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Multi-level Support Vector Regression analysis to identify condition-specific regulatory networks

ABSTRACT: The identification of gene regulatory modules is an important yet challenging problem in computational biology. While many computational methods have been proposed to identify regulatory modules, their initial success is largely compromised by a high rate of false positives, especially when applied to human cancer studies. New strategies are needed for reliable regulatory module identification. We present a new approach, namely multi-level support vector regression (ml-SVR), to systematically identify conditionspecific regulatory modules. The approach is built upon a multi-level analysis strategy designed for suppressing false positive predictions. With this strategy, a regulatory module becomes ever more significant as more relevant gene sets are formed at finer levels. At each level, a two-stage support vector regression (SVR) method is utilized to help reduce false positive predictions by integrating binding motif information and gene expression data; a significant analysis procedure is followed to assess the significance of each regulatory module. We applied our method to breast cancer cell line data to identify condition-specific regulatory modules associated with estrogen treatment. Experimental results show that our method can identify biologically meaningful regulatory modules related to estrogen signaling and action in breast cancer. Three independent total RNA samples were extracted for each cell line (MCF-7 and MCF-7-stripped) and the samples were arrayed using Affymetrix GeneChip HG-U133A. MCF-7-stripped denotes estrogen-deprived MCF-7 human breast cancer cells, which were grown in the absence of estrogen for 96 hours. We analyzed the enriched motifs and their targets for the genes significantly down-regulated in MCF-7-stripped cells as compared to MCF-7 cells.

ORGANISM(S): Homo sapiens

SUBMITTER: Wang Yue

PROVIDER: S-ECPF-GEOD-20700 | biostudies-other |

REPOSITORIES: biostudies-other

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Project description:MotivationSignificant efforts have been made to acquire data under different conditions and to construct static networks that can explain various gene regulation mechanisms. However, gene regulatory networks are dynamic and condition-specific; under different conditions, networks exhibit different regulation patterns accompanied by different transcriptional network topologies. Thus, an investigation on the topological changes in transcriptional networks can facilitate the understanding of cell development or provide novel insights into the pathophysiology of certain diseases, and help identify the key genetic players that could serve as biomarkers or drug targets.ResultsHere, we report a differential dependency network (DDN) analysis to detect statistically significant topological changes in the transcriptional networks between two biological conditions. We propose a local dependency model to represent the local structures of a network by a set of conditional probabilities. We develop an efficient learning algorithm to learn the local dependency model using the Lasso technique. A permutation test is subsequently performed to estimate the statistical significance of each learned local structure. In testing on a simulation dataset, the proposed algorithm accurately detected all the genes with network topological changes. The method was then applied to the estrogen-dependent T-47D estrogen receptor-positive (ER+) breast cancer cell line datasets and human and mouse embryonic stem cell datasets. In both experiments using real microarray datasets, the proposed method produced biologically meaningful results. We expect DDN to emerge as an important bioinformatics tool in transcriptional network analyses. While we focus specifically on transcriptional networks, the DDN method we introduce here is generally applicable to other biological networks with similar characteristics.AvailabilityThe DDN MATLAB toolbox and experiment data are available at http://www.cbil.ece.vt.edu/software.htm.

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Multi-level Support Vector Regression analysis to identify condition-specific regulatory networks

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