Abstract:
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A large number of genes have recently been implicated in risk for autism and these discoveries serve as a springboard for additional explorations into the neurobiology of the disorder. Quantification of gene expression, by sequencing RNA from either single cells or bulk tissue of brain, can be a critical step in such investigations. Each technology, however, brings analytical challenges. We address these challenges with novel statistical tools, including constructing a coherent hierarchical tree of cell types; novel nonparametric approaches to estimate cell-specific gene-gene networks; identification of critical marker genes using sparsity; and deconvolution to estimate subject and cell-type-specific gene expression.
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