Alquicira-Hernandez Jose, Powell Joseph E
Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW 2010, Australia.
Computational Genomics, Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD 4072, Australia.
Bioinformatics. 2021 Aug 25;37(16):2485-2487. doi: 10.1093/bioinformatics/btab003.
Data sparsity in single-cell experiments prevents an accurate assessment of gene expression when visualized in a low-dimensional space. Here, we introduce Nebulosa, an R package that uses weighted kernel density estimation to recover signals lost through drop-out or low expression.
Nebulosa can be easily installed from www.github.com/powellgenomicslab/Nebulosa.
Supplementary data are available at Bioinformatics online.
单细胞实验中的数据稀疏性使得在低维空间中可视化时无法准确评估基因表达。在此，我们介绍了Nebulosa，这是一个R软件包，它使用加权核密度估计来恢复因数据丢失或低表达而丢失的信号。
可以从www.github.com/powellgenomicslab/Nebulosa轻松安装Nebulosa。
补充数据可在《生物信息学》在线获取。
