# Cascade Forest-Based Model for Prediction of RNA Velocity
## 1 Introduction  
With the rapid development and innovation of single-cell RNA sequencing (scRNA-seq) technology and related bioinformatics methods [1; 2; 3; 4; 5; 6; 7; 8], high-throughput single-cell data are emerging in large quantities, and \"scRNA-seq technology\" has become an important tool for molecular biology research. Compared with traditional cell-based RNA-seq, scRNA-seq can better reflect the molecular biological processes within a specific cell population. Currently, the use of single-cell RNA sequencing (scRNA-seq) data for cell trajectory inference is one of the pressing research issues [9; 10; 11; 12; 13; 14; 15]. Many researchers have proposed many algorithms for cell trajectory inference, for example, TSCAN [16] constructs a minimum spanning tree (MST) based on the center of mass of a cluster of cells and then infers the pseudo-temporal order of cells. There is also a class of algorithms to infer trajectories based on the graph structure. For instance, [PERSON] [17] and SLICER [18] construct intercellular graphs based on KNN graphs, and SoptSC [19] uses intercellular similarity matrices for inferring trajectories. [PERSON] et al. [20] introduce the concept of RNA velocity. This concept develops new ways to study cellular dynamics by linking data to underlying molecular dynamics. Therefore, the predictive models of cellular dynamics can be achieved. RNA velocity is an indicator of transcript dynamics that predicts future changes in cellular state, and it allows for reliable estimation of relative temporal derivatives of gene expression state for studies of cell differentiation, lineage development, and dynamic changes in cellular components in the tumor microenvironment. RNA velocity recovers targeted information by distinguishing between newly transcribed pre mRNA (unspliced) and mature mRNA (spliced), the latter of which can be detected from the presence of introns in standard single-cell RNA-seq protocols. The change in mRNA abundance is referred to as RNA velocity. [PERSON] et al. [20] propose the original steady-state model algorithm, named velocity, for RNA velocity assumes that the transcriptional phase lasts long enough to reach steady-stateequilibrium and that equilibrium mRNA levels can be approximated by simplifying linear regression with co-splicing rates. [PERSON] et al. (2019) propose a model called scVelo algorithm, which includes a stochastic, kinetic model and steady-state model. [PERSON] et al. (2020) take a bold stab at RNA velocity research by using RNA velocity prediction as a supervised learning problem for classification, dividing the cell state space into segments of equal size by direction as classes. The estimated RNA velocity vector is treated as ground truth, which we counted as a true positive if the predicted direction is located in the same segment as the original target direction, and so on. In this paper, we study the supervised learning problem based on RNA velocity prediction classification using internally generated mixed cell lineage datasets and a number of complex public datasets, which are well annotated with cell-type labels. We evaluate six integrated classifiers (XGBoost (2019); RandomForest (2019), LightGBM (2020), NGBoost (2020), TabNet (2021), Cascade Forest (2021)) in the problem of RNA velocity prediction. This can make biologically meaningful predictions. The paper is organized as followed. The second section introduces the concept and theory of RNA velocity estimation. The cascade forest structure model and the evaluation indicators to be used in subsequent experiments are introduced in this chapter. Finally, the information of the relevant data set is briefly described. In the third section, experimental results are analyzed, such as preprocessing the data set, evaluating the experimental results of the data set, using accuracy, the kappa coefficient, and evaluating the benchmark performance of the classifier. Finally, the stability of the performance of the cascade forest model is further analyzed by parameter comparison.