scSemiGCN: boosting cell-type annotation from noise-resistant graph neural networks with extremely limited supervision.
Journal:
Bioinformatics (Oxford, England)
Published Date:
Feb 1, 2024
Abstract
MOTIVATION: 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.