# scTransSort: Transformers for Intelligent Annotation of Cell Types by Gene Embeddings

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## Abstract

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## 1. Introduction

## 2. Materials and Methods

#### 2.1. Datasets

#### 2.2. Framework of scTransSort

#### 2.3. Loss Function and Parameters Setting

## 3. Results

#### 3.1. Evaluation Metrics

_{1-score}. Since we are solving a multi-category problem with unbalanced data in each category, we choose macro precision, macro recall, and macro F

_{1-score}. These metrics have different emphases. Accuracy indicates the percentage of correct prediction types across all cells and focuses on assessing the ability of the model to correctly classify samples. In contrast, the macro F

_{1-score}focuses on assessing the sensitivity of the model. The MCC focuses on predicting the classification performance of models in unbalanced datasets. A dataset containing at least two cell types was selected to calculate the macro F

_{1-score}and MCC. All the evaluation metrics used are detailed in Table 2. The evaluation parameters used in this paper, TP, FP, FN, and TN, represent positive samples predicted by the model to be positive, negative samples predicted by the model to be positive, positive samples predicted by the model to be negative, and negative samples predicted by the model to be negative, respectively.

#### 3.2. Performance on Internal Datasets

#### 3.3. Performance and Robustness Compared with Other Methods

#### 3.4. The Effect of Feature Order of Input Data on Model Performance

#### 3.5. The Effect of Different Patches on Model Performance

## 4. Discussion

## Supplementary Materials

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

- Shao, X.; Lu, X.; Liao, J.; Chen, H.; Fan, X. New avenues for systematically inferring cell-cell communication: Through single-cell transcriptomics data. Protein Cell
**2020**, 11, 866–880. [Google Scholar] [CrossRef] [PubMed] - Li, X.; Wang, C.Y. From bulk, single-cell to spatial RNA sequencing. Int. J. Oral Sci.
**2021**, 13, 1–6. [Google Scholar] [CrossRef] [PubMed] - Healey, H.M.; Bassham, S.; Cresko, W.A. Single-cell Iso-Sequencing enables rapid genome annotation for scRNAseq analysis. Genetics
**2022**, 220, iyac017. [Google Scholar] [CrossRef] [PubMed] - Andrews, T.S.; Hemberg, M. Identifying cell populations with scRNASeq. Mol. Asp. Med.
**2018**, 59, 114–122. [Google Scholar] [CrossRef] [PubMed] - Pasquini, G.; Arias, J.E.R.; Schäfer, P.; Busskamp, V. Automated methods for cell type annotation on scRNA-seq data. Comput. Struct. Biotechnol. J.
**2021**, 19, 961–969. [Google Scholar] [CrossRef] [PubMed] - Shaw, R.; Tian, X.; Xu, J. Single-cell transcriptome analysis in plants: Advances and challenges. Mol. Plant
**2021**, 14, 115–126. [Google Scholar] [CrossRef] - Wolf, F.A.; Angerer, P.; Theis, F.J. SCANPY: Large-scale single-cell gene expression data analysis. Genome Biol.
**2018**, 19, 1–5. [Google Scholar] [CrossRef] [Green Version] - Butler, A.; Hoffman, P.; Smibert, P.; Papalexi, E.; Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol.
**2018**, 36, 411–420. [Google Scholar] [CrossRef] - Wang, B.; Zhu, J.; Pierson, E.; Ramazzotti, D.; Batzoglou, S. Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning. Nat. Methods
**2017**, 14, 414–416. [Google Scholar] [CrossRef] - Kiselev, V.Y.; Kirschner, K.; Schaub, M.T.; Andrews, T.; Yiu, A.; Chandra, T. SC3: Consensus clustering of single-cell RNA-seq data. Nat. Methods
**2017**, 14, 483–486. [Google Scholar] [CrossRef] [Green Version] - Plass, M.; Solana, J.; Wolf, F.A.; Ayoub, S.; Misios, A.; Glažar, P.; Obermayer, B.; Theis, F.J.; Kocks, C.; Rajewsky, N. Cell type atlas and lineage tree of a whole complex animal by single-cell transcriptomics. Science
**2018**, 360, eaaq1723. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Fincher, C.T.; Wurtzel, O.; de Hoog, T.; Kravarik, K.M.; Reddien, P.W. Cell type transcriptome atlas for the planarian Schmidtea mediterranea. Science
**2018**, 360, eaaq1736. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Han, X.; Wang, R.; Zhou, Y.; Fei, L.; Sun, H.; Lai, S.; Saadatpour, A.; Zhou, Z.; Chen, H.; Ye, F.; et al. Mapping the mouse cell atlas by microwell-seq. Cell
**2018**, 172, 1091–1107.e17. [Google Scholar] [CrossRef] [Green Version] - Tabula Muris Consortium. Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris. Nature
**2018**, 562, 367–372. [Google Scholar] [CrossRef] - Cao, J.; Spielmann, M.; Qiu, X.; Huang, X.; Ibrahim, D.M.; Hill, A.J.; Zhang, F.; Mundlos, S.; Christiansen, L.; Steemers, F.J.; et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature
**2019**, 566, 496–502. [Google Scholar] [CrossRef] [PubMed] - Chen, K.; Ozturk, K.; Contreras, R.L.; Simon, J.; McCann, S.; Chen, W.J.; Carter, H.; Fraley, S.I. Phenotypically supervised single-cell sequencing parses within-cell-type heterogeneity. iScience
**2021**, 24, 101991. [Google Scholar] [CrossRef] - McKellar, D.W.; Walter, L.D.; Song, L.T.; Mantri, M.; Wang, M.F.; De Vlaminck, I.; Cosgrove, B.D. Large-scale integration of single-cell transcriptomic data captures transitional progenitor states in mouse skeletal muscle regeneration. Commun. Biol.
**2021**, 4, 1–12. [Google Scholar] [CrossRef] [PubMed] - Abdelaal, T.; Michielsen, L.; Cats, D.; Hoogduin, D.; Mei, H.; Reinders, M.J.T.; Mahfouz, A. A comparison of automatic cell identification methods for single-cell RNA sequencing data. Genome Biol.
**2019**, 20, 1–19. [Google Scholar] [CrossRef] [Green Version] - Zhang, Y.; Aevermann, B.D.; Bakken, T.E.; Miller, J.A.; Hodge, R.D.; Lein, E.S.; Scheuermann, R.H. FR-Match: Robust matching of cell type clusters from single cell RNA sequencing data using the Friedman–Rafsky non-parametric test. Brief. Bioinform.
**2021**, 22, bbaa339. [Google Scholar] [CrossRef] - Heydari, A.A.; Davalos, O.A.; Zhao, L.; Hoyer, K.K.; Sindi, S.S. ACTIVA: Realistic single-cell RNA-seq generation with automatic cell-type identification using introspective variational autoencoders. Bioinformatics
**2022**, 38, 2194–2201. [Google Scholar] [CrossRef] - Huang, Y.; Zhang, P. Evaluation of machine learning approaches for cell-type identification from single-cell transcriptomics data. Brief. Bioinform.
**2021**, 22, bbab035. [Google Scholar] [CrossRef] [PubMed] - Dong, X.; Chowdhury, S.; Victor, U.; Li, X.; Qian, L. Semi-supervised Deep Learning for Cell Type Identification from Single-Cell Transcriptomic Data. IEEE/ACM Trans. Comput. Biol. Bioinform.
**2022**, 1, 1. [Google Scholar] [CrossRef] [PubMed] - Aran, D.; Looney, A.P.; Liu, L.; Wu, E.; Fong, V.; Hsu, A.; Chak, S.; Naikawadi, R.P.; Wolters, P.J.; Abate, A.R.; et al. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nat. Immunol.
**2019**, 20, 163–172. [Google Scholar] [CrossRef] [PubMed] - De Kanter, J.K.; Lijnzaad, P.; Candelli, T.; Margaritis, T.; Holstege, F.C. CHETAH: A selective, hierarchical cell type identification method for single-cell RNA sequencing. Nucleic Acids Res.
**2019**, 47, e95. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Kiselev, V.Y.; Yiu, A.; Hemberg, M. scmap: Projection of single-cell RNA-seq data across data sets. Nat. Methods
**2018**, 15, 359–362. [Google Scholar] [CrossRef] [PubMed] - Boufea, K.; Seth, S.; Batada, N.N. scID uses discriminant analysis to identify transcriptionally equivalent cell types across single-cell RNA-seq data with batch effect. iScience
**2020**, 23, 100914. [Google Scholar] [CrossRef] - Alquicira-Hernandez, J.; Sathe, A.; Ji, H.P.; Nguyen, Q.; Powell, J.E. scPred: Accurate supervised method for cell-type classification from single-cell RNA-seq data. Genome Biol.
**2019**, 20, 1–17. [Google Scholar] [CrossRef] [Green Version] - Ma, F.; Pellegrini, M. ACTINN: Automated identification of cell types in single cell RNA sequencing. Bioinformatics
**2020**, 36, 533–538. [Google Scholar] [CrossRef] - Zhang, A.W.; O’Flanagan, C.; Chavez, E.A.; Lim, J.L.; Ceglia, N.; McPherson, A. Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling. Nat. Methods
**2019**, 16, 1007–1015. [Google Scholar] [CrossRef] - Pliner, H.A.; Shendure, J.; Trapnell, C. Supervised classification enables rapid annotation of cell atlases. Nat. Methods
**2019**, 16, 983–986. [Google Scholar] [CrossRef] - Zhang, Z.; Luo, D.; Zhong, X.; Choi, J.H.; Ma, Y.; Wang, S.; Mahrt, E.; Guo, W.; Stawiski, E.W.; Modrusan, Z.; et al. SCINA: A semi-supervised subtyping algorithm of single cells and bulk samples. Genes
**2019**, 10, 531. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Tan, Y.; Cahan, P. SingleCellNet: A computational tool to classify single cell RNA-Seq data across platforms and across species. Cell Syst.
**2019**, 9, 207–213.e2. [Google Scholar] [CrossRef] [PubMed] - Shao, X.; Yang, H.; Zhuang, X.; Liao, J.; Yang, P.; Cheng, J.; Lu, X.; Chen, H.; Fan, X. scDeepSort: A pre-trained cell-type annotation method for single-cell transcriptomics using deep learning with a weighted graph neural network. Nucleic Acids Res.
**2021**, 49, e122. [Google Scholar] [CrossRef] [PubMed] - Yu, S.; Wang, M.; Pang, S.; Song, L.; Qiao, S. Intelligent fault diagnosis and visual interpretability of rotating machinery based on residual neural network. Measurement
**2022**, 196, 111228. [Google Scholar] [CrossRef] - Yu, S.; Wang, M.; Pang, S.; Song, L.; Zhai, X.; Zhao, Y. TDMSAE: A transferable decoupling multi-scale autoencoder for mechanical fault diagnosis. Mech. Syst. Signal Process.
**2023**, 185, 109789. [Google Scholar] [CrossRef] - Brown, T.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J.D.; Dhariwal, P. Language models are few-shot learners. Adv. Neural Inf. Process. Syst.
**2020**, 33, 1877–1901. [Google Scholar] - Lepikhin, D.; Lee, H.; Xu, Y.; Chen, D.; Firat, O.; Huang, Y. Gshard: Scaling giant models with conditional computation and automatic sharding. arXiv
**2020**, arXiv:2006.16668. [Google Scholar] - Dosovitskiy, A.; Beyer, L.; Kolesnikov, A.; Weissenborn, D.; Zhai, X.; Unterthiner, T. An image is worth 16×16 words: Transformers for image recognition at scale. arXiv
**2020**, arXiv:2010.11929. [Google Scholar] - Khan, S.; Naseer, M.; Hayat, M.; Zamir, S.W.; Khan, F.S.; Shah, M. Transformers in vision: A survey. ACM Comput. Surv.
**2022**, 54, 1–41. [Google Scholar] [CrossRef] - Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, A.; Gomez, A.; Kaiser, Ł.; Polosukhin, L. Attention is all you need. Adv. Neural Inf. Process. Syst.
**2017**, 30. [Google Scholar] - Wang, Q.; Li, B.; Xiao, T.; Zhu, J.; Li, C.; Wong, D.F.; Chao, L.S. Learning deep transformer models for machine translation. arXiv
**2019**, arXiv:1906.01787. [Google Scholar] - Baevski, A.; Auli, M. Adaptive input representations for neural language modeling. arXiv
**2018**, arXiv:1809.10853. [Google Scholar] - Rosenblatt, F. The Perceptron, a Perceiving and Recognizing Automaton Project Para. Master’s Thesis, Cornell Aeronautical Laboratory, Buffalo, NY, USA, 1957. [Google Scholar]

**Figure 1.**The architecture of the scTranSort. (

**a**) The gene patch-embedding layer. Receive scRNA-seq data, transform it into a two-dimensional matrix of gene expression, and generate gene-embedding patches; (

**b**) The transformer encoder block. It consists of a multi-head self-attention mechanism and a fully connected feedforward network to obtain a high-dimensional vector representation of the input sequence; (

**c**) The linear classifier layer. The input represented by the high-dimensional vector is mapped to a set of cate-gory probabilities to obtain the final classification result.

**Figure 2.**Performance of scTransSort on internal test datasets. (

**a**) Accuracy of scTransSort in annotating cells from 35 human tissues. (

**b**) Accuracy of scTransSort in annotating cells from 26 mouse tissues. The bar graph shows the number of cells per tissue.

**Figure 3.**Confusion matrix of scTransSort classification results on the human fetal eye dataset. The accuracies in the graphs represent the accuracy of the predicted results for each cell label, respectively. The bars represent the number of cells in the training dataset for each cell label.

**Figure 4.**Performance comparison of scTransSort on human external test datasets (accessed on 1 June 2022). (

**a**) Heat maps and boxplots of accuracy comparison for different methods on 18 datasets from 9 tissues; (

**b**) Heat maps and boxplots of the mean F1 score comparison; (

**c**) Heat maps and boxplots of the mean MCC comparison. The bolded font indicates the top-ranked method for each dataset; (

**d**) The bubble charts summarize the accuracy, mean F1 score, and mean MCC of the different methods in each tissue.

**Figure 5.**Performance comparison of scTransSort on mouse external test datasets (accessed on 1 June 2022). (

**a**) Heat maps and boxplots of accuracy comparisons for different methods on 29 datasets from 12 tissues; (

**b**) Heat maps and boxplots of the mean F1 score comparison; (

**c**) Heat maps and boxplots of the mean MCC comparison. The bolded font indicates the top-ranked method for each dataset; (

**d**) The bubble charts summarize the accuracy, mean F1 score, and mean MCC of the different methods in each tissue.

**Figure 6.**Confusion matrix of prediction results on external test datasets. (

**a**) Confusion matrix for predicting results on the human lung dataset; (

**b**) Confusion matrix for predicting results on the mouse lung dataset. The accuracies in the graphs represent the accuracies of the predicted results for each cell label. The bars represent the number of training cell samples and the number of test cell samples for each cell type.

**Figure 7.**Performance of scTransSort under different inputs. (

**a**) Experiments on cells containing 35 human tissues; (

**b**) Experiments on cells containing 26 mouse tissues. The line chart shows the accuracy of cell classification predictions for each group of data under different input modes. Each color line represents a specific input mode. The bar chart displays the standard deviation of the results obtained with different inputs.

**Figure 8.**Performance of scTransSort at different patch parameter settings. (

**a**) Experiments on cells containing 35 human tissues; (

**b**) Experiments on cells containing 26 mouse tissues. The line chart shows the accuracy of cell classification predictions for each group of data under different model parameter settings (patch size). Each color line represents a specific parameter setting. The bar chart displays the standard deviation of the results obtained with different patch parameter settings.

Parameters | Range |
---|---|

patch_size | 16 |

batch_size | 64 |

epoch | 50 |

initial_lr | 1 × 10^{−3} |

end_lr | 1 × 10^{−5} |

weight_decay | 1 × 10^{−4} |

Optimizer | SGD |

Activation | GeLU |

Actual Positive | Actual Negative | |
---|---|---|

Predicted Positive | TP | FP |

Predicted Negative | FN | TN |

Precision | TP/(TP+FP) | |

Recall | TP/(TP+FN) | |

Accuracy (ACC) | (TP+TN)/(TP+FP+FN+TN) | |

Matthews correlation coefficient (MCC) | MCC = $\frac{\mathrm{T}\mathrm{P}\times \mathrm{T}\mathrm{N}-\mathrm{F}\mathrm{P}\times \mathrm{F}\mathrm{N}}{\sqrt{\left(\mathrm{T}\mathrm{P}+\mathrm{F}\mathrm{P}\right)\left(\mathrm{T}\mathrm{P}+\mathrm{F}\mathrm{N}\right)\left(\mathrm{T}\mathrm{N}+\mathrm{F}\mathrm{P}\right)\left(\mathrm{T}\mathrm{N}+\mathrm{F}\mathrm{N}\right)}}$ | |

F_{1-score} | F_{1-score} = 2$\times \frac{\mathrm{P}\mathrm{r}\mathrm{e}\mathrm{c}\mathrm{i}\mathrm{s}\mathrm{i}\mathrm{o}\mathrm{n}\times \mathrm{R}\mathrm{e}\mathrm{c}\mathrm{a}\mathrm{l}\mathrm{l}}{\mathrm{P}\mathrm{r}\mathrm{e}\mathrm{c}\mathrm{i}\mathrm{s}\mathrm{i}\mathrm{o}\mathrm{n}+\mathrm{R}\mathrm{e}\mathrm{c}\mathrm{a}\mathrm{l}\mathrm{l}}$ | |

Standard Deviation | s =$\sqrt{\frac{{\sum}_{\mathrm{i}=1}^{\mathrm{n}}{\left({\mathrm{x}}_{\mathrm{i}}-\stackrel{-}{\mathrm{x}}\right)}^{2}}{\mathrm{n}-1}}$ $($where n is the number of data, ${\mathrm{x}}_{\mathrm{i}}\mathrm{i}\mathrm{s}$ the i-th data, and $\stackrel{-}{\mathrm{x}}\mathrm{i}\mathrm{s}\mathrm{t}\mathrm{h}\mathrm{e}\mathrm{a}\mathrm{r}\mathrm{i}\mathrm{t}\mathrm{h}\mathrm{m}\mathrm{e}\mathrm{t}\mathrm{i}\mathrm{c}\mathrm{m}\mathrm{e}\mathrm{a}\mathrm{n}\mathrm{o}\mathrm{f}\mathrm{t}\mathrm{h}\mathrm{e}\mathrm{n}\mathrm{d}\mathrm{a}\mathrm{t}\mathrm{a}.$) |

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## Share and Cite

**MDPI and ACS Style**

Jiao, L.; Wang, G.; Dai, H.; Li, X.; Wang, S.; Song, T.
scTransSort: Transformers for Intelligent Annotation of Cell Types by Gene Embeddings. *Biomolecules* **2023**, *13*, 611.
https://doi.org/10.3390/biom13040611

**AMA Style**

Jiao L, Wang G, Dai H, Li X, Wang S, Song T.
scTransSort: Transformers for Intelligent Annotation of Cell Types by Gene Embeddings. *Biomolecules*. 2023; 13(4):611.
https://doi.org/10.3390/biom13040611

**Chicago/Turabian Style**

Jiao, Linfang, Gan Wang, Huanhuan Dai, Xue Li, Shuang Wang, and Tao Song.
2023. "scTransSort: Transformers for Intelligent Annotation of Cell Types by Gene Embeddings" *Biomolecules* 13, no. 4: 611.
https://doi.org/10.3390/biom13040611