Effective Clustering for Single Cell Sequencing Cancer Data.
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| Title: | Effective Clustering for Single Cell Sequencing Cancer Data. |
|---|---|
| Authors: | Ciccolella, Simone1 (AUTHOR) simone. ciccolella@unimib.it, Patterson, Murray2 (AUTHOR) mpatterson@ cs.gsu.edu, Bonizzoni, Paola1 (AUTHOR) paola.bonizzoni@unimib.it, Della Vedova, Gianluca1 (AUTHOR) gianluca.dellavedova@unimib.it |
| Source: | IEEE Journal of Biomedical & Health Informatics. Nov2021, Vol. 25 Issue 11, p4068-4078. 11p. |
| Subjects: | Cancer cells, Sequential analysis, Linear programming, Phylogeny |
| Abstract: | Single cell sequencing (SCS) technologies provide a level of resolution that makes it indispensable for inferring from a sequenced tumor, evolutionary trees or phylogenies representing an accumulation of cancerous mutations. A drawback of SCS is elevated false negative and missing value rates, resulting in a large space of possible solutions, which in turn makes it difficult, sometimes infeasible using current approaches and tools. One possible solution is to reduce the size of an SCS instance — usually represented as a matrix of presence, absence, and uncertainty of the mutations found in the different sequenced cells — and to infer the tree from this reduced-size instance. In this work, we present a new clustering procedure aimed at clustering such categorical vector, or matrix data — here representing SCS instances, called celluloid. We show that celluloid clusters mutations with high precision: never pairing too many mutations that are unrelated in the ground truth, but also obtains accurate results in terms of the phylogeny inferred downstream from the reduced instance produced by this method. We demonstrate the usefulness of a clustering step by applying the entire pipeline (clustering + inference method) to a real dataset, showing a significant reduction in the runtime, raising considerably the upper bound on the size of SCS instances which can be solved in practice. Our approach, celluloid: clustering single cell sequencing data around centroids is available at https://github.com/AlgoLab/celluloid/ under an MIT license, as well as on the Python Package Index (PyPI) at https://pypi.org/project/celluloid-clust/ [ABSTRACT FROM AUTHOR] |
| Copyright of IEEE Journal of Biomedical & Health Informatics is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 153789538 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Effective Clustering for Single Cell Sequencing Cancer Data. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ciccolella%2C+Simone%22">Ciccolella, Simone</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> simone. ciccolella@unimib.it</i><br /><searchLink fieldCode="AR" term="%22Patterson%2C+Murray%22">Patterson, Murray</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> mpatterson@ cs.gsu.edu</i><br /><searchLink fieldCode="AR" term="%22Bonizzoni%2C+Paola%22">Bonizzoni, Paola</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> paola.bonizzoni@unimib.it</i><br /><searchLink fieldCode="AR" term="%22Della+Vedova%2C+Gianluca%22">Della Vedova, Gianluca</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> gianluca.dellavedova@unimib.it</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22IEEE+Journal+of+Biomedical+%26+Health+Informatics%22">IEEE Journal of Biomedical & Health Informatics</searchLink>. Nov2021, Vol. 25 Issue 11, p4068-4078. 11p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Cancer+cells%22">Cancer cells</searchLink><br /><searchLink fieldCode="DE" term="%22Sequential+analysis%22">Sequential analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Linear+programming%22">Linear programming</searchLink><br /><searchLink fieldCode="DE" term="%22Phylogeny%22">Phylogeny</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Single cell sequencing (SCS) technologies provide a level of resolution that makes it indispensable for inferring from a sequenced tumor, evolutionary trees or phylogenies representing an accumulation of cancerous mutations. A drawback of SCS is elevated false negative and missing value rates, resulting in a large space of possible solutions, which in turn makes it difficult, sometimes infeasible using current approaches and tools. One possible solution is to reduce the size of an SCS instance — usually represented as a matrix of presence, absence, and uncertainty of the mutations found in the different sequenced cells — and to infer the tree from this reduced-size instance. In this work, we present a new clustering procedure aimed at clustering such categorical vector, or matrix data — here representing SCS instances, called celluloid. We show that celluloid clusters mutations with high precision: never pairing too many mutations that are unrelated in the ground truth, but also obtains accurate results in terms of the phylogeny inferred downstream from the reduced instance produced by this method. We demonstrate the usefulness of a clustering step by applying the entire pipeline (clustering + inference method) to a real dataset, showing a significant reduction in the runtime, raising considerably the upper bound on the size of SCS instances which can be solved in practice. Our approach, celluloid: clustering single cell sequencing data around centroids is available at https://github.com/AlgoLab/celluloid/ under an MIT license, as well as on the Python Package Index (PyPI) at https://pypi.org/project/celluloid-clust/ [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of IEEE Journal of Biomedical & Health Informatics is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1109/JBHI.2021.3081380 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 4068 Subjects: – SubjectFull: Cancer cells Type: general – SubjectFull: Sequential analysis Type: general – SubjectFull: Linear programming Type: general – SubjectFull: Phylogeny Type: general Titles: – TitleFull: Effective Clustering for Single Cell Sequencing Cancer Data. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ciccolella, Simone – PersonEntity: Name: NameFull: Patterson, Murray – PersonEntity: Name: NameFull: Bonizzoni, Paola – PersonEntity: Name: NameFull: Della Vedova, Gianluca IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2021 Type: published Y: 2021 Identifiers: – Type: issn-print Value: 21682194 Numbering: – Type: volume Value: 25 – Type: issue Value: 11 Titles: – TitleFull: IEEE Journal of Biomedical & Health Informatics Type: main |
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