Method for rapidly and automatically identifying cell subsets of streaming data

Abstract

The invention discloses a method for rapidly and automatically identifying cell subsets of flow cytometry data (streaming data for short). According to the method disclosed by the invention, automatic cell subset identification is realized by mapping the streaming data to a matrix and classifying the location points of the matrix, and the method specifically comprises the following steps: projecting the data into a 128*128 matrix according to a two-dimension analysis mode of the streaming data, thereby obtaining an initialization matrix of the data; classifying the location points of the matrix by adopting a cyclic maximizing method, and finally, mapping the location point classification result back to the original data, thereby obtaining the final cell subset classification result. According to the method disclosed by the invention, the cell subsets in the streaming data can be rapidly identified, the data analysis efficiency is improved, and the influence of artificial subjective factors on the analysis result is avoided. The method for identifying the cell subsets of streaming data is accurate in result, short in analysis time and high in analysis efficiency and can be applied to the current automatic streaming data analysis.

Description

technical field [0001] The invention relates to an automatic analysis technology of flow data, in particular to a method for quickly identifying cell subgroups in the flow data. technical background [0002] Flow cytometry is a technique that can accurately and quickly perform multi-parameter quantitative analysis of the physicochemical and biological properties of biological cells and sort specific cell groups. The principle is to excite the hydrodynamically focused cells one by one with a micron-scale laser beam, collect and record the multi-angle scattered light and multi-wavelength labeled fluorescent signals induced by each cell, and analyze the data of the cell population through multiple optical channels The cluster analysis of the method realizes the high-precision quantitative detection of samples. Typically, the scattered light and fluorescence signals induced by individual cells are recorded as individual events, all of which are aggregated into a complete flow p...

AI Technical Summary

Problems solved by technology

Aiming at this problem, a modified spectral clustering method is currently proposed, which solves the problem of long calculation time caused by large sample size to a certain extent, but due to the pre-processing of the data, part of the biological information contained in the data may be lost during processing

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