CopyMix : Mixture model based single-cell clustering and copy number profiling using variational inference

Negar Safinianaini*, Camila P.E. De Souza, Andrew Roth, Hazal Koptagel, Hosein Toosi, Jens Lagergren

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

Investigating tumor heterogeneity using single-cell sequencing technologies is imperative to understand how tumors evolve since each cell subpopulation harbors a unique set of genomic features that yields a unique phenotype, which is bound to have clinical relevance. Clustering of cells based on copy number data obtained from single-cell DNA sequencing provides an opportunity to identify different tumor cell subpopulations. Accordingly, computational methods have emerged for single-cell copy number profiling and clustering; however, these two tasks have been handled sequentially by applying various ad-hoc pre- and post-processing steps; hence, a procedure vulnerable to introducing clustering artifacts. We avoid the clustering artifact issues in our method, CopyMix, a Variational Inference for a novel mixture model, by jointly inferring cell clusters and their underlying copy number profile. Our probabilistic graphical model is an improved version of the mixture of hidden Markov models, which is designed uniquely to infer single-cell copy number profiling and clustering. For the evaluation, we used likelihood-ratio test, CH index, Silhouette, V-measure, total variation scores. CopyMix performs well on both biological and simulated data. Our favorable results indicate a considerable potential to obtain clinical impact by using CopyMix in studies of cancer tumor heterogeneity.

Original languageEnglish
Article number108257
Pages (from-to)1-17
Number of pages17
JournalComputational Biology and Chemistry
Volume113
DOIs
Publication statusPublished - Dec 2024
MoE publication typeA1 Journal article-refereed

Keywords

  • Cancer
  • Copy number profiling
  • Mixture models
  • Single-cell
  • Tumor clonal decomposition
  • Variational inference

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