Multi-parameter flow cytometer data rapid automatic grouping and gating method

Abstract

The invention provides a multi-parameter flow cytometer data rapid automatic grouping and gating method. The method comprises the following steps: 1) processing flow cytometer data through kernel entropy component analysis; 2) grouping the flow cytometer data through a clustering method based on vector included angle; 3) with a kernel entropy principal component obtained after kernel entropy component analysis being a coordinate axis, drawing a two-dimensional or three-dimensional scatter diagram; and 4) carrying out gating on cells according to category labels obtained through clustering analysis to realize cell grouping.

Description

technical field [0001] The invention relates to the field of biomedical detection, in particular to a fast and automatic grouping and gating method for multi-parameter flow cytometry data. Background technique [0002] With the development of precision medicine and gene biology, Flow Cytometer has become the most important tool for biological research and clinical diagnosis, and is widely used in biological and biomedical research. Flow cytometry data analysis is one of the difficulties in flow cytometry, and its main purpose is to identify and divide subpopulation cells in the sample. When performing multi-parameter flow cytometry data analysis, traditional data analysis methods usually use a two-dimensional scatter diagram that can display the parameters of two measurement channels, and analyze the data by manually setting a gate. The coordinates of the two-dimensional scatter diagram The axis parameter can be forward scatter light (SSC), side scatter light (FSC) or fluor...

AI Technical Summary

Problems solved by technology

[0004] (1) The process is cumbersome and inefficient

Since the two-dimensional scatter diagram drawn by the traditional method can only display two-dimensional parameters at a time, if the number of streaming data parameters is n, two parameters are randomly selected as the horizontal and vertical coordinates, and the scatter diagram may need to be drawn The number is Analysis is inefficient, time-consuming and wastes resources;

[0005] (2) Higher requirements for operators

[0006] (3) The grouping results are subjective, easily affected by the operator's experience, and have poor repeatability

Although the above research has realized the automatic gating of cells through different algorithms, before setting the gating, the operator still needs to manually set the coordinate axis parameters of the scatter plot according to the relevant background knowledge and sample staining strategy, and has not realized automatic clustering in the true sense.

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