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Dirichlet composition distribution for compositional data with zero components: An application to fluorescence in situ hybridization (FISH) detection of chromosome.

ABSTRACT: Zeros in compositional data are very common and can be classified into rounded and essential zeros. The rounded zero refers to a small proportion or below detection limit value, while the essential zero refers to the complete absence of the component in the composition. In this article, we propose a new framework for analyzing compositional data with zero entries by introducing a stochastic representation. In particular, a new distribution, namely the Dirichlet composition distribution, is developed to accommodate the possible essential-zero feature in compositional data. We derive its distributional properties (e.g., its moments). The calculation of maximum likelihood estimates via the Expectation-Maximization (EM) algorithm will be proposed. The regression model based on the new Dirichlet composition distribution will be considered. Simulation studies are conducted to evaluate the performance of the proposed methodologies. Finally, our method is employed to analyze a dataset of fluorescence in situ hybridization (FISH) for chromosome detection.

SUBMITTER: Tang ML

PROVIDER: S-EPMC9300144 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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Dirichlet composition distribution for compositional data with zero components: An application to fluorescence in situ hybridization (FISH) detection of chromosome.

Tang Man-Lai ML Wu Qin Q Yang Sheng S Tian Guo-Liang GL

Biometrical journal. Biometrische Zeitschrift 20211216 4

Zeros in compositional data are very common and can be classified into rounded and essential zeros. The rounded zero refers to a small proportion or below detection limit value, while the essential zero refers to the complete absence of the component in the composition. In this article, we propose a new framework for analyzing compositional data with zero entries by introducing a stochastic representation. In particular, a new distribution, namely the Dirichlet composition distribution, is devel ...[more]

PMID: 34914842

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Project description:The large size and complex polyploid nature of many genomes has often hampered genomics development, as is the case for several plants of high agronomic value. Isolating single chromosomes or chromosome arms via flow sorting offers a clue to resolve such complexity by focusing sequencing to a discrete and self-consistent part of the whole genome. The occurrence of sufficient differences in the size and or base-pair composition of the individual chromosomes, which is uncommon in plants, is critical for the success of flow sorting. We overcome this limitation by developing a robust method for labeling isolated chromosomes, named Fluorescent In situ Hybridization In suspension (FISHIS). FISHIS employs fluorescently labeled synthetic repetitive DNA probes, which are hybridized, in a wash-less procedure, to chromosomes in suspension following DNA alkaline denaturation. All typical A, B and D genomes of wheat, as well as individual chromosomes from pasta (T. durum L.) and bread (T. aestivum L.) wheat, were flow-sorted, after FISHIS, at high purity. For the first time in eukaryotes, each individual chromosome of a diploid organism, Dasypyrum villosum (L.) Candargy, was flow-sorted regardless of its size or base-pair related content. FISHIS-based chromosome sorting is a powerful and innovative flow cytogenetic tool which can develop new genomic resources from each plant species, where microsatellite DNA probes are available and high quality chromosome suspensions could be produced. The joining of FISHIS labeling and flow sorting with the Next Generation Sequencing methodology will enforce genomics for more species, and by this mightier chromosome approach it will be possible to increase our knowledge about structure, evolution and function of plant genome to be used for crop improvement. It is also anticipated that this technique could contribute to analyze and sort animal chromosomes with peculiar cytogenetic abnormalities, such as copy number variations or cytogenetic aberrations.

Dataset Information

Dirichlet composition distribution for compositional data with zero components: An application to fluorescence in situ hybridization (FISH) detection of chromosome.

Publications

Dirichlet composition distribution for compositional data with zero components: An application to fluorescence in situ hybridization (FISH) detection of chromosome.

Similar Datasets

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