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scRNA-seq dropouts serve as a signal for tissue heterogeneity in autism spectrum disorder

2020-05 , Spencer, Collin

Analysis of single-cell RNA-sequencing (scRNA-seq) data is plagued by dropouts, zero counts for mRNA transcripts due to low mRNA in individual cells and inefficient mRNA capture. Dropouts are traditionally treated as an error to be corrected through normalization while performing unsupervised clustering of single cells based on highly expressed, variable transcripts. A novel algorithm, co-occurrence clustering, treats dropouts as a signal and binarizes scRNA-seq data for cell clustering, producing the same clusters as Seurat. Previous application of Seurat to single nuclear RNA-sequencing (snRNA-seq) data taken from the prefrontal cortex (PFC) and anterior cingulate cortex (ACC) of patients with autism spectrum disorder (ASD) found no difference in clusters between brain regions. This seems at odds with literature suggesting tissue-specific emergence of co-expression networks and regional specialization in the brain. We applied co-occurrence clustering to ASD samples to parse interregional heterogeneity between the PFC and ACC and identify novel cell clusters.

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