Sex bias evaluation of classic and novel Housekeeping Genes in adipose tissue
through the massive analysis of transcriptomics data

Figure 1. Data-analysis workflow. This study consisted of seven main block-steps: 1. The collection of public microarray information located at GEO (Gene Expression Omnibus) database with Python and R. 2. Raw data pre-processing and probe annotation. 3. Statistical data analysis with three different statistics to get the gene expression variability in adipose tissue samples of Hsa and Mmu, considering the biological sex as a variable. 4. Meta-analysis by Rank Products. 5. Functional annotation with Gene Ontology (GO) terms. 6. GTEX-based gene expression filtering, to select potential reference genes suitable to compare both sexes in gene expression analyses. 7. Experimental validation by qPCR.