Direct estimation and inference of higher-level correlations from lower-level measurements with applications in gene-pathway and proteomics studies.
Biostatistics
; 2024 Jul 31.
Article
em En
| MEDLINE
| ID: mdl-39083810
ABSTRACT
This paper tackles the challenge of estimating correlations between higher-level biological variables (e.g. proteins and gene pathways) when only lower-level measurements are directly observed (e.g. peptides and individual genes). Existing methods typically aggregate lower-level data into higher-level variables and then estimate correlations based on the aggregated data. However, different data aggregation methods can yield varying correlation estimates as they target different higher-level quantities. Our solution is a latent factor model that directly estimates these higher-level correlations from lower-level data without the need for data aggregation. We further introduce a shrinkage estimator to ensure the positive definiteness and improve the accuracy of the estimated correlation matrix. Furthermore, we establish the asymptotic normality of our estimator, enabling efficient computation of P-values for the identification of significant correlations. The effectiveness of our approach is demonstrated through comprehensive simulations and the analysis of proteomics and gene expression datasets. We develop the R package highcor for implementing our method.
Texto completo:
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Bases de dados:
MEDLINE
Idioma:
En
Revista:
Biostatistics
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Estados Unidos