Inference of Causal Networks from Time-Varying Transcriptome Data via Sparse Coding

Temporal analysis of genome-wide data can provide insights into the underlying mechanism of the biological processes intwo ways. First, grouping the temporal data provides a richer, more robust representation of the underlying processes thatare co-regulated. The net result is a significant dimensional reduction of the genome-wide array data into a smaller set ofvocabularies for bioinformatics analysis. Second, the computed set of time-course vocabularies can be interrogated for apotential causal network that can shed light on the underlying interactions. The method is coupled with an experiment forinvestigating responses to high doses of ionizing radiation with and without a small priming dose. From a computationalperspective, inference of a causal network can rapidly become computationally intractable with the increasing number ofvariables. Additionally, from a bioinformatics perspective, larger networks always hinder interpretation. Therefore, ourmethod focuses on inferring the simplest network that is computationally tractable and interpretable. The method firstreduces the number of temporal variables through consensus clustering to reveal a small set of temporal templates. It thenenforces simplicity in the network configuration through the sparsity constraint, which is further regularized by requiringcontinuity between consecutive time points. We present intermediate results for each computational step, and apply ourmethod to a time-course transcriptome dataset for a cell line receiving a challenge dose of ionizing radiation with andwithout a prior priming dose. Our analyses indicate that (i) the priming dose increases the diversity of the computedtemplates (e.g., diversity of transcriptome signatures); thus, increasing the network complexity; (ii) as a result of the primingdose, there are a number of unique templates with delayed and oscillatory profiles; and (iii) radiation-induced stressresponses are enriched through pathway and subnetwork studies.