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Experimental planning considerations

Understanding the steps in the experimental process of RNA extraction and preparation of RNA-Seq libraries is helpful for designing an RNA-Seq experiment, but there are special considerations that should be highlighted that can greatly affect the quality of a differential expression analysis.

These important considerations include:

  1. Number and type of replicates
  2. Avoiding confounding
  3. Addressing batch effects

We will go over each of these considerations in detail, discussing best practice and optimal design.

Replicates

Experimental replicates can be performed as technical replicates or biological replicates.

Image credit: Klaus B., EMBO J (2015) 34: 2727-2730

In the days of microarrays, technical replicates were considered a necessity; however, with the current RNA-Seq technologies, technical variation is much lower than biological variation and technical replicates are unneccessary.

In contrast, biological replicates are absolutely essential. For differential expression analysis, the more biological replicates, the better the estimates of biological variation and the more precise our estimates of the mean expression levels. This leads to more accurate modeling of our data and identification of more differentially expressed genes.

Image credit: Liu, Y., et al., Bioinformatics (2014) 30(3): 301–304

As the figure above illustrates, biological replicates are of greater importance than sequencing depth. The figure shows the relationship between sequencing depth and number of replicates on the number of differentially expressed genes identified [1]. Note that an increase in the number of replicates tends to return more DE genes than increasing the sequencing depth. Therefore, generally more replicates are better than higher sequencing depth, with the caveat that higher depth is required for detection of lowly expressed DE genes and for performing isoform-level differential expression.

Replicates are almost always preferred to greater sequencing depth for bulk RNA-Seq. However, guidelines depend on the experiment performed and the desired analysis. Below we list some general guidelines for replicates and sequencing depth to help with experimental planning:

Confounding

A confounded RNA-Seq experiment is one where you cannot distinguish the separate effects of two different sources of variation in the data.

For example, we know that sex has large effects on gene expression, and if all of our control mice were female and all of the treatment mice were male, then our treatment effect would be confounded by sex. We could not differentiate the effect of treatment from the effect of sex.

To AVOID confounding:

Batch effects

Batch effects are a significant issue for RNA-Seq analyses, since you can see significant differences in expression due solely to the batch effect.

Image credit: Hicks SC, et al., bioRxiv (2015)

How to know whether you have batches?

If any of the answers is ‘No’, then you have batches.

Best practices regarding batches:


Exercise

Your experiment has three different treatment groups, A, B, and C. Due to the lengthy process of tissue extraction, you can only isolate the RNA from two samples at the same time. You plan to have 4 replicates per group.

  1. Fill in the RNA isolation column of the metadata table. Since we can only prepare 2 samples at a time and we have 12 samples total, you will need to isolate RNA in 6 batches. In the RNA isolation column, enter one of the following values for each sample: group1, group2, group3, group4, group5, group6. Make sure to fill in the table so as to avoid confounding by batch of RNA isolation.

  2. BONUS: To perform the RNA isolations more quickly, you devote two researchers to perform the RNA isolations. Fill in their initials to the researcher column for the samples they will prepare: use initials AB or CD.

sample treatment sex replicate RNA isolation
sample1 A F 1  
sample2 A F 2  
sample3 A M 3  
sample4 A M 4  
sample5 B F 1  
sample6 B F 2  
sample7 B M 3  
sample8 B M 4  
sample9 C F 1  
sample10 C F 2  
sample11 C M 3  
sample12 C M 4