SHISMA: SHape-driven inference of significant cell type-specific subnetworks from tiMe series single-cell...

Recent advances in DNA and RNA sequencing technologies and the gradual decrease in costs have allowed to design serial experiments with timestamps, even at single cell resolution. This possibility unlocks a finer level of detail, as well as a huge amount of noisy information to decode. Tools inferring regulatory networks, or patterns, from this type of data often focus on trajectories, disregarding local shapes and fundamental time series primitives. Moreover, they fail to target the analysis on a few meaningful results, reporting large and noisy outputs that need further downstream analysis.

We describe SHISMA, a novel tool to infer significant cell type-specific co-dynamic gene subnetworks, from time series transcriptomic data, with strong statistical guarantees in terms of P value. SHISMA leverages isolated cell populations thanks to single-cell resolution, constructing cell type-specific pseudobulk time-series datasets. It then exploits a recently proposed time series primitive, the Bag-of-Receptive-Fields, adapted to discretize shorter temporal data and retain local shapes. SHISMA extracts significant groups of genes by performing a random walk approach on a protein-protein interaction network, with nodes identified by genes and scores derived from the shape-induced representation of the data, while properly validating via permutation and correcting for multiple hypothesis testing. Our extensive experimental evaluation on synthetic data shows that our tool is able to retrieve specific and significant subnetworks from time series transcriptomic data. Moreover, the subnetworks identified by SHISMA on real-world data confirm its ability to retrieve known cell type-specific processes, as well as potentially novel patterns and co-dynamic mechanisms.

https://github.com/antoniocollesei/SHISMA.