Approximate distance correlation for selecting highly interrelated genes across datasets.

ABSTRACT: With the rapid accumulation of biological omics datasets, decoding the underlying relationships of cross-dataset genes becomes an important issue. Previous studies have attempted to identify differentially expressed genes across datasets. However, it is hard for them to detect interrelated ones. Moreover, existing correlation-based algorithms can only measure the relationship between genes within a single dataset or two multi-modal datasets from the same samples. It is still unclear how to quantify the strength of association of the same gene across two biological datasets with different samples. To this end, we propose Approximate Distance Correlation (ADC) to select interrelated genes with statistical significance across two different biological datasets. ADC first obtains the k most cor

SUBMITTER: Shen Q 

PROVIDER: S-EPMC8604336 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature