Recap and Future Directions in Chip-Seek Analysis

The previous two exercises have given us a glimpse of the later stages of a typical gypsy growth flow. In this video, we'll delve deeper into the results you can expect to see from a chip-seq analysis and how this will help us better understand the mechanisms driving the observed differences between our two groups of samples.

One of the first questions you want to ask of a data set like this, where there is evidence for a systematic difference between groups, is what does this heat map of Sopra correlations clearly show? The answer is 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. This type of plot is useful in assessing sample quality, as we'll learn more about in the next chapter.

Knowing that the correlations between samples conform to the expected patterns is useful, but it doesn't really tell you much about what the differences between groups are. Looking at a heatmap of individual peaks across samples can be a bit more informative. This heat map shows different samples as columns and different peaks as rows, with the color of each peak corresponding to the height of that peak. As we can see, each group has its own set of high and low intensity peaks.

This type of plot will help us gain a better understanding of what the observed differences in protein binding actually mean. It's very helpful to associate observed peaks with genes, as this provides valuable information about the molecular mechanisms underlying these differences. The next chapter will cover how to compare peaks between groups, which is essential for uncovering real differences between primary and treatment-resistant tumors at a molecular level.

The plot we're looking at visualizes overlap in genes associated with peak odds in the two groups of samples. Each vertical bar corresponds to the size of one subset, while the dot below the bars indicates which group these genes were observed in. As we can see, many of the genes that are associated with peaks in one condition don't have any presence in the other. This provides us with a list of genes for each condition that can serve as a starting point to investigate real differences between primary and treatment-resistant tumors at a molecular level.

By uncovering common themes among the functions of these genes, we'll learn more about how to do this in chapter 4. Now it's time to take a closer look at how to interpret the results from our chip-seq analysis.

"WEBVTTKind: captionsLanguage: enlet's recap what you've just learned the previous two exercises have given you a glimpse of the later stages of a typical gypsy growth flow in this video we'll talk a bit more about the results you can expect to see from a chip seek analysis and how this will help us to better understand the mechanisms driving the observed differences between our two groups of samples one of the first questions you want to ask of a data set like this where there is evidence for a systematic difference between groups this heat map of sopra 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 learn more about how to do this in the next chapter knowing that the correlations between samples conform to the expected patterns is useful but doesn't really tell you much about what the differences between groups are looking at a height of individual Peaks across samples can be a bit more informative this heat map shows different samples as columns and different peaks as rows the color of each sir corresponds to the height 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 Peaks between groups in Chapter three 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 visualizes overlap in genes associated with peak odds in the two groups of samples each of the vertical bars corresponds to the size of one sub set the dot below the bars indicates 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 piece in the other this provides you a list of genes for each condition that can serve as a starting point to investigate 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 now it's time to take a closer look how to do all thelet's recap what you've just learned the previous two exercises have given you a glimpse of the later stages of a typical gypsy growth flow in this video we'll talk a bit more about the results you can expect to see from a chip seek analysis and how this will help us to better understand the mechanisms driving the observed differences between our two groups of samples one of the first questions you want to ask of a data set like this where there is evidence for a systematic difference between groups this heat map of sopra 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 learn more about how to do this in the next chapter knowing that the correlations between samples conform to the expected patterns is useful but doesn't really tell you much about what the differences between groups are looking at a height of individual Peaks across samples can be a bit more informative this heat map shows different samples as columns and different peaks as rows the color of each sir corresponds to the height 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 Peaks between groups in Chapter three 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 visualizes overlap in genes associated with peak odds in the two groups of samples each of the vertical bars corresponds to the size of one sub set the dot below the bars indicates 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 piece in the other this provides you a list of genes for each condition that can serve as a starting point to investigate 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 now it's time to take a closer look how to do all the\n"