Frequency heatmap in r


  • Frequency Heatmaps Usage
  • Create Heatmap in R (3 Examples) | Base R, ggplot2 & plotly Package
  • R-bloggers
  • Building Heatmaps in R with ggplot2 package
  • How do I use it? Enter your SNP without the haplogroup prefix and hit enter or click the button. Q: My SNP didn't load anything. For my haplogroup research, J2b, we rely on the YFull tree because it's the only tree that contains samples from all testing companies, including many samples from scientific studies conducted in otherwise undersampled regions. Q: Is the highest intensity area the origin of the haplogroup?

    A: Possibly but not necessarily. A relative frequency heatmap is just that - it isn't implying any theoretical migration between points but a haplogroup researcher may be able to draw certain conclusions based on their deeper knowledge of upstream and related lineages. A relative frequency heatmap may be able to provide a better understanding of the approximate origin of a subclade that has many samples, some of which are in more heavily sampled regions, some in less sampled regions.

    Because regional sampling rate has been factored out of the equation, you can look at the map to get a better idea of the true relative frequency of your lineage than you would be able to from just looking at a list of samples on a tree and doing a daunting amount of mental math.

    Understanding Founder Effects One important pitfall to avoid are founder effects. This is when one man, many years after the founding of the subclade you are looking at, had a multitude of sons. The sheer number of this mans sons can make the relative frequency for where they live higher than the actual origin of their more distant ancestor.

    My haplogroup J-L has a relative high frequency around Mordovia due to a single man having migrated there around years ago.

    Their descendants are now Mokshas and Mishar Tatars. Recognizing Founder Effects It's not possible to determine founder effects by simply looking at the frequency heatmap absent knowledge of the samples.

    To identify a possible founder effect in your haplogroup, zoom in to see the individual samples. Click on the samples to see their position on the YFull tree. You are looking at a founder effect if all the samples are below one particular lineage of your haplogroup. Zooming in or Mordovia and clicking the samples, I find they are all downstream J-Y This is how you can determine that the high frequency is caused by a founder effect.

    Founder Effect is Obscuring My Haplogroup An extreme local maximum caused by a founder effect can sometimes result in other samples contributing a very faint, near invisible blue. To make the regional differentiation of these fainter areas clearer, increase the intensity.

    Increase or decrease the intensity. By increasing the intensity more areas of the map will become red. Note that, as you increase the intensity, more areas of the map will depict the same red hue though they are not truly equal in relative frequency. This is fine if you don't care about that and are more interested in seeing regional differentiation elsewhere. A Type of Heatmap Free of Founder Effects I have an idea for a different type of heatmap calculated almost the same as a relative frequency heatmap but that gives samples under each sibling equal total weight.

    This would no longer be a true relative frequency heatmap but would be immune to founder effects. Q: A sample from one region is contributing much more intensity than the others. Is this a bug? If each sample is given the same weight, the result is not a relative frequency heatmap but a frequency heatmap. Frequency heatmaps will have higher intensity in areas where people live and do not depict the true relative frequency of a haplogroup, which is more interesting to know.

    Hovering over Austria and India you can see India is sampled at over 6 times less per capita. So one Indian, on average, equals about six Austrians. Low Sample Size? If you don't have very many samples you will have a less reliable relative frequency heatmap.

    To go up a level in your tree, hit the back button below the text input field. Thank You Thank you to YFull for letting me use their tree and samples to compute the heatmaps. Thanks all the YFull customers for having tested and for contributing to public haplogroup research. Special thanks to Thomas Krahn of YSEQ whose idea it was for me to create the heatmaps and who has provided significant ideas and financially supported the development.

    Or if you are already a satisfied customer, consider recommending their services to your friends. You'll be tested down to the most recently discovered and testable SNP. The YFull YTree is the tree of mankind.

    The more who test and get on this tree, the greater our knowledge of where our ancestors lived and how we are all related to one another.

    Figure 1: Default Heatmap in Base R. Figure 1 illustrates the output of the previous R code. By default, the heatmap function returns a heatmap with column and row names as well as a dendrogram.

    Furthermore, we can modify the colors of the heatmap by specifying our own color range with the colorRampPalette function. The ggplot2 package requires a long data format. We can create this data format with the reshape packageā€¦ install. As you can see, the melt function created the two columns X1 and X2, which are containing every possible row and column combination, and a third column with the name value, which is containing the corresponding values.

    In order to draw a heatmap with the ggplot2 package, we also need to install and load ggplot2: install. As you can see based on Figure 4, the patter of the heatmap cells is the same as in Base R. However, the general layout is in the typical ggplot2 style. Of cause, ggplot2 also provides options for the modification of our heatmap. Again, the patter is the same, but the general plot style is different. The plotly package also provides additional options for the modification of the heatmap.

    As you can see based on Figure 7, the Greys specification created a heatmap in greyscale. Note that the plotly package show its graphics in the RStudio viewer instead of the RStudio plot window.

    For that reason you need to export these plots differently. Also note that there are many other packages for the creation of heatmaps in R available. In my opinion, however, Base R, ggplot2, and plotly provide the best solutions.

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    Frequency Heatmaps Usage

    By default, the heatmap function returns a heatmap with column and row names as well as a dendrogram. Furthermore, we can modify the colors of the heatmap by specifying our own color range with the colorRampPalette function. The ggplot2 package requires a long data format. We can create this data format with the reshape packageā€¦ install.

    As you can see, the melt function created the two columns X1 and X2, which are containing every possible row and column combination, and a third column with the name value, which is containing the corresponding values. In order to draw a heatmap with the ggplot2 package, we also need to install and load ggplot2: install.

    Create Heatmap in R (3 Examples) | Base R, ggplot2 & plotly Package

    As you can see based on Figure 4, the patter of the heatmap cells is the same as in Base R. However, the general layout is in the typical ggplot2 style. Of cause, ggplot2 also provides options for the modification of our heatmap. Again, the patter is the same, but the general plot style is different. The plotly package also provides additional options for the modification of the heatmap. Q: My SNP didn't load anything. For my haplogroup research, J2b, we rely on the YFull tree because it's the only tree that contains samples from all testing companies, including many samples from scientific studies conducted in otherwise undersampled regions.

    Q: Is the highest intensity area the origin of the haplogroup? A: Possibly but not necessarily. A relative frequency heatmap is just that - it isn't implying any theoretical migration between points but a haplogroup researcher may be able to draw certain conclusions based on their deeper knowledge of upstream and related lineages.

    A relative frequency heatmap may be able to provide a better understanding of the approximate origin of a subclade that has many samples, some of which are in more heavily sampled regions, some in less sampled regions. Because regional sampling rate has been factored out of the equation, you can look at the map to get a better idea of the true relative frequency of your lineage than you would be able to from just looking at a list of samples on a tree and doing a daunting amount of mental math.

    Understanding Founder Effects One important pitfall to avoid are founder effects. This is when one man, many years after the founding of the subclade you are looking at, had a multitude of sons. The sheer number of this mans sons can make the relative frequency for where they live higher than the actual origin of their more distant ancestor.

    My haplogroup J-L has a relative high frequency around Mordovia due to a single man having migrated there around years ago. Their descendants are now Mokshas and Mishar Tatars. Recognizing Founder Effects It's not possible to determine founder effects by simply looking at the frequency heatmap absent knowledge of the samples.

    R-bloggers

    To identify a possible founder effect in your haplogroup, zoom in to see the individual samples. Click on the samples to see their position on the YFull tree. You are looking at a founder effect if all the samples are below one particular lineage of your haplogroup. Zooming in or Mordovia and clicking the samples, I find they are all downstream J-Y This is how you can determine that the high frequency is caused by a founder effect.

    Founder Effect is Obscuring My Haplogroup An extreme local maximum caused by a founder effect can sometimes result in other samples contributing a very faint, near invisible blue.

    Building Heatmaps in R with ggplot2 package

    To make the regional differentiation of these fainter areas clearer, increase the intensity. Increase or decrease the intensity. By increasing the intensity more areas of the map will become red.


    thoughts on “Frequency heatmap in r

    • 15.08.2021 at 11:42
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      It not absolutely approaches me. Perhaps there are still variants?

      Reply
    • 17.08.2021 at 18:18
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      I will know, many thanks for an explanation.

      Reply
    • 20.08.2021 at 22:24
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      Magnificent phrase and it is duly

      Reply

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