Epidemiology & Biostatistics // Biostatistics

Ronglai Shen, PhD

Assistant Attending Biostatistician
Office Phone:
646-735-8089
Office Fax:
646-735-0010
E-mail:
shenr@mskcc.org
Education:
University of Michigan

Current Research Interests

Dr. Shen's research interests are in statistical and computational methods for high-dimensional cancer genomic data. Her current work focuses on class discovery and classification problems given multiple “omic” data types where genomic, epigenomic, and transcriptomic aberrations in a tumor are measured in an integrated fashion. She is currently leading a 2-year project funded by the Starr Cancer Consortium to implement integrative approaches for the identification of novel tumor subtypes and associated cancer genes in various cancer types and to functionally validate the findings using large-scale RNAi and cDNA screening platforms through collaboration with investigators at Cold Spring Harbor Laboratory. She is also implementing her method in analyzing multidimensional genomic profiling data generated by the NIH and NHGRI Cancer Genome Atlas (TCGA) project. Her current and past research work includes: iCluster, a regularized joint latent variable model for identifying molecular subtypes by integrating DNA copy number, mRNA expression, and DNA methylation profiles measured in the same set of tumors; CMM, a Bayesian Cell Mixture Model for reconstructing tumor-wise protein expression measured by tissue microarrays; ELDA, an Eigengene-based Linear Discriminant Analysis for tumor classification problems using gene expression microarray data; and Meta-analysis of microarrays, a two-stage Bayesian mixture modeling strategy for cross-study analysis of gene expression microarrays. Dr. Shen actively engages in collaborative work with clinical investigators in the lung cancer group as well as other disease groups at MSKCC.

Her current and past research work includes:

  • iCluster: a regularized joint latent variable model for identifying molecular subtypes by integrating DNA copy number, mRNA expression, and DNA methylation profiles measured in the same set of tumors.
  • CMM: a Bayesian Cell Mixture Model for reconstructing tumor-wise protein expression measured by tissue microarrays.
  • ELDA: an Eigengene-based Linear Discriminant Analysis for tumor classification problems using gene expression microarray data.
  • Meta-analysis of microarrays: a two-stage Bayesian mixture modeling strategy for cross-study analysis of gene expression microarrays.

Dr. Shen actively engages in collaborative work with clinical investigators in the lung cancer group as well as other disease groups at MSKCC.

Publications by Ronglai Shen

Selected Bibliography

Shen R, Olshen AB, Ladanyi M. Integrative Clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis. Bioinformatics 2009; 25(22): 2906-12.

Regales L, Gong Y, Shen R, Stanchina E, Vivanco I, Goel A, Koutcher JA, Spassova M, Ouerfelli O, Mellinghoff IK, Zakowski MF, Politi KA, Pao W. Novel dual targeting of EGFR can overcome a major drug resistance mutation in EGFR-mutant lung cancer. Journal of Clinical Investigation 2009; 119(10):3000-10.

Shen R, Taylor JM, Ghosh D. Reconstructing tumor-wise protein expression in tissue microarray studies using a Bayesian cell mixture model. Bioinformatics 2008; 24(24):2880-6.

Shen R, Ghosh D, Chinnaiyan AM, Meng Z. (2006). Eigengene Based Linear Discriminant Model for Gene Expression Data Analysis. Bioinformatics 22(21):2635-42.

Shen R, Ghosh D, Chinnaiyan A. (2004). Prognostic Meta-signature of Breast Cancer Developed by Two-stage Mixture Modeling of Microarray Data. BMC Genomics 5(1):94.

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