Robust Rank Aggregation meetodi rakendamine autoimmuunsete sihtmärkide prioritiseerimiseks valgukiipidel
Failid
Kuupäev
2016
Autorid
Ajakirja pealkiri
Ajakirja ISSN
Köite pealkiri
Kirjastaja
Abstrakt
Autoimmuunhaigused on tänapäeva maailmas väga sagedased. Üha enam ja enam haigusi on seotud autoimmuunsete protsessidega. Autoimmuunreaktsioon on protsess, mille käigus immuunsüsteem toodab antikehasid (autoantikehad) organismi enda rakkude vastu. Autoimmuunhaiguste põhjused ja mehhanismid on aga veel selgeks tegemata. Üheks võimaluseks, kuidas autoimmuunhaigusi õppida on välja selgitada, miks kindlad rakud ja iseäranis just valgud on autoantikehade märklauaks. Selle eesmärgi saavutamiseks on välja töötatud mitmesuguseid tehnoloogiaid, kuhu kuuluvad ka valgukiibid. See tehnoloogia võimaldab hinnata autoantikehade kogust patsiendi seerumis 9000 unikaalse inimese valgu vastu. Seega, rakendades andmeanalüüsi meetodeid on bioinformaatikud võimelised tuvastama autoantikehade märklaudvalke. Teades neid valke, saavad bioloogid läbi viia edasisi katseid ning formuleerida uusi hüpoteese autoimmuunhaiguste mehhanismide ja esinemise kohta. Traditsioonilised andmeanalüüsi meetodid keskenduvad ainult selliste valkude leidmisele, mis erinevad kõige kindlamalt tervete ja patsientide grupi vahel. Need meetodid aga jätavad kõrvale fakti, et märklaudvalkude repertuaar võib patsientide vahel oluliselt erineda. Seega võib isegi üksikjuhtum sisaldada olulist informatsiooni haiguse mehhanismide mõistmisel. Käesolevas lõputöös pakume välja, et Robust Rank Aggregation (RRA) algoritmi saab kasutada adaptiivse meetodina leidmaks reaktiivsete valkude (märklaudvalkude) laia repertuaari. Me võrdlesime klassikaliste analüüsimeetodite otstarbekust ja efektiivsust RRA-ga nii sünteetilistel kui ka pärisandmetel. Katsed sünteetilise andmehulgaga ehk andmehulgaga, mille puhul on reaktiivsed valgud teada näitavad, et RRA ületab teisi meetodeid olles samal ajal vähem mõjutatud “mürast”. Rakendades RRA-d pärisandmetel ning viies läbi rikastusanalüüsi iga meetodi kohta saadud reaktiivsete valkude listidega, saime me sarnase arvu valke, mis olid bioloogilise ja immuunvastusega seotud klassides üleesindatud.
Autoimmune diseases are very common in the modern world. More and more diseases associated with an autoimmune process. Autoimmune reaction is a process in which the immune system produces antibodies (autoantibodies) that attack organism’s own cells. Causes and mechanisms of autoimmune diseases are yet to be understood. One of the ways to study autoimmunity is to explore reasons why certain cells and particularly proteins were attacked by autoantibodies. To achieve this, many technologies have been developed and one of which is Protein microarray. This technology allows estimating the amount of autoantibodies in patient serum against 9000 unique human proteins. Consequently, applying methods of data analysis on this data, bioinformaticians might be able to identify proteins that attract prevalent amount of autoantibodies. Knowing these proteins, biologists could conduct experiments and formulate new hypotheses about mechanisms of work and appearance of autoimmune diseases. Common data analysis methods focused on how to select only the most reliably differing proteins between healthy and diseased groups. Moreover, ignoring the fact that in the case of an autoimmune disease - the repertoire of the affected proteins can differ greatly between patients. So even single cases of high protein reactivity may carry important information for understanding the mechanisms of disease. In this thesis, we propose to apply Robust Rank Aggregation algorithm as an adaptive method to identify a wide repertoire of reactive proteins. We compared expediency and effectiveness of the classical methods of analysis, method recently applied by biologists and RRA on synthetic and real data. Experiments on synthetic data sets with known reactive proteins show that RRA outperforms these methods while also being more robust to incorporated noise. Applying RRA on real data and conducting an enrichment analysis on lists of reactive proteins for each method, we got comparable numbers of proteins overrepresented in the classes associated with biological and immune responses.
Autoimmune diseases are very common in the modern world. More and more diseases associated with an autoimmune process. Autoimmune reaction is a process in which the immune system produces antibodies (autoantibodies) that attack organism’s own cells. Causes and mechanisms of autoimmune diseases are yet to be understood. One of the ways to study autoimmunity is to explore reasons why certain cells and particularly proteins were attacked by autoantibodies. To achieve this, many technologies have been developed and one of which is Protein microarray. This technology allows estimating the amount of autoantibodies in patient serum against 9000 unique human proteins. Consequently, applying methods of data analysis on this data, bioinformaticians might be able to identify proteins that attract prevalent amount of autoantibodies. Knowing these proteins, biologists could conduct experiments and formulate new hypotheses about mechanisms of work and appearance of autoimmune diseases. Common data analysis methods focused on how to select only the most reliably differing proteins between healthy and diseased groups. Moreover, ignoring the fact that in the case of an autoimmune disease - the repertoire of the affected proteins can differ greatly between patients. So even single cases of high protein reactivity may carry important information for understanding the mechanisms of disease. In this thesis, we propose to apply Robust Rank Aggregation algorithm as an adaptive method to identify a wide repertoire of reactive proteins. We compared expediency and effectiveness of the classical methods of analysis, method recently applied by biologists and RRA on synthetic and real data. Experiments on synthetic data sets with known reactive proteins show that RRA outperforms these methods while also being more robust to incorporated noise. Applying RRA on real data and conducting an enrichment analysis on lists of reactive proteins for each method, we got comparable numbers of proteins overrepresented in the classes associated with biological and immune responses.