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This article mainly explains "how to use edgeR's TMM normalization algorithm". Interested friends may wish to have a look. The method introduced in this paper is simple, fast and practical. Let's let the editor take you to learn how to use edgeR's TMM normalization algorithm.

We all know that the quantitative method of raw count cannot be compared directly between samples. Therefore, in the difference analysis, the original expression data will be normalized.

There are many factors that make raw count unable to compare directly, the most common of which are the following two factors.

1. Sequencing quantity

The composition of 2.RNA

The impact of sequencing quantity on count is easy to understand. The larger the amount of data sequenced, the more reads there will be. How does the composition of RNA affect the expression level?

Due to the tissue specificity and time specificity of RNA, we can not guarantee that the type and quantity of RNA expressed in the two samples are exactly the same. Suppose that the type of RNA in two samples An and B is twice as much as that in A, and the common RNA expression is the same. In the case of the same sequencing amount, the common RNA expression in A will be twice as high as that in B. thus it can be seen that the composition of RNA in different samples will also affect the detected RNA expression.

In normalization, the usual method is to consider only the same RNA between samples, and then eliminate the influence of sequencing quantity.

The normalization algorithm of DESeq2 only considers the genes whose expression is greater than zero in all samples, which is also due to the consideration of the same RNA composition. EdgeR adopts the strategy of reference sample. Firstly, one sample is selected from all the samples as a reference. When normalizing other samples, only the common RNA between the reference sample and the sample to be normalized is considered.

The code for selecting the reference sample is as follows

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