CausalXtract, a flexible pipeline to extract causal effects from live-cell time-lapse imaging data
- CNRS UMR168, Institut Curie, Université PSL, Sorbonne Université, France
- Department of Electronic Engineering, University of Rome Tor Vergata, Italy
- INSERM U830, Institut Curie, Université PSL, France
Figures
Figure 1 with 1 supplement
CausalXtract pipeline.
(a) Live-cell tumor ecosystem reconstituted ex vivo (Nguyen et al., 2018) using the tumor-on-chip technology (‘Materials and methods’). (b) CausalXtract’s live-cell image feature extraction module …
Figure 1—figure supplement 1
Time series of cellular features extracted from the tumor ecosystems.
Example of time series of cellular features extracted by CausalXtract’s feature extraction module (CellHunter+) from the tumor ecosystems analyzed in this study (Figure 1a). It includes two …
Figure 2 with 4 supplements
Relation to Granger–Schreiber temporal causality and tMIIC benchmarking against PC and PCMCI+.
(a) The signature of Granger–Schreiber temporal causality is a vanishing Transfer Entropy, that is, (‘Materials and methods’). In the time-unfolded causal network framework, it implies (i) the …
Figure 2—figure supplement 1
Benchmark assessment of CausalXtract’s causal discovery module (tMIIC) using generated time-series datasets.
(a) Example of a 15-node causal network to generate benchmark time-series datasets based on linear combinations of contributions (Appendix 1). Examples of temporal causal networks reconstructed by …
Figure 2—figure supplement 2
CausalXtract insensitivity to an overestimated maximum lag τ.
Figure 2—figure supplement 3
CausalXtract sensitivity to non-stationary variables.
(a) Example of a temporal causal network model () with a low-frequency periodic input () applied to X8 and a time-linear trend applied to X13. Corresponding temporal causal networks inferred by …
Figure 2—figure supplement 4
Benchmark assessment of CausalXtract’s causal discovery module (tMIIC) using more complex time-series datasets.
(a) Example of a 15-node causal network to generate more complex benchmark time-series datasets based on nonlinear combinations of contributions (Appendix 2). Examples of temporal causal networks …
Figure 3 with 2 supplements
Application of CausalXtract to time-lapse images of tumor ecosystems reconstituted ex vivo.
(a) Summary causal network inferred by CausalXtract. The underlying time-unfolded causal network is shown in Figure 3—figure supplement 1. Red (resp. blue) edges correspond to positive (resp. …
Figure 3—figure supplement 1
Time-unfolded causal network inferred by CausalXtract.
(a) Time-unfolded causal network assuming stationary dynamics of cellular ecosystems implying translational time invariance of the inferred causal network. (b) Only edges involving at least one …
Figure 3—figure supplement 2
Robustness of CausalXtract’s temporal causal networks to variations in sampling rate.
Summary causal networks inferred by CausalXtract using different sampling rates (). (a) ts and ts, in time step units (1 ts = 2 min). (b) ts, and ts, as chosen automatically by …
Videos
Video 1
Example of tracking of cancer and immune cells and of their mutual interactions in the absence of cell division and apoptosis event.
Video 2
Example of tracking of cancer and immune cells and of their mutual interactions in the presence of a cell division event.
Video 3
