0000000001246158

AUTHOR

Sara P. Garcia

showing 1 related works from this author

Assessment of computational methods for the analysis of single-cell ATAC-seq data

2019

Abstract Background Recent innovations in single-cell Assay for Transposase Accessible Chromatin using sequencing (scATAC-seq) enable profiling of the epigenetic landscape of thousands of individual cells. scATAC-seq data analysis presents unique methodological challenges. scATAC-seq experiments sample DNA, which, due to low copy numbers (diploid in humans), lead to inherent data sparsity (1–10% of peaks detected per cell) compared to transcriptomic (scRNA-seq) data (10–45% of expressed genes detected per cell). Such challenges in data generation emphasize the need for informative features to assess cell heterogeneity at the chromatin level. Results We present a benchmarking framework that …

Epigenomicslcsh:QH426-470Test data generationComputer scienceCellATAC-seqComputational biologyBiologyClusteringTranscriptomeMice03 medical and health scienceschemistry.chemical_compound0302 clinical medicinemedicineAnimalsHumansProfiling (information science)scATAC-seqnatural sciencesEpigeneticsFeature matrixCluster analysislcsh:QH301-705.5GeneTransposaseVisualization030304 developmental biologySparse matrix0303 health sciencesFeaturizationDimensionality reductionResearchComputational BiologySequence Analysis DNADimensionality reductionChromatinBenchmarkinglcsh:Geneticsmedicine.anatomical_structurelcsh:Biology (General)chemistryRegulatory genomicsSingle-Cell AnalysisPeak calling030217 neurology & neurosurgeryDNA
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