In the recent years, there has been an increasing amount of single-cell sequencing studies, producing a considerable number of new data sets. This has particularly affected the field of cancer analysis, where more and more articles are published using this sequencing technique that allows for capturing more detailed information regarding the specific genetic mutations on each individually sampled cell. As the amount of information increases, it is necessary to have more sophisticated and rapid tools for analyzing the samples. To this goal, we developed plastic (PipeLine Amalgamating Single-cell Tree Inference Components), an easy-to-use and quick to adapt pipeline that integrates three different steps: (1) to simplify the input data, (2) to infer tumor phylogenies, and (3) to compare the phylogenies. We have created a pipeline submodule for each of those steps and developed new in-memory data structures that allow for easy and transparent sharing of the information across the tools implementing the above steps. While we use existing open source tools for those steps, we have extended the tool used for simplifying the input data, incorporating two machine learning procedures-which greatly reduce the running time without affecting the quality of the downstream analysis. Moreover, we have introduced the capability of producing some plots to quickly visualize results.

Ali, S., Ciccolella, S., Lucarella, L., Della Vedova, G., Patterson, M. (2021). Simpler and Faster Development of Tumor Phylogeny Pipelines. JOURNAL OF COMPUTATIONAL BIOLOGY, 28(11), 1142-1155 [10.1089/cmb.2021.0271].

Simpler and Faster Development of Tumor Phylogeny Pipelines

Ciccolella S.;Lucarella L.;Della Vedova Gianluca.;
2021

Abstract

In the recent years, there has been an increasing amount of single-cell sequencing studies, producing a considerable number of new data sets. This has particularly affected the field of cancer analysis, where more and more articles are published using this sequencing technique that allows for capturing more detailed information regarding the specific genetic mutations on each individually sampled cell. As the amount of information increases, it is necessary to have more sophisticated and rapid tools for analyzing the samples. To this goal, we developed plastic (PipeLine Amalgamating Single-cell Tree Inference Components), an easy-to-use and quick to adapt pipeline that integrates three different steps: (1) to simplify the input data, (2) to infer tumor phylogenies, and (3) to compare the phylogenies. We have created a pipeline submodule for each of those steps and developed new in-memory data structures that allow for easy and transparent sharing of the information across the tools implementing the above steps. While we use existing open source tools for those steps, we have extended the tool used for simplifying the input data, incorporating two machine learning procedures-which greatly reduce the running time without affecting the quality of the downstream analysis. Moreover, we have introduced the capability of producing some plots to quickly visualize results.
Articolo in rivista - Articolo scientifico
autoencoder; cancer analysis; dimensionality reduction; ridge regression; single-cell sequencing;
English
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Ali, S., Ciccolella, S., Lucarella, L., Della Vedova, G., Patterson, M. (2021). Simpler and Faster Development of Tumor Phylogeny Pipelines. JOURNAL OF COMPUTATIONAL BIOLOGY, 28(11), 1142-1155 [10.1089/cmb.2021.0271].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/352768
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