This is the repository of the source code and results of performance comparison of ACAM against Garnett, CellAssign, SingleR, Seurat transferData, deCS and SCSA. If you want to use ACAM, see the package https://github.com/yuc0824/ACAM.

Automatic Cell type Annotation Method (ACAM) is proposed based on marker genes' information. This method first finds the representative clusters by searching for the consistent subgroups across the results of several popular clustering methods. Such technique guarantees that the cells in the same cluster have very high probabilities of being from the same cell type. Then by selecting the features that discriminate one cluster from all the remaining cells, the potential marker genes are identified. The cell types are determined by defining a cell type importance score to match these marker genes with the validated ones. For those cells that do not belong to any of these clusters, we use $k$-nearest neighbors to determine their cell type.

• celltype: Consensus tables of cell types and numbers between 'Y_xxx.raw' and 'Y_xxx' are in the fold celltype for some dataset needed.
• markers: Markers of different forms to cater for the use of different methods. They are given in the fold begin with markers.
• results: In the fold results:
  • 'Y_xxx.raw': The original labels.
  • 'Y_xxx': The numeric form of 'Y_xxx.raw'.
  • 'xxx.comb': Clustering results for checking convenience, since the time cost of clustering may be long.
  • 'umap_xxx': The umap dimensional reduction form of the data.
  • 'cds1_xxx', 'Y_xxx_cellassign', 'Y_xxx_singleR', 'xxx.predictions','deCS_xxx','SCSA_seurat_xxx','SCSA_scran_xxx': comparison results of Garnett, CellAssign, SingleR, Seurat transferData, deCS, SCSA_seurat and SCSA_scran respectively.
  • 'Yfinal_xxx': ACAM annotation results.
• vignettes: Figures of the flowchart and visualizations are in the fold vignettes.

Annotation performance comparison. Results of the compared methods using four evaluation metrics: Accuracy, Balanced Accuracy, Macro F1-Score and MCC on seven real-world datasets are shown.

Rank of the eight methods. The performance rank of each method according to four performance metrics: Accuracy, Balanced Accuracy, Macro F1-Score and MCC on seven datasets is shown. Lower rank represents better performance (one is the best and eight is the worst).

Two-dimensional visualization of the annotation results for dataset Kidney using UMAP.

Two-dimensional visualization of the annotation results for dataset Kidney using UMAP.

Two-dimensional visualization of the annotation results for dataset Kidney using UMAP.