When: Monday, November 16th at 11:00 a.m.
Where: TTIC, 6045 S Kenwood Avenue, 5th Floor, Room 526
Speaker: Matthew Stephens, Univ. of Chicago
Title: False Discovery Rates – a new deal

Abstract: False Discovery Rate (FDR) methodology, first put forward by
Benjamini and Hochberg, and further developed by many authors –
including Storey, Tibshirani, and Efron – is now one of the most
widely used statistical methods in genomics, among other areas of
application.
A typical genomics workflow consists of i) estimating thousands of
effects, and their associated
p values; ii) feeding these p values to software (e.g. the widely used
qvalue package) to estimate
the FDR for any given significance threshold. In this talk we take a
fresh look at this problem,
and highlight two deficiencies of this standard pipeline that we
believe could be improved. First, current methods, being
based directly on p values (or z scores), fail to fully account for
the fact that some measurements are more precise
than others. Second, current methods assume that the least significant
p values (those near 1) are all null – something
that initially appears intuitive, but will not necessarily hold in
practice. We suggest simple approaches to address
both issues, and demonstrate the potential for these methods to
increase the number of discoveries at a given FDR threshold. We also
discuss the connection between
this problem and shrinkage estimation, and problems involving sparsity
more generally.