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Anchor Clustering for million-scale immune repertoire sequencing data
Haiyang Chang1, Daniel A Ashlock1, Steffen P Graether2
1Department of Mathematics and Statistics, University of Guelph, 50 Stone Rd E, Guelph, ON, N1G 2W1, Canada.
BMC Bioinformatics
|January 25, 2024
Summary
Anchor Clustering efficiently groups millions of immune receptor gene sequences. This novel method overcomes computational challenges, enabling faster analysis and a deeper understanding of immune repertoire data.
Area of Science:
- Immunology
- Bioinformatics
- Computational Biology
Background:
- Clustering immune repertoire data is computationally intensive due to extensive pairwise sequence comparisons.
- Millions of antigen receptor gene sequences present a significant challenge for traditional analysis methods.
- Existing methods struggle with the scale and complexity of immune repertoire datasets.
Approach:
- Developed Anchor Clustering, an unsupervised method to identify similar sequences.
- Utilizes a Point Packing algorithm to select maximally spaced anchor sequences.
- Calculates genetic distances to anchors, creating distance vectors for iterative clustering.
Key Points:
- Anchor Clustering significantly reduces computational cost compared to pairwise methods.
- Achieves comparable clustering quality with remarkable speed.
- Handles millions of antigen receptor gene sequences in minutes.
- Offers a flexible and memory-saving approach for large-scale data.
Conclusions:
- Enables meta-analysis of immune repertoire data across diverse studies.
- Facilitates a more comprehensive understanding of the immune repertoire data landscape.
- Provides a scalable solution for analyzing complex immunological datasets.
