How to process and correct the batch effect of GEO data in R language
This article Xiaobian introduces in detail for you "R language how to correct GEO data batch effect", the content is detailed, the steps are clear, and the details are handled properly. I hope that this "R language how to deal with correcting GEO data batch effect" article can help you solve your doubts, following the editor's ideas slowly in depth, together to learn new knowledge.
What is batch effect?
Data of different platforms, different periods of the same platform, different reagents of the same sample, and data of different times of the same sample all produce a kind of batch effect. If this effect is widespread, enough attention should be paid to it, otherwise it will lead to the failure of the whole experiment and the final conclusion.
Let me briefly explain what is meant by batch effect. Compared with the experimental group and the control group, the different treatments are disease and non-disease (when sequencing, the disease is first detected, and then normal), and then you get a lot of differentially expressed genes through analysis. Now the question is, whether the result of this differential expression is related to the factor you want to study, or time, in this question, time will become a factor that interferes with the experimental results, and this effect is batch effect.
How to detect whether there is such an effect?
The simplest thing is to record the time variable in the experiment, and then cluster the differentially expressed genes to see if they are all related to time, and if so, to prove the existence of batch effect.
Similarly, if there is a batch effect between data from different platforms, it cannot be easily integrated.
Everyone may ask standardization, will it get rid of batch effect?
The answer is that it can be weakened and cannot be fundamentally eliminated. As shown in the figure below, b is the result of standardization of a, and there is no problem all the time from the sample, but at the genetic level, there is still an obvious clustering by time in the batch effect,d diagram in the c map, it is obvious that the differential expression is mainly caused by time.
Correct batch effect
If we solve this batch problem, we can not only make the experiment more reliable, but also, what is more, we can do joint analysis of multiple chips. There are two ways to correct the batch effect:
1. Use the combat package in sva to correct the batch effect
Here's an example: install the necessary R package and load it, and comat is in the sva package.
# install the package, add images in advance to speed up the installation if (! requireNamespace ("BiocManager", quietly=TRUE)) {install.packages ("BiocManager")} options (BioC_mirror= "https://mirrors.tuna.tsinghua.edu.cn/bioconductor")local({r")
