With the rapid advancement in high throughput technology, extensive
repeated measurements have been taken to monitor the system-wide dynamics
in many scientific investigations. A typical example is temporal gene
expression studies, in which a series of micorarray experiments are
conducted sequentially during a biological process, e.g., cell cycle
microarray experiments. At each time point, mRNA expression levels of
thousands of genes are measured simultaneously. Collected over time,
a gene's "temporal expression profile" gives the scientist some clues
on what role this gene might play during the process. A group of genes
with similar profiles are often "co-regulated" or participants of a
common and important biological function. Thus clustering genes into
homogeneous groups is a crucial first step to decipher the underlying
mechanism. In addition to the time factor, such repeated measurements
often contain other covariates, e.g., replicates at each time point,
species in comparative genomics studies, and treatment groups in
case-control studies, as well as many factors in a factorial designed
experiment. Incorporation of multiple covariates adds another layer of
complexity. Clustering methods taking all these factors into account
are still lacking.
In this talk, I will present a penalized clustering method for large
scale data with multiple covariates through a functional data approach.
Simulation studies and real-data examples will be presented to investigate
the empirical performance of the proposed method. Open-source code is
available in the R package MFDA.
Meet the speaker in Room 212 Cockins Hall at 4:30
p.m. Refreshments will be served.
