Book chapter
Progression Reconstruction from Unsynchronized Biological Data using Cluster Spanning Trees
Bioinformatics Research and Applications, pp.136-147
Lecture Notes in Computer Science, Springer International Publishing
05/27/2016
DOI: 10.1007/978-3-319-38782-6_12
Abstract
Identifying the progression-order of an unsynchronized set of biological samples is crucial for comprehending the dynamics of the underlying molecular interactions. It is also valuable in many applied problems such as data denoising and synchronization, tumor classification and cell lineage identification. Current methods that attempt solving this problem are ultimately based either on polynomial and piece-wise approximation of the unknown generating function or its reconstruction through the use of spanning trees. Such approaches face difficulty when it is necessary to factor-in complex relationships within the data such as partial ordering or bifurcating or multifurcating progressions. We propose the notion of Cluster Spanning Trees (CST) that can model both linear as well as the aforementioned complex progression relationships in data. Through a number of experiments on synthetic data sets as well as datasets from the cell cycle, cellular differentiation, and phenotypic screening, we show that the proposed CST approach outperforms the previous approaches in reconstructing the temporal progression of the data.
Details
- Title: Subtitle
- Progression Reconstruction from Unsynchronized Biological Data using Cluster Spanning Trees
- Creators
- Ryan Eshleman - San Francisco State UniversityRahul Singh - San Francisco State University
- Resource Type
- Book chapter
- Publication Details
- Bioinformatics Research and Applications, pp.136-147
- Publisher
- Springer International Publishing; Cham
- Series
- Lecture Notes in Computer Science
- DOI
- 10.1007/978-3-319-38782-6_12
- eISSN
- 1611-3349
- ISSN
- 0302-9743
- Language
- English
- Date published
- 05/27/2016
- Academic Unit
- Computer Science
- Record Identifier
- 9984446453602771
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