Learning interpretable causal networks from very large datasets, application to 400,000 medical records of breast cancer patients

Ribeiro-Dantas, Marcel da Câmara; Li, Honghao; Cabeli, Vincent; Dupuis, Louise; Simon, Franck; Hettal, Liza; Hamy, Anne-Sophie; Isambert, Hervé

doi:10.1016/j.isci.2024.109736

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

Simon,

Comes,

Tocci

et al. 2024

Preprint

1

0

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Live-cell microscopy routinely provides massive amount 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 particular, we find that cancer associated fibroblasts directly inhibit cancer cell apoptosis, independently from anti-cancer treatment. CausalXtract uncovers also multiple antagonistic effects at different time delays. Hence, CausalXtract provides a unique computational tool to interpret live-cell imaging data for a range of fundamental and translational research applications.

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miic: Learning Causal or Non-Causal Graphical Models Using Information Theory

Simon,

Tocci,

Lagrange

et al. 2017

CRAN: Contributed Packages

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

Simon,

Comes,

Tocci

et al. 2024

Preprint

0

View full text Add to dashboard Cite

Live-cell microscopy routinely provides massive amount 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 particular, we find that cancer associated fibroblasts directly inhibit cancer cell apoptosis, independently from anti-cancer treatment. CausalXtract uncovers also multiple antagonistic effects at different time delays. Hence, CausalXtract provides a unique computational tool to interpret live-cell imaging data for a range of fundamental and translational research applications.

show abstract

Learning interpretable causal networks from very large datasets, application to 400,000 medical records of breast cancer patients

Cited by 4 publications

References 47 publications

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

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

miic: Learning Causal or Non-Causal Graphical Models Using Information Theory

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

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