The invention discloses a lightweight optimal clustering
selection method for scRNA-seq data. The lightweight optimal clustering
selection method comprises the following steps: 1, acquiring and preprocessing an scRNA-seq
data matrix; 2, performing layer-by-layer
dimensionality reduction on data by using a multi-layer
perceptron encoder to obtain a low-dimensional
feature matrix; and 3, constructing an undirected
cell map, and enhancing and removing
cell map
noise through an NE network. And 4, reconstructing features by using graph
convolution, inputting the low-dimensional
feature matrix and the
cell graph into a simplified graph
convolution module, and updating the reconstructed
feature matrix round by round. And 5, carrying out
cell clustering on the
characteristic matrix after each round of updating by using self-optimization clustering to obtain cell representation. And 6, judging and evaluating the clustering performance of the current round, storing an optimal clustering result, and carrying out feature reconstruction and self-optimization clustering of the next round. And 7, outputting and analyzing final optimal feature information. According to the method, scRNA-seq data can be well processed, low-dimensional embedded features can be extracted, tag prediction is more accurate, the requirement for hardware resources is low, and an efficient method is provided for analyzing biological characteristics of single cell data.