Human brain cell typespecific gene coexpression associated with autism spectrum disorder Autism spectrum disorder (ASD) is a complex neuropsychiatric disorder with substantial phenotypic and genetic heterogeneity. Until now, about a thousand diverse genes have been associated with ASD, while it remains elusive that how disruptions in these different genes can lead to a common clinical phenotype. Therefore, it is essential to understand how ASD candidate genes relate to each other and identify potential shared molecular pathways. Human brain is a highly heterogeneous organ involving multiple cell types. Different functions in different types of cells may be dysregulated in ASD; investigating functional interactions between ASD candidate genes in normal human brain cells may shed new light on the genetic heterogeneity of ASD. To this end, we construct cell typeassociated gene coexpression networks based on human brain cell gene expression data. Then we identify seven cell typespecific gene modules and analyze the specific gene functions in each cell type. We also identify six ASDassociated gene modules and study the dysregulated functions in ASD. Lastly, we obtain two ASDassociated cell typespecific gene modules for studying the cell typespecific aberrant functions in ASD. It is found that ASDassociated astrocytesspecific gene modules are relevant to endocytosis, neuron differentiation and cell projection organization, while ASDassociated neuronsspecific gene modules are relevant to presynapse, glutamatergic synapse and neuron projection morphogenesis. Our method has been proven to be effective in discovering ASDassociated cell typespecific gene expression pattern. Our findings can promote the study of the heterogeneity of ASD in gene expression between different cell types, providing new insights into the molecular mechanisms underlying the pathogenesis of ASD.

TexAnASD: Text Analytics for ASD Risk Gene Predictions Autism Spectrum Disorder (ASD) is an extreme neurodevelopmental disease affecting 1 in every 59 children in the United States, and approximately 1% of US population. The clinical traits of the disorder include noticeable deficits in social interactions, language development and in many cases very narrow and repetitive interests and behaviors. ASD is highly heritable genetic disease, but the known causes including the biomarkers associated with ASD form only the tip of the iceberg. Over the past decade extensive research on exome sequences revealed only about one hundred genetic biomarkers with very high confidence. Number of putative ASD causing genes is rapidly growing with the advent of new technologies while researchers are struggling now to assess which genes are the true causing genes. Manual curation of each of the long list of genes is a cumbersome process that requires huge amount of expert workhours, and is therefore expensive. An in silico prediction method can assist the human experts to check only a shortlist of genes that were filtered through a machine learning system. Most of the existing ASD gene prediction algorithms involve highperformance computing platform to analyze largescale genetic data which is counterintuitive to the actual benefit of using an in silico method in the first place. We propose TexAnASD, a text analytics based ASD gene prediction algorithm that only utilizes what we know about each gene that we learn from published literatures. The proposed method outperforms most of the stateoftheart ASD associated gene prediction methods. Moreover, the method offers an inexpensive model than those of the other competing solutions in terms of computational complexity and running time. All source codes, dataset, predictions and functional insights are available at http:ml.cse.ucdenver.eduresearchTexAnASD.