Motivation: The transition from evaluating a single time point to examining the entire dynamic evolution of a system is possible only in the presence of the proper framework. The strong variability of dynamic evolution makes the definition of an explanatory procedure for data fitting and clustering challenging.Results: We developed CONNECTOR, a data-driven framework able to analyze and inspect longitudinal data in a straightforward and revealing way. When used to analyze tumor growth kinetics over time in 1599 patient-derived xenograft growth curves from ovarian and colorectal cancers, CONNECTOR allowed the aggregation of time-series data through an unsupervised approach in informative clusters. We give a new perspective of mechanism interpretation, specifically, we define novel model aggregations and we identify unanticipated molecular associations with response to clinically approved therapies.Availability and implementation: CONNECTOR is freely available under GNU GPL license at https://qbioturin.github. io/connector and https://doi.org/10.17504/protocols.io.8epv56e74g1b/v1.

CONNECTOR, fitting and clustering of longitudinal data to reveal a new risk stratification system

Pernice, Simone;Sirovich, Roberta;Grassi, Elena;Viviani, Marco;Ferri, Martina;Alessandrì, Luca;Tortarolo, Dora;Calogero, Raffaele A;Trusolino, Livio;Bertotti, Andrea;Beccuti, Marco;Olivero, Martina;Cordero, Francesca
2023-01-01

Abstract

Motivation: The transition from evaluating a single time point to examining the entire dynamic evolution of a system is possible only in the presence of the proper framework. The strong variability of dynamic evolution makes the definition of an explanatory procedure for data fitting and clustering challenging.Results: We developed CONNECTOR, a data-driven framework able to analyze and inspect longitudinal data in a straightforward and revealing way. When used to analyze tumor growth kinetics over time in 1599 patient-derived xenograft growth curves from ovarian and colorectal cancers, CONNECTOR allowed the aggregation of time-series data through an unsupervised approach in informative clusters. We give a new perspective of mechanism interpretation, specifically, we define novel model aggregations and we identify unanticipated molecular associations with response to clinically approved therapies.Availability and implementation: CONNECTOR is freely available under GNU GPL license at https://qbioturin.github. io/connector and https://doi.org/10.17504/protocols.io.8epv56e74g1b/v1.
2023
39
5
1
8
https://academic.oup.com/bioinformatics/article/39/5/btad201/7133735
Longitudinal data, functional data analysis, growth curves
Pernice, Simone; Sirovich, Roberta; Grassi, Elena; Viviani, Marco; Ferri, Martina; Sassi, Francesco; Alessandrì, Luca; Tortarolo, Dora; Calogero, Raffaele A; Trusolino, Livio; Bertotti, Andrea; Beccuti, Marco; Olivero, Martina; Cordero, Francesca
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1914011
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