Abstract Motivation: Autism spectrum disorder (ASD) has a strong, yet heterogeneous, genetic component. Among the various methods that are being developed to study it, one that is gaining popularity is the incorporation of transcriptomic data with known mutations associated to the disorder, often using the SFARI Gene list to characterise the latter. Results: SFARI genes were found not to be significantly associated to differential gene expression patterns, nor enriched in co-expression modules with strong module-diagnosis correlation, however, it was confirmed that they do provide useful insights when using network analysis and machine learning models that are able to incorporate information from the whole gene co-expression network. A statistically significant bias related to level of expression was found in the SFARI genes and SFARI scores, which was found to influence transcriptomic results at gene, module and whole-network levels, as well as other ASD gene-scoring systems. This dataset contains all of the data used in our paper cited in this data submission for reproducibility and re-use purposes. The files are described in detail in the source and file structure description section of this entry.
Simpson, Ian; Navarro, Magdalena. (2021). SFARI Genes and where to find them; classification modelling to identify genes associated with Autism Spectrum Disorder from RNA-seq data, [dataset]. University of Edinburgh. School of Informatics. https://doi.org/10.7488/ds/2980.