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How to use Mfuzz for time series expression pattern clustering analysis, many novices are not very clear about this, in order to help you solve this problem, the following editor will explain in detail for you, people with this need can come to learn, I hope you can get something.

In the previous article, we introduced the STEM software, which can be used for gene expression pattern clustering analysis based on time series data. Here is another R-packet Mfuzz with the same functionality. The address of this R packet is as follows

Https://www.bioconductor.org/packages/release/bioc/html/Mfuzz.html

Mfuzz uses a new clustering algorithm fuzzy c-means algorithm, which is called soft clustering algorithm in the literature. Compared with hard clustering algorithms such as K-means, it reduces the interference of noise to clustering results to a certain extent, and this algorithm effectively defines the relationship between genes and cluster, that is, whether genes belong to a certain cluster, the corresponding value is memebership.

For analysis, we only need to provide the data of gene expression. It should be noted that Mfuzz defaults that the data you provide is the normalized expression, which means that the expression must be directly compared between samples. For the two quantitative methods of FPKM and TPM, they can be compared directly between samples, but for the quantitative results of count, we must first normalize them. You can use edgeR or DESeq to get the normalized data for subsequent analysis.

Assuming that you already have the normalized expression, you can get the clustering results through the following steps

1. Pretreatment

Preprocessing includes reading data and removing genes that are too low in expression or change too little between different time points. The code is as follows

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