Trajectory inference in single cell data: A systematic literature review

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Ishrat Jahan Emu
Sumon Ahmed


Recent advances in single-cell transcriptomics have made it possible to explore the dynamic mechanisms of immunology in a high-throughput and objective manner. Unsupervised trajectory inference methods attempt to automatically reconstruct the developmental path cells are following by using a mixture of cells at various stages of development. In the past few years, there have been a multitude of new techniques for deducing the trajectory of a single cell from its data. This paper proposes that new researchers might focus on these criteria by examining the strategies and challenges of existing methodologies. Using specific databases (Scopus, Google Scholar and IEEE Xplore), these single cell data trajectory inference studies from 2016 to 2022 were reviewed. We have adhered to the PRISMA structure. Three databases and the most recent works on trajectory inference have been selected. The majority of studies compared their results to those of previously established methods. Several challenges were identified. Additionally, we attempted to identify the most recent work strategies. This may aid future researchers in locating suitable strategies.


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Emu, I. J., & Sumon Ahmed. (2022). Trajectory inference in single cell data: A systematic literature review. Systematic Literature Review and Meta-Analysis Journal, 3(3), 109–116.


Ahmed, S., Rattray, M. & Boukouvalas, A. (2019). GrandPrix: scaling up the Bayesian GPLVM for single-cell data. Bioinformatics, 47-54. DOI:

Bergen, V., Lange, M., Peidli, S., Wolf, F.A. & Theis, F.J. (2020). Generalizing RNA velocity to transient cell states through dynamical modeling. Nat. Biotechnol., 1408–1414. DOI:

Campbell, K.R. and Yau, C. (2017). Switchde: inference of switch-like differential expression along single-cell trajectories. Bioinformatics, 1241-1242. DOI:

Cannoodt, R., Saelens, W., Sichien, D., Tavernier, S., Janssens, S., Guilliams, M., Lambrecht, B., De Preter, K. and Saeys, Y. (2016). SCORPIUS improves trajectory inference and identifies novel modules in dendritic cell development. bioRxiv, 079509. DOI:

Cao, J. et al. (2018). Joint profiling of chromatin accessibility and gene expression in thousands of single cells. Science, 1380–1385. DOI:

Coifman, R.R. and Lafon,S. (2006). Diffusion maps. Appl. Comput. Harmon., 5-30. DOI:

Gan, Y., Li, N., Guo, C., Zou, G., Guan, J. and Zhou, S. (2021). TiC2D: trajectory inference from single-cell RNA-seq data based on consensus clustering. IEEE/ACM Transactions on Computational Biology and Bioinformatics.

Goodarzi, M., Maletic, N., Gutiérrez, J., Sark, V. and Grass, E. (2019). Next-cell prediction based on cell sequence history and intra-cell trajectory. In 2019 22nd Conference on Innovation in Clouds. DOI:

Haghverdi, L., B¨uttner,M., Wolf,A., Buettner,F. and Theis,F.J. (2016). Diffusion pseudotime robustly reconstructs lineage branching. Nat.Methods, 845–848. DOI:

Ji, Z. and Ji, H. (2016). TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis. Nucleic acids research, e117-e117. DOI:

Jiangyong Wei, Tianshou Zhou, Xinan Zhang, Tianhai Tian. (2021). DTFLOW: Inference and Visualization of Single-cell Pseudotime Trajectory Using Diffusion Propagation,Genomics. Proteomics & Bioinformatics, 306-318. DOI:

Li, D., Velazquez, J.J., Ding, J., Hislop, J., Ebrahimkhani, M.R. and Bar-Joseph, Z. (2022). TraSig: inferring cell-cell interactions from pseudotime ordering of scRNA-Seq data. Genome biology, 1-19. DOI:

Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2009). Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement (Reprinted from Annals of Internal Medicine). Physical Therapy, 873–880. DOI:

Mondal, P.K., Saha, U.S. and Mukhopadhyay, I. (2021). PseudoGA: cell pseudotime reconstruction based on genetic algorithm. Nucleic Acids Research, 49(14), pp.7909-7924. Nucleic Acids Research, 7909-7924. DOI:

Qiu, X., Mao,Q., Tang,Y., Wang,L., Chawla,R., Pliner,H.A. and Trapnell,C. (2017). Reversed graph embedding resolves complex single-cell trajectories. Nat. Methods, 979–982. DOI:

Stone, J.V. (2014). Independent Component Analysis: A Tutorial Introduction. MIT Press.

Street, K., Risso,D., Fletcher,R.B., Das,D., Ngai,J., Yosef,N.,Purdom,E. and Dudoit,S. (2018). Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genomics. DOI:

Sun, N., Yu, X., Li, F., Liu, D., Suo, S., Chen, W., Chen, S., Song, L., Green, C.D., McDermott, J. and Shen, Q. (2017). Inference of differentiation time for single cell transcriptomes using cell population reference data. Nature communications, 1-12. DOI:

Svensson, V., Vento-Tormo, R. & Teichmann, S. A. (2018). Exponential scaling of single-cell RNA-seq in the past decade. Nat. Protoc, 599–604. DOI:

Tanay, A., & Regev, A. (2017). Scaling single-cell genomics from phenomenology to mechanism. Nature, 331-338. DOI:

Tran, T.N. and Bader, G.D. (2020). Tempora: cell trajectory inference using time-series single-cell RNA sequencing data. PLoS computational biology, e1008205. DOI:

Trapnell, C., Cacchiarelli,D., Grimsby,J., Pokharel,P., Li,S., Morse,M., Lennon,N.J., Livak,K.J., Mikkelsen,T.S. and Rinn,J.L. (2014). The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol., 381–386. DOI:

Tsuyuzaki, K., Sato,H., Sato,K. and Nikaido,I. (2020). Benchmarking principal component analysis for large-scale single-cell RNA-sequencing. Genome Biol, 9. DOI:

Van der Maaten, L. a. (2008). Visualizing High-Dimensional Data Using t-SNE. J. Mach. Learn. Res., 2579–2605.

Wei, J., Zhou, T., Zhang, X. and Tian, T. (2019). SCOUT: a new algorithm for the inference of pseudo-time trajectory using single-cell data. Computational Biology and Chemistry, 111-120. DOI:

Wolf, F.A., Angerer, P. and Theis, F.J. (2018). SCANPY: large-scale single-cell gene expression data analysis. Genome biology, 1-5. DOI:

Wolf, F.A., Hamey, F.K., Plass, M., Solana, J., Dahlin, J.S., Göttgens, B., Rajewsky, N., Simon, L. and Theis, F.J. (2019). PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells. Genome biology, 1-9. DOI:

Xie, J., Yin, Y. and Wang, J. (2021). TIPD: A Probability Distribution-Based Method for Trajectory Inference from Single-Cell RNA-Seq Data. Interdisciplinary Sciences: Computational Life Sciences, 652-665. DOI:

Y. Gan, N. Li, C. Guo, G. Zou, J. Guan and S. Zhou. (2021). TiC2D: trajectory inference from single-cell RNA-seq data based on consensus clustering. IEEE/ACM Transactions on Computational Biology and Bioinformatics. DOI:

Zheng, Z., Qiu, X., Wu, H., Chang, L., Tang, X., Zou, L., Li, J., Wu, Y., Zhou, J., Jiang, S. and Wan, Y. (2021). TIPS: trajectory inference of pathway significance through pseudotime comparison for functional assessment of single-cell RNAseq data. Briefings in bioinformatics. DOI: