Tissues, organs and organisms are complex biological systems. They are objects of many studies aiming at characterizing their biological processes. Understanding how they work and how they interact in healthy and unhealthy samples gives the possibility to interfere, correcting and preventing dysfunctions, possibly leading to diseases. Recent advances in single-cell technologies are expanding our capabilities to profile at single-cell resolution various molecular layers, by targeting the transcriptome, the genome, the epigenome and the proteome. The number of single-cell datasets, their size and the diverse modalities they describe is continuously increasing, prompting the need to develop robust methods to integrate multiomic datasets, whether paired from the same cells or, most challenging, from unpaired separate experiments. The integration of different source of information results in a more comprehensive description of the whole system. Most published methods allow the integration of limited number of omics (generally two) and make assumptions about their inter-relationships. They often impose the conversion of a data modality into the other one (e.g., ATAC peaks converted in a gene activity matrix). This step introduces an important level of approximation, which could affect the analysis later performed. Here we propose MOWGAN (Multi Omic Wasserstein Generative Adversarial Network), a deep-learning based framework to simulate paired multimodal data supporting high number of modalities (more than two) and agnostic about their relationships (no assumption is imposed). Each modality is embedded into feature spaces with same dimensionality across all modalities. This step prevents any conversion between data modalities. The embeddings are sorted based on the first Laplacian Eigenmap. Mini-batches are selected by a Bayesian ridge regressor to train a Wasserstein Generative Adversarial Network with gradient penalty. The output of the generative network is used to bridge real unpaired data. MOWGAN was prototyped on public data for which paired and unpaired RNA and ATAC experiments exists. Evaluation was conducted on the ability to produce data integrable with the original ones, on the amount of shared information between synthetic layers and on the ability to impose association between molecular layers that are truly connected. The organization of the embeddings in mini-batches allows MOWGAN to have a network architecture independent of the number of modalities evaluated. Indeed, the framework was also successfully applied to integrate three (e.g., RNA, ATAC and protein or histone modification data) and four modalities (e.g., RNA, ATAC, protein, histone modifications). MOWGAN’s performance was evaluated in terms of both computational scalability and biological meaning, being the latter the most important to avoid erroneous conclusion. A comparison was conducted with published methods, concluding that MOWGAN performs better when looking at the ability to retrieve the correct biological identity (e.g., cell types) and associations. In conclusion, MOWGAN is a powerful tool for multi-omics data integration in single-cell, which answer most of the critical issues observed in the field.

Tessuti, organi e organismi sono sistemi biologici complessi, oggetto di studi che mirano alla caratterizzazione dei loro processi biologici. Comprendere il loro funzionamento e la loro interazione in campioni sani e malati consente di interferire, correggere e prevenire le disfunzioni dalle quali si sviluppano possibilmente le malattie. I recenti sviluppi nelle tecnologie di sequenziamento single-cell stanno ampliano la capacità di profilare, a livello di singola cellula, diversi layer molecolari (trascrittoma, genoma, epigenoma, proteoma). Il numero, la grandezza e le diverse modalità dei dataset prodotti è in continua crescita. Ciò spinge allo sviluppo di robusti metodi per l’integrazione di dataset multiomici, che siano essi descrittivi o meno delle stesse cellule. L’integrazione di più fonti di informazione produce una descrizione più ampia e completa dell’intero sistema analizzato. La maggior parte dei sistemi di integrazione disponibili ad oggi consente l’analisi simultanea di un numero limitato di omiche (generalmente due) e richiede conoscenze pregresse riguardo le loro relazioni. Questi metodi spesso impongono la traduzione di una modalità nelle variabili espresse da un altro dato (ad esempio, i picchi di ATAC vengono convertiti in gene activity matrix). Questo step introduce un livello di approssimazione nel dato che potrebbe pregiudicare le analisi svolte in seguito. Da qui nasce MOWGAN (Multi Omic Wasserstein Generative Adversarial Network), un framework basato sul deep-learning, per la simulazione di dati multimodali appaiati in grado di supportare un alto numero di dataset (più di due) e agnostico sulle relazioni che intercorrono tra loro (non viene imposta alcuna assunzione). Ogni modalità viene proiettata in uno spazio descrittivo ridotto, le cui dimensioni sono fissate per tutti i datasets. Questo processo previene la traduzione tra modalità. Le cellule, descritte da vettori nello spazio ridotto, vengono ordinate in base alla prima componente della loro Laplacian Eigenmap. Un regressore Bayesian viene successivamente applicato per selezionare i mini-batch con i quali viene allenata una particolare architettura di deep-learning, la Wasserstein Generative Adversarial Network with gradient penalty. La componente generativa della rete restituisce in uscita un nuovo dataset, appaiato, che viene utilizzato come ponte per il passaggio di informazioni tra i dataset originali. Lo sviluppo di MOWGAN è stato condotto con l’ausilio di dati pubblici per i quali erano disponibili osservazioni di RNA e ATAC sia per le stesse cellule, che per cellule differenti. La valutazione dei risultati è stata condotta sulla base della capacità del dato prodotto di essere integrato con il dato originale. Inoltre, il dato sintetico deve avere informazione condivisa tra le diverse omiche. Questa deve rispettare la natura biologica del dato: le associazioni non devono essere presenti tra entità cellulari rappresentanti tipi cellulari differenti. L’organizzazione del dato in mini-batch consente a MOWGAN di avere una architettura di rete indipendente dal numero di modalità considerate. Infatti, il framework è stato applicato anche per l’integrazione di tre (RNA, ATAC e proteine, RNA ATAC e modificazioni istoniche) e quattro modalità (RNA, ATAC, proteine e modificazioni istoniche). Il rendimento di MOWGAN è stato dunque valutato in termini di scalabilità computazionale (integrazione di molteplici datasets) e significato biologico, essendo quest’ultimo il più importante per non giungere a conclusioni errate nello studio in essere. È stato eseguito un confronto con altri metodi già disponibili in letteratura, riscontrando la maggiore capacità di MOWGAN di creare associazioni inter-modali tra entità cellulari realmente legate. In conclusione, MOWGAN è uno strumento potente per l’integrazione di dati multi-modali in single-cell, che risponde a molte delle problematiche riscontrate nel campo.

(2023). Integration of heterogeneous single cell data with Wasserstein Generative Adversarial Networks. (Tesi di dottorato, Università degli Studi di Milano-Bicocca, 2023).

Integration of heterogeneous single cell data with Wasserstein Generative Adversarial Networks

GIANSANTI, VALENTINA
2023

Abstract

Tissues, organs and organisms are complex biological systems. They are objects of many studies aiming at characterizing their biological processes. Understanding how they work and how they interact in healthy and unhealthy samples gives the possibility to interfere, correcting and preventing dysfunctions, possibly leading to diseases. Recent advances in single-cell technologies are expanding our capabilities to profile at single-cell resolution various molecular layers, by targeting the transcriptome, the genome, the epigenome and the proteome. The number of single-cell datasets, their size and the diverse modalities they describe is continuously increasing, prompting the need to develop robust methods to integrate multiomic datasets, whether paired from the same cells or, most challenging, from unpaired separate experiments. The integration of different source of information results in a more comprehensive description of the whole system. Most published methods allow the integration of limited number of omics (generally two) and make assumptions about their inter-relationships. They often impose the conversion of a data modality into the other one (e.g., ATAC peaks converted in a gene activity matrix). This step introduces an important level of approximation, which could affect the analysis later performed. Here we propose MOWGAN (Multi Omic Wasserstein Generative Adversarial Network), a deep-learning based framework to simulate paired multimodal data supporting high number of modalities (more than two) and agnostic about their relationships (no assumption is imposed). Each modality is embedded into feature spaces with same dimensionality across all modalities. This step prevents any conversion between data modalities. The embeddings are sorted based on the first Laplacian Eigenmap. Mini-batches are selected by a Bayesian ridge regressor to train a Wasserstein Generative Adversarial Network with gradient penalty. The output of the generative network is used to bridge real unpaired data. MOWGAN was prototyped on public data for which paired and unpaired RNA and ATAC experiments exists. Evaluation was conducted on the ability to produce data integrable with the original ones, on the amount of shared information between synthetic layers and on the ability to impose association between molecular layers that are truly connected. The organization of the embeddings in mini-batches allows MOWGAN to have a network architecture independent of the number of modalities evaluated. Indeed, the framework was also successfully applied to integrate three (e.g., RNA, ATAC and protein or histone modification data) and four modalities (e.g., RNA, ATAC, protein, histone modifications). MOWGAN’s performance was evaluated in terms of both computational scalability and biological meaning, being the latter the most important to avoid erroneous conclusion. A comparison was conducted with published methods, concluding that MOWGAN performs better when looking at the ability to retrieve the correct biological identity (e.g., cell types) and associations. In conclusion, MOWGAN is a powerful tool for multi-omics data integration in single-cell, which answer most of the critical issues observed in the field.
ANTONIOTTI, MARCO
SCHETTINI, RAIMONDO
CITTARO, DAVIDE
Integrazione dati; Dati multimodali; Deep Learning; WGAN-GP; Single cell
Data integration; Multimodal data; Deep Learning; WGAN-GP; Single cell
INF/01 - INFORMATICA
English
17-feb-2023
INFORMATICA
35
2021/2022
open
(2023). Integration of heterogeneous single cell data with Wasserstein Generative Adversarial Networks. (Tesi di dottorato, Università degli Studi di Milano-Bicocca, 2023).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/404516
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