Informative community structure revealed using Arabidopsis time series transcriptome data via partitioned local depth

Transcriptome studies that provide temporal information about transcript abundance facilitate identification of gene regulatory networks (GRNs). Inferring GRNs from time series data using computational modeling remains a central challenge in systems biology. Commonly employed clustering algorithms identify modules of like-responding genes but do not provide information on how these modules are interconnected. These methods also require users to specify parameters such as cluster number and size, adding complexity to the analysis. To address these challenges, we used a recently developed algorithm, partitioned local depth (PaLD), to generate cohesive networks for 4 time series transcriptome datasets (3 hormone and 1 abiotic stress dataset) from the model plant Arabidopsis thaliana. PaLD provided a cohesive network representation of the data, revealing networks with distinct structures and varying numbers of connections between transcripts. We utilized the networks to make predictions about GRNs by examining local neighborhoods of transcripts with highly similar temporal responses. We also partitioned the networks into groups of like-responding transcripts and identified enriched functional and regulatory features in them. Comparison of groups to clusters generated by commonly used approaches indicated that these methods identified modules of transcripts that have similar temporal and biological features, but also identified unique groups, suggesting that a PaLD-based approach (supplemented with a community detection algorithm) can complement existing methods. These results revealed that PaLD could sort like-responding transcripts into biologically meaningful neighborhoods and groups while requiring minimal user input and producing cohesive network structure, offering an additional tool to the systems biology community to predict GRNs.