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S. Dan Li Current position: Senior Research Scientist, Informatics, Eli Lilly and Company Education: Ph.D. (1999) Cell & Molecular Biology, University of Texas Southwestern Medical Center at Dallas; Gene regulation in B lymphocyte differentiation.

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s dan li
S. Dan Li
  • Current position: Senior Research Scientist, Informatics, Eli Lilly and Company
  • Education:
    • Ph.D. (1999) Cell & Molecular Biology, University of Texas Southwestern Medical Center at Dallas; Gene regulation in B lymphocyte differentiation.
    • M.S. (2001) Computer Science, University of Texas at Dallas
    • M.S. (2008, expected) Applied Statistics, Purdue University
  • Professional experience:
    • 2001-2002: Postdoctoral Scientist, Tularik Inc. South San Francisco
    • 2002-present: Research Scientist, Sr. Research Scientist, Eli Lilly and Company
  • Main research interests: comparative functional genomics; cancer informatics
comparative analysis of gene expression between the sage and microarray platforms
Comparative analysis of gene expression between the SAGE and microarray platforms
  • Goal: To test the data comparability between SAGE and microarray technologies, through examining the expression pattern of genes under normal physiological states across multiple tissues
  • Results:
    • Significant discrepancies between the two platforms; only 30-40% genes exhibited positive correlations
    • The discrepancies are not caused by heterogeneity of tissue sources, microarray probe designs, mRNA abundance, or gene function
    • The discrepancies can be at least partially explained by:
      • Errors in SAGE tag annotation
      • Splice variants
      • SAGE tags and array probesets represent different regions of the same genes
  • Reference: Li S., Li Y. H., Wei T., Su E. W., Duffin K., and Liao B. (2006). Too much data, but little inter-changeability: A lesson learned from mining public data on tissue specificity of gene expression. Biology Direct 1, 33
evaluation of model experimental systems cancer cell lines vs primary tumors
Evaluation of Model Experimental Systems- Cancer cell lines vs. primary tumors
  • Q1: If the cell lines represent the tumor tissues that they are originally derived from?
    • Correlation analysis suggests that 51 of NCI60 cell lines represent their presumed tumor origin.
  • Q2: What cancer subtypes or tumor stages do the cell lines represent?
    • Classification based on supervised learning. Examples: 6 NSCLC lines represent stage 2/3 tumors.

Stage I

Stage II

Stage III/IV

A549

NCI-H460, EKVX, HOP-92, NCI-H226, HOP-62

evaluation of model experimental systems cancer cell lines vs primary tumors4
Evaluation of Model Experimental Systems- Cancer cell lines vs. primary tumors
  • Q3: What pathways are activated in primary cancers and cell lines?
    • Derive gene expression signature representing the activated pathways and predict pathway activity in primary tumors and cell lines. Example: Ras pathway is activated in 70% of lung adenocarcinomas and 4 NSCLC cell lines.
  • Reference: Wang H., Huang S., Shou J., Su E. W., Onyia J. E., Liao B., and Li S. (2006). Comparative analysis and integrative classification of NCI60 cell lines and primary tumors using gene expression profiling data. BMC Genomics 7, 166.

Ras

Control

Active

Inactive