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

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

Subnetwork enrichment analysis of template 3 with priming dose.

<p>Subnetwork enrichment analysis of template 3 with priming dose.</p

Illustration of the sliding window regression.

<p>Illustration of the sliding window regression.</p

Causal networks for the 5 templates without priming dose in <b>Figure 2D</b>.

<p>Causal networks for the 5 templates without priming dose in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042306#pone-0042306-g002" target="_blank"><b>Figure 2D</b></a>.</p

Causal networks for the 8 templates with priming dose in <b>Figure 2C</b>.

<p>Causal networks for the 8 templates with priming dose in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042306#pone-0042306-g002" target="_blank"><b>Figure 2C</b></a>.</p

Transcriptome profile of E2F family and RB1 indicates down-regulation for the treatment group with the priming dose.

<p>RB1 remains unchanged in the absence of the priming dose. Dash red-lines correspond to adaptive response.</p

Pathway and subnetwork enrichment without the priming dose with the default p-value of 0.05.

<p>Pathway and subnetwork enrichment without the priming dose with the default p-value of 0.05.</p

Temporal profiles of endpoints in <b>Figure 4</b> with a significant fold change.

<p>Dash red-lines correspond to adaptive response.</p

Analysis of temporal profile.

<p>Consensus clustering indicates A) 8 clusters for transcript data with the priming dose and B) 5 clusters in the control group. Each cluster, in A) and B), corresponds to a unique temporal profile as shown in C) and D), respectively.</p

Ariadne signaling pathway and subnetwork enrichment with priming dose with the default p-value of 0.05.

<p>Ariadne signaling pathway and subnetwork enrichment with priming dose with the default p-value of 0.05.</p