News: Sept 2021  Bing and Yu's co-1st author paper on maternal blood lipidomics analysis in severe preeclampsia is published by Journal of Lipid Research.
News: Sept 2021  Lana is going to AMIA Women's Leadership Program!
News: July 2021  Our single cell analysis webtool GranatumX is accepted provionally by Genomics, Proteomics and Bioinformatics.
News: June 2021  The 1st author manuscript of Dr. Olivier Poirion (previous postdoc) on DeepProg, an ensemble of deep-learning and machine-learning methods to predict patient survival using multi-omics data, has been accepted by Genome Medicine
News: June 2021  The collaborative paper with Dr. Elisseeff's lab on Domino, a method for modeling intercellular interactions in scRNA-Seq data, has been accepted by Nature Biomedical Engineering.
News: Feb 2021  The 1st-author paper of Paula Benny (our past postdoc in Hawaii time) on "reduced placenta microbiome diversity and abundance associated with maternal pre-pregnancy obesity" is accepted by FASEB.
News: Feb 2021  Zhucheng and Zheng's co-1st author paper on using 2-stage Cox-nnet model to integrate pathological and gene expression data to predict liver cancer patient survival is accepted by Nucleic Acid Research: Genomics and Bioinformatics. Congratulations to them!
News: Feb 2021  The position paper that Dr Garmire collaborated with preeclampsia clinical research experts Dr. James Roberts (U Pitts) and others is accepted by Hypertension.
News: Jan 2021  Dr. Guarav Shetty will be joining our lab as a new postdoc, starting March 1st.
News: Jan 2021  Di Wang's 1st author paper "Cox-nnet v2.0: improved neural-network based survival prediction extended to large-scale EMR data" is published by Bioinformatics. Congratulations!
News: Jan 2021  XinYing Fang's 1st author paper "Lilikoi V2.0: a deep learning-enabled, personalized pathway-based R package for diagnosis and prognosis predictions using metabolomics data" is published by GigaScience. Congratulations!
News: Jan 2021  In supporting female trainees and mentors, Dr Garmire published the editorial essay "Mentorship is not co-authorship: a revisit to mentorship" in Genome Biology.
Upcoming invited/accepted talks:
Data Innovation Lab invited speaker, UVA, 2022. Joint Statistical Meeting (JSM), 2022.
Potential collaborators: Garmire Group has recently acquired a 10x chromium system for single cell RNA and DNA sequencing. We welcome collaboration projects on experimentations and bioinformatics. Please send an inquiry to Dr. Garmire: lgarmire at med dot umich dot edu
Postdoc and PhD student candidates: Garmire Group is recruiting computationally competent and motivated postdocs and research scientists (PhD + postdoc training) who are passionate to cure cancers with the computational biology approach. Previous rigorous training in quantative sciences is required. The lab currently has opennings for rotational PhD students and MS students who can pass a 24hr-quiz based on projects. We can not admit students ourselves, sorry!
Lana Garmire

Lana Garmire, Ph.D. (CV) Linkedin Social Twitter Social
Associate Professor (with tenure)
Email: LGarmire at med dot umich dot edu

Primary Faculty
Department of Computational Medicine and Bioinformatics

Affiliated Faculty
Department of Biomedical Engineering
Department of Biostatistics

Members
Rogel Cancer Center, Core member

Dr. Garmire is an awardee of US Presidential Early Career Scientisits and Engineers in 2019, the highest honor bestowed to the most outstanding early career scientisits and engineers in the United States. She is a nationally and internationally recognized expert in translational bioinformatics. Before joining University of Michigan DCMB department, she rapidly rose to tenure (Dec. 2012 to Jun. 2017) at University of Hawaii Cancer Center. Garmire has won numerous competitive federal grant awards (~10 million) as the PI.

Dr. Garmire obtained the MA degree in Statistics (2005) and PhD degree in Comparative Biochemistry (Computational Biology focus, 2007), both from UC-Berkeley. She did her postdoctoral training (2008-2011) with Prof. Shankar Subramaniam in the Bioengineering Dept. and Prof. Christopher Glass in the Department of Cellular and Molecular Medicine, UC-San Diego. In 2012, she won the first bioinformatics NIH SBIR grant for Asuragen Inc (a spin-off of Ambion, the RNA company), and resumed the tenure-track faculty position since Dec 2012. She has published ~80 papers in top quality journals including Cell, Nature Communications, Genome Biology, Genome Medicine, Clinical Cancer Resarch. She contributed as the senior corresponding author in the majority of them. She delivered over 60 invited talks to institutes including National Library of Medicine (NLM) and National Academy of Sciences (NAS). She has mentored over 50 junior faculty, MD fellows, postdocs, graduate students and undergraduates of various academic backgrounds, in Biology, Mathematics, Phyiscs, (bio)Statistics, Bioengineering, Computer Science and Electrical Engineering. Most PhD and postdoc trainees became faculty or senior scientists in private sectors. She has served on various NIH study sections and currently a standing member of BDMA study section. She is on the editorial advisory board for Genome Biology and Journal of Proteome Research.

In her spare time, Dr. Garmire runs around her two young children and an academic husband. In the spare time of her spare time, she tweets about science, gender and racial disparity. She is a strong advocate for women in STEM, minority and under represented groups.

Research Interests

The major research interests of Garmire Group
single-cell sequencing and bioinformatics
Integrative omics/clinic data analysis
Translational bioinformatics of cancers and immune-related diseases, such as classifying biomarkers for cancer prognosis and diagnosis
Use high-throughput methods (next-generation sequencing method etc.) to study non-coding RNAs, such as microRNAs and intergenic non-coding RNAs
Pregnancy adversities and fetal orgin of cancers, public health genomics

Established Collaborators

Major established collaborators
Yale University:
Dr. Sherman Weissman (Sterling Professor and a member of National Academy of Sciences)
Dr. Rong Fan (Associate Professor of Biomedical Engineering)
Dr. Richard Flavell (Sterling Professor, a member of National Academy of Sciences)
Dr. Naftali Kaminski (Division Chief of Yale Pulmonary, Critical Care & Sleep Medicine)
University of Pennsyvinia:
Dr. Jason Moore (Founding Director of Institute for Biomedical Informatics and an AAAS member)
University of Michigan:
Dr. Rita Loch-Caruso (EHS)
Dr. Kelly Bakulski (Epidemiology)
Dr. John Meeker (EHS)
Dr. Veera Baladandayuthapani (Biostat)
Dr. Duxin Sun (Pharmacology)
Dr. Anna Lok (GI)
Dr. Neehar Parikh (GI)
Dr. Maria Westerhoff (Pathology)
Dr. Elizabeth Langen (OBGYN)
University of Hawaii School of Medicine:
Dr. William Boisvert
Drs. Dena Towner & Men-Jean Lee (OBGYNs)
Dr. Steven Ward (Director of Institute of Biogenesis Research)
Dr. Monika Ward (Institute of Biogenesis Research)
Dr. Ben Fogelgren (Department of Biochemistry, Physiology and Anatomy)
University of Hawaii Cancer Center:
Dr. Wei Jia (Associate Director of Shared Resources)
Dr. Herbert Yu (Director of Epidemiology Program)
Dr. Charles Rosser (Director of Clinical and Translational Research Program)

Establishing Collaborators

Other establishing collaborators
Yale University:
Dr. Vasilis Vasiliou (EHS chair)
University of Hawaii:
Drs. Qing Li in MBBE program

Photos

Principal Investigator

Lana Garmire, Ph.D.

Lana is a proud and longest-serving member of the Garmire group. She hopes to create an inclusive and diversified group, where all enjoy learning and exploring science.

Email: lgarmire@med.umich.edu

Postdoc

Bing He, Ph.D.

Bing got his bachelor’s degree from Xi’an Jiaotong University and Ph.D.'s degree from Hong Kong Baptist University. He is a bioinformatics scientist with experiences on single-cells, genomics, transcriptomics, mass spectrometry-based proteomics, protein networks and databases. He specializes in complex diseases and personalized medicine. His current research focuses on single-cell heterogeneity, drug repurposing, combination therapeutics and machine learning.

Email: hbing@umich.edu

Stefan Stanojevic, Ph.D.

Stefan obtained his BSc in mathematics and physics from Brandeis University and his PhD in theoretical physics from Brown University. He is interested in machine learning applied to multi-omics data.

Email: stanojes@umich.edu

PhD Student

Yijun Li

Yijun Li is a PhD candidate in the department of Biostatistics. She earned a Bachelor degree in Statistics and Mathematics at Duke University and a Masters degree in Biostatistics here in University of Michigan. Yijun’s primary research interest is in spatial transcriptomics, neuroimaging and developing novel methodology that combines traditional statistical methods with deep learning.

Email: liyijun@umich.edu

Qianhui Huang

Qianhui is a PhD candidate in the DCMB. Qianhui received her dual bachelor’s degree in biology and applied mathematics from the University of Pittsburgh and master’s degree in biostatistics from the University of Michigan. Her research interest involves scRNA-seq analysis, GWAS analysis, DNA methylation analysis and expanding her knowledge in deconvolution problems in omics analysis.

Email: qhhuang@umich.edu

Yuheng Du

Yuheng is a PhD student in the DCMB. Yuheng obtained her BS degree in Biology and Computer Science from Brandeis University, and MS degree in Biostatistics from University of Michigan. She is currently working on DNA methylation and single-cell RNA-seq analysis.

Email: yhdu@umich.edu

Research Specialist

Xiaotong Yang

Xiaotong Yang is a research associate at Dr. Garmire’s lab. She received her dual BS degree in statistics and economics and her MS degree in applied statistics from the University of Michigan, Ann Arbor. She’s currently working with DNA methylation microarray data and electronic health record (EHR) data in preeclamptic pregnancies. Her research interests include applying statistical and machine learning methods to genomic and epigenomic data.

Email: xtyang@umich.edu

Shashank Yadav

Shashank is a research associate in the group. He obtained his bachelors and masters degree in Biochemical Engineering and Biotechnology from the Indian Institute of Technology Delhi, New Delhi, India. He is currently working on understanding cell-cell interactions in cancer prognosis.

Email: shyadav@umich.edu, shashank.yadav1@alumni.iitd.ac.in

Master Student Research Assistant

Thatchayut Unjitwattana

Thatchayut obtained his bachelor's degree in Computer Engineering from Chiang Mai University, Thailand. He is currently an M.S. student in Biomedical Engineering at University of Michigan. His research interests include using machine learning approaches for biomarker discovery and predictive analyses.

Email: thatchau@umich.edu

Zhixin Mao

Zhixin Mao received her Bachelor’s degree in mathematics at Rutgers University, and she is getting a Master’s degree in Statistics at University of Michigan. She is currently working with cord blood DNA methylation data and downstream analysis in preeclampsia.

Email: zhmao@umich.edu

Xingwen Wei

After getting a Bachelor of Science degree of Computer Science from Lafayette College in PA, Xingwen is getting a Master’s degree in Data Science in University of Michigan. He is working on DeepProg webapps for Dr. Garmire.

Email: weixw@umich.edu

Research Associate

Alumni

Cameron Lassiter

Cameron obtained his BS degree in Physiology and Neurobiology from the University of Connecticut, and an MS from John Hopkins University and the University of Maryland, Baltimore in Bioinformatics and Biomedical Sciences, respectively. He has previously worked in both industry and academia on a mixture of pre-clinical and clinical projects; including chemotherapy-induced peripheral neuropathy, lupus, cerebral ataxia, and hepatitis C; involving omics data sets and wet-lab validation. He is interested in bridging the gap between computational and experimental knowledge.

Email: CLassiter@cc.hawaii.edu

Breck Yunits

Breck obtained his BS degree in Economics from Duke University. He previously worked at Microsoft on cloud computing and did research on programming languages and data visualization. He is interested in machine learning applied to genomics and biomarkers.

byunits@cc.hawaii.edu

Thomas Wolfgruber

Thomas received his BS degree in Computer Engineering from Santa Clara University and PhD in Molecular Biosciences and Bioengineering at the University of Hawaii at Manoa. He has researched the genomics and epigenomics of centromeres, and is interested in making software for research.

Email: twolfgruber@cc.hawaii.edu

Cédric Arisdakessian

Cedric received his Engineering degree from the Ecole Centrale de Lyon (ECL) and specialized in Computer Sciences. In addition, he obtained his MS degree in Biomathematics, Biostatistics, Bioinformatics and Public Health from Lyon I university. His research interests include machine learning approaches for biomarker discovery and predictive analyses using multiomics data.

Email: carisdak@hawaii.edu

Xun Zhu

Xun obtained his BS degree in Applied Mathematics from Tianjing Polytechnic University, China, and MS degree in Applied Mathematics from University of Southern California. His research interests include single-cell RNA-seq analysis.

Email: xunzhu@cc.hawaii.edu

Ryan Schlueter

Ryan Schlueter obtained his BS in biological sciences from the College of William and Mary and his medical degree (D.O.) from the Virginia College of Osteopathic Medicine in Blacksburg, VA. He then completed residency training in Obstetrics and Gynecology at the University of Buffalo, SUNY. Currently he is a fellow at the University of Hawaii in Maternal-Fetal Medicine. Research interests include maternal obesity and preeclampsia, stillbirth, and modifiable risk factors for maternal disease.

Email: rjschlue@hawaii.edu

Paula Benny

Paula obtained her B.Sc (Hons) in Life Sciences from the National University of Singapore. She also has a M.Sc in Biomedical Sciences and a PhD in Biochemistry. Her research interests include genetics, pathophysiology and cancer.

Email: pbenny@cc.hawaii.edu

Fadhl Alakwaa

Fadhl Alakwaa obtained his Master’s degree in Biomedical engineering at Cairo University in Giza, Egypt. He then completed his PhD in the same department, working on extracting biological knowledge from biological databases. His research was mainly on modeling human gene-gene interaction using Bayesian networks and biclustering. His research interests include but are not limited to Genomic Cancer Biology, Bioinformatics, Gene Regulatory Network, Biological Networks, Molecular Biology, and Biotechnology.

Email: falakwaa@cc.hawaii.edu

Olivier Poirion

Olivier obtained an Engineering degree in Bioprocess Sciences with minor in Bioinformatics from Ecole Nationale Superieure d'Agronomie et des Industries Alimentaires (ENSAIA), France and a Ph.D. in Evolutionary Genomics, delivered by the Ecole Centrale de Lyon (ECL), France. Olivier is interested in machine learning and datamining analyses, applied to genomics.

Email: opoirion@hawaii.edu

Sijia Huang

Sijia Huang, PhD (Aug. 2013 - Dec. 2017) is current a postdoc researcher in the group of Prof. Jason Moore, Director of Biomedical Informatics Insitute at U Penn. She obtained her BS degree in Financial Statistics in HuaZhong University of Science and Technology in China, and MS degree in biostatistics from University of Florida. Her PhD thesis was in biomarker classification and genomic/clinic data integration in the context of breast cancers.

Email: shuang@cc.hawaii.edu

Kumardeep Chaudhary

Kumardeep, Postdoc (April 2016- Dec 2017). Kumar is current a senior postdoc in School of Medicine, Mount Sainai. He was a postdoc in Garmire group and relocated for family reason, he did his BSc (Gold Medal) in Zoology (Hons.) from Panjab University, Chandigarh (India) followed by MSc in Systems Biology and Bioinformatics from the same University. He earned his Doctorate from Bioinformatics Centre at Institute of Microbial Technology, Chandigarh (IMTECH-JNU PhD program). His broad interests include: Personalized Medicine, Next Generation Sequencing, Biomaker identification, survival analysis, Bioinformatics, Computational Biology, GWAS and Medical Health. His ultimate goal is to improvise upon translational health for the benefit of mankind using high-throughput data.

Email: kchaudhary@cc.hawaii.edu

Travers Ching

Travers, PhD (2013-2017) is currently a computational biologist in Adaptive Biotechnologies Inc, Seattle. Travers graduated with PhD in May 2017 from Garmire group. He obtained his BS degree in Applied Physics with minor in Biomedical Engineering from Cornell University, and MS degree in microbiology from University of Hawaii at Manoa. He has broad interest in methylation data analysis, RNA-Seq sample size estimation, and integration of RNA-Seq, methylation data in breast cancer and lung cancers.

Email: tching@cc.hawaii.edu

Michael Ortega

Michael obtained his PhD in Developmental and Reproductive Biology with the Institute for Biogenesis Research at the University of Hawaii while working on replication and DNA damage in gametes and preimplantation embryos. His current research interests include understanding human health and disease by utilizing high-throughput sequencing and multi-omics approaches to investigate critical issues in RNA and cancer biology. He is concentrated in exploring these issues by developing methods for single-cell research and data analysis.

Email: maortega@hawaii.edu

Joshua Chen

Joshua obtained his BS degree in Biomedical Engineering from the University of Miami and is currently working on his MS in Electrical Engineering at the University of Hawaii at Manoa. He plans on attending medical school afterwards. He is currently working on a miRNA project.

Email: jschen2@hawaii.edu

Austin Tasato

Austin Tasato majors in Electrical Engineering at University of Hawaii at Manoa. He worked with Xun Zhu on developing Granatum, an scRNA-seq analysis online platform.

Email: atasato@hawaii.edu

Runmin Wei

Runmin obtained his MS degree in Pharmaceutical Science from Shanghai Jiaotong University, China.

Email: rwei@cc.hawaii.edu

Jonathan Uejbe

Jonathan Uejbe is an undergraduate student pursuing his Bachelor's of Science in Electrical Engineering. His interests include new computational methods and algorithms in data analysis. He currently is working on new computational techniques to analyze GWAS data.

Email: uejbe@cc.hawaii.edu

Nicole Chong

Nicole Chong is a 3rd year undergraduate student with 4.0 GPA majoring in Biology (with Mathematical Biology Certificate) at University of Hawaii at Manoa. She is working on breast cancer biomarker classification project in collaboration with Sijia Huang, using an integrative metabolomics and transcriptomics approach.

Email: nchong6@hawaii.edu

Jeffery Li

Jeffery Li is a sophomore with 4.0 GPA majoring in Biomedical Engineering at John Hopkins University. A Punahou graduate and local Hawaiian, he returns to UH for summer session, and is working on prediction of global gene expression using gene methylation patterns.

James Ha

James Ha is a sophomore with 4.0 GPA majoring in Biology at Caltech. A Punahou graduate and local Hawaiian, he returns to UH for summer session, and is working on global methylation changes of cord blood in pre-eclampsia.

Mark Menor

Information and Computer Science Department, University of Hawaii at Manoa.

Cameron Yee

Undergraduate intern, Neuroscience Major, University of Washington.

Liangqun Lu

Liangqun obtained her BS degree in Biological Sciences and MS degree in Bioinformatics, both from China Agricultural University.

Email: llu@cc.hawaii.edu

News

Publications

Preprints

Badowsky C, He B, Garmire LX . Blood-derived lncRNAs as potential biomarkers for early cancer diagnosis: The Good, the Bad and the Beauty. (preprint)

Yile Chen, Bing He, Yu Liu, Max T. Aung, Zaira Rosario-Pabón, Carmen M. Vélez-Vega, Akram Alshawabkeh, José F. Cordero, John D. Meeker, Garmire LX . Maternal plasma lipids are involved in the pathogenesis of preterm birth. (preprint)

Vahed M, , Vahed M, Garmire LX . BML: a versatile web server for bipartite motif discovery. (preprint)

Li YJ, Stanojevic S, He B, Jing Z, Huang Q , Kang J, Garmire , Benchmarking Computational Integration Methods for Spatial Transcriptomics Data (preprint)

He B, Garmire , ASGARD: A Single-cell Guided pipeline to Aid Repurposing of Drugs (preprint)

2021

77. He B , Liu Y, , Maurya MR, Benny P, Lassiter C, Li H , Subraminiam S, Garmire LX . The maternal blood lipidome is indicative of the pathogenesis of severe preeclampsia. J Lipid Res . 2021 Sep 18;100118. (link)

76. Garmire D, Zhu X, Mantravadi A, Huang Q, Yunits B, Liu Y, Wolfgruber T, Poirion O, Zhao T, Arisdakessian C, Stanojevic S, Garmire LX . GranatumX: A community engaging, modularized and flexible software environment for single-cell analysis. Provisional Acceptance. Proteomics, Genomics and Bioinformatics(preprint)

75. Piorion O, Chaudhary K, Huang S, Garmire LX , Multi-omics-based pan-cancer prognosis prediction using an ensemble of deep-learning and machine-learning models. Genome Medicine. 2021 ( link )

74. Cherry C, Maestas DR, Han J, ..., Garmire LX , Elisseeff JH, Intercellular signaling dynamics from a single cell atlas of the biomaterials response. Accepted. Nature Biomedical Engineering. ( link )

73. Paula A Benny, Fadhl M. Al-Akwaa , Corbin Dirkx, Ryan J. Schlueter, Thomas K. Wolfgruber, Ingrid Y. Chern , Suzie Hoops, Dan Knights, Lana X. Garmire . Placenta microbiome diversity is associated with maternal pre-pregnancy obesity and placenta biogeography. FASEB. ( link)

72. Zhucheng Zhan, Zheng Jing, Bing He , Noshad Hossenei, Maria Westerhoff, Eun-Young Choi, Lana Garmire . Two-stage biologically interpretable neural-network models for liver cancer prognosis prediction using histopathology and transcriptomic data. Accepted. Nucleic Acid Research: Genomics and Bioinformatics. (preprint)

71. Alakwaa F, Garmire LX , Savelieff MG. Correction to "Bioinformatics Analysis of Metabolomics Data Unveils Association of Metabolic Signatures with Methylation in Breast Cancer". J Proteome Res . ( link )

70. Wang Di, He Kevin, Garmire LX . Cox-nnet v2.0: improved neural-network based survival prediction extended to large-scale EMR dataset. Bioinformatics. (link)

69. Fang X, Liu Y, Ren Z, Du Y, Huang Q, Garmire LX. Lilikoi V2.0: a deep learning-enabled, personalized pathway-based R package for diagnosis and prognosis predictions using metabolomics data. Gigascience. 10(1):giaa162. doi: 10.1093/gigascience/giaa162. ( link )

68. Garmire LX . Mentorship is not co-authorship: a revisit to mentorship. Genome Biology. 22(1):2. ( link )

2020

67. Brito JJ, Li J, Moore JH, Greene CS, Nogoy NA, $Garmire LX , $Mangul S.Corrigendum to: Recommendations to enhance rigor and reproducibility in biomedical research. Gigascience. 2020. ( link )

66. Li H, Huang SJ, Liu Y, Garmire LX , Single Cell Transcriptome Research in Human Placenta, Reproduction. (link)

65. He Bing, Garmire LX . Prediction of repurposed drugs for treating lung injury in COVID-19. F1000 Research. 9:609 ( link )

64. Huang QH, Liu Y, Du Y, Garmire LX , Evaluation of cell type deconvolution R packages on single cell RNA-seq data, accepted, Genomics, Proteomics and Bioinformatics. (preprint)

63. Brito JJ, Li J, Moore JH, Greene CS, Nogoy NA, $Garmire LX , $Mangul S. Recommendations to enhance rigor and reproducibility in biomedical research. Gigascience. 2020. 9(6) ( link )

62. Du Y, Huang Q, Arisdakessian C, Garmire L , evaluation of STAR and Kallisto aligners on single cell RNA-Seq data, G3. ( link )

61. Schlueter, RJ, Al-Akwaa FM, Benny PA, Gurary A, Xie G, Jia W, Chun X, Chern I, Garmire L, Metabolomics profile of cord blood is associated with maternal pre-pregnant obesity in a multi-ethnic cohort, Journal of Proteome Research. 19(4):1361-1374 ( link )

60. Chen B, Garmire LX , Calvisi BF,...,Chen X, Harnessing Big 'Omics' Data and AI for Drug Discovery in Hepatocellular Carcinoma, Nature Reviews. Gastroenterology & Hepatology. 17(4):238-251 ( link )

59. Benny PA, Alakwaa FM, Schlueter RJ, Lassiter CB, Garmire LX . A review of omics approaches to study preeclampsia. Placenta. 2020. 92:17-27. ( link )

58.Liu QZ, Ha MJ, Bhattacharyya R, Garmire L , Baladandayuthapani V, Network-Based Matching of Patients and Targeted Therapies for Precision Oncology. Pacific Symposium on Biocomputing 2020 ( link )

2019

57. Garmire L , From Hawaii to PECASE, tips of success from a female bioinformatician. Genome Biology.20(1):271. ( link )

56. Garmire LX , Guo-cheng Yuan, Rong Fan, Gene Yeo, John Quackenbush, Single Cell Analysis, what is in the future? ( link )

55. J Olender, BD Wang, K Nguyen, T Ching , C Samtal, Y JI, J Rim, L Garmire , P Latham, NH Lee, Identification and cloning of a novel FGF3 splice variant involved in Arfican American prostate cancer disparities, Molecular Cancer Research, ( link )

54. Zhu X, Garmire LX , Book chapters: chapter 19: Data analysis in Single Cell Omics. Single-cell Omics, Vol. 1 (Elsevier)

53. The Pediatric Cell Atlas: Defining the Growth Phase of Human Development at Single-Cell Resolution, Dev. Cell, 2019 ( link )

52. Benny P, Yamasato K, ..., Ching T, Garmire LX, Berry M, Towner D, Evaluation of a maternal cardiovascular gene array in early on-set preeclampia in a dominantly Asian cohort. PLoS One. ( link )

51. Arisdakessian C, Poirion O, Yunits B, Zhu X, Garmire LX . DeepImpute: an accurate, fast and scalable deep neural network method to impute single-cell RNA-Seq data. Genome Biology. 20(1):211. ( link)

2018

50. Stein-O' Brien, Arora R, Culhane AC, Favorov A, Garmire LX , Greene C, Goff LA, Li Y, Ngom A, Yanxun Xu Y, Fertig EJ. Entering the matrix: factorization uncovers knowledge from omics. Trands in Genetics. 34(10):790-805. ( link)

49. Alakwaa F, Huang S, Yunits B and Garmire LX . Lilikoi: an R package for personalized pathway-based classification modeling using metabolomics data. Gigascience. 7(12). ( link)

48. Ching T, Zhu X, Garmire LX. Cox-nnet: an artificial neural network Cox regression for prognosis prediction, PLoS Comp Biol. ( link )

47. Chaudhary K, Lu L, Poirion O, Garmire LX , Multi-modal meta-analysis of 1494 hepatocellular carcinoma samples reveals vast impacts of consensus driver genes on phenotypes. Clinical Cancer Research (IF=10.2) ( link)

46. Poirion O, Zhu X, Ching T, Garmire L Using Single Nucleotide Variations of Single-Cell RNA-Seq to identify tumor Subpopulation and Genotype-phenotype Links. Nature Communications. 20;9(1):4892. ( link)

45. Poirion O, Chaudhary K, Garmire LX , Deep Learning data integration for better risk stratification models of bladder cancer. AMIA Jt Summits Transl Sci Proc. 2018 May 18;2017:197-206

2017

44. Ortega M, Poirion O, Zhu X, Huang SJ , Sebra R, Garmire LX , Using Single-Cell Multiple Omics Approaches to Resolve Tumor Heterogeneity. Clinical and Translational Medicine (Springer). 2017. 6(1):46

(link)

43. Ching T, Garmire LX , Pan-cancer analysis of expressed single nucleotide variants in long inter-genic non-coding RNA. Pac Symp Biocomput. 2018;23:512-523.

(link )

42. Al-Akwaa F, Chaudhary K, Garmire LX , Deep learning predicts estrogen receptor status in breast cancer metabolomics data. Journal of Proteome Research. (link)

41. Huang SJ, Chaudhary K, Garmire LX, More is better: recent progress in multi-omics data integration, accepted, Frontiers in Genetics. (link)

40. Zhu X, Wolfgruber T, Tasato A, Arisdakessian C, Garmie D, Garmire LX, Granatum: A graphical single cell RNA-Seq analysis pipeline for genomics scientists, Genome Medicine, 9(1):108. ( link)

39. Chaudhary K, Poirion O, Lu L, Garmire LX, Deep Learning based multi-omics integration robustly predicts survivals in liver cancer. Clinical Cancer Research ( link)

38. Wang BD, Ceniccola K, Hwang S, Andrawis R, Horvath A, Freeman JA, Knapp S, Ching T, Garmire LX, Pate lV, Garcia-Blanco MA, Patierno SR, Lee NH, Aberrant Alternative Splicing in African American Prostate Cancer: novel driver of tumor aggressiveness and drug resistance, accepted, Nature Communications.

37. Han B, Park HK, Wang H, Panneerselvam J, Shen Y, Zhang J, Li L, Lee YH, Su M, Ching T, Garmire LX , Jia W, Yu H, Fei P, HDBR1 Modulates U2 snRNP Function to Maintain RNA Populations, Contributing to the Suppression of Human Cancer Development, accepted, Oncogene.

36. Greene CS, Garmire LX , Gilbert JA, Ritchie DR, Hunt L, Celebrate parasites, accepted, Nature Genetics. ( link)

35. Feng N, Wang Y, Zheng M, Yu X, Lin H, Ma RN, Shi O, Zheng X, Gao M, Yu H, Garmire L, Qian B. Genome-wide analysis of DNA methylation and their associations with long noncoding RNA/mRNA expression in non-small-cell lung cancer. Accepted, Epigenomics. ( link)

2016

34. Yang J, Tanaka Y, Seay M, Li Z, Jin JQ, Garmire L, Zhu X , Euskirchen G, Synder M, Li W, Park IH, Pan X, Weissman SM. Single cell transcriptomics reveals unanticipated features of early hematopoietic precursors. Nucleic Acids Research (2016). ( link)

33. Zhu X, Ching T, Pan X, Weissman S, Garmire LX. Detecting heterogeneity in single-cell RNA-Seq data by non-negative matrix factorization, PeerJ 5:e2888 ( link)

32. Garmire LX, Gliske S, Nguyen QC, Chen JH, Nemati S, VAN Horn JD, Moore JH, Shreffler C, Dunn M. The training of next generation data scientists in biomedicine. Pac Symp Biocomput. 2016;22:640-645. ( link)

31. Feng N§, Ching T§, Wang Y, Liu B, Lin H,Shi O,Zhang X, Yao Y, Hua L, Zheng X, Gao M, Yu H#, Garmire LX #, Qian B#. Analysis of Microarray Data on Gene Expression and Methylation to Identify Long Non-coding RNAs in Non-small Cell Lung Cancer. Scientific Reports 6 (2016). (#: co-corresponding authors) ( link)

30. Lu L, McCurdy S, Huang S, Zhu X, Peplowska K, Tiirikainen M, Boisvert WA, Garmire LX, Time Series miRNA-mRNA integrated analysis reveals critical miRNAs and targets in macrophage polarization. Scientific Reports 6 (2016). ( link)

29. Poirion A§, Zhu X§, Ching T, Garmire LX Single-Cell Transcriptomics Bioinformatics and Computational Challenges,7:163.Frontiers in Genetics (2016). ( link)

28. Huang S*, Kou L*, Furuya H, Yu CH, Kattan M, Goodison S, Garmire LX, Rosser CJ, A nomogram derived by combination of demographic and biomarker data improves the non-invasive evaluation of patients at risk for bladder cancer, accepted, Cancer Epidemiology, Biomarkers and Prevention, 2016 Jul 6. pii: cebp.0260.

(link)

27. Wei R, De Vivo I, Huang S, Zhu X, Risch, H, Moore JH, Yu H, Garmire LX, Meta-dimensional data integration identifies critical pathways for susceptibility, tumorigenesis and progression of endometrial cancer, Oncotarget, 2016 Jul 9. doi: 10.18632/oncotarget.10509. ( link)

26. Ching T, Peplowska K, Huang S, Zhu X , Shen Y, Molnar J, Yu H, Tiirikainen M, Fogelgren B, Fan R, Garmire LX . Pan-cancer analyses reveal a panel of biologically and clinically relevant lincRNAs for tumour diagnosis, subtyping and prognosis, 2016, 7:62-72, EBioMedicine. ( link)

25. Huang S, Chong N, Lewis NE, Jia W, Xie G, Garmire LX. Novel personalized pathway-based metabolomics models reveal key metabolic pathways for breast cancer diagnosis, 8(1):34, Genome Medicine (2016). ( link)

2015

24. Ching T, Masaki J, Weirather J, Garmire LX . Non-coding yet non-trivial: a review on the genomics of long intergenic non-coding RNAs, 8:44. doi: 10.1186/s13040-015-0075-z, BioData Mining. ( link)

23. Xie G, Zhou B, Zhao Y, Qiu Y, Zhao X, Garmire LX , Yu H, Yen Y, Jia W, Lowered circulating aspartate is a metabolic feature of human breast cancer,Oncotarget, 6 (32), 33369-81. ( link)

22. Li J, Ching T, Huang S, Garmire LX, Using Epigenomics Data to Predict Differential Gene Expression in Lung Cancer, BMC Bioinformatics. 2015;16 Suppl 5:S10 ( link)

21. Ching T, Ha J,Song MA, Tiirikainen M, Molnar J Berry M, Towner D, Garmire LX, Genome-scale hypomethylation in the cord blood cells associated with early onset preeclampsia, Clinical Epigenetics. 2015 Mar 13;7(1):21. ( link)

20. Gagliani N, Iseppon A, Vesely CA, Brockmann L, Palm NW, Zeote MR, Licona-Limon P, Paiva R, Ching T, Zi X, Fan R, Garmire L, Geginat J, Stockinger B, Esplugues E, Huber S, Flavell R, Th17 cells transdifferentiate into regulatory T cells during resolution of inflammation, Nature. 2015 Apr 29. doi: 10.1038/nature14452. ( link)

2014

19. Menor M, Ching T, Garmire D, Zhu X, Garmire LX, mirMark: a site-level and UTR-level classifier for miRNA target prediction. Genome Biology. Oct 25;15(10):500. ( link)

18. Han L, Zi XY, Garmire LX, Pan XH, Weissman SM, Fan R, Co-detection and sequencing of genes and transcripts from the same single cells enabled by a microfluidics platform, Sci Rep. Sep 26;4:6485. doi: 10.1038/srep06485. ( link)

17. Ching T, Huang S, Garmire LX: power analysis and sample size estimation for RNA-Seq differential expression, RNA. 2014 Sep 22. [Epub ahead of print]. ( link)

16. Huang S, Yee C, Ching T, Yu H, Garmire LX, A novel model to combine clinical and pathway-based transcriptomic information for the prognosis prediction of breast cancer, PLOS Computational Biology. Sep 18;10(9):e1003851. ( link)

15. Ching T,Song MA, Tiirikainen M, Berry M, Towner D, Garmire LX, Global hypermehtylation coupled with promoter hypomethylation in the chorioamniotic membranes of early onset preeclampsia. Mol. Hum. Reprod. Sep;20(9):885-904. ( link)

2013

14. Garmire LX, Subramaniam S. The poor performance of TMM on microRNA-Seq. RNA 19, 735-6 (2013). PMCID: PMC3683907. ( link)

13. Hadd AG, Houghton J, Choudhary A, Sah S, Chen L, Marko AC, Sanford T, Buddavarapu K, Krosting J, Garmire LX, Wylie D, Shinde R, Beaudenon S, Alexander EK, Mambo E, Adai AT, Latham GJ. Targeted, high-depth, next-generation sequencing of cancer genes in formalin-fixed, paraffin-embedded and fine-needle aspiration tumor specimens. J Mol Diagn 15, 234-47 (2013). ( link)

Pre-Garmire Group time. 2012 and earlier

12. Colas A, McKeithan W, Cunningham T, Bushway P, Garmire LX, Duester G, Subramaniam S, Mercola M, Whole genome microRNA screening identifies let-7 And mir-18 as regulators of germ layer formation during early embryogenesis, Genes & Development 26, 2567-79 (2012).PMCID: PMC3521625. ( link)

11. Wu Y, Garmire LX, Fan R, Dynamic analysis of intercellular signaling reveals a mechanistic transition in tumor microenvironment, Integrative Biology,(Camb) 4, 1478-86 (2012).PMCID: PMC3502715. ( link)

10. Nathan S*, Garmire LX*, McDonald J, Norihito S, Reichart D, Heudobler D, Raetz CR, Murphy RC, Merril AH, Brown A, Dennis EA, Li AC, Fahy E, Subramaniam S, Quehenberger O, Russell DW, and Glass CK, Regulated accumulation of desmosterol integrates macrophage lipid metabolism and inflammatory responses. Cell 151, 138-52 (2012) (*: equal contribution). PMCID: PMC3464914. ( link)

9. Garmire LX, Subramaniam S. Evaluation of microRNA-Seq normalization methods. RNA 18, 1279-1288 (2012). PMCID: PMC3358649. ( link)

8. Garmire LX, Garmire DG, Huang W, Yao J, Glass CK, Subramaniam S. A global clustering approach to identify intergenic non-coding RNA, with application in mouse macrophages. PLoS ONE 6(9):e24051 (2011). PMCID: PMC3184070. ( link)

7. Wang KC, Garmire LX, Young A, Nguyen P, Trinh A, Subramaniam S, Wang NP, Shyy J, Li J, Chien S, Role of miR-23b in flow-regulation of microRNA signature and cell growth in endothelial Cells, Proc Natl Acad Sci U S A 107, 3234-9 (2010). PMCID: PMC2840325. ( link)

6. Garmire LX, Shen ZX, Briggs S, Yeo G, Glass CK, Subramaniam S, Regulatory Network of microRNAs in RAW 264.7 Macrophage Cells. Proceedings of 32nd International Conference of the IEEE Eng Med Biol Soc 6198-201 (2010). ( link)

5. Garmire LX, Hunt CA, In silico methods for unraveling the mechanistic complexities of intestinal absorption: metabolism-efflux transport interactions. Drug Metab Dispos 36,1414-24 (2008). PMC, in process. ( link)

4. Garmire LX, Garmire DG, Hunt CA, An in silico transwell device for drug transport and drug-drug interaction studies. Pharmaceutical Research 24, 2171-86 (2007). Featured Article. ( link)

3. Garmire LX, Mechanistic study of enzyme-efflux transporter relations using in silico devices. Lecture Notes in Engineering and Computer Science 2167, 34-39 (2007). ( link)

2. Grant MR, Hunt CA, Xia L*, Fata JM, Bissell MJ, Modeling mammary gland morphogenesis as a reaction-diffusion process, Proceedings of the 26th Annual International Conference of the IEEE EMBS, San Francisco, CA, USA September 1-5 (2004) (*: Maiden name). ( link)

1. Fan T, Xia L*, Han Y. Mitochondrion and apoptosis. Acta Biochica et Biophysica Sinica 33, 7-12 (2001) (*: Maiden name). ( link)

Software

  • cox-nnet - predict patient prognosis by extending Cox Regression to the non-linear neural network framework
  • SSrGE - Using Single Nucleotide Variations in Single-Cell RNA-Seq to Identify Tumor Subpopulations and Genotype-phenotype Linkage
  • Lilikoi - an R package for personalized pathway-based classification modeling using metabolomics data
  • Granatum - a single-cell RNA-Seq analysis platform for bench scientists
  • GranatumX - a community engaging, flexible computing environment for single-cell RNA-Seq analysis platform connecting tool developers with bench scientists
  • NMFEM - cluster scRNA-Seq samples using NMF, extract important genes, and generate PPI network modules from these genes
  • mirMark - random forest based machine learning method to predict the microRNA targets both at site-level and UTR-level
  • RNASeqPowerCalculator - R code to calculate the power and sample size for RNA-Seq differential expression
  • MetaboloPathwayModel - R code to build pathway-based metabolomics diagnosis models