Abstract
BackgroundWith the abundant information produced by microarray technology, various approaches have been proposed to infer transcriptional regulatory networks. However, few approaches have studied subtle and indirect interaction such as genetic compensation, the existence of which is widely recognized although its mechanism has yet to be clarified. Furthermore, when inferring gene networks most models include only observed variables whereas latent factors, such as proteins and mRNA degradation that are not measured by microarrays, do participate in networks in reality.ResultsMotivated by inferring transcriptional compensation (TC) interactions in yeast, a stepwise structural equation modeling algorithm (SSEM) is developed. In addition to observed variables, SSEM also incorporates hidden variables to capture interactions (or regulations) from latent factors. Simulated gene networks are used to determine with which of six possible model selection criteria (MSC) SSEM works best. SSEM with Bayesian information criterion (BIC) results in the highest true positive rates, the largest percentage of correctly predicted interactions from all existing interactions, and the highest true negative (non-existing interactions) rates. Next, we apply SSEM using real microarray data to infer TC interactions among (1) small groups of genes that are synthetic sick or lethal (SSL) to SGS1, and (2) a group of SSL pairs of 51 yeast genes involved in DNA synthesis and repair that are of interest. For (1), SSEM with BIC is shown to outperform three Bayesian network algorithms and a multivariate autoregressive model, checked against the results of qRT-PCR experiments. The predictions for (2) are shown to coincide with several known pathways of Sgs1 and its partners that are involved in DNA replication, recombination and repair. In addition, experimentally testable interactions of Rad27 are predicted.ConclusionSSEM is a useful tool for inferring genetic networks, and the results reinforce the possibility of predicting pathways of protein complexes via genetic interactions.
Highlights
With the abundant information produced by microarray technology, various approaches have been proposed to infer transcriptional regulatory networks
stepwise structural equation modeling algorithm (SSEM) is applied to infer transcriptional compensation (TC) and TD interactions for (1) small groups of genes that are synthetic sick or lethal (SSL) to SGS1, and (2) SGS1 or RAD27 with their SSL partners from 51 genes involved in yeast DNA synthesis and repair that are of interest
We have carried out an extensive simulation to evaluate eight criteria used in commercial structural equation modeling (SEM) software, such as Mplus version 3 [17]
Summary
With the abundant information produced by microarray technology, various approaches have been proposed to infer transcriptional regulatory networks. The proposed algorithm (SSEM) was motivated by inferring transcriptional compensation (TC) networks of SGS1 (or RAD27) and its synthetic sick or lethal (SSL) partners [3,4]. SSEM can be applied to infer other types of networks, such as transcriptional regulatory networks. Since genetic networks derived from model organisms, such as yeast, are likely to be conserved in humans the prediction of TC and TD may shed light on pathways that cause complex human diseases. With the abundant information produced by microarray technology, various approaches have been proposed to infer genetic networks or transcriptional regulatory networks. We refer to [7] (in Additional file 1) for a thorough review of the models
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