Abstract
BackgroundThe molecular assays that test gene expression, transcriptional, and epigenetic regulation are increasingly diverse and numerous. The information generated by each type of assay individually gives an insight into the state of the cells tested. What should be possible is to add the information derived from separate, complementary assays to gain higher-confidence insights into cellular states. At present, the analysis of multi-dimensional, massive genome-wide data requires an initial pruning step to create manageable subsets of observations that are then used for integration, which decreases the sizes of the intersecting data sets and the potential for biological insights. Our Significance-based Modules Integrating the Transcriptome and Epigenome (SMITE) approach was developed to integrate transcriptional and epigenetic regulatory data without a loss of resolution.ResultsSMITE combines p-values by accounting for the correlation between non-independent values within data sets, allowing genes and gene modules in an interaction network to be assigned significance values. The contribution of each type of genomic data can be weighted, permitting integration of individually under-powered data sets, increasing the overall ability to detect effects within modules of genes. We apply SMITE to a complex genomic data set including the epigenomic and transcriptomic effects of Toxoplasma gondii infection on human host cells and demonstrate that SMITE is able to identify novel subnetworks of dysregulated genes. Additionally, we show that SMITE outperforms Functional Epigenetic Modules (FEM), the current paradigm of using the spin-glass algorithm to integrate gene expression and epigenetic data.ConclusionsSMITE represents a flexible, scalable tool that allows integration of transcriptional and epigenetic regulatory data from genome-wide assays to boost confidence in finding gene modules reflecting altered cellular states.
Highlights
The molecular assays that test gene expression, transcriptional, and epigenetic regulation are increasingly diverse and numerous
Integrative analysis increases study power Significance-based Modules Integrating the Transcriptome and Epigenome (SMITE) increases the power of analysis at four levels: (1) by analyzing combined genomic signals from multi-level genomics experiments and avoiding the inflated type I error that characterizes pairwise comparisons of genomic signals; (2) by combining incomplete data sets so that having one missing signal will not eliminate a gene from analysis; (3) by allowing prioritization of the most important signals and genomic contexts for further downstream analysis; and (4) by implementing methods to analyze groups of genes within networks or pathways together
SMITE identifies novel dysregulated functional modules in T. gondii-infected human cells In Additional file 1: Table S2, we show two sets of criteria that we used to score the T. gondii human foreskin fibroblasts (HFF) data called reduced (SMITE-R) and full (SMITE-F) models that illustrate how a researcher can use SMITE with varied weighting to identify varied gene modules
Summary
The molecular assays that test gene expression, transcriptional, and epigenetic regulation are increasingly diverse and numerous. Our Significance-based Modules Integrating the Transcriptome and Epigenome (SMITE) approach was developed to integrate transcriptional and epigenetic regulatory data without a loss of resolution. There is a need for a flexible method integrating genomic assay data into a single score that can be used to identify functionally important pathways for further study. We describe an intuitive gene scoring system that combines transcriptional and epigenetic regulatory data sets, an approach we call Significance-based Modules Integrating the Transcriptome and Epigenome (SMITE). SMITE provides a useful and intuitive answer to the most important question in integrative genomics: what we can learn from integrating multiple sources of high-resolution information instead of considering each source separately?
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