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Robust protocols and automation now enable large-scale single-cell RNA and ATAC sequencing experiments and their application on biobank and clinical cohorts. However, technical biases introduced during sample acquisition can hinder solid, reproducible results and a systematic benchmarking is required before entering large-scale data production. Here, we report the existence and extent of gene expression and chromatin accessibility artifacts introduced during sampling and identify experimental and computational solutions for their prevention.

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  • Downloaded 2,591 times
  • Download rankings, all-time:
    • Site-wide: 2,737 out of 101,137
    • In genomics: 502 out of 6,270
  • Year to date:
    • Site-wide: 625 out of 101,137
  • Since beginning of last month:
    • Site-wide: 19,216 out of 101,137

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Distribution of downloads per paper, site-wide


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