scBFA: modeling detection patterns to mitigate technical noise in large-scale single-cell genomics data
scBFA: modeling detection patterns to mitigate technical noise in large-scale single-cell genomics data
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DOI：
10.1186/s13059-019-1806-0
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发表时间：
2019-09-09
期刊：
影响因子：
12.3
通讯作者：
Quon, Gerald
中科院分区：
文献类型：
--
作者：
Li, Ruoxin;Quon, Gerald
特征测量的技术差异，如基因表达和基因位点可及性，是大规模单细胞基因组数据集的关键挑战。我们表明，通过单独分析特征检测模式并忽略特征量化测量，可以缓解scRNA-seq和scatac-seq数据集中的这种技术差异。当数据集具有相对于量化噪声较低的检测噪声时，这一结果成立。我们使用我们的新框架scBFA演示了检测模式模型的最新性能，用于细胞类型识别和轨迹推断。在现有的流水线中，也可以在一行R代码中实现性能提升。
Technical variation in feature measurements, such as gene expression and locus accessibility, is a key challenge of large-scale single-cell genomic datasets. We show that this technical variation in both scRNA-seq and scATAC-seq datasets can be mitigated by analyzing feature detection patterns alone and ignoring feature quantification measurements. This result holds when datasets have low detection noise relative to quantification noise. We demonstrate state-of-the-art performance of detection pattern models using our new framework, scBFA, for both cell type identification and trajectory inference. Performance gains can also be realized in one line of R code in existing pipelines.