Compositional data analysis applied to human microbiome network reconstruction

Fuschi, Alessandro (2020) Compositional data analysis applied to human microbiome network reconstruction. [Laurea magistrale], Università di Bologna, Corso di Studio in Fisica [LM-DM270], Documento ad accesso riservato.
Documenti full-text disponibili:
[img] Documento PDF (Thesis)
Full-text accessibile solo agli utenti istituzionali dell'Ateneo
Disponibile con Licenza: Salvo eventuali più ampie autorizzazioni dell'autore, la tesi può essere liberamente consultata e può essere effettuato il salvataggio e la stampa di una copia per fini strettamente personali di studio, di ricerca e di insegnamento, con espresso divieto di qualunque utilizzo direttamente o indirettamente commerciale. Ogni altro diritto sul materiale è riservato

Download (3MB) | Contatta l'autore


The comprehension of the human gut microbiome has been made possible by technological advances for performing culture-independent analyzes. Next Generation Sequencing techniques produce discrete counts as a result, describing only the relative abundances of each identified bacterial species: such data are of compositional type. Unfortunately the classic methods of analysis on this type of data can lead to completely wrong conclusions: the development of analysis methods for compositional data is still an open issue. The purpose of this work is the description of several analyzes based on compositional data of human gut microbiome. The first result, obtained with t-SNE dimensionality reduction algorithm, is that a different sample clustering was obtained based on the metrics used to define neighborhood. Next ,I applied a biomarker identification method based on the log-ratio variance, a statistical observable used with compositional data, that allowed to identify bacterial species associated to our case/control study design. In the last part I analyzed the problem of the reconstruction of networks of bacterial species. The main objective of the network analysis was to characterize microbiota ecosystem of healthy and infected subjects in our database. Several methods have been proposed to characterize the complex relationships between bacterial populations: in this work I applied the SPIEC-EASI method to reconstruct the correlation structure of the data, and compared the different results obtained. Finally, I propose a new method inspired by Kendall’s Tau correlation, adapted to the peculiarities of compositional data, that provided promising results. This research was made possible thanks to the collaboration with Prof. George Weinstock at the Jackson Laboratory research center (USA). One of the researche aims of JAX is the study and understanding of the gut microbiota for diagnostic purposes (in our case related to infection and diabetes).

Tipologia del documento
Tesi di laurea (Laurea magistrale)
Autore della tesi
Fuschi, Alessandro
Relatore della tesi
Correlatore della tesi
Corso di studio
Curriculum E: Fisica applicata
Ordinamento Cds
Parole chiave
Compositional Theory,Network Analysis,data analysis,human microbiome
Data di discussione della Tesi
23 Ottobre 2020

Altri metadati

Statistica sui download

Gestione del documento: Visualizza il documento