MANOCCA: a robust and computationally efficient test of covariance in high-dimension multivariate omics data.

ABSTRACT: Multivariate analysis is becoming central in studies investigating high-throughput molecular data, yet, some important features of these data are seldom explored. Here, we present MANOCCA (Multivariate Analysis of Conditional CovAriance), a powerful method to test for the effect of a predictor on the covariance matrix of a multivariate outcome. The proposed test is by construction orthogonal to tests based on the mean and variance and is able to capture effects that are missed by both approaches. We first compare the performances of MANOCCA with existing correlation-based methods and show that MANOCCA is the only test correctly calibrated in simulation mimicking omics data. We then investigate the impact of reducing the dimensionality of the data using principal component analysis when the

SUBMITTER: Boetto C 

PROVIDER: S-EPMC11163461 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature