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This article shows you how WGCNA can mine key genes from module. The content is concise and easy to understand. It will definitely make your eyes shine. I hope you can get something through the detailed introduction of this article.

After identifying the modules associated with phenotypic data, genes can be further screened in the modules. To facilitate screening, the following three statistics are defined for each gene

1. Connectivity

In the previous article, we briefly mentioned this concept, which is similar to the concept of degree of nodes in the network, except that in the weighted co-expression network, because each edge represents the correlation between two genes, corresponding to a numerical value, the connectivity of a gene in the co-expression network is defined as the sum of all the edges connected to the gene.

In addition, according to whether the connected gene and the gene are located in the same module, the edges can be divided into two categories, and the gene is in the same module, defined as within, located in different modules, and defined as out. In WGCNA, the degree of connectivity can be calculated by the intramodularConnectivity function, as follows

# calculate the adjacency value between genes ADJ1=abs (cor (datExpr,use= "p")) ^ calculate connectivity Alldegrees1=intramodularConnectivity (ADJ1, colorh2)

The results of the calculation are as follows

> head (Alldegrees1) kTotal kWithin kOut kDiffGene1 31.80186 28.37595 3.425906 24.95005Gene2 28.88249 26.47896 2.403522 24.07544Gene3 25.38600 23.11852 2.267486 20.85103

KTotal represents the degree of connectivity of all edges of the gene, KWithin represents the degree of connectivity of the edges of the gene under the same module, and KOut represents the degree of connectivity of the edges of the gene under different module, so KTotal is the sum of KWithin and KOut, and KDiff represents the difference between KWithin and KOut.

In module, there will be the concept of hub gene. The so-called hub gene is the gene with the highest degree of connectivity under the module. Note that only the edge under the module is considered at this time, which is the KWithin above.

2. Module member-ship

Referred to as MM, by analyzing the correlation between the expression of the gene and the first principal component of module, namely module eigengene, we can get the MM value, so the MM value is essentially a correlation coefficient. If the MM value of the gene and a module is 0, it means that the gene does not belong to this module;. If the absolute value of MM is close to 1, it means that the gene has a high correlation with the module.

In WGCNA, the code to calculate the MM value between the gene and the module is as follows

DatKME = signedKME (datExpr,datME,outputColumnName= "MM.")

The first parameter is the amount of gene expression, and the second parameter is the Module Eigengene value. The results are as follows

> head (datKME) MM.blue MM.brown MM.green MM.grey MM.turquoise MM.yellowGene1 0.6830511 0.11547756-0.007124794 0.2840109 0.9481457 0.09588170Gene2 0.6342657 0.02257975 0.080277091 0.3029967 0.9356343 0.06889483Gene3-0.6198067-0.1253 1203 0.008372054-0.2776929-0.9121710-0.17852211Gene4 0.5966736 0.06469079 0.049862112 0.2671967 0.9052030 0.11707603Gene5 0.6642214 0.14369720-0.017975774 0.2442237 0.9017972-0. 01038067Gene6-0.6018161-0.15167072 0.006667131-0.2053897-0.9192597-0.171389603. Gene signigicancer

Referred to as GS, the correlation between the gene expression and the corresponding phenotypic value is analyzed, and the final correlation coefficient is GS. GS reflects the correlation between gene expression and phenotypic data. The code for calculating GS is as follows.

GS1=as.numeric (cor (yMagneDatExpr, use= "p"))

Through the above three quantitative indicators, it is convenient to screen the genes under module. In general, through the correlation analysis of module and phenotypic data, we can screen the specific modules related to a certain phenotype of interest. When digging genes under this module, we can use MM and GS as filtering means. The example is as follows

FilterGenes= abs (GS1) > .2 & abs (datKME$MM.brown) > .8

Suppose that brown is the phenotypic module we found, and the key genes can be defined as those with a GS value of more than 0.2 and an MM value of more than 0.8 with the brown phenotype. After screening the key genes, their functions can be further explored by functional enrichment analysis.

The above is how WGCNA digs key genes from module. Have you learned any knowledge or skills? If you want to learn more skills or enrich your knowledge reserve, you are welcome to follow the industry information channel.

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