Predictability of human differential gene expression

• DOI: 10.1073/pnas.1802973116
• Publication Date: 2019-03-08T00:25:55Z
• Authors: Megan Crow; Nathaniel Lim; Sara Ballouz; Paul Pavlidis; Jesse Gillis
• Keywords: Graft Rejection; Lung Neoplasms; neoplasms; specificity; 3102 Bioinformatics and Computational Biology; transcriptomics; computational biology; Essential; Recurrence; replicability; analysis and processing; Gene Regulatory Networks; anzsrc-for: 31 Biological Sciences; RNA structure; genes; Cancer; modification; 0303 health sciences; Tumor; Genes, Essential; cancer types; bioinformatics; Genomics; 3. Good health; PNAS Plus; diseases & disorders; biomarker; Female; anzsrc-for: 3102 Bioinformatics and Computational Biology; metaanalysis; 610; Breast Neoplasms; 612; Adenocarcinoma; Sensitivity and Specificity; differential expression; 03 medical and health sciences; breast cancer; Rare Diseases; transcriptomes; Breast Cancer; Genetics; Biomarkers, Tumor; cancer; Humans; genetics & nucleic acid processing; Probability; genomics and proteomics; function; Electronic Data Processing; Gene Expression Profiling; Human Genome; Investigative techniques and equipment; Human Genetics; DNA; Kidney Transplantation; Genes; Gene Expression Regulation; ROC Curve; gene expression; Women's Health; structure and function; Transcriptome; Biomarkers; 31 Biological Sciences

Differential expression (DE) is commonly used to explore molecular mechanisms of biological conditions. While many studies report significant results between their groups of interest, the degree to which results are specific to the question at hand is not generally assessed, potentially leading to inaccurate interpretation. This could be particularly problematic for metaanalysis where replicability across datasets is taken as strong evidence for the existence of a specific, biologically relevant signal, but which instead may arise from recurrence of generic processes. To address this, we developed an approach to predict DE based on an analysis of over 600 studies. A predictor based on empirical prior probability of DE performs very well at this task (mean area under the receiver operating characteristic curve, ∼0.8), indicating that a large fraction of DE hit lists are nonspecific. In contrast, predictors based on attributes such as gene function, mutation rates, or network features perform poorly. Genes associated with sex, the extracellular matrix, the immune system, and stress responses are prominent within the “DE prior.” In a series of control studies, we show that these patterns reflect shared biology rather than technical artifacts or ascertainment biases. Finally, we demonstrate the application of the DE prior to data interpretation in three use cases: (i) breast cancer subtyping, (ii) single-cell genomics of pancreatic islet cells, and (iii) metaanalysis of lung adenocarcinoma and renal transplant rejection transcriptomics. In all cases, we find hallmarks of generic DE, highlighting the need for nuanced interpretation of gene phenotypic associations.