Dr. Lana Garmire – PECASE Recipient 2016
By receiving a 2016 PECASE, Lana Garmire, Ph.D., has been recognized for her research on single cell technologies allowing investigation of human cancer heterogeneity at an unprecedented level. As an Associate Professor of Computational Medicine and Bioinformatics at University of Michigan, Dr. Garmire uses data driven approach to study diseases such as cancer. Current research areas include single-cell bioinformatics and genomics, multi-omics data integration. Dr. Garmire obtained her Ph.D. in Comparative Biochemistry from Berkeley. She completed her postdoctoral training at UC-San Diego.
What do you think the impact of this award will have on your research career?
This award has set my career on a very good starting platform. It gives me much support to dedicate my energy to study single cell genomics and bioinformatics, a very fascinating and exciting research area, where the landscape is rapidly evolving.
What is your best career advice to young investigators?
Work hard, stay focused, be resilient, and actively learn from your mentors and role models, then you will be successful in your own way.
What do you think might be the most likely (and meaningful) discovery to emerge in your field in the next five? Ten years?
The multi-modality analysis and multi-scale data integration are happening. Artificial intelligence and machine learning methods, such as deep-learning, will be really useful to handle the ever-growing single cell data, along with imaging, phenotype and clinical data. Integrating them across the scales, from single-cell to populations, will be the way to cure many diseases, including cancer.
A selection of publications related to this grant include:
Poirion O, Zhu X, Ching T, Garmire LX. Single-cell transcriptomic bioinformatics and computational challenges, 2016, 7:163 Frontiers in Genetics. PMID: 27708664.
Huang SJ, Chaudhary K, Garmire LX. More is Better: Recent Progress in Multi-omics Integration Methods, Frontiers in Genetics. 2017. 8:84. doi: 10.3389/fgene.2017.00084. eCollection. PMID: 28670325.
Zhu X, Ching T, Pan X, Weissman S, Garmire LX. Detecting Heterogeneity in Single-cell RNA-Seq Data by Non-negative Matrix Factorization. PeerJ. 2017. 5(11):e2888. PMID: 28133571.
Zhu X, Wolfgruber T, Tasato A, Garmire DG, Garmire LX, Granatum. A Graphical Single Cell RNA-Seq Analysis, Genome Medicine. 2017. 9(1):108. PMID: 29202807.
Ortega M, Poirion O, Zhu X, Huang SJ, Wolfgruber T, Sebra R, Garmire LX. Using Single-Cell Multiple Omics Approaches to Resolve Tumor Heterogeneity. Clinical Translational Medicine. 2017. 6(1):46. PMID: 29285690.
Poirion O, Zhu X, Ching T, Garmire LX. Using Single Nucleotide Variations in Single-cell RNA-Seq to Identify Tumor Subpopulations and Genotype-phenotype Linkage, Nature Communications. 2018. 9(1):4892. doi: 10.1038/s41467-018-07170-5.
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. In revision. Preprint: https://www.biorxiv.org/content/10.1101/353607v1.
Zhu X, Yunits B, Wolfgruber T, Poirion O, Arisdakessian C, Garmire LX . GranatumX: A community engaging and flexible software environment for single-cell analysis. Submitted: https://www.biorxiv.org/content/10.1101/385591v2.
Dr. Lana Garmire
Last Reviewed: August 26, 2019