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    <titleInfo>
      <title>Souporcell : robust clustering of single-cell RNA-seq data by genotype without reference genotypes</title>
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      <namePart type="family">Heaton</namePart>
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    <abstract>Souporcell clusters single-cell RNA-seq data using genotype information without the use of a genotype reference. Methods to deconvolve single-cell RNA-sequencing (scRNA-seq) data are necessary for samples containing a mixture of genotypes, whether they are natural or experimentally combined. Multiplexing across donors is a popular experimental design that can avoid batch effects, reduce costs and improve doublet detection. By using variants detected in scRNA-seq reads, it is possible to assign cells to their donor of origin and identify cross-genotype doublets that may have highly similar transcriptional profiles, precluding detection by transcriptional profile. More subtle cross-genotype variant contamination can be used to estimate the amount of ambient RNA. Ambient RNA is caused by cell lysis before droplet partitioning and is an important confounder of scRNA-seq analysis. Here we develop souporcell, a method to cluster cells using the genetic variants detected within the scRNA-seq reads. We show that it achieves high accuracy on genotype clustering, doublet detection and ambient RNA estimation, as demonstrated across a range of challenging scenarios.</abstract>
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      <titleInfo>
        <title>Nature Methods</title>
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      <part>
        <detail type="volume">
          <number>17</number>
        </detail>
        <detail type="volume">
          <number>6</number>
        </detail>
        <extent unit="pages">
          <list> 615-620</list>
        </extent>
      </part>
      <originInfo>
        <dateIssued>2020</dateIssued>
      </originInfo>
      <identifier type="issn">1548-7091</identifier>
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    <identifier type="uri">https://www.documentation.ird.fr/hor/fdi:010079057</identifier>
    <identifier type="doi">10.1038/s41592-020-0820-1</identifier>
    <identifier type="issn">1548-7091</identifier>
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