Pooling across cells to normalize single-cell RNA sequencing data with many zero counts.

> 商业许可源文 · EUROPE_PMC · [CC-BY](https://creativecommons.org/licenses/by/)

书目信息

引用：Lun AT, Bach K, Marioni JC. (2016). Pooling across cells to normalize single-cell RNA sequencing data with many zero counts. Genome biology. PMID 27122128 · PMC4848819 · DOI 10.1186/s13059-016-0947-7

证据类型：PRIMARY_RESEARCH

主题：rna-seq、single-cell

被引次数（采集时）：941

原始记录：[Europe PMC](https://europepmc.org/article/MED/27122128)

来源许可：[CC-BY](https://creativecommons.org/licenses/by/)

作者摘要（按来源许可复用）

Normalization of single-cell RNA sequencing data is necessary to eliminate cell-specific biases prior to downstream analyses. However, this is not straightforward for noisy single-cell data where many counts are zero. We present a novel approach where expression values are summed across pools of cells, and the summed values are used for normalization. Pool-based size factors are then deconvolved to yield cell-based factors. Our deconvolution approach outperforms existing methods for accurate normalization of cell-specific biases in simulated data. Similar behavior is observed in real data, where deconvolution improves the relevance of results of downstream analyses.

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