M3Drop：用于单细胞RNA测序的基于缺失值的特征选择
M3Drop: dropout-based feature selection for scRNASeq.
作者信息
Andrews Tallulah S, Hemberg Martin
机构信息
Department of Cellular Genetics, Wellcome Trust Sanger Institute, Hinxton, Cambridgshire, UK.
出版信息
Bioinformatics. 2019 Aug 15;35(16):2865-2867. doi: 10.1093/bioinformatics/bty1044.
MOTIVATION
Most genomes contain thousands of genes, but for most functional responses, only a subset of those genes are relevant. To facilitate many single-cell RNASeq (scRNASeq) analyses the set of genes is often reduced through feature selection, i.e. by removing genes only subject to technical noise.
RESULTS
We present M3Drop, an R package that implements popular existing feature selection methods and two novel methods which take advantage of the prevalence of zeros (dropouts) in scRNASeq data to identify features. We show these new methods outperform existing methods on simulated and real datasets.
AVAILABILITY AND IMPLEMENTATION
M3Drop is freely available on github as an R package and is compatible with other popular scRNASeq tools: https://github.com/tallulandrews/M3Drop.
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
动机
大多数基因组包含数千个基因，但对于大多数功能反应而言，只有其中一部分基因是相关的。为便于进行许多单细胞RNA测序（scRNASeq）分析，通常通过特征选择来减少基因集，即去除仅受技术噪声影响的基因。
结果
我们展示了M3Drop，这是一个R包，它实现了现有的流行特征选择方法以及两种利用scRNASeq数据中零值（缺失值）的普遍性来识别特征的新方法。我们表明，这些新方法在模拟数据集和真实数据集上均优于现有方法。
可用性与实现
M3Drop作为一个R包可在github上免费获取，并且与其他流行的scRNASeq工具兼容：https://github.com/tallulandrews/M3Drop。
补充信息
补充数据可在《生物信息学》在线获取。
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