GiniClust3: a fast and memory-efficient tool for rare cell type identification
GiniClust3: a fast and memory-efficient tool for rare cell type identification
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DOI：
10.1186/s12859-020-3482-1
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发表时间：
2020-04-25
影响因子：
3
通讯作者：
Yuan, Guo-Cheng
中科院分区：
文献类型：
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作者：
Dong, Rui;Yuan, Guo-Cheng
背景随着单细胞RNA测序技术的快速发展，高分辨率的细胞类型分析成为可能。已经开发了许多方法来鉴定稀有细胞类型。然而，现有的方法仍然不能扩展到大型数据集，限制了它们的实用性。为了克服这一限制，我们提出了一个新的软件包，称为GiniClust3，这是GiniClust2的扩展和显着的速度和内存效率比以前的版本。结果使用GiniClust3，仅需约7 h即可从包含超过100万个细胞的数据集中识别常见和罕见细胞簇。细胞类型定位和扰动分析表明，GiniClust3可以鲁棒地识别细胞簇。总之，这些结果表明GiniClust3是识别常见和罕见细胞群体的强大工具，并且可以处理大数据集。giniquitter3是在开源python包中实现的，可以在。
Background With the rapid development of single-cell RNA sequencing technology, it is possible to dissect cell-type composition at high resolution. A number of methods have been developed with the purpose to identify rare cell types. However, existing methods are still not scalable to large datasets, limiting their utility. To overcome this limitation, we present a new software package, called GiniClust3, which is an extension of GiniClust2 and significantly faster and memory-efficient than previous versions. Results Using GiniClust3, it only takes about 7 h to identify both common and rare cell clusters from a dataset that contains more than one million cells. Cell type mapping and perturbation analyses show that GiniClust3 could robustly identify cell clusters. Conclusions Taken together, these results suggest that GiniClust3 is a powerful tool to identify both common and rare cell population and can handle large dataset. GiniCluster3 is implemented in the open-source python package and available at .