scTrans: Sparse attention powers fast and accurate cell type annotation in single-cell RNA-seq data
Fig 2
Comparison annotation performance across multiple datasets and scales. (a) The average accuracy and f1-macro of each tissue in MCA datasets. (b) The accuracy and f1-macro violin plot, including 31 tissues of MCA datasets, with each point representing the average annotation result of a tissue. (c) The accuracy and f1-macro violin plot of PBMC160k and scBloodNL datasets, with 10 percent of stratified sampling label cell, five times repeated experiments at different randomized seed. (d) The runtime performance of scTrans and comparative methods at MCA, PBMC160k and scBloodNL datasets. The figure on the left shows the running time of the 31 tissues in MCA, and the number of cells increases with the x-coordinate. The figure on the right shows the running time of four methods at PBMC160k and scBloodNL datasets. (e) The average accuracy and f1-macro under 1r = 1, 5δ, and 10n = 4 labeled cells in 31 tissues of MCA.