Zhandong Liu Lab

Cellular heterogeneity is present in almost all gene expression profiles. However, transcriptome analysis of tissue specimens often ignores the cellular heterogeneity present in these samples. Standard deconvolution algorithms require prior knowledge of the cell type frequencies within a tissue or their in vitro expression profiles.

Furthermore, these algorithms tend to report biased estimations. Here, we describe a Digital Sorting Algorithm (DSA) for extracting cell-type specific gene expression profiles from mixed tissue samples that is unbiased and does not require prior knowledge of cell type frequencies. Source code of the proposed algorithm is freely available.

Selecting genes and pathways indicative of disease is a central problem in computational biology. This problem is especially challenging when parsing multi-dimensional genomic data. A number of tools, such as L1-norm based regularization and its extensions elastic net and fused lasso, have been introduced to deal with this challenge. However, these approaches tend to ignore the vast amount of a priori biological network information curated in the literature.

We propose the use of graph Laplacian regularized logistic regression to integrate biological networks into disease classification and pathway association problems. Simulation studies demonstrate that the performance of the proposed algorithm is superior to elastic net and lasso analyses. Utility of this algorithm is also validated by its ability to reliably differentiate breast cancer subtypes using a large breast cancer dataset recently generated by the Cancer Genome Atlas (TCGA) consortium. Many of the protein-protein interaction modules identified by our approach are further supported by evidence published in the literature. Source code of the proposed algorithm is freely available.

Combinatorial therapies play increasingly important roles in combating complex diseases. Due to the huge cost associated with experimental methods in identifying optimal drug combinations, computational approaches can provide a guide to limit the search space and reduce cost. However, few computational approaches have been developed for this purpose and thus there is a great need of new algorithms for drug combination prediction.

Here we proposed to formulate the optimal combinatorial therapy problem into two complementary mathematical algorithms, Balanced Target Set Cover (BTSC) and Minimum Off-Target Set Cover (MOTSC). Given a disease gene set, BTSC seeks a balanced solution that maximizes the coverage on the disease genes and minimizes the off-target hits at the same time. MOTSC seeks a full coverage on the disease gene set while minimizing the off-target set. Through simulation, both BTSC and MOTSC demonstrated a much faster running time over exhaustive search with the same accuracy. When applied to real disease gene sets, our algorithms not only identified known drug combinations, but also predicted novel drug combinations that are worth further testing.

[25] S. Yamamoto, M. Jaiswal, W.-L. Charng, T. Gambin, E. Karaca, G. Mirzaa, W. Wiszniewski, H. Sandoval, N. A. Haelterman, B. Xiong, K. Zhang, V. Bayat, G. David, T. Li, K. Chen, U. Gala, T. Harel, D. Pehlivan, S. Penney, L. E. L. M. Vissers, J. de Ligt, S. N. Jhangiani, Y. Xie, S. H. Tsang, Y. Parman, M. Sivaci, E. Battaloglu, D. Muzny, Y.-W. Wan, Z. Liu, A. T. Lin-Moore, R. D. Clark, C. J. Curry, N. Link, K. L. Schulze, E. Boerwinkle, W. B. Dobyns, R. Allikmets, R. A. Gibbs, R. Chen, J. R. Lupski, M. F. Wangler, and H. J. Bellen, “A Drosophila genetic resource of mutants to study mechanisms underlying human genetic diseases.,” Cell, vol. 159, no. 1, pp. 200–214, Sep. 2014

[24] J. Ye, J. Fan, S. Venneti, Y.-W. Wan, B. R. Pawel, J. Zhang, L. W. S. Finley, C. Lu, T. Lindsten, J. Cross, G. Qing, Z. Liu, M. C. Simon, J. D. Rabinowitz, and C. B. Thompson, “Serine catabolism regulates mitochondrial redox control during hypoxia.,” Cancer Discov, Sep. 2014.

[23] E. Yang, Y. Baker, P. Ravikumar, G. I. Allen, Z. Liu, “Mixed Graphical Models via Exponential Families”, In Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, pp. 1042-1050. 2014.

[22] Kaifang Pang; Ying-Wooi Wan; William T. Choi; Lawrence A. Donehower; Jingchun Sun; Dhruv Pant; Zhandong Liu, “Combinatorial Therapy Discovery using Mixed Integer Linear Programming”, Bioinformatics 2014; doi: 10.1093/bioinformatics/btu046 [pdf]

[21] Ying-Wooi Wan, Claire M. Mach, Genevera Allen, Matthew L. Anderson, and Zhandong Liu ” On the Reproducibility of TCGA Ovarian Cancer MicroRNA Profiles” PLOS one 2014. [pdf]

[20] Ying-Wooi Wan, John Nagorski, Genevera Allen, Zhaohui Li, Zhandong Liu “Identifying Cancer Biomarkers through a network regularized cox model,” IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS), 2013.

[19] Sangeetha Mahadevan, Shu Wen, Ying-Wooi Wan, Hsiu-Huei Peng, Subhendu Otta, Zhandong Liu, Michelina Iacovino, Elisabeth M. Mahen, Michael Kyba, Bekim Sadikovic, Ignatia B. Van den Veyver, “NLRP7 affects trophoblast lineage differentiation, binds to overexpressed YY1 and alters CpG methylation”, Human Molecular Genetics, 2013. [pdf]

[18] E. Yang, P. Ravikumar, G. I. Allen, and Z. Liu, “On Poisson Graphical Models”, In Neural Information Processing Systems (NIPS), 2013. [pdf]

[17] E. Yang, P. Ravikumar, G. I. Allen, and Z. Liu, “Conditional Random Fields via Univariate Exponential Families”, In Neural Information Processing Systems (NIPS), 2013 [pdf]

[16] W. Zhang, W.Y. Wan, G. I. Allen, K. Pang, M. L. Anderson, and Z. Liu “Molecular pathway identification using biological network-regularized logistic models”, BMC Genomics, vol. 14, supp. 8, p. S7, 2013 [pdf]

[15] G. I. Allen and Z. Liu, “A Local Poisson Graphical Model for Inferring Genetic Networks from Next Generation Sequencing Data”, IEEE Transactions on NanoBioscience, 12:3, 1-10, 2013.

[14] S. M. Hawkins, H. A. Loomans, Y.-W. Wan, T. Ghosh-Choudhury, D. Coffey, W. Xiao, Z. Liu, H. Sangi-Haghpeykar, and M. L. Anderson, “Expression and Functional Pathway Analysis of Nuclear Receptor NR2F2 in Ovarian Cancer.,” J. Clin. Endocrinol. Metab., May 2013.

[13] Y. Zhong, Y.-W. Wan, K. Pang, L. M. Chow, and Z. Liu, “Digital sorting of complex tissues for cell type-specific gene expression profiles.,” BMC Bioinformatics, vol. 14, no. 1, p. 89, Mar. 2013. [pdf]

[12] K. Han, V. A. Gennarino, Y. Lee, K. Pang, K. Hashimoto-Torii, S. Choufani, C. S. Raju, M. C. Oldham, R. Weksberg, P. Rakic, Z. Liu, and H. Y. Zoghbi, “Human-specific regulation of MeCP2 levels in fetal brains by microRNA miR-483-5p.,” Genes Dev., vol. 27, no. 5, pp. 485–490, Mar. 2013. [pdf]

[11] Y. Zhong and Z. Liu, “Gene expression deconvolution in linear space.,” Nat. Methods, vol. 9, no. 1, pp. 8–9, Jan. 2012. [pdf]

[10] G. Allen and Z. Liu, “A Log-Linear Graphical Model for Inferring Genetic Networks from High-Throughput Sequencing Data,” The IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2012), 2012.

[9] E. Yang, P. K. Ravikumar, G. I. Allen, and Z. Liu, “Graphical Models via Generalized Linear Models,” NIPS, vol. 25, pp. 1367–1375, 2012.

[8] G. B. W. Wertheim, T. W. Yang, T.-C. Pan, A. Ramne, Z. Liu, H. P. Gardner, K. D. Dugan, P. Kristel, B. Kreike, M. J. van de Vijver, R. D. Cardiff, C. Reynolds, and L. A. Chodosh, “The Snf1-related kinase, Hunk, is essential for mammary tumor metastasis,” Proceedings of the National Academy of Sciences, vol. 106, no. 37, pp. 15855–15860, Jan. 2009. [pdf]

[7] A. V. Ivanova, S. V. Ivanov, L. Prudkin, D. Nonaka, Z. Liu, A. Tsao, I. Wistuba, J. Roth, and H. I. Pass, “Mechanisms of FUS1/TUSC2 deficiency in mesothelioma and its tumorigenic transcriptional effects,” Mol. Cancer, vol. 8, p. 91, 2009. [pdf]

[6] Z. Liu, M. Wang, J. Alvarez, M. Bonney, C.-C. Chen, C. D’Cruz, T.-C. Pan, M. Tadesse, and L. Chodosh, “Singular value decomposition-based regression identifies activation of endogenous signaling pathways in vivo,” Genome Biol., vol. 9, no. 12, p. R180, 2008. [pdf]

[5] Z. Liu, S. Venkatesh, and C. Maley, “Sequence space coverage, entropy of genomes and the potential to detect non-human DNA in human samples,” BMC Genomics, vol. 9, no. 1, p. 509, 2008. [pdf]

[4] H. I. Pass, D. Lott, F. Lonardo, M. Harbut, Z. Liu, N. Tang, M. Carbone, C. Webb, and A. Wali, “Asbestos exposure, pleural mesothelioma, and serum osteopontin levels.,” N. Engl. J. Med., vol. 353, no. 15, pp. 1564–1573, Oct. 2005. [pdf]

[3] S. A. Krawetz, S. Draghici, R. Goodrich, Z. Liu, and G. C. Ostermeier, “In silico and wet-bench identification of nuclear matrix attachment regions.,” Methods Mol. Med., vol. 108, pp. 439–458, 2005.

[2] H. I. Pass, Z. Liu, A. Wali, R. Bueno, S. Land, D. Lott, F. Siddiq, F. Lonardo, M. Carbone, and S. Draghici, “Gene expression profiles predict survival and progression of pleural mesothelioma.,” Clin. Cancer Res., vol. 10, no. 3, pp. 849–859, Feb. 2004. Featured Cover Article [pdf]

[1] G. C. Ostermeier, Z. Liu, R. P. Martins, R. R. Bharadwaj, J. Ellis, S. Draghici, and S. A. Krawetz, “Nuclear matrix association of the human beta-globin locus utilizing a novel approach to quantitative real-time PCR.,” Nucleic Acids Res., vol. 31, no. 12, pp. 3257–3266, Jun. 2003. [pdf]