Inferring gene regulatory network from variable delay high temporal data

Abstract

BACKGROUND: Recent advances in the live cell imaging techniques have enabled us to closely observe the cell lineages with high temporal resolution. Gene expression patterns in these cell lineages suggest regulatory pathways, which help us to decode various underlying mechanisms in developmental biology. Inferring gene networks from temporal data is fundamental but still a long standing challenge. Quite a number of gene network inference methods are proposed. However, every method has its own biases. Network Inference gets complicated with the dynamic delay associated to the gene regulation and the number of time points. This advocates the need of new gene network inference methods that can effectively handle the variable delay in high temporal live cell imaging data. METHOD: Here we design a new gene network inference algorithm based on the dynamic local alignment of time series gene expression data. The novelty of our method is the use of gapped alignment to handle the variable delay in gene regulation. The local alignment can even detect the short term gene regulations in the cell lineages, that are undetectable by traditional correlation and Mutual Information based methods. RESULTS: We tested our method on both synthetic and C. elegans live cell imaging data and compared its performance against other popular methods like MIC, ARACNE and Banjo. The area under the curve (AUC) of our method is observed to be significantly higher when compared to others. Besides the well-established regulatory relationships we also predicted few new relationships that are worthy of subsequent experimental investigation.