Cell lineage analysis, the most concise understanding is the evolutionary tree of the cell field, usually refers to a certain type of progenitor cells, under certain conditions, there are multiple developmental trajectories and fates, the process of change is similar to the process of complex tree branch changes. Therefore, the goal of this analysis is to restore complex cell fate changes using single-cell data. The results of the analysis can be summarized as two points: 1. Cellular fate: 2. The marker gene of different fate cells.
Taking Monocle for cell lineage analysis as an example, the purpose of Monocle cell lineage analysis is to understand how cells are transformed by a biological process of gene expression changes. Each cell can be thought of as a point in a high-dimensional space, each dimension describing the expression of a different gene in the genome. The procedure for identifying changes in gene expression is equivalent to learning the trajectory that cells follow in this space. However, the more dimensions in the analysis, the more difficult it is to learn the trajectory. However, many genes usually coexist with each other. Therefore, a variety of different algorithms can be used to reduce the dimensionality of the data . Therefore, the core of pedigree analysis is to visualize the close relationship between cells through the dimensionality reduction of gene expression data.
Pedigree skeleton
The input data to be analyzed includes three main parts: the gene list, the expression matrix, and the cell type. A basic pedigree change map (lineage frame map) can be obtained by dimensionally reducing the data, encoding each cell to a position in the trajectory, and specifying the starting point of the trajectory according to the existing knowledge.
Graph spectrum frame 2. Genealogical Hierarchical Clustering Heat Map (Complex Heatmap)
The pedigree frame diagram can be used to see the phylogenetic relationships between cells. Of course, by hierarchically clustering similar gene sets, the heat map also allows us to identify the cell types that develop in each branch, as shown below:
Figure 2 pedigree hierarchical clustering heat map The figure above shows the distribution of the Cluster obtained from the pedigree skeleton in each segment of the pedigree. The name (number) label corresponds to the status label of each segment of the tree.
3. Pedigree (each trunk) score calculation (stemness score)
After obtaining the basic pedigree structure, the results need to be evaluated and optimized. Therefore, the pedigree needs to be scored. In general, scoring calculations follow several procedures:
(1) Select the stemness gene unique to each branch (2) Calculate the expression value based on the selected gene, calculate the lineage score, and correct the lineage skeleton map. (3) Normalize the UMI to the TPM data structure.
After the calibration, the scores are high and low, leaving reliable, delete unreliable data cells, and display them separately:
Figure 3 shows the left cell and knockout cell lineage map after correction, and the right picture shows the corrected cell lineage map. 4. Differential genetic analysis
After obtaining the pedigree, it is necessary to analyze which of the different differential genes are unique in each different branch, so that it is easy to understand the characteristics and biological processes of each branch. Based on the Monocle2 regression model, genes that change with fate during development can be detected and the differences in expression between different branches can be well calculated and visualized.
After getting the difference gene, we can show it in the following ways:
(1) Multi-directional kinetic curves of differential genes
The characteristic genes are classified into different branches, and the evolutionary trajectory is plotted on the abscissa, and the dynamic curve is drawn:
Figure 4 Gene dynamics curve In the above figures, each curve corresponds to the kinetics of the gene in a particular lineage.
(2) Branch_ heatmap of differential genes
According to the differential gene, the gene expression heat map of each different branch can be displayed in a form to visually show the changes of different genes in each different branch:
The results of the above four-step process demonstrate the main results after cell lineage analysis. For specific branches, we can also show it in other ways. However, in either case, simulating cell lineage changes and screening for characteristic genes in each branch are the primary outcomes and priorities of the analysis. With such a high bioinformatics analysis, I believe that the interpretation of single-cell data in each specific situation can provide tremendous help.
The above analysis results are independently completed by the bioinformatics experts of Shanghai Biochip Co., Ltd. If you have related requirements, please contact Shanghai Biochip Co., Ltd. service platform.
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