# Predicting Algorithm of Tissue Cell Ratio Based on Deep Learning Using Single-Cell RNA Sequencing
## 1 Introduction  
Bulk RNA sequencing (RNA-Seq) is used to sequence cell populations whose results reflect the average expression of the overall cell populations. However, cell populations include many different types of cells, and bulk RNA-Seq data can not reveal cellular heterogeneity [1; 2]. Therefore, investigating the composition of cell populations is crucial to study the heterogeneity of cells. Inferring the cellular composition of a sample involves determining the existing cell types and corresponding proportions, which is also called cell deconvolution. This reflects many aspects of studying cell heterogeneity. For example, tumor cells have strong cellular heterogeneity and, by assessing the makeup of immune cells in the tumor microenvironment, we can learn the degree of tumor invasion, judge relevant cancer prognostic biomarkers, and analyze the patient's prognosis and survival condition [3; 4; 5]. Cell deconvolution can be used to standardize immune profiles in human health and disease, explore pathogenesis, etc. [6; 7; 8; 9; 10]. It can also help in the study of cellularheterogeneity during organ development [11; 12]. It contributes to determining whether the changes in tissue cell composition are caused by changes in gene expression of specific cell populations or by changes in the composition. Therefore, studying cell deconvolution is essential. The gene expression value of a heterogeneous sample can be modeled as a linear combination of gene expression values for all cell types in the sample. It is represented by a matrix that the gene expression information of the sample (S) = the gene expression information of each cell type (C) \(\times\) the ratio of cell types in the sample (R) [13]. The derivation of R through S and C, that is, using a specific express matrix of cell type C, is called a reference-based method, and the method of directly deriving C through S is called a non-reference-based method. A precondition for cell-reference-based deconvolution methods is that the number of equations (number of genes) far exceeds the number of unknowns (total number of cell types in the cell population) [14]. These methods rely heavily on the design of C, and the acquisition of C depends on the marker genes of various cell types, where marker genes are also called signature genes. Therefore, acquiring C can be challenging. By comparing the gene expression of different cell types, a set of genes are screened as signature genes to distinguish one cell type from others. We generally use \"highly expressed, cell-specific\" genes. Then, gene values are continually compared for selection and, finally, the top n genes of each cell type are combined to form a signature gene set [15]. Therefore, the design of C is regarded as a screening problem of marker genes. Gini coefficients, Jensen Shannon divergence, or feature selection can also be used for filtering [16; 17; 18; 19]. Cellmapper assumes that marker genes are associated, and starts with one marker gene in the search for marker genes [20]. Machine learning methods, such as support vector regression, can be used to estimate the probability that a gene is a signature gene, such as CIBERSORT and CIBERSORTx [21; 22]. CIBERSORT provides 22 immune cell types of C. Both CIBERSORTx and CIBERSORT provide C for some cell types. In the current reference-based cell deconvolution algorithms, the design of C is still the primary consideration. However, specific cell type expression matrices should vary for tissue specificity; for example, either different tissues or different regions of the tissue should use different cell expression profiles. Even in samples from the same source, batch effects caused by different experimental equipment and time points will introduce data noise [23; 24]. However, the above method uses the average value of gene expression. Currently, the use of non-uniform values of C containing more cell types can be used to partially address the above problems [25].