A micro-droplet data analysis method and system

Abstract

The invention discloses a micro-droplet data analysis method and a system, which can cluster and analyze the micro-droplet data efficiently and improve the accuracy and stability of the analysis result. The method comprises the following steps: step S1, obtaining m sample data from a micro-droplet sample, wherein m >> 3; Step S2: adopting the K-mean algorithm to classify m sample data and get a Vic signal cluster, fam signal cluster and noise signal cluster; Step S3, calculating a ratio result of the fam signal cluster and the vic signal cluster, and executing step S5 when the ratio result iswithin a threshold range, otherwise, executing step S4; step S4: optimizing and correcting the Vic signal cluster by the K-mean algorithm, or optimizing and correcting the fam signal cluster by the DBSCAN algorithm, and returning to step S3; step S5, outputting a cluster distribution map of the Vic signal cluster, the fam signal cluster and the noise signal cluster, and obtaining a data analysis result of a micro-droplet sample. The system comprises the method proposed in the technical proposal.

Description

technical field [0001] The invention relates to the technical field of biological reaction numerical simulation calculations, in particular to a micro-droplet data analysis method and system. Background technique [0002] Droplet digital PCR technology is a new type of nucleic acid detection method. Its principle is to evenly distribute each sample reaction solution into a large number of emulsion-wrapped micro-droplets, and perform PCR amplification independently in each micro-droplet. Then, the fluorescent signals of the droplets are detected one by one by a method similar to flow cytometry, the number of positive reaction wells and negative reaction wells is determined, and the number of nucleic acid copies in the sample is calculated by the statistical method Poisson distribution. [0003] In the process of analyzing the micro-droplet data, the core step is to cluster the micro-droplets, and then output the analysis results of the micro-droplet data according to the clus...

AI Technical Summary

Problems solved by technology

The existing micro-droplet data analysis methods usually use a two-dimensional scatter diagram that can display the parameters of two measurement channels, and perform cluster analysis on the data in the two-dimensional scatter diagram by manually setting a gate. With the rapid development, the data volume of detection parameters has also increased exponentially, and the traditional analysis method of manually setting gates is no longer able to meet the huge micro-droplet data analysis business.

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