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  "Title": "Flexible, probabilistic metrics for quality control of scRNA-seq\ndata",
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  "Description": "Single-cell RNA-sequencing (scRNA-seq) has made it\npossible to profile gene expression in tissues at high\nresolution.  An important preprocessing step prior to\nperforming downstream analyses is to identify and remove cells\nwith poor or degraded sample quality using quality control (QC)\nmetrics.  Two widely used QC metrics to identify a\n‘low-quality’ cell are (i) if the cell includes a high\nproportion of reads that map to mitochondrial DNA encoded genes\n(mtDNA) and (ii) if a small number of genes are detected. miQC\nis data-driven QC metric that jointly models both the\nproportion of reads mapping to mtDNA and the number of detected\ngenes with mixture models in a probabilistic framework to\npredict the low-quality cells in a given dataset.",
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