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Molecular genetic analyses in Origanum (Lamiaceae) taxa in Türkiye
(01. Izmir Institute of Technology, 2022) Frary, Anne; Frary, Anne; Frary, Anne; 04.03. Department of Molecular Biology and Genetics; 04. Faculty of Science; 01. Izmir Institute of Technology
Medicinal and aromatic plants (MAPs) belonging to the genus, Origanum L. (Lamiaceae), are called “oregano”. They are economically important and beneficial for trade, medicine, food, cosmetics, and ornamental purposes with their bioactive compound diversity and richness. Although Türkiye is the gene center for generation, speciation, and diversification of oregano throughout the world, their uncontrolled consumption and other factors threaten their status. According to Ietswaart (1980), there are ten morphological sections in the genus. Of these, 25 taxa (including 13 endemics) and 13 hybrids from eight sections grow naturally in Türkiye. The cross-pollinating and gynodioecious nature of oreganos makes their taxonomic classification difficult. In this dissertation, molecular markers (EST-SSRs and SRAPs) were used to assess the complex evolutionary relationships in a herbarium and the Turkish national AARI Gene Bank collection. Cross hybridization due to high gene flow was found to be the main source of genetic diversity within both collections. In both collections, the highest gene flow was observed between two sections, ANA and BRE, with diecious flowers which supports their frequent hybridization when compared to gynodioecious oreganos in nature. The Aegean and Mediterranean regions had the highest gene flow among all regions, while five province pairs had the highest gene flow among all provinces. In conclusion, molecular markers were shown to be a useful tool for examinations of genetic diversity and evolution in oregano.
Development of a Unified Analysis Framework for Multicolor Flow Cytometry Data Based on Quasi-Supervised Learning
(Izmir Institute of Technology, 2017) Köktürk Güzel, Başak Esin; Karaçalı, Bilge; Karaçalı, Bilge; 03.05. Department of Electrical and Electronics Engineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology
In this dissertation, automatic compensation and gating strategies are investigated for multi-color flow cytometry data analysis. We propose two clustering algorithms that combine the quasi-supervised learning algorithm with an expectation-maximization routine for automatic gating. The quasi-supervised learning algorithm estimates the posterior probabilities of the different cell populations at each sample in a dataset in a manner that does not involve fitting a parametric model to the data. We have developed two different binary divisive clustering algorithms based on expectation maximization with responsibility values calculated using the quasi-supervised learning algorithm instead of the probabilistic models used in conventional expectation maximization applications. Our clustering algorithms determine the number of clusters in run-time by measuring the overlap between the estimated clusters in each provisional division and comparing it with the previous one to determine whether the division is warranted or not. Since this type of clustering is indifferent to the underlying distribution of dataset, it is well suited to automatic flow cytometry gating. The second clustering algorithm improves upon the first one using a simulated annealing approach. Its iterative structure allows finding the global minimum of a cost functional that achieves the best separation point by gradually smoothing the decision regions in each iteration. Finally, we have developed a joint diagonalization and clustering method for automatic compensation of flow data based on the methods above. The proposed method identifies cell sub groups using the annealing-based model-free expectation-maximization algorithm and finds a data transformation matrix that achieves orthogonality of the covariance structure of each identified cell cluster using fast Frobenius diagonalization. We have tested all proposed algortihms on both synthetically created datasets and real multi-color flow cytometry datasets. The results show that our automated gating algorithms are very successful in identifying the distinct cell groups so long as there is enough statistical evidence for their presence. In addition, the automated compensation procedure was also successfully applied on the synthetically created dataset and real multi-color flow cytometry data of lymphocytes that are a low autofluorescence cell group. However, the automated compensation algorithm needs further study to be generalized to high autofluorescence cell types where proper compensation does not necessarily coincide with an orthogonal covariance structure.

Phd Degree / Doktora

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