Ronglai Shen, PhD

Assistant Attending Biostatistician
Office Phone:
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 recently led a two-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-wide 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 Memorial Sloan Kettering Cancer Center.

Publications by Ronglai Shen

Shen R, Olshen AB, Ladanyi M. (2009)  Integrative clustering of multiple genomic data  types using a joint latent variable model with application to breast and lung cancer subtype analysis. Bioinformatics. 25:2906-12. *The iCluster method we developed was used in a landmark study to discover novel integrated subtypes of breast cancer (Curtis et al., Nature, 2012, 486:346-52).

Shen R, Wang S, Mo Q. (2012) Sparse integrative clustering of multiple omics data sets. Annals of Applied Statistics. 7:269-294.

Hammerman PS, Hayes DN, Wilkerson MD, Schultz N, Bose R, Chu A, Collisson EA, Cope L, Creighton CJ, Getz G, Herman JG, Johnson BE, Kucherlapati R, Ladanyi M, Maher CA, Robertson G, Sander C, Shen R, Sinha R, Sivachenko A, Thomas RK, Travis WD, Tsao MS, Weinstein JN, Wigle DA, Baylin SB, Govindan R, Meyerson M. (2012) Comprehensive genomic characterization of squamous cell lung cancers. Cancer Genome Atlas Research Network. Nature. 27:519-25 *We contributed to the integrated classification of squamous cell lung cancers using multidimensional data.

Mo Q, Wang S, Seshan VE, Olshen AB, Schultz N, Sander C, Powers RS, Ladanyi M, and Shen R*. (2013) Pattern discovery and cancer gene identification in integrated cancer genomic data. Proceedings of the National Academy of Sciences. 12:4245-4250 *Corresponding author