Automatic rapid flow cytometer data grouping method

Abstract

The invention provides an automatic rapid flow cytometer data grouping method. The method comprises steps that firstly, the flow cell data is processed through employing a major constituent analysis method, including sub methods that 1), a sample matrix X is standardized to acquire a standardized matrix X*; 2), a correlation coefficient matrix is acquired, characteristic decomposition is further carried out, and characteristic constants lambda1> / =lambda2> / =...> / =lambdap and characteristic vectors a1, a2...ap corresponding to the characteristic constants are acquired; 3), the quantity k of major constituents is determined according to a major constituent variance contribution rate; and 4), according to a characteristic vector U=[lambda1, lambda2...lambdak] corresponding to the k major constituents, a characteristic vector matrix W=X*U of the sample data for the k major constituents is acquired; secondly, the flow cells are clustered through utilizing the improved K-means algorithm to acquire group type labels; thirdly, a major constituent with the largest contribution rate is set as a coordinate axis to draft a scatter diagram; and fourthly, automatic grouping is realized.

Description

technical field [0001] The invention relates to the field of biomedical detection, in particular to a method for fast and automatic grouping of flow cytometer data. Background technique [0002] Flow cytometry has become the most important tool for biological research and clinical diagnosis. Flow cytometry is a multi-parameter, rapid analysis or sorting of suspended cells or other particles. technology. Flow cytometry can detect a variety of physical and chemical properties of a single cell, and at the same time obtain a scattered light signal (SC) representing the cell volume and particle size and a variety of fluorescent pulse signals (FL) representing the content of each antigen from the cell, and extract the signal. Characteristic parameters such as peak value, pulse width and area. The scattered light and fluorescence signals induced by each cell are recorded as a single event, and all events are aggregated into complete flow data of the tested cell population. [00...

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Problems solved by technology

However, the two-dimensional scatter diagram can only analyze the parameters of two dimensions at a time. Since the multi-parameter streaming data has high dimensions and a large amount of data, if the number of streaming data parameters is n, two parameters are randomly selected as horizontal, The ordinate, the number of scatter plots that can be drawn is Usually, in a scatterplot drawn with randomly selected coordinate axis parameters, the distinction of cell subgroups is not obvious, requiring the operator to have a higher level of professional knowledge and select a specific combination of parameters for analysis to obtain a more ideal grouping As a result, the process is cumbersome and time-consuming

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