SCA analysis on setA using kinase targets as latent space

We developed for the functional mining of single cell clusters based on Sparsely-Connected Autoencoder (SCA). SCA analysis on setA was also done using a latent space based on kinase targets, but we cannot find any robust association with reference cluster.

我们基于稀疏连接自编码器（Sparsely-Connected Autoencoder，SCA）开发了用于单细胞簇功能挖掘的分析方法。针对数据集setA的SCA分析同样采用了基于激酶靶点的隐空间，但未发现其与参考簇存在稳健的关联。