# Predicting Algorithm of Tissue Cell Ratio Based on Deep Learning Using Single-Cell RNA Sequencing
## 3 Results and Discussion
### Test on a Manual Batch Samples  
On the trained model, we used the fourth simulated dataset to make predictions to judge the performance of the trained model on the simulated tissue; that is, by determining the error between the predicted value and the actual value. We counted the data and calculated the number of data where the predicted cell ratio differs from the actual cell ratio by 5% and 10%. Among the 500 data used in this paper, 417 data in Bell met the 5% condition, 332 data in CD4 Tcells met the 5% condition, 300 data in CD8 Tcells met the 5% condition, 424 data in Monocytes met the 5% condition, 402 data in Unknown were eligible, and 316 data in NK met the 5% condition. A total of 94.3% of the data met the condition of having a difference of less than 10% between the predicted proportion of cells and the actual proportion of cells. Then, we evaluated the performance of Autoptrc's prediction using RMSE, PCC, and LCC. The lower the RMSE calculated from the true and predicted values, the closer the values to each other. The higher the PCC and LCC values, the stronger the correlation between the predicted value and the true value. We compared the performance of Autoptrc with that of CIBERSORT (CS), CIBERSORTx (CSx), MusiC, and CPM. As Figures 2-4 show, the comparison of the five methods on different datasets shows that the performance of each method on these datasets is not the same, and there is a certain fluctuation range. Of the four datasets, Autoptrc's RMSE is at least 0.072 on the data6k dataset, and Autoptrc is the only method having a PCC over 0.94 on the three datasets. Autoptrc's performance on all four datasets is lower than that of MusiC and CPM in terms of RMSE. CS has the most stable RMSE, and Autoptrc has the smallest dynamic range. The performance of Autoptrc is basically on par with that of CSx. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Batch Size & 128 \\ Steps & 2000 \\ Learning Rate & 0.001 \\ Optimized Algorithm & Adam \\ Loss Function & MSE \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The hyperparameters of the Autoptrc network. Finally, we evaluated the average performance of the methods on all datasets. As shown in Table 2, the RMSE of the Autoptrc model is the lowest at 0.081, which is the same as the RMSE of the Autoptrc model. Figure 4: LCC of CPM, CS, CSx, MusiC, and Autoptrc on four datasets. Figure 3: PCC of CPM, CS, CSx, MusiC, and Autoptrc on four datasets. Figure 2: RMSE of CPM, CS, CSx, MusiC, and Autoptrc on four datasets. as that of CSx. The RMSE values of the remaining three methods are all above 0.11. The PCC of Autoptcr reaches a maximum of 0.903, and the PCC of CSx reaches 0.896, which is higher than that of CPM. This method improves the result by 50%, and is 10% higher than that of CS and 3% higher than that of MusiC. The LCC data of Autoptcr reaches 0.851, which is also the highest. This illustrates that Autoptcr has better deconvolution performance than that of the other methods and can predict unit deconvolution. Comparing the overall predicted data and the actual data, in Figure 5 the horizontal axis represents the true value and the vertical axis represents the predicted value. A data point falling on the y = x line means that the two attributes are the same. Therefore, the data tend to the line y = x, which represents the higher accuracy of the data prediction. We can see that the CS values occupy the entire plane. From the donorC, data6k, and data8k datasets, the predicted data of Autoptcr tends to the y = x line and is more concentrated, indicating that it is close to predicting the actual value. The minor errors indicate that the data have high stability compared to those of other algorithm models. The biased data are shown in Figure 5.  
The predicted value of the CPM method is always lower than the true value in the picture. On the data8k dataset, all methods do not perform very well; that is, this poor performance is not only seen in the Autoptcr model.