# Predicting Algorithm of Tissue Cell Ratio Based on Deep Learning Using Single-Cell RNA Sequencing
## 4 Conclusions  
Machine learning and deep learning have been widely used in the field of bioinformatics [39]. Autoptrc uses convolutional neural networks, which represent a new solution for cell deconvolution. Autoptrc does not use a reference-based cell deconvolution protocol, and no longer depends on the average expression matrix data of a specific cell type. Therefore, we no longer need to design a complex data preprocessing process to normalize the average expression matrix of a specific cell type.  
Autoptrc is highly robust to noise and deviation. It has obvious advantages over ordinary mathematical models for feature extraction and modeling. Its convolutional layers are responsible for extracting the connections between genes. The layer can abstract these implicit connections into features. In addition, Autoptrc can create different signatures based on different data, so it is beneficial for tissue-specific studies. Because of the natural advantages of the network, nodes are good at mining abstract features from noise and deviation data. The experimental results show that Autoptrc can effectively eliminate the noise and deviation caused by the collection process in the experimental data (such as the batch effect). Compared with the traditional deconvolution algorithm that relies on the design of the average expression matrix of a specific cell type and linear regression, Autoptrc has higher prediction accuracy in most cases, and the model is more tolerant of noise and deviation.  
Autoptrc also has many outstanding issues. The training of the deep learning model requires a large quantity of data. However, there is little labelling information in bulk RNA-Seq data. Therefore, the model uses artificially simulated data. The simulated data are generated by subsampling the ScRNA-Seq data of the target tissue. Therefore, we speculate that the prediction will be more accurate if some real tissue sample data are added to the artificial simulation data. Determining if Autoptrc can be used for cross-species data will also be one of our future research works.  
In conclusion, Autoptrc is robust to noise and bias. The model is easy to understand and extend. As a result of the development of deep learning technology, we can apply attention mechanisms and long short-term memory networks to cellular deconvolution. We expect deep learning techniques to be a popular research topic for cellular deconvolution.  
Conceptualization, Z.L.; Data curation, X.L. (Xinrong Lv) and M.Q.; Formal analysis, K.B.; Funding acquisition, Z.L.; Investigation, Y.Y.; Methodology, X.L. (Xinrong Lv); Project administration, Z.L.; Resources, Z.L.; Software, X.L. (Xinrong Lv) and D.L.; Supervision, P.Z.; Validation, X.C.; Visualization, X.L. ([PERSON]); Writing original draft, X.L. ([PERSON]); Writing, review and editing, Z.L. All authors have read and agreed to the published version of the manuscript.  
The research was greatly supported by NNSF (National Natural Science Foundation of China), with Grant No. 61672328 and 61672323, and the research is also supported by the Science and Research Plan of Luoyang Branch of Henan Tobacco Company (No. 2020410300270078).  
Figure 8: LCC of CPM, CSx, MusiC, and Autoptrc on PBMC2.  
**Institutional Review Board Statement:** The simulated PBMC samples were obtained from Kevin ([[https://figshare.com/s/e59a03885](https://figshare.com/s/e59a03885) ec4c4 dk153f]([https://figshare.com/s/e59a03885](https://figshare.com/s/e59a03885) ec4c4 dk153f) (accessed on 6 April 2022)). The PBMC2 data were downloaded from GEO ([[https://www.ncbi.nlm.nih.gov/geo/](https://www.ncbi.nlm.nih.gov/geo/)]([https://www.ncbi.nlm.nih.gov/geo/](https://www.ncbi.nlm.nih.gov/geo/)) (accessed on 6 April 2022)) with accession number GSE107011.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the author.  
**Conflicts of Interest:** The authors declare no conflict of interest.