Handling missing rows in multi-omics data integration: multiple imputation in multiple factor analysis framework

Summary Malignant progression of normal tissue is typically driven by complex networks of somatic changes, including genetic mutations, copy number aberrations, epigenetic changes, and transcriptional reprogramming. To delineate aberrant multi-omic tumor features that correlate with clinical outcomes, we present a novel pathway-centric tool based on the multiple factor analysis framework called padma. Using a multi-omic consensus representation, padma quantifies and characterizes individualized pathway-specific multi-omic deviations and their underlying drivers, with respect to the sampled population. We demonstrate the utility of padma to correlate patient outcomes with complex genetic, epigenetic, and transcriptomic perturbations in clinically actionable pathways in breast and lung cancer.

Download Full-text

Simultaneous analysis of distinct Omics data sets with integration of biological knowledge: Multiple Factor Analysis approach.

BMC Genomics ◽

10.1186/1471-2164-10-32 ◽

2009 ◽

Vol 10 (1) ◽

pp. 32 ◽

Cited By ~ 62

Author(s):

Marie de Tayrac ◽

Sebastien Le ◽

Marc Aubry ◽

Jean Mosser ◽

Francois Husson

Keyword(s):

Factor Analysis ◽

Simultaneous Analysis ◽

Analysis Approach ◽

Biological Knowledge ◽

Data Sets ◽

Omics Data ◽

Multiple Factor Analysis ◽

Multiple Factor

Download Full-text

Individualized multi-omic pathway deviation scores using multiple factor analysis

10.1101/827022 ◽

2019 ◽

Author(s):

Andrea Rau ◽

Regina Manansala ◽

Michael J. Flister ◽

Hallgeir Rui ◽

Florence Jaffrézic ◽

...

Keyword(s):

Factor Analysis ◽

Patient Outcomes ◽

Copy Number ◽

Normal Tissue ◽

Genetic Mutations ◽

Epigenetic Changes ◽

Analysis Framework ◽

Multiple Factor Analysis ◽

Transcriptional Reprogramming ◽

Multiple Factor

SummaryMalignant progression of normal tissue is typically driven by complex networks of somatic changes, including genetic mutations, copy number aberrations, epigenetic changes, and transcriptional reprogramming. To delineate aberrant multi-omic tumor features that correlate with clinical outcomes, we present a novel pathway-centric tool based on the multiple factor analysis framework called padma. Using a multi-omic consensus representation, padma quantifies and characterizes individualized pathway-specific multi-omic deviations and their underlying drivers, with respect to the sampled population. We demonstrate the utility of padma to correlate patient outcomes with complex genetic, epigenetic, and transcriptomic perturbations in clinically actionable pathways in breast and lung cancer.

Download Full-text

0311 - Effect of ammonia on the dynamics of anaerobic digestion microbiome: omics data integration in a time-course context

10.26226/morressier.5b5199beb1b87b000ecee94b ◽

2018 ◽

Author(s):

Olivier Chapleur

Keyword(s):

Anaerobic Digestion ◽

Data Integration ◽

Time Course ◽

Omics Data ◽

Omics Data Integration

Download Full-text

Multi-omics data integration reveals correlated regulatory features of triple negative breast cancer

Molecular Omics ◽

10.1039/d1mo00117e ◽

2021 ◽

Author(s):

Kevin Chappell ◽

Kanishka Manna ◽

Charity L. Washam ◽

Stefan Graw ◽

Duah Alkam ◽

...

Keyword(s):

Breast Cancer ◽

Data Integration ◽

Triple Negative Breast Cancer ◽

Triple Negative ◽

Biological Pathways ◽

Omics Data ◽

Insight Into ◽

Omics Data Integration

Multi-omics data integration of triple negative breast cancer (TNBC) provides insight into biological pathways.

Download Full-text

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

Scientific Reports ◽

10.1038/s41598-021-81200-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Mario Zanfardino ◽

Rossana Castaldo ◽

Katia Pane ◽

Ornella Affinito ◽

Marco Aiello ◽

...

Keyword(s):

User Interface ◽

Data Integration ◽

Graphical User Interface ◽

Data Science ◽

Heterogeneous Data ◽

Biological Information ◽

Omics Data ◽

Correlation Clustering ◽

Downstream Analysis ◽

Omics Data Integration

AbstractAnalysis 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.

Download Full-text