Pattern recognition in lymphoid malignancies using CytoGPS and Mercator

Zachary B. Abrams, Dwayne G. Tally, Lin Zhang, Caitlin E. Coombes, Philip R.O. Payne, Lynne V. Abruzzo, Kevin R. Coombes

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Background: There have been many recent breakthroughs in processing and analyzing large-scale data sets in biomedical informatics. For example, the CytoGPS algorithm has enabled the use of text-based karyotypes by transforming them into a binary model. However, such advances are accompanied by new problems of data sparsity, heterogeneity, and noisiness that are magnified by the large-scale multidimensional nature of the data. To address these problems, we developed the Mercator R package, which processes and visualizes binary biomedical data. We use Mercator to address biomedical questions of cytogenetic patterns relating to lymphoid hematologic malignancies, which include a broad set of leukemias and lymphomas. Karyotype data are one of the most common form of genetic data collected on lymphoid malignancies, because karyotyping is part of the standard of care in these cancers. Results: In this paper we combine the analytic power of CytoGPS and Mercator to perform a large-scale multidimensional pattern recognition study on 22,741 karyotype samples in 47 different hematologic malignancies obtained from the public Mitelman database. Conclusion: Our findings indicate that Mercator was able to identify both known and novel cytogenetic patterns across different lymphoid malignancies, furthering our understanding of the genetics of these diseases.

Original languageEnglish
Article number100
JournalBMC bioinformatics
Volume22
Issue number1
DOIs
StatePublished - Dec 2021

Keywords

  • CytoGPS
  • Karyotype
  • Lymphoid malignancies
  • Mercator
  • Pattern recognition

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