7 research outputs found
A Model-Based Method for Gene Dependency Measurement
<div><p>Many computational methods have been widely used to identify transcription regulatory interactions based on gene expression profiles. The selection of dependency measure is very important for successful regulatory network inference. In this paper, we develop a new method–DBoMM (Difference in BIC of Mixture Models)–for estimating dependency of gene by fitting the gene expression profiles into mixture Gaussian models. We show that DBoMM out-performs 4 other existing methods, including Kendall’s tau correlation (TAU), Pearson Correlation (COR), Euclidean distance (EUC) and Mutual information (MI) using <em>Escherichia coli, Saccharomyces cerevisiae, Drosophila melanogaster</em>, <em>Arabidopsis thaliana</em> data and synthetic data. DBoMM can also identify condition-dependent regulatory interactions and is robust to noisy data. Of the 741 <em>Escherichia coli</em> regulatory interactions inferred by DBoMM at a 60% true positive rate, 65 are previously known interactions and 676 are novel predictions. To validate the new prediction, the promoter sequences of target genes regulated by the same transcription factors were analyzed and significant motifs were identified.</p> </div
DBoMM can catch the conditional dependent interactions and distinguish the real gene interactions from the background.
<p>The expression profiles of two interacting genes (a) and non-interacting genes (c) are fitted into a bivariate mixture Gaussian distribution (joint distribution with different colors). The expression profiles of two interacting genes (b) and non-interacting genes (d) are separately fitted into two univariate mixture Gaussian distribution (marginal distribution). The blue and green lines represent the distribution of the two genes respectively. The contours correspond to the joint densities implied by DBoMM.</p
Motifs detected for TF and .
<p>(a). The regulatory motif detected in the promoters of the 19 inferred target operons(upper) compared to the motif identified in PRODORIC. (b). The regulatory motif detected in the promoters of 8 inferred target operons(upper) compared to the motif identified in PRODORIC(lower).</p
DBoMM is robust to noise.
<p>Different levels of noise are introduced to the datasets. The numbers in the legend correspond to the noise levels, e.g. “noisy2” means 20% of noise introduced. DBoMM remains stable with up to 60% of noise. X axis: recall; y axis: precision.</p
A comparison of different methods using PR-curve.
<p>(a). <i>E.coli</i> dataset and the reference network from RegulonDB; (b). <i>Yeast</i> datset and the reference network from YEASTRACT; (c). <i>Drosophila</i> dataset and the reference network from DroID; (d). <i>Arabidopsis</i> datset and the reference network from AGRIS. X axis: recall; Y axis: precision. In general, DBoMM out-performs other 4 methods using various datasets.</p
The distributions of different similarity scores.
<p>The distributions of different similarity scores.</p
DBoMM can identify the conditional dependent regulatory interactions between two genes.
<p>The experimental conditions are classified into 6 different clusters based on the expression profiles of two genes (<i>lexA</i> and <i>recA</i>). Cn represents the index of the cluster.</p