scSemiGCN: boosting cell-type annotation from noise-resistant graph neural networks with extremely limited supervision

Jue Yang, Weiwen Wang, Xiwen Zhang

Abstract: Cell-type annotation is fundamental in revealing cell heterogeneity for single-cell data analysis. Although a host of works have been developed, the low signal-to-noise-ratio single-cell RNA-sequencing data that suffers from batch effects and dropout still poses obstacles in discovering grouped patterns for cell types by unsupervised learning and its alternative–semi-supervised learning that utilizes a few labeled cells as guidance for cell-type annotation.