Model-based methods for identifying periodically expressed genes based on time course microarray gene expression data

  • Luan Y
  • Li H
  • 26


    Mendeley users who have this article in their library.
  • 82


    Citations of this article.


Motivation: The expressions of many genes associated with certain
periodic biological and cell cycle processes such as circadian rhythm
regulation are known to be rhythmic. Identification of the genes
whose time course expressions are synchronized to certain periodic
biological process may help to elucidate the molecular basis of many
diseases, and these gene products may in turn represent drug targets
relevant to those {diseases.Results:} We propose in this paper a
statistical framework based on a shape-invariant model together with
a false discovery rate {(FDR)} procedure for identifying periodically
expressed genes based on microarray time-course gene expression data
and a set of known periodically expressed guide genes. We applied
the proposed methods to the {Î}±-factor, cdc15 and cdc28 synchronized
yeast cell cycle data sets and identified a total of 1010 cell-cycle-regulated
genes at a {FDR} of 0.5% in at least one of the three data sets
analyzed, including 89 (86%) of 104 known periodic transcripts.
We also identified 344 and 201 circadian rhythmic genes in vivo in
mouse heart and liver tissues with {FDR} of 10 and 2.5%, respectively.
Our results also indicate that the shape-invariant model fits the
data well and provides estimate of the common shape function and
the relative phases for these periodically regulated {genes.Availability:}
Matlab programs are available on request from the {authors.Supplementary}

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document


  • Y. Luan

  • H. Li

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free