Automatic cell type annotation using supervised classification: A systematic literature review

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Nazifa Tasnim Hia
Sumon Ahmed


Single-cell sequencing gives us the opportunity to analyze cells on an individual level rather than at a population level. There are different types of sequencing based on the stage and portion of the cell from where the data are collected. Among those Single Cell RNA seq is most widely used and most application of cell type annotation has been on Single-cell RNA seq data. Tools have been developed for automatic cell type annotation as manual annotation of cell type is time-consuming and partially subjective. There are mainly three strategies to associate cell type with gene expression profiles of single cell by using  marker genes databases, correlating expression data, transferring levels by supervised classification. In this SLR, we present a comprehensive evaluation of the available tools and the underlying approaches to perform automated cell type annotations on scRNA-seq data.


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Hia, N. T., & Sumon Ahmed. (2022). Automatic cell type annotation using supervised classification: A systematic literature review . Systematic Literature Review and Meta-Analysis Journal, 3(3), 99–108.


Alquicira-Hernandez, J., Sathe, A., Ji, H. P., Nguyen, Q., & Powell, J. E. (2019). ScPred: Accurate supervised method for cell-type classification from single-cell RNA-seq data. Genome Biology, 20(1), 1–17. DOI:

Goyal, M., Serrano, G., Argemi, J., Shomorony, I., Hernaez, M., & Ochoa, I. (2022). JIND: joint integration and discrimination for automated single-cell annotation. Bioinformatics. DOI:

Ji, X., Tsao, D., Bai, K., Tsao, M., & Zhang, X. (2022). scAnnotate: an automated cell type annotation tool for single-cell RNA-sequencing data. BioRxiv, 2022.02.19.481159. DOI:

Johnson, T. S., Wang, T., Huang, Z., Yu, C. Y., Wu, Y., Han, Y., Zhang, Y., Huang, K., & Zhang, J. (2019). LAmbDA: Label ambiguous domain adaptation dataset integration reduces batch effects and improves subtype detection. Bioinformatics, 35(22), 4696–4706. DOI:

Kitchenham, B. A., Mendes, E., & Travassos, G. H. (2007). Cross versus within-company cost estimation studies: A systematic review. IEEE Transactions on Software Engineering, 33(5), 316–329. DOI:

Lin, Y., Cao, Y., Kim, H. J., Salim, A., Speed, T. P., Lin, D. M., Yang, P., & Yang, J. Y. H. (2020). scClassify: sample size estimation and multiscale classification of cells using single and multiple reference. Molecular Systems Biology, 16(6). DOI:

Ma, F., & Pellegrini, M. (2020). ACTINN: Automated identification of cell types in single cell RNA sequencing. Bioinformatics, 36(2), 533–538. DOI:

Mädler, S. C., Julien-Laferriere, A., Wyss, L., Phan, M., Sonrel, A., Kang, A. S. W., Ulrich, E., Schmucki, R., Zhang, J. D., Ebeling, M., Badi, L., Kam-Thong, T., Schwalie, P. C., & Hatje, K. (2021). Besca, a single-cell transcriptomics analysis toolkit to accelerate translational research. NAR Genomics and Bioinformatics, 3(4). DOI:

Pasquini, G., Rojo Arias, J. E., Schäfer, P., & Busskamp, V. (2021a). Automated methods for cell type annotation on scRNA-seq data. In Computational and Structural Biotechnology Journal (Vol. 19, pp. 961–969).

Pasquini, G., Rojo Arias, J. E., Schäfer, P., & Busskamp, V. (2021b). Automated methods for cell type annotation on scRNA-seq data. Computational and Structural Biotechnology Journal, 19, 961–969. DOI:

Pliner, H. A., Shendure, J., & Trapnell, C. (2019). Supervised classification enables rapid annotation of cell atlases. Nature Methods, 16(10), 983–986. DOI:

Qi, R., Wu, J., Guo, F., Xu, L., & Zou, Q. (2021). A spectral clustering with self-weighted multiple kernel learning method for single-cell RNA-seq data. Briefings in Bioinformatics, 22(4). DOI:

Ray, S., & Schönhuth, A. (2020). MarkerCapsule: Explainable Single Cell Typing using Capsule Networks. In bioRxiv. DOI:

Shasha, C., Tian, Y., Mair, F., Miller, H., BioRxiv, R. G.-, & 2021, U. (n.d.). Superscan: Supervised Single-Cell Annotation. Biorxiv.Org. Retrieved May 12, 2022, from DOI:

Tan, Y., & Cahan, P. (2019). SingleCellNet: A Computational Tool to Classify Single Cell RNA-Seq Data Across Platforms and Across Species. Cell Systems, 9(2), 207-213.e2. DOI:


Upadhyay, P., Genetics, S. R.-F. in, & 2022, U. (n.d.). A Regularized Multi-Task Learning Approach for Cell Type Detection in Single-Cell RNA Sequencing Data. Europepmc.Org. Retrieved May 12, 2022, from DOI:

Wagner, F., & Yanai, I. (2018). Moana: A robust and scalable cell type classification framework for single-cell RNA-Seq data. BioRxiv. DOI:

Wang, S., Pisco, A. O., McGeever, A., Brbic, M., Zitnik, M., Darmanis, S., Leskovec, J., Karkanias, J., & Altman, R. (2019). Unifying single-cell annotations based on the Cell Ontology. BioRxiv, 810234. DOI:

Xie, P., Gao, M., Wang, C., Zhang, J., Noel, P., Yang, C., Von Hoff, D., Han, H., Zhang, M. Q., & Lin, W. (2019). SuperCT: A supervised-learning framework for enhanced characterization of single-cell transcriptomic profiles. Nucleic Acids Research, 47(8), 1–12. DOI:

Xu, C., Lopez, R., Mehlman, E., Regier, J., Jordan, M. I., & Yosef, N. (2021). Probabilistic harmonization and annotation of single‐cell transcriptomics data with deep generative models. Molecular Systems Biology, 17(1). DOI: