12P3270X062-233C KJ3203X1-BA1 CE4001S2T2B4 EMERSON distance matrix MODULE

Description

12P3270X062-233C KJ3203X1-BA1 CE4001S2T2B4 EMERSON distance matrix MODUL

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System clustering method is the earliest and most commonly used clustering algorithm in gene chip data analysis research. The specific steps are as follows:
As shown in Figure 2. Each column of CE4001S2T2B4 represents different conditions or samples under different conditions, and each row represents the gene number. The expression level of each gene is represented by the standardized log (R/G) 2.
② Calculate the classification statistics between all genes: calculate the correlation coefficient or distance coefficient between all genes through the module of classification statistics provided by the software.
③ Establish a distance matrix for Gene Gene.
④ Establishing a phylogenetic tree: Based on the score of Gene Gene’s distance matrix, first find the distance

The two genes closest to CE4001S2T2B4 are merged, and then two groups with similar distances are found and merged again until all genes are merged into one group. This software mainly adopts single link method, complete link method, average link method, shortest distance method, longest distance method, intermediate distance method, centroid method, class average method, variable class average method, and sum of squared deviations method.
⑵ CE4001S2T2B4 dynamic clustering method
CE4001S2T2B4 This software uses K-means clustering and K-median clustering methods. The specific algorithm steps are as follows:
① Select a gathering point. This software adopts the method of using any K samples or the first K samples as condensation points and numerical interpolation to find condensation points to select condensation points.
② Initial classification. This software adopts the following methods for initial classification. After selecting a batch of clusters, each cluster forms its own category, and the samples are sequentially classified into the category of the closest cluster. The center of gravity of that category is immediately recalculated to replace the original cluster, and the classification of the next sample is calculated until all samples are classified.

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