Stabilized Reconstruction of Signaling Networks from Single-Cell Cue-Response Data.

ABSTRACT: Inferring cell-signaling networks from high-throughput data is a challenging problem in systems biology. Recent advances in cytometric technology enable us to measure the abundance of a large number of proteins at the single-cell level across time. Traditional network reconstruction approaches usually consider each time point separately, resulting thus in inferred networks that strongly vary across time. To account for the possibly time-invariant physical couplings within the signaling network, we extend the traditional graphical lasso with an additional regularizer that penalizes network variations over time. ROC evaluation of the method on in silico data showed higher reconstruction accuracy than standard graphical lasso. We also tested our approach on single-cell mass cytometry data of

SUBMITTER: Kumar S 

PROVIDER: S-EPMC6985232 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature