SIMLR: A Tool for Large-Scale Genomic Analyses by Multi-Kernel Learning
원문 초록
We here present SIMLR (Single-cell Interpretation via Multi-kernel LeaRning), an open-source tool that implements a novel framework to learn a sample-to-sample similarity measure from expression data observed for heterogenous samples. SIMLR can be effectively used to perform tasks such as dimension reduction, clustering, and visualization of heterogeneous populations of samples. SIMLR was benchmarked against state-of-the-art methods for these three tasks on several public datasets, showing it to be scalable and capable of greatly improving clustering performance, as well as providing valuable insights by making the data more interpretable via better a visualization. Availability and Implementation SIMLR is available on GitHub in both R and MATLAB implementations. Furthermore, it is also available as an R package on http://bioconductor.org.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
ALPCAHUS: Subspace Clustering for Heteroscedastic Data
Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering
CNTK: Microsoft's Open-Source Deep-Learning Toolkit
Chainer: a Next-Generation Open Source Framework for Deep Learning
Tri-Learn Graph Fusion Network for Attributed Graph Clustering
Car Object Counting and Position Estimation via Extension of the CLIP-EBC Framework
토론
이 논문과 연결된 포럼 글이 아직 없습니다.