CausalXtract, a flexible pipeline to extract causal effects from live-cell time-lapse imaging data.

ABSTRACT: Live-cell microscopy routinely provides massive amounts of time-lapse images of complex cellular systems under various physiological or therapeutic conditions. However, this wealth of data remains difficult to interpret in terms of causal effects. Here, we describe CausalXtract, a flexible computational pipeline that discovers causal and possibly time-lagged effects from morphodynamic features and cell-cell interactions in live-cell imaging data. CausalXtract methodology combines network-based and information-based frameworks, which is shown to discover causal effects overlooked by classical Granger and Schreiber causality approaches. We showcase the use of CausalXtract to uncover novel causal effects in a tumor-on-chip cellular ecosystem under therapeutically relevant conditions. In parti

SUBMITTER: Simon F 

PROVIDER: S-EPMC11741518 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature