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Comparative Study
. 2003 Sep 2;100(18):10146-51.
doi: 10.1073/pnas.1732547100. Epub 2003 Aug 21.

Comparing the continuous representation of time-series expression profiles to identify differentially expressed genes

Ziv Bar-Joseph  1 , Georg Gerber, Itamar Simon, David K Gifford, Tommi S Jaakkola
Affiliations
Comparative Study

Comparing the continuous representation of time-series expression profiles to identify differentially expressed genes

Ziv Bar-Joseph et al. Proc Natl Acad Sci U S A. .

Abstract

We present a general algorithm to detect genes differentially expressed between two nonhomogeneous time-series data sets. As increasing amounts of high-throughput biological data become available, a major challenge in genomic and computational biology is to develop methods for comparing data from different experimental sources. Time-series whole-genome expression data are a particularly valuable source of information because they can describe an unfolding biological process such as the cell cycle or immune response. However, comparisons of time-series expression data sets are hindered by biological and experimental inconsistencies such as differences in sampling rate, variations in the timing of biological processes, and the lack of repeats. Our algorithm overcomes these difficulties by using a continuous representation for time-series data and combining a noise model for individual samples with a global difference measure. We introduce a corresponding statistical method for computing the significance of this differential expression measure. We used our algorithm to compare cell-cycle-dependent gene expression in wild-type and knockout yeast strains. Our algorithm identified a set of 56 differentially expressed genes, and these results were validated by using independent protein-DNA-binding data. Unlike previous methods, our algorithm was also able to identify 22 non-cell-cycle-regulated genes as differentially expressed. This set of genes is significantly correlated in a set of independent expression experiments, suggesting additional roles for the transcription factors Fkh1 and Fkh2 in controlling cellular activity in yeast.

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Figures

Fig. 1.
Fig. 1.
Complete algorithm for identifying differentially expressed genes in time-series data.
Fig. 2.
Fig. 2.
Differentially expressed cell cycle genes. (A) Percentage of genes bound by the nine factors in the entire set of 800 cell-cycle-regulated genes and the set identified by our algorithm. As can be seen, all the relevant factors are significantly enriched for genes in the selected set (P values are in parentheses after the factor name). (B) Results of clustering the 56 selected genes. Cluster 1 is composed of genes that are affected directly and clusters 2 and 3 contain genes with second-order effects. See Results and Discussion.
Fig. 3.
Fig. 3.
Genes identified by our algorithm that were missed by the clustering method used in ref. . (Upper) Five of the cell-cycle-regulated genes. WT expression is represented by the solid line, and knockout by the dashed line. A P value appears to the right of the gene name. As can be seen, all these genes displayed significant reduction in their cycling ability. In addition, all the above genes are bound by Fkh1/2, Ace2, or Swi5, indicating that our algorithm identified a relevant set of genes. (Lower) Five of the noncycling genes. Note that some of these genes seem to be cycling in the knockout experiment, whereas they are not cycling in the WT experiment (see Results and Discussion).
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