Interpreting transcriptional changes using causal graphs: new methods and their practical utility on public networks

Abstract

BackgroundInference of active regulatory cascades under specific molecular and environmental perturbations is a recurring task in transcriptional data analysis. Commercial tools based on large, manually curated networks of causal relationships offering such functionality have been used in thousands of articles in the biomedical literature. The adoption and extension of such methods in the academic community has been hampered by the lack of freely available, efficient algorithms and an accompanying demonstration of their applicability using current public networks.ResultsIn this article, we propose a new statistical method that will infer likely upstream regulators based on observed patterns of up- and down-regulated transcripts. The method is suitable for use with public interaction networks with a mix of signed and unsigned causal edges. It subsumes and extends two previously published approaches and we provide a novel algorithmic method for efficient statistical inference. Notably, we demonstrate the feasibility of using the approach to generate biological insights given current public networks in the context of controlled in-vitro overexpression experiments, stem-cell differentiation data and animal disease models. We also provide an efficient implementation of our method in the R package QuaternaryProd available to download from Bioconductor.ConclusionsIn this work, we have closed an important gap in utilizing causal networks to analyze differentially expressed genes. Our proposed Quaternary test statistic incorporates all available evidence on the potential relevance of an upstream regulator. The new approach broadens the use of these types of statistics for highly curated signed networks in which ambiguities arise but also enables the use of networks with unsigned edges. We design and implement a novel computational method that can efficiently estimate p-values for upstream regulators in current biological settings. We demonstrate the ready applicability of the implemented method to analyze differentially expressed genes using the publicly available networks.

A cancer pharmacogenomic screen powering crowd-sourced advancement of drug combination prediction

“…Alternatively, more generic signatures of dynamic (e.g. transcriptional) output may first be used to identify a mechanistic rationale 19,20,21,22 to which causative genetic or epigenetic events can then be inferred and aligned as predictive features 23,24 . A surprising result of our Challenge, however, suggested only modest improvement to prediction from inclusion of all data in SC1A compared to only genetics in SC1B.…”

ModEx: A text mining system for extracting mode of regulation of Transcription Factor-gene regulatory interaction

“…To demonstrate the utility of our annotated network, we used our network in conjunction with a directional enrichment analysis algorithm [52,53] to identify drivers of differential expressed genes. We utilized quaternaryProd, a gene set enrichment algorithm that can take advantage of direction of regulation on causal biological interaction graphs to identify regulators of differential gene expression.…”

“…We utilized quaternaryProd, a gene set enrichment algorithm that can take advantage of direction of regulation on causal biological interaction graphs to identify regulators of differential gene expression. The qua-ternaryProd algorithm takes a differential gene expression profile along with an annotated transcriptional regulatory net- [52]. The Network inputted to quaternaryProd is assumed to encapsulate knowledge of TF-DNA interactions.…”

Causal Inference Engine: A platform for directional gene set enrichment analysis and inference of active transcriptional regulators

“…Tel: +1-617-287-7486; Fax: +1-617-287-6433; Email: kourosh.zarringhalam@umb.edu of genes based on known TF-gene interactions (3). Another class of algorithms, which are the main focus of this work, use prior biological knowledge on biomolecular interactions to link a differential gene expression (DGE) profile to upstream regulators (e.g., TFs) (4,5,6,7). The essential ingredients of these algorithms are (i) a DGE profile, (ii) a network of biomolecular interactions, and (iii) an inference algorithm to query the network.…”