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This article mainly explains "what is the use of DESeq2". The content of the explanation in the article is simple and clear, and it is easy to learn and understand. Please follow the editor's train of thought to study and learn "what is the use of DESeq2"?

For RNA_Seq, obtaining the quantitative results of genes / transcripts is only the first step, only the summary of sequencing data, and the same work can also be obtained directly through the chip.

No matter whether it is chip or sequencing, quantification is only to quantify the expression abundance of transcripts in organisms, and only one quantitative result can not get effective biological conclusions. In order to answer biological questions, follow-up difference analysis is needed.

Because there are many quantitative methods, such as raw count, TPM, RPKM/FPKM and so on, the distribution of expression is different in different quantitative methods, so the software and algorithms used in difference analysis will be different. In this paper, the R packet DESeq2 is introduced, and the difference analysis is mainly based on the quantitative results of raw count.

DESeq2 requires that the input quantitative result is in the form of raw count. Raw count is actually calculated according to the reads number, so it must be all integers.

Because the sequencing quantity of different samples is not the same, the raw count can not be compared directly between samples, that is to say, the raw count of the same gene in sample An is larger than the raw count in sample B, which does not mean that the expression level in An is high. The large number may be caused by the large number of reads sequenced in sample A.

In order to analyze the differences between samples, it is necessary to normalize the original raw count expression data. In DESeq2, a coefficient, called sizefactor, is calculated for each sample through the estimateSizeFactors function, as shown below

Dds dds sizeFactors (dds) sample1 sample2 sample3 sample41.010543 1.033960 1.023026 1.001038

The specific calculation process is as follows:

Each row of the original expression matrix represents a gene, and each column represents a sample, which is expressed by counts. After the log conversion, the mean value of each gene in all samples is calculated. The code is as follows

Loggeomeans 0]))

It should be noted that when calculating the median, the genes are filtered and the following two conditions need to be met.

1. The expression of the gene was more than 0 in this sample.

two。 The expression of the gene was greater than 0 in all samples, and the sum after log was not 0.

In fact, the second condition already contains the first condition, in the original expression matrix, there must be genes with zero expression in some samples, so in the final calculation of the median, only some genes will be used.

After the sizefactor of each sample is calculated, the original expression of the sample is divided by the sizefactor of the sample, and the normalized expression is obtained.

For the normalization of raw count, the essence is to eliminate the influence of different total sequencing amount of different samples, and reflect on the expression matrix, that is, the sum of each column is different. There is a linear relationship between the sizefactor calculated by DESeq2 and the sum of each column, as shown below

So sizefactors can be used for normalization.

Thank you for your reading, the above is the content of "what is the use of DESeq2". After the study of this article, I believe you have a deeper understanding of what the use of DESeq2 is, and the specific use needs to be verified in practice. Here is, the editor will push for you more related knowledge points of the article, welcome to follow!

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