Sorting points into neighborhoods (SPIN): data analysis and visualization by ordering distance matrices
Open accessAbstract
SUMMARY: We introduce a novel unsupervised approach for the organization and visualization of multidimensional data. At the heart of the method is a presentation of the full pairwise distance matrix of the data points, viewed in pseudocolor. The ordering of points is iteratively permuted in search of a linear ordering, which can be used to study embedded shapes. Several examples indicate how the shapes of certain structures in the data (elongated, circular and compact) manifest themselves visually in our permuted distance matrix. It is important to identify the elongated objects since they are often associated with a set of hidden variables, underlying continuous variation in the data. The problem of determining an optimal linear ordering is shown to be NP-Complete, and therefore an iterative search algorithm with O(n3) step-complexity is suggested. By using sorting points into neighborhoods, i.e. SPIN to analyze colon cancer expression data we were able to address the serious problem of sample heterogeneity, which hinders identification of metastasis related genes in our data. Our methodology brings to light the continuous variation of heterogeneity--starting with homogeneous tumor samples and gradually increasing the amount of another tissue. Ordering the samples according to their degree of contamination by unrelated tissue allows the separation of genes associated with irrelevant contamination from those related to cancer progression. AVAILABILITY: Software package will be available for academic users upon request.
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Pseudoalignment reduces quantification time by two orders of magnitude without compromising accuracy.
“by replacing read alignment with pseudoalignment, i.e., by only identifying the transcripts from which a read could have originated without determining the exact sequence alignments between them, the time required to perform quantification (assigning expression values to genes) is decreased by two orders of magnitude without compromising accuracy.”
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scRNA-seq reads can be used to cluster cells using features other than explicitly quantified gene expression.
“Interestingly, scRNA-seq reads can be used to cluster cells based on features other than explicitly quantified gene expression.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [121]
The BackSPIN algorithm has been extensively validated using computational and biological validation tools.
“The algorithm has by now been extensively validated using both computational (including comparisons to PCA, and independent-component analysis, tSNE, and GP-LVM) biological validation tools 9, 63–66.”
From Molecular interrogation of hypothalamic organization reveals distinct dopamine neuronal subtypes (2017) · cites this paper as [65]
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Self-citation
- Authors
- Tsafrir, D., Tsafrir, I., Ein-Dor, L., Zuk, O., Notterman, D. A., & Domany, E.
- Affiliations
- Weizmann Institute of Science (IL)
- Corresponding author
- Tsafrir, D.
- ORCID
- Zuk, O.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2005-02-18
- Volume
- 21
- Issue
- 10
- Pages
- 2301-2308
- ISSN
- 0266-7061 (ISSN-L)
- 1367-4803
- 1460-2059
- OpenAlex
- W2136941630
- Cited by
- 146
- References
- 23
- Citations by year
- 2026: 5 · 2025: 7 · 2024: 2 · 2023: 11 · 2022: 4 · 2021: 8 · 2020: 10 · 2019: 12 · 2018: 6 · 2017: 4 · 2016: 14 · 2015: 14 · 2014: 2 · 2013: 2 · 2012: 4
- FWCI
- 3.15
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Computer applications in the biosciences (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Rutgers Cancer Institute of New Jersey
- National Institutes of Health
- National Cancer Institute (5 P01 CA 65930-06, P01CA065930)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Topological and Geometric Data Analysis
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Clustering Algorithms Research
- Keywords
- high-dimensional data visualization, linear ordering, NP-completeness, sample heterogeneity, metastasis-associated genes, tissue contamination
- MeSH terms
- Algorithms, Colonic Neoplasms, Computer Graphics, Computer Simulation, Data Interpretation, Statistical, Diagnosis, Computer-Assisted, Humans, Models, Biological, Neoplasm Proteins, Pattern Recognition, Automated, Software, Biomarkers, Tumor, User-Computer Interface, Cluster Analysis, Information Storage and Retrieval, Gene Expression Profiling
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