MuSA: a graphical user interface for multi-OMICs data integration in radiogenomic studies

Abstract

Analysis of large-scale omics data along with biomedical images has gaining a huge interest in predicting phenotypic conditions towards personalized medicine. Multiple layers of investigations such as genomics, transcriptomics and proteomics, have led to high dimensionality and heterogeneity of data. Multi-omics data integration can provide meaningful contribution to early diagnosis and an accurate estimate of prognosis and treatment in cancer. Some multi-layer data structures have been developed to integrate multi-omics biological information, but none of these has been developed and evaluated to include radiomic data. We proposed to use MultiAssayExperiment (MAE) as an integrated data structure to combine multi-omics data facilitating the exploration of heterogeneous data. We improved the usability of the MAE, developing a Multi-omics Statistical Approaches (MuSA) tool that uses a Shiny graphical user interface, able to simplify the management and the analysis of radiogenomic datasets. The capabilities of MuSA were shown using public breast cancer datasets from TCGA-TCIA databases. MuSA architecture is modular and can be divided in Pre-processing and Downstream analysis. The pre-processing section allows data filtering and normalization. The downstream analysis section contains modules for data science such as correlation, clustering (i.e., heatmap) and feature selection methods. The results are dynamically shown in MuSA. MuSA tool provides an easy-to-use way to create, manage and analyze radiogenomic data. The application is specifically designed to guide no-programmer researchers through different computational steps. Integration analysis is implemented in a modular structure, making MuSA an easily expansible open-source software.

Future artificial intelligence tools and perspectives in medicine

“…This type of radiomic pipelines uses mainly the imaging features (e.g., shape, texture, deep features) with clinical variables (e.g., age, gender, treatment, survival, etc.). In this context, radiomic features could be used to predict the molecular variable and /or combined to build a predictive model based on radiogenomics and multi-omics data (16,(50)(51)(52)(53)(54)(55)(56)(57)(58).…”

BCU Imaging Biobank, an Innovative Digital Resource for Biomedical Research Collecting Imaging and Clinical Data From Human Healthy and Pathological Subjects

“…Imaging biobanks collect high quality digital images, such as magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET), together with raw data, associated metadata and measurements to allow the extraction of quantitative radiomic features from images, which might evolve into so-called "imaging biomarker" [3,4,5]. These new biobanks can contribute to developing innovative research fields such as radiomics and radiogenomics [6,7,8].…”

ImaGene: a web-based software platform for tumor radiogenomic evaluation and reporting

“…In addition, users require a flexibility to select from a variety of AI model types (mainly regression based popular in radiogenomic domain; Attiyeh et al , 2019 ; Depeursinge et al , 2018 ) for training and testing, and consequently postanalyzing or comparing results using quality control metrices such as RMSE, STDEV, R 2 and AUC to arrive at reliable imaging-omics associations ( Gevaert et al , 2020 ). Multiomics Statistical Approaches is another tool used for radiogenomic studies that provides correlation heat maps and principal component analysis plots ( Zanfardino et al , 2021 ). However, it lacks a feature prediction ability imparted by AI-based methods ( Zanfardino et al , 2021 ).…”