# Differential CLIP binding analysis

#### **How many replicates do I need?**

One measure to address this question is to calculate correlation between replicates, which helps to determine the number required to make a statistical analysis meaningful i.e. in the case that your replicates are very variable, then you will need more replicates.

*written by Charlotte Capitanchik* 

#### **How can I control for changes in gene expression between my conditions?**

Within DeSeq2 and EdgeR it is possible to include a covariate in your analysis, enabling you to include an expression dataset alongside the CLIP data. Additionally some tools that were originally developed for MeRIP-Seq appear to also work in the case of CLIP and specifically have the capacity to include an expression dataset (for example [QNB](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-017-1808-4)). The various approaches tested for MeRIP-Seq analysis [(McIntyre et al., 2019)](https://paperpile.com/c/3c2FI2/8eQz) are thus inspiring for CLIP analysis too. In my own hands, I find that the DeSeq2 approach is the most forgiving.

*written by Charlotte Capitanchik* 

**If you compare RNA binding targets of a protein between different tissues, does the data need to be adjusted for differential gene expression across tissues?**

Often, a change in binding targets between tissues will be due to differential gene expression. To try and distinguish whether this is the case, you might want to correlate the log2 fold change in gene expression between two tissues with the log2 fold change in CLIP peak signal. To perform an analysis that adjusts for differential gene expression see above, but this is probably more relevant in a treatment vs. control situation than in a tissue vs. tissue comparison.

*written by Charlotte Capitanchik*<br>


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