How to use ggpubr

Today, I would like to share with you the relevant knowledge of how to use ggpubr. The content is detailed and the logic is clear. I believe most people still know too much about this knowledge, so share this article for your reference. I hope you can get something after reading this article. Let's take a look at it.

How to define differences:

When it comes to differences, the first thing that comes to mind must be biological differences, such as the multiple of the difference in the expression of the same gene between two samples, which is generally acceptable from 1.2, 1.5 and 2 times (in the transcriptome, it is generally 2 times as a screening index. Editor thinks 1.2, 1.5 is also acceptable).

On the other hand, we should also consider random errors, because we cannot eliminate errors, and perfect-looking data may also be caused by random errors, so we should consider statistical differences in addition to biological differences.

The above two differences exist objectively, of course, we hope that the data difference is caused by experimental processing, but the random error exists objectively, so the smaller the probability of random error, the better.

How to measure random error?

P-value (P-value), which is no stranger to us, is a parameter used to determine the results of hypothesis testing. To put it bluntly, P-value represents a possibility and measures the probability of random errors. In statistics, it is generally required that the P value is less than 0.05; if P-value=0.05, it means that there is a 5% probability that our experimental results are caused by random errors.

We often use this argument p