GapClust takes advantage of the gap between minor cluster and neighbouring abundant cluster to let rare cells within minor cluster stand out through delicately designed statistics. Meanwhile, GapClust does not struggle to search for rare cell informative genes like most of the competitors, but learns the cluster size as well as rare cells using simple arithmetic calculation.
R >= 3.4.0
R users can easily install GapClust by running following code in R console.
# Install devtools first if it has not been installed on your R environment. Please try "install.packages("devtools")" in R console.
devtools::install_github("fabotao/GapClust")
Seurat >= 3.1.0
rflann >=1.8.4 (GitHub: YeeJeremy/rflann)
irlba >=2.3.3
e1071 >= 1.7-0.1
In terms of rare cell detection, GapClust offers best performance compared with other methods, quantified by F1 score.
As to compuation time and memory utilization, GapClust also displays unrivaled speed and memory efficiency, in comparison with other methods.
This software package is distributed under MIT license.
This work is free to use for academic and research purposes. Please contact maintainer for commercial use of this work.