - Robert J Coffey
- Epithelial Biology Center, Vanderbilt University Medical Center, USA
- Department of Medicine, Vanderbilt University Medical Center, USA
| | | | - Julia Wrobel
- Department of Biostatistics and Bioinformatics, Emory University, USA
| | - Siyuan Ma
- Department of Biostatistics, Vanderbilt University, USA
| | - Ken S Lau
- Program of Chemical and Physical Biology, Vanderbilt University School of Medicine, USA
- Epithelial Biology Center, Vanderbilt University Medical Center, USA
- Department of Surgery, Vanderbilt University Medical Center, USA
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, USA
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