Depth unsupervised single cell clustering method based on Gaussian mixture graph variational auto-encoder

Abstract

The invention discloses a depth unsupervised single cell clustering method based on a Gaussian mixture graph variational auto-encoder. The method comprises the following steps: initializing a gene regulatory network A by using a protein-protein interaction relationship PPIs (or regulatory element interaction HiChIP); initializing a cell cluster C of each cell by using a K-means method; enabling the gene regulatory network A and single-cell gene expression data X (or regulatory element opening degree data X) to pass through a graph encoder to obtain a hidden layer; obtaining a cell cluster C, and sampling from a Gaussian mixture model GMM to obtain a cell low-dimensional representation Z; predicting a gene regulatory network A by using a decoder; calculating a loss function, performing back propagation to update A and GCN, and repeating the steps until convergence; and outputting a gene regulatory network A, a cell low-dimensional representation Z and a cell cluster C. Clustering of cells and dimensionality reduction of cell expression are completed in the process of constructing the gene regulatory network A.

Description

technical field [0001] The invention relates to the technical field of single-cell clustering, in particular to a deep unsupervised single-cell clustering method based on Gaussian mixture graph variational autoencoder. Background technique [0002] Single-cell sequencing technology refers to a technology for high-throughput sequencing analysis of the genome, transcriptome, and epigenome at the single-cell level. It plays an important role in tumor, developmental biology, microbiology, neuroscience and other fields, and is becoming the focus of life science research. One of the first tasks to be solved in single-cell sequencing is to identify the cell types contained in the sample, that is, to complete cell clustering based on unsupervised algorithms. Single-cell clustering enables the establishment of a comprehensive reference for all cell types at different developmental stages in an organism or tissue, which, in addition to providing a deeper understanding of basic biolog...

AI Technical Summary

Problems solved by technology

However, each method has problems worth noting: chromVAR can only analyze groups of peaks, and lacks the judgment of individual peaks; while scABC relies heavily on label samples with high sequencing depth, and for data with many missing values, Especially for scATAC-seq data, the Spearman correlation coefficient may be misdefined

[0005] In short, most of the input data of existing single-cell clustering methods are cell gene expression or chromatin openness, ignoring the interaction relationship between these genes or open regions, and only using vector form as model input

Second, because the interaction between genes or open regions is not considered, existing methods can only achieve single-cell clustering and cannot simultaneously predict cell-type-specific gene regulatory networks.

Images