ChIP-seq results summary

1. ChIP-seq results summary

Let's recap what you have just learned. The previous two exercises have given you a glimps of the later stages of a typical ChIP-seq workflow. In this video we'll talk a bit more about the results you can expect to see from a ChIP-seq analysis and and how this will help us to better understand the mechanisms driving the observed differences between our two groups of samples.

2. High-level differences between samples

One of the first question you want to ask of a dataset like this is whether there is evidence for a systematic difference between groups. This heatmap of sample correlations clearly shows that samples form blocks according to the group they belong to. Notice that group membership is indicated by red and blue bars along the side of the plot. Plots like this one are useful in assessing sample quality. You'll learn more about how to do this in the next chapter.

3. Peak intensity heatmap

Knowing that the correlations between samples conform to the expected pattern is useful but doesn't really tell you much about what the differences between groups are. Looking at the height of individual peaks across samples can be a bit more informative. This heatmap shows different samples as columns and different peaks as rows. The color of each cell corresponds to the hight of that peak. As you can see each group has its own set of high and low-intensity peaks. You'll learn more about how to compare peak sets between groups in Chapter 3.

4. Interpreting ChIP-seq results

To gain a better understanding of what the observed differences in protein binding actually mean it is very helpful to associate observed peaks with genes. This plot visualises the overlap in genes associated with peak calls in the two groups of samples. Each of the vertical bars corresponds to the size of one subset. The dots below the bars indicate which groups these genes were observed in. As you can see, many of the genes that are associated with peaks in one condition don't have any peaks in the other. This provides you with a list of genes for each condition that can serve as a starting point to investigate the real differences between primary and treatment-resistant tumors at a molecular level in much more detail by uncovering common themes among the functions of these genes. You will learn more about how to do that in Chapter 4.

5. Let's practice!

Now it's time to take a closer look at how you do all this in practice.

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