Meta Analyses of Heterogeneous Omics Data

Projektinformationen

MAHOD

ID Finanzhilfevereinbarung: 101106986

DOI

10.3030/101106986

Das Projekt endete am 31 Mai 2026

EK-Unterschriftsdatum 10 Juli 2023

Startdatum 18 März 2024

Enddatum 17 Juni 2026

Finanziert unter

• Marie Skłodowska-Curie Actions (MSCA)

Gesamtkosten

Keine Daten

EU-Beitrag

€ 181 152,96

Koordiniert durch

UNIVERSIDAD DE GRANADA
Spain

Periodic Reporting for period 1 - MAHOD (Meta Analyses of Heterogeneous Omics Data)

Berichtszeitraum: 2024-03-18 bis 2026-06-17

Zusammenfassung vom Kontext und den Gesamtzielen des Projekts

Omics experiments are commonly disseminated as tabular datasets containing relatively few biological samples but thousands of measured variables. This combination may make statistical estimates unstable requiring many parallel hypothesis tests that increase the risk of false discoveries, or apparent differences between experimental groups that arise through random variation rather than a reproducible biological effect. Such findings can direct subsequent research towards hypotheses that are not genuinely promising.

Traditional meta-analysis combines evidence from multiple studies by comparing effect estimates for the same or sufficiently similar measurements, such as the abundance of a shared clinical marker. However, many potentially related omics studies contain neither the same samples nor the same measured variables and therefore cannot be combined using conventional approaches. Meta Analyses of Heterogeneous Omics Data (MAHOD) aims to develop a mathematical and statistical framework for evaluating such datasets by exploiting their common experimental design. For example, comparable treatment and control groups, even when their observations and measurement modalities do not overlap.

Arbeit, die ab Beginn des Projekts bis zum Ende des durch den Bericht erfassten Berichtszeitraums geleistet wurde, und die wichtigsten bis dahin erzielten Ergebnisse

This project investigated methods for analysing heterogeneous datasets in which features were not directly comparable between studies, as well as heteromodal datasets obtained using entirely different measurement technologies. It also furthered a simulation-based method for estimating the number of study participants required to achieve a specified level of statistical power under relatively mild assumptions.

Fortschritte, die über den aktuellen Stand der Technik hinausgehen und voraussichtliche potenzielle Auswirkungen (einschließlich der bis dato erzielten sozioökonomischen Auswirkungen und weiter gefassten gesellschaftlichen Auswirkungen des Projekts)

MAHOD demonstrated that, for certain types of chemical instrumentation, transforming signals into the frequency domain can make statistical inference more robust to drift occurring in the time domain. It also established conditions under which datasets obtained using entirely different measurement modalities can be analysed jointly through a transformation with a unique solution. Methods were evaluated for assessing agreement between these datasets while accounting for possible ambiguities in their low-rank representations. More broadly, the project outlined strategies for analysing raw instrumental signals through linear transformations in the frequency domain when an equivalent analysis is not feasible in the time domain.