Yazar "Dalkilic, Mehmet" seçeneğine göre listele

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    ccImpute: an accurate and scalable consensus clustering based algorithm to impute dropout events in the single-cell RNA-seq data
    (Bmc, 2022) Malec, Marcin; Kurban, Hasan; Dalkilic, Mehmet
    Background: In recent years, the introduction of single-cell RNA sequencing (scRNA-seq) has enabled the analysis of a cell's transcriptome at an unprecedented granularity and processing speed. The experimental outcome of applying this technology is a M x N matrix containing aggregated mRNA expression counts of M genes and N cell samples. From this matrix, scientists can study how cell protein synthesis changes in response to various factors, for example, disease versus non-disease states in response to a treatment protocol. This technology's critical challenge is detecting and accurately recording lowly expressed genes. As a result, low expression levels tend to be missed and recorded as zero - an event known as dropout. This makes the lowly expressed genes indistinguishable from true zero expression and different than the low expression present in cells of the same type. This issue makes any subsequent downstream analysis difficult. Results: To address this problem, we propose an approach to measure cell similarity using consensus clustering and demonstrate an effective and efficient algorithm that takes advantage of this new similarity measure to impute the most probable dropout events in the scRNA-seq datasets. We demonstrate that our approach exceeds the performance of existing imputation approaches while introducing the least amount of new noise as measured by clustering performance characteristics on datasets with known cell identities. Conclusions: cclmpute is an effective algorithm to correct for dropout events and thus improve downstream analysis of scRNA-seq data. cclmpute is implemented in R and is available at https://github.com/khazum/ccImpute.
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    DCEM: An R package for clustering big data via data-centric modification of Expectation Maximization
    (Elsevier, 2022) Sharma, Parichit; Kurban, Hasan; Dalkilic, Mehmet
    Clustering is intractable, so techniques exist to give a best approximation. Expectation Maximization (EM), initially used to impute missing data, is among the most popular. Parameters of a fixed number of probability distributions (PDF) together with the probability of a datum belonging to each PDF are iteratively computed. EM does not scale with data size, and this has hampered its current use. Using a data-centric approach, we insert hierarchical structures within the algorithm to separate high expressive data (HE) from low expressive data (LE): the former greatly affects the objective function at some iteration i, while LE does not. By alternating using either HE or HE+LE, we significantly reduce run-time for EM. We call this new, data-centric EM, EM*. We have designed and developed an R package called DCEM (Data Clustering with Expectation Maximization) to emphasize that data is driving the algorithm. DCEM is superior to EM as we vary size, dimensions, and separability, independent of the scientific domain. DCEM is modular and can be used as either a stand-alone program or a pluggable component. DCEM includes our implementation of the original EM as well. To the best of our knowledge, there is no open source software that specifically focuses on improving EM clustering without explicit parallelization, modified seeding, or data reduction. DCEM is freely accessible on CRAN (Comprehensive R Archive Network). (C) 2021 The Author(s). Published by Elsevier B.V.
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    Density-functional tight-binding approach for the structural analysis and electronic structure of copper hydride metallic nanoparticles
    (Elsevier, 2019) Kurban, Hasan; Kurban, Mustafa; Dalkilic, Mehmet
    We perform a theoretical investigation using the density functional tight-binding (DFTB) approach for the structural analysis and electronic structure of copper hydride (CuH) metallic nanoparticles (NPs) of different size (from 0.7 to 1.6 nm). By increasing the size of CuH NPs, the number of bonds, segregation phenomena and radial distribution function (RDF) of binary Cu-Cu, Cu-H and H-H interactions are analyzed using new implementations in R code. The results reveal that the number of Cu-Cu bonds is more than that of Cu-H while the number of H-H bonds are the less. Thus, a large amount of H atoms prefers to connect to Cu atoms. The increase in the size of the NPs contributes to their stabilization because of the increase in the interaction of H-H bonding. The segregation of Cu and H atoms shows that Cu atoms tend to co-locate at the center, while H atoms tend to reside on the surface. From the density of state (DOS) analysis, CuH NPs shows a metallic character which is compatible with experimental data. HOMO and Fermi levels decrease from -3.555 to -3.443 eV and from -3.510 to -3.441 eV. Herein, an increase in the size contributes to the stabilization of CuH NP due to decrease in the HOMO energies.
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    Tailoring the structural properties and electronic structure of anatase, brookite and rutile phase TiO2 nanoparticles: DFTB calculations
    (Elsevier, 2020) Kurban, Hasan; Dalkilic, Mehmet; Temiz, Selcuk; Kurban, Mustafa
    In this study, we perform a theoretical investigation using the density functional tight-binding (DFTB) approach for the structural analysis and electronic structure of anatase, brookite and rutile phase TiO2 nanoparticles (NPs). Our results show that the number of Ti-O bonds is greater than that of O-O, while the number of Ti-Ti bonds is fewer. Thus, large amounts of O atoms prefer to connect to Ti atoms. The increase in the temperature of the NPs contributes to an increase in the interaction of Ti-O bonding, but a decrease in the O-O bonding. The segregation of Ti and O atoms shows that Ti atoms tend to co-locate at the center, while O atoms tend to reside on the surface. Increasing temperature causes a decrease of the bandgap from 3.59 to 2.62 eV for the brookite phase, which is much more energetically favorable compared to the bulk, while it could increase the bandgap from 3.15 to 3.61 eV for anatase phase. For three-phase TiO2 NPs, LUMO and Fermi levels decrease. The HOMO level of anatase phase NP decreases, but it increases for brookite and rutile phase TiO2 nanoparticles. An increase in the temperature contributes to the stabilization of anatase phase TiO2 NP due to a decrease in the HOMO energies.