A clustering algorithm for the single cell analysis of mixtures

Fuente: arXiv
Saved in:
Bibliographic Details
Main Author: Cowell, Robert G.
Format: Preprint
Published: 2025
Subjects:
Online Access:
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1866918159216279552
author Cowell, Robert G.
author_facet Cowell, Robert G.
contents A probabilistic clustering algorithm is proposed for the analysis of forensic DNA mixtures in which individual cells are isolated and short tandem repeats are amplified using the polymerase chain reaction to generate single cell electropherograms. The task of the algorithm is to use the peak height information in the electropherograms to group the cells according to their contributors. Using a recently developed experimental set of individual cell electropherograms, a large set of simulations shows that the proposed clustering algorithm has excellent performance in correctly grouping single cells, and for assigning likelihood ratios for persons of interest (of known genotype).
format Preprint
id arxiv_https___arxiv_org_abs_2510_10614
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle A clustering algorithm for the single cell analysis of mixtures
Cowell, Robert G.
Applications
Quantitative Methods
A probabilistic clustering algorithm is proposed for the analysis of forensic DNA mixtures in which individual cells are isolated and short tandem repeats are amplified using the polymerase chain reaction to generate single cell electropherograms. The task of the algorithm is to use the peak height information in the electropherograms to group the cells according to their contributors. Using a recently developed experimental set of individual cell electropherograms, a large set of simulations shows that the proposed clustering algorithm has excellent performance in correctly grouping single cells, and for assigning likelihood ratios for persons of interest (of known genotype).
title A clustering algorithm for the single cell analysis of mixtures
topic Applications
Quantitative Methods
url https://arxiv.org/abs/2510.10614