CausalXtract: a flexible pipeline to extract causal effects from live-cell time-lapse imaging data
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eLife assessment
In this fundamental study, the authors describe a new data processing pipeline that can be used to discover causal interactions from time-lapse imaging data. The utility of this pipeline was convincingly illustrated using tumor-on-chip ecosystem data. The newly developed pipeline could be used to better understand cell-cell interactions and could also be applied to perform temporal causal discovery in other areas of science, meaning this work could potentially have a wide range of applications.
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Abstract
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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eLife assessment
In this fundamental study, the authors describe a new data processing pipeline that can be used to discover causal interactions from time-lapse imaging data. The utility of this pipeline was convincingly illustrated using tumor-on-chip ecosystem data. The newly developed pipeline could be used to better understand cell-cell interactions and could also be applied to perform temporal causal discovery in other areas of science, meaning this work could potentially have a wide range of applications.
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Reviewer #1 (Public review):
Summary:
This paper presents a data processing pipeline to discover causal interactions from time-lapse imaging data, and convicingly illustrates it on a challenging application for the analysis of tumor-on-chip ecosystem data.
The core of the discovery module is the original tMIIC method of the authors, which is shown in supplementary material to compare favourably to two state-of-the-art methods on synthetic temporal data on a 15 nodes network.
Strengths:
This paper tackles the problem of learning causal interactions from temporal data which is an open problem in presence of latent variables.
The core of the method tMIIC of the authors is nicely presented in connection to Granger-Schreiber causality and to the novel graphical conditions used to infer latent variables and based on a theorem about transfer …
Reviewer #1 (Public review):
Summary:
This paper presents a data processing pipeline to discover causal interactions from time-lapse imaging data, and convicingly illustrates it on a challenging application for the analysis of tumor-on-chip ecosystem data.
The core of the discovery module is the original tMIIC method of the authors, which is shown in supplementary material to compare favourably to two state-of-the-art methods on synthetic temporal data on a 15 nodes network.
Strengths:
This paper tackles the problem of learning causal interactions from temporal data which is an open problem in presence of latent variables.
The core of the method tMIIC of the authors is nicely presented in connection to Granger-Schreiber causality and to the novel graphical conditions used to infer latent variables and based on a theorem about transfer entropy.
tMIIC compares favourably to PC and PCMCI+ methods using different kernels on synthetic datasets generated from a network of 15 nodes.
A full application to tumor-on-chip cellular ecosystems data including cancer cells, immune cells, cancer-associated fibroblasts, endothelial cells and anti cancer drugs, with convincing inference results with respect to both known and novel effects between those components and their contact.
The code and dataset are available online for the reproducibility of the results.
Weaknesses:
The references to "state-of-the-art methods" concerning the inference of causal networks should be more precise by giving citations in the main text, and better discussed in general terms, both in the first section and in the section of presentation of CausalXtract. It is only in the legend of the figures of the supplementary material that we get information.
Of course, comparison on our own synthetic datasets can always be criticized but this is rather due to the absence of common benchmark and I would recommend the authors to explicitly propose their datasets as benchmark to the community.
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Reviewer #2 (Public review):
Summary:
The authors propose a methodology to perform causal (temporal) discovery. The approach appears to be robust and is tested in the different scenarios: one related with live-cell imaging data, and another one using synthetic (mathematically defined) time series data. They compare the performance of their findings against another well-know method by using metrics like F-score, precision and recall,
Strengths:
Performance, robustness, the text is clear and concise, The authors provide the code to review.
Weaknesses:
One concern could be the applicability of the method in other areas like climate, economy. For those areas, public data are available and might be interesting to test how the method performs with this kind of data.
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