# Predicting Algorithm of Tissue Cell Ratio Based on Deep Learning Using Single-Cell RNA Sequencing
## 1 Introduction
CPM [26] and MusiC [27] have also been proposed to use ScRNA data to generate tissue-specific signature gene matrices. Because some mutant cells and unknown cells have no reference, the design of their feature matrix is very difficult. After designing C, because the number of equations (the number of genes) far exceeds the number of unknowns (the total number of cell types in the cell population), cell deconvolution can be viewed as solving the overdetermined system of equations. The least squares method is commonly used for solving overdetermined equations, such as in DWLS [28], SpatialDWLS [29], and MusiC. Non-reference-based methods commonly use algorithms involving non-negative matrix factorization [30; 31]. Non-negative matrix factorization needs to generate initializations of C and R. However, the matrix factorization problem may obtain multiple global optimal solutions under the non-negative constraints of the base matrix and the proportional matrix [32]. To obtain a proper solution, new constraints must be added, but cell composition ratios are still hard to find. Therefore, it is desirable to use the gene expression data of the sample as a model input only, and no longer rely on a specific gene expression profile. Deep learning is a good solution. CNNs are widely used in bioinformatics. For example, CNNs have great potential in biological signal processing of one-dimensional data with specific repetitive patterns, such as genome sequences [33], and two-dimensional matrix data, such as the time-frequency matrix of biological signals. The nodes of the model are robust to noise and other deviations. It is difficult but necessary to build a model with high deconvolution performance and robustness to noise and errors using a deep learning approach such as a CNN. Deep learning requires a large amount of labelled data, but labelled information about bulk RNA-Seq is insufficient. Therefore, we use ScRNA-Seq to simulate the generated bulk RNA data and make predictions based on simulated bulk samples and real samples. ScRNA-Seq provides unbiased, reproducible, high-resolution, and high-throughput transcriptional analysis of single cells, reflecting all gene expression levels in single cells [34]. Single-cell metabolomics examines changes in the metabolic status markers of single cells over time, and faces the problem of the lack of a linear relationship between the number of cell matrices and the number of cell products [35]. Single-cell proteomics can distinguish cell types by differential protein information. Its difficulty lies mainly in the trace amount of protein in a single cell [36]. Three studies have analyzed the cellular heterogeneity from the different perspectives of cells. At present, ScRNA-Seq is the most mature discipline. In this study, a cellular deconvolution scheme named Autoptcr was constructed. Autoptcr is an algorithm that does not depend on a specific expression matrix reference of the cell type. It can infer cell proportions of tissue directly from the tissue's gene expression data. Autoptcr first applies a CNN to perform cell deconvolution. Autoptcr is trained on simulated ScRNA-Seq data, which solves the difficulty of deep learning training due to the lack of label information of bulk RNA data. It can fully examine the inner relationship between genes and extract the hidden features from ScRNA-Seq data. We compared Autoptcr with other algorithms, and found that it has better deconvolution performance, which means greater accuracy in predicting the proportion of cell types in heterogeneous tissues. The network nodes are robust to data noise and errors. Autoptcr has high prediction accuracy and strong anti-noise ability.