Evidence map›Paper›PMID 29925568›Full record

ArticleMolecular systems biology2018

Multi-Omics Factor Analysis-a framework for unsupervised integration of multi-omics data sets.

Ricard Argelaguet, Britta Velten, Damien Arnol, Sascha Dietrich, Thorsten Zenz, John C Marioni, Florian Buettner, Wolfgang Huber, Oliver Stegle

Abstract readValidation Study
In one paragraph

Article in Molecular systems biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 828 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
828citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

828 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  15. Antibody-drug conjugate engineering: from design to efficacy and safety.Signal transduction and targeted therapy · 2026
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768 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Ricard ArgelaguetEuropean Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, UK.ORCID 0000-0003-3199-3722
Britta VeltenEuropean Molecular Biology Laboratory (EMBL), Heidelberg, Germany.ORCID 0000-0002-8397-3515
Damien ArnolEuropean Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, UK.ORCID 0000-0003-2462-534X
Sascha DietrichHeidelberg University Hospital, Heidelberg, Germany.ORCID 0000-0002-0648-1832
Thorsten ZenzHeidelberg University Hospital, Heidelberg, Germany.ORCID 0000-0001-7890-9845
John C MarioniEuropean Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, UK.ORCID 0000-0001-9092-0852
Florian BuettnerEuropean Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, UK fbuettner.phys@gmail.com wolfgang.huber@embl.de oliver.stegle@embl.de.ORCID 0000-0001-5587-6761
Wolfgang HuberEuropean Molecular Biology Laboratory (EMBL), Heidelberg, Germany fbuettner.phys@gmail.com wolfgang.huber@embl.de oliver.stegle@embl.de.ORCID 0000-0002-0474-2218
Oliver StegleEuropean Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, UK fbuettner.phys@gmail.com wolfgang.huber@embl.de oliver.stegle@embl.de.ORCID 0000-0002-8818-7193

Funding

Cancer Research UK 22231Medical Research Council MR/M01536X/1
6 · The paper itself

Abstract

Multi-omics studies promise the improved characterization of biological processes across molecular layers. However, methods for the unsupervised integration of the resulting heterogeneous data sets are lacking. We present Multi-Omics Factor Analysis (MOFA), a computational method for discovering the principal sources of variation in multi-omics data sets. MOFA infers a set of (hidden) factors that capture biological and technical sources of variability. It disentangles axes of heterogeneity that are shared across multiple modalities and those specific to individual data modalities. The learnt factors enable a variety of downstream analyses, including identification of sample subgroups, data imputation and the detection of outlier samples. We applied MOFA to a cohort of 200 patient samples of chronic lymphocytic leukaemia, profiled for somatic mutations, RNA expression, DNA methylation and

Indexed as

Datasets as TopicAntineoplastic AgentsComputational BiologyComputer SimulationHumansLeukemia, Lymphocytic, Chronic, B-CellModels, StatisticalOxidative StressSoftwareTranscriptomeAntineoplastic Agentsdata integrationdimensionality reductionmulti‐omicspersonalized medicinesingle‐cell omics

Identifiers

PMID29925568
PMCPMC6010767

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.