High-dimensional covariance matrices tests for analyzing multi-tumor gene expression data
- 7 July 2021
- journal article
- research article
- Published by SAGE Publications in Statistical Methods in Medical Research
- Vol. 30 (8), 1904-1916
- https://doi.org/10.1177/09622802211009257
Abstract
By collecting multiple sets per subject in microarray data, gene sets analysis requires characterize intra-subject variation using gene expression profiling. For each subject, the data can be written as a matrix with the different subsets of gene expressions (e.g. multiple tumor types) indexing the rows and the genes indexing the columns. To test the assumption of intra-subject (tumor) variation, we present and perform tests of multi-set sphericity and multi-set identity of covariance structures across subjects (tumor types). We demonstrate by both theoretical and empirical studies that the tests have good properties. We applied the proposed tests on The Cancer Genome Atlas (TCGA) and tested covariance structures for the gene expressions across several tumor types.Funding Information
- National Basic Research Program of China (2015CB856004)
- National Natural Science Foundation of China (11531001)
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