%0 Journal Article %1 noauthororeditor %A Verrou, Klio-Maria %A Tsamardinos, Ioannis %A Papoutsoglou, Georgios %D 2020 %J Cytometry part A, Special Issue: Machine Learning for Single Cell Data, %K mxmcausalpath %N 3 %R https://doi.org/10.1002/cyto.a.23976 %T Learning Pathway Dynamics from Single‐Cell Proteomic Data: A Comparative Study %U https://onlinelibrary.wiley.com/doi/full/10.1002/cyto.a.23976 %V 97 %X Single‐cell platforms provide statistically large samples of snapshot observations capable of resolving intrercellular heterogeneity. Currently, there is a growing literature on algorithms that exploit this attribute in order to infer the trajectory of biological mechanisms, such as cell proliferation and differentiation. Despite the efforts, the trajectory inference methodology has not yet been used for addressing the challenging problem of learning the dynamics of protein signaling systems. In this work, we assess this prospect by testing the performance of this class of algorithms on four proteomic temporal datasets. To evaluate the learning quality, we design new general‐purpose evaluation metrics that are able to quantify performance on (i) the biological meaning of the output, (ii) the consistency of the inferred trajectory, (iii) the algorithm robustness, (iv) the correlation of the learning output with the initial dataset, and (v) the roughness of the cell parameter levels though the inferred trajectory. We show that experimental time alone is insufficient to provide knowledge about the order of proteins during signal transduction. Accordingly, we show that the inferred trajectories provide richer information about the underlying dynamics. We learn that established methods tested on high‐dimensional data with small sample size, slow dynamics, and complex structures (e.g. bifurcations) cannot always work in the signaling setting. Among the methods we evaluate, Scorpius and a newly introduced approach that combines Diffusion Maps and Principal Curves were found to perform adequately in recovering the progression of signal transduction although their performance on some metrics varies from one dataset to another. The novel metrics we devise highlight that it is difficult to conclude, which one method is universally applicable for the task. Arguably, there are still many challenges and open problems to resolve. © 2020 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

@article{noauthororeditor, abstract = {Single‐cell platforms provide statistically large samples of snapshot observations capable of resolving intrercellular heterogeneity. Currently, there is a growing literature on algorithms that exploit this attribute in order to infer the trajectory of biological mechanisms, such as cell proliferation and differentiation. Despite the efforts, the trajectory inference methodology has not yet been used for addressing the challenging problem of learning the dynamics of protein signaling systems. In this work, we assess this prospect by testing the performance of this class of algorithms on four proteomic temporal datasets. To evaluate the learning quality, we design new general‐purpose evaluation metrics that are able to quantify performance on (i) the biological meaning of the output, (ii) the consistency of the inferred trajectory, (iii) the algorithm robustness, (iv) the correlation of the learning output with the initial dataset, and (v) the roughness of the cell parameter levels though the inferred trajectory. We show that experimental time alone is insufficient to provide knowledge about the order of proteins during signal transduction. Accordingly, we show that the inferred trajectories provide richer information about the underlying dynamics. We learn that established methods tested on high‐dimensional data with small sample size, slow dynamics, and complex structures (e.g. bifurcations) cannot always work in the signaling setting. Among the methods we evaluate, Scorpius and a newly introduced approach that combines Diffusion Maps and Principal Curves were found to perform adequately in recovering the progression of signal transduction although their performance on some metrics varies from one dataset to another. The novel metrics we devise highlight that it is difficult to conclude, which one method is universally applicable for the task. Arguably, there are still many challenges and open problems to resolve. © 2020 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.}, added-at = {2020-04-15T09:49:24.000+0200}, author = {Verrou, Klio-Maria and Tsamardinos, Ioannis and Papoutsoglou, Georgios}, biburl = {https://www.bibsonomy.org/bibtex/2e3bfa8becd8b4b2537754b410e035264/mensxmachina}, doi = {https://doi.org/10.1002/cyto.a.23976}, interhash = {cfaefe697d477e338b1b5b57bc0e7335}, intrahash = {e3bfa8becd8b4b2537754b410e035264}, journal = {Cytometry part A, Special Issue: Machine Learning for Single Cell Data,}, keywords = {mxmcausalpath}, number = 3, timestamp = {2021-03-08T12:03:01.000+0100}, title = {Learning Pathway Dynamics from Single‐Cell Proteomic Data: A Comparative Study}, url = {https://onlinelibrary.wiley.com/doi/full/10.1002/cyto.a.23976}, volume = 97, year = 2020 }