Correlating gene expression levels with transcription factor binding sites facilitates identification of key transcription factors from transcriptome data.

ABSTRACT: Identification of key transcription factors from transcriptome data by correlating gene expression levels with transcription factor binding sites is important for transcriptome data analysis. In a typical scenario, we always set a threshold to filter the top ranked differentially expressed genes and top ranked transcription factor binding sites. However, correlation analysis of filtered data can often result in spurious correlations. In this study, we tested four methods for creating the gene expression inputs (ranked gene list) in the correlation analysis: star coordinate map transformation (START), expression differential score (ED), preferential expression measure (PEM), and the specificity measure (SPM). Then, Kendall's tau correlation statistical algorithms implementing the standard (

SUBMITTER: Huang T 

PROVIDER: S-EPMC11638204 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature