Differential analysis method and system based on single cell samples of mixed experimental group and control group

Abstract

The invention relates to a difference analysis method based on single cell samples of a mixed experimental group and a control group, which comprises the following steps: a, acquiring data, and carrying out quality control and standardization processing on the data, the data comprising experimental data and open source data; b, screening out hypervariant gene data from the standardized data, and carrying out dimensionality reduction on the screened data; c, performing multiple rounds of clustering analysis of different parameters on the single cells according to the data subjected to dimension reduction, and calculating the composition proportion of a cell subset experimental group and a control group of each round of clustering analysis to obtain an optimal result; and d, carrying out difference analysis on the cell subpopulation. The invention further relates to a difference analysis system based on the single cell samples of the mixed experimental group and the control group. According to the invention, the cell subgroup formed by the specific group can be better found, so that the difference analysis of the experimental group and the control group at the single cell level is realized.

Description

technical field [0001] The invention relates to a difference analysis method and system based on single cell samples of mixed experimental group and control group. Background technique [0002] High-throughput single-cell technology is currently one of the hot research directions in the field of bioinformatics, and it has led to a shift in the field of genomics, that is, from the analysis of whole tissues to the detailed and comprehensive study of single cells, providing insights into different organisms. Insights into cellular diversity in tissues. Many current single-cell studies aim to identify cell types (eg, B cells, T cells, and subsets of cells expressing unique genes), with little focus on analyzing experimental (ie, disease) and control groups at the single-cell level The single-cell expression difference of all single cells, and the overall difference analysis of all single cells directly according to the experimental group and the control group, the difference si...

AI Technical Summary

Problems solved by technology

However, there is a problem that has been seldom paid attention to in single-cell research, that is, whether it is possible to find a common expression pattern (that is, to express certain genes) in single cells from disease samples and control samples, and whether the single cells of disease samples or control samples subpopulation of cells

[0007] In tissue-level studies, researchers directly use gene expression data of tissue samples from experimental and control groups for differential analysis, but this analysis method is not feasible at the single-cell level

Images