March 22nd SFASA Seminar
Speaker:
Mark R. Segal, PhD, Professor, Department
of Epidemiology and Biostatistics; Director, Center for Bioinformatics
& Molecular Biostatistics; Department of Epidemiology and Biostatistics,
UCSF
Time:
March 22nd, Thursday, 5pm - 6pm (4:30-5pm pre-seminar
social)
Location:
UCSF China Basin Landing, Room 6702 (specific directions to classrooms: http://www.epibiostat.ucsf.edu/general/cbl.html)
Title:
Querying Genomic Databases: Refining the Connectivity Map
Abstract: The
advent of high throughput biotechnologies, that can efficiently measure gene
expression on a global basis, has led to the creation and population of correspondingly
rich databases and compendia. Such repositories have the potential to add
enormous scientific value beyond that provided by individual studies which, due
largely to cost considerations, are typified by small sample sizes.
Accordingly, substantial effort has been invested in devising analysis schemes
for utilizing gene expression repositories. Here, we focus on one such scheme,
the connectivity map, that was developed with the express purpose of
identifying drugs with putative efficacy against a given disease, where the
disease in question is characterized by a gene expression signature. In view of
the enormous costs and poor success rates of established drug development
pipelines, the promise seemingly demonstrated by early use of the connectivity
map is of profound importance. The success of the connectivity map is belied by
its simplicity. The aforementioned signature serves as a query which is
applied to a customized database of (differential) gene expression experiments
designed to elicit response to a wide range of
drugs, across of spectrum of concentrations, durations, and cell lines.
Such application is effected by computing a per experiment score that measures
"closeness" between the signature and the experiment. Top
scoring experiments, and the attendant drug(s), are then deemed relevant to the
disease underlying the query. Inference supporting such elicitations is
pursued via resampling. In this talk, we revisit two key aspects of the
connectivity map implementation. Firstly, we develop new approaches to
measuring closeness for the common scenario wherein the query constitutes an
ordered list. These involve using metrics proposed for analyzing
partially ranked data. Secondly, we advance an alternate inferential
approach based on generating empiric null distributions that exploit the scope,
and capture dependencies, embodied by the database. Using these
refinements we illustrate select results from a comprehensive re-evaluation of
connectivity map findings.
Transportation/Parking: 1) Right at the Caltrain station. 2) If
driving, 2-hour parking in the neighborhood as well as reasonably priced
parking in lot A for the Giants. The building parking is expensive. 3) If
by BART, transfer at Embarcadero to the N-Judah or T-train (both inbound)
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