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Računski postopki za odkrivanje celičnih tipov v vizualizacijah podatkov scRNA
FURLAN, ANEJA (Author), Zupan, Blaž (Mentor) More about this mentor... This link opens in a new window

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Abstract
V genomiki se v zadnjem času, zahvaljujoč napredku tehnologije, veliko raziskuje na področju transkriptomike posameznih celic, ki ponuja nov, drugačen vpogled v funkcionalno različnost celic. Pridobivanje takih podatkov se začne z izolacijo RNA iz posameznih celic, zato jih lahko krajše označimo kot podatke scRNA (ang. single-cell RNA). V diplomskem delu predlagamo metodo za odkrivanje celičnih tipov na vizualizacijah podatkov scRNA. Metoda prejme podatke o izražanju genov v celicah in seznam genov, značilen za izbran tip celice. Visokorazsežne izrazne profile celic vloži v dvorazsežen prostor, primeren za vizualizacijo v obliki razsevnega diagrama, in nato na podlagi ocen obogatenosti soseščin celic glede na podan seznam genov odkrije regije celic izbranega celičnega tipa. Uspešnost predlagane metode smo preverili na treh različnih naborih podatkov nedavno opravljenih študij sekvenciranja na nivoju posameznih celic. Metoda se je izkazala za robustno glede na začetno vložitev v 2D prostor in glede na izbrano velikost obravnavanih soseščin. Dobili smo spodbudne rezultate, vendar je tu še veliko prostora za dopolnitve in izboljšave. Predvsem bi bilo smiselno metodo preveriti še na večjem naboru podatkov.

Language:Slovenian
Keywords:bioinformatika, enocelična genomika, vizualizacije podatkov, analiza obogatenosti, scRNA
Work type:Bachelor thesis/paper (mb11)
Organization:FRI - Faculty of computer and information science
Year:2018
Views:251
Downloads:196
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Secondary language

Language:English
Title:A computational approach for cell-type discovery in tow-dimensional of scRNA gene expression data
Abstract:
The popularity of single-cell analysis has risen due to recent advancements in single-cell transcriptomics, especially in sequencing technologies. Single-cell analysis provides us with a new perspective of cellular data and helps us study cellular heterogeneity. Expression profiles of individual cells can be derived with single-cell RNA sequencing (scRNA data) and corresponding gene expressions. In the Thesis we propose an approach for cell type discovery in such data. Input to the proposed approach is a gene expression matrix, along with a set of marker genes, typical for a specific cell type. Our method starts with visualizing data in 2D and then finds regions of chosen cell type by measuring functional enrichments across local neighborhoods and estimating their significances. We applied the proposed approach on three datasets from recent single-cell sequencing studies. We got encouraging results and showed the approach to be robust to dimensionality reduction and neighborhood size. There is still room for improvement and in order to further illustrate full functionality of the approach, more testing on larger datasets would be required.

Keywords:bioinformatics, single-cell genomics, data visualizations, enrichment analysis, scRNA

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