Compression of quantification uncertainty for scRNA-seq counts.
Compression of quantification uncertainty for scRNA-seq counts.
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DOI：
10.1093/bioinformatics/btab001
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发表时间：
2021-07-19
期刊：
影响因子：
--
通讯作者：
Love MI
中科院分区：
文献类型：
--
作者：
Van Buren S;Sarkar H;Srivastava A;Rashid NU;Patro R;Love MI
来自单细胞RNA-seq（scRNA-seq）数据的基因表达的定量估计由于映射到多个基因的读段而具有固有的不确定性。许多现有的scRNA-seq定量管道忽略了多重映射读段，因此低估了许多基因的预期读段计数。Alevin解释了多重映射读数，并允许产生反映定量不确定性的“推断性复制”。以前的方法在将这些复制合并到统计分析中时已经显示出改进的性能，但是这些复制的存储和使用增加了计算时间和内存需求。我们证明，仅存储来自一组推理重复（“压缩”）的平均值和方差足以捕获基因水平的量化不确定性，同时将磁盘存储降低到原始存储的9%，并且在加载数据时将内存使用降低到6%。使用这些值，我们从负二项分布生成“伪推断”重复，并提出了一个一般程序，将这些重复到一个拟议的统计测试框架。当将此程序应用于基于遗传学的差异表达分析时，我们发现对于具有高水平定量不确定性的基因，假阳性减少了三分之一以上。我们还扩展了Swish方法，将伪推理复制，并证明了在计算时间和内存使用方面的改进，而没有任何性能损失。最后，我们表明，丢弃多映射读段会导致真实的数据集中功能重要基因的计数被显著低估。 makeInfReps和splitSwish在R/Bioconductor fishpond包中实现，可在https://bioconductor.org/packages/fishpond上获得。分析和模拟数据集可以在该论文的GitHub repo中找到，网址为https://github.com/skvanburen/scUncertaintyPaperCode。 补充数据可在Bioinformatics在线获得。
Quantification estimates of gene expression from single-cell RNA-seq (scRNA-seq) data have inherent uncertainty due to reads that map to multiple genes. Many existing scRNA-seq quantification pipelines ignore multi-mapping reads and therefore underestimate expected read counts for many genes. alevin accounts for multi-mapping reads and allows for the generation of ‘inferential replicates’, which reflect quantification uncertainty. Previous methods have shown improved performance when incorporating these replicates into statistical analyses, but storage and use of these replicates increases computation time and memory requirements. We demonstrate that storing only the mean and variance from a set of inferential replicates (‘compression’) is sufficient to capture gene-level quantification uncertainty, while reducing disk storage to as low as 9% of original storage, and memory usage when loading data to as low as 6%. Using these values, we generate ‘pseudo-inferential’ replicates from a negative binomial distribution and propose a general procedure for incorporating these replicates into a proposed statistical testing framework. When applying this procedure to trajectory-based differential expression analyses, we show false positives are reduced by more than a third for genes with high levels of quantification uncertainty. We additionally extend the Swish method to incorporate pseudo-inferential replicates and demonstrate improvements in computation time and memory usage without any loss in performance. Lastly, we show that discarding multi-mapping reads can result in significant underestimation of counts for functionally important genes in a real dataset. makeInfReps and splitSwish are implemented in the R/Bioconductor fishpond package available at https://bioconductor.org/packages/fishpond. Analyses and simulated datasets can be found in the paper’s GitHub repo at https://github.com/skvanburen/scUncertaintyPaperCode. Supplementary data are available at Bioinformatics online.
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