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dc.contributor.advisorGoodisman, Michael
dc.contributor.authorSpencer, Collin
dc.date.accessioned2021-06-30T17:36:55Z
dc.date.available2021-06-30T17:36:55Z
dc.date.created2020-05
dc.date.submittedMay 2020
dc.identifier.urihttp://hdl.handle.net/1853/64830
dc.description.abstractAnalysis 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.
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherGeorgia Institute of Technology
dc.subjectscRNA-seq
dc.subjectsnRNA-seq
dc.subjectsingle cell
dc.subjectsingle-cell
dc.subjectASD
dc.subjectautism
dc.subjectautism spectrum disorder
dc.subjectPFC
dc.subjectACC
dc.subjectprefrontal cortex
dc.subjectanterior cingular cortex
dc.subjectco-occurreence clustering
dc.subjectclustering
dc.subjectcluster Seurat
dc.subjectcooccurreence cluster
dc.subjectneural circuit
dc.subjectgenetics
dc.subjectgenes
dc.subjectASD genes
dc.subjectautism genes
dc.subjectautism genetics
dc.subjecttranscriptomics
dc.subjectmachine learning
dc.subjectsingle nucleus
dc.subjectnormalization
dc.subjectbrain region
dc.subjectheterogeneity
dc.titlescRNA-seq dropouts serve as a signal for tissue heterogeneity in autism spectrum disorder
dc.typeUndergraduate Research Option Thesis
dc.description.degreeUndergraduate
dc.contributor.departmentBiology
thesis.degree.levelUndergraduate
dc.contributor.committeeMemberGibson, Greg
dc.contributor.committeeMemberQiu, Peng
dc.date.updated2021-06-30T17:36:55Z


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