Single Cell Trajectory Inference Based on Mouse Embryo Dataset
DOI:
https://doi.org/10.54097/2qvrb769Keywords:
Trajectory Inference, Single Cell Inference Dataset, Dimension Reduction, Clustering, T-cell.Abstract
Constructing cell differentiation trajectories helps understand the development process of normal tissues or provides pathology-related information. This work analyses data from a mouse model of fetal growth restriction. In data processing, this work encodes for genes, uses PCA dimension reduction to reduce the cost of calculating, and uses the Louvain algorithm to cluster cells into seven different types including DN, DP-L, DP-M1, DP-M2, DP-M3, T-cell, and unknown, and then infer cell differentiation trajectories by DDRTree. By analyzing cell differentiation trajectories, the cell differentiation process can be known. Besides that, it can be found that the growth of DP cells can be divided into different steps due to differences in gene expression level.
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