This strategy is useful if you suspect the population is inbreeding Jerome Goudet, personal communication. These shuffling schemes have been implemented for the index of association, but there may be other summary statistics you can use shufflepop for.
All you have to do is use the function replicate. You could use this method to replicate the resampling times and then create a histogram to visualize a distribution of what would happen under different assumptions of panmixia. Phylogenetically uninformative loci are those that have only one sample differentiating from the rest.
This can lead to biased results when using multilocus analyses such as the index of association Brown, Feldman, and Nevo ; Smith et al. These nuisance loci can be removed with the following function. Essentially, this means that any locus with fewer than 2 observations differing will be removed. The user can also specify a fraction of observations for the cutoff eg.
Now what happens when you have all informative loci. We"ll use the nancycats data set, which has microsatellite loci. It is important to note that this is searching for loci with a specified genotype frequency as fixed heterozygous sites are also uninformative:. In populations with mixed sexual and clonal reproduction, it common to have multiple samples from the same population that have the same set of alleles at all loci.
Here, we introduce tools for tracking MLGs within and across populations in genind , and genlight objects from the adegenet package. Note that genclone and snpclone objects are optimal for these analyses. Counting the number of MLGs in a population is the first step for these analyses as they allow us to see how many clones exist.
With the genclone object, This information is already displayed when we view the object. If we need to store the number of MLGs as a variable, we can simply run the mlg command. Since the number of individuals exceeds the number of multilocus genotypes, we conclude that this data set contains clones. Let"s examine what populations these clones belong to.
Since you have the ability to define hierarchical levels of your data set freely, it is quite possible to see some of the same MLGs across different populations.
Tracking them by hand can be a nightmare with large data sets. Luckily, mlg. Analyze the MLGs that cross populations within your data set. This has three output modes. Alternate outputs are described with indexreturn and df. The vector for this flag can be produced by this function as you will see later in this vignette. You can use these in the mlgsub flag, or you can use them to subset the columns of an MLG table. This is useful for making graphs in ggplot2.
Should the populations be printed to screen as they are processed? We can see what MLGs cross different populations and then give a vector that shows how many populations each one of those MLGs crosses. The output of this function is a list of MLGs, each containing a vector indicating the number of copies in each population.
We"ll count the number of populations each MLG crosses using the function sapply to loop over the data with the function length. We can also create a table of MLGs per population as well as bar graphs to give us a visual representation of the data. This is achieved through the function mlg. This function will produce a matrix containing counts of MLGs columns per population rows. If there are not populations defined in your data set, a vector will be produced instead.
If TRUE , a bar plot will be printed for each population with more than one individual. An example of a bar-chart produced by mlg. Note that this data set would produce several such charts but only the chart for Norway is shown here. The MLG table is not limited to use with poppr. In fact, one of the main advantages of mlg. One example is to create a rarefaction curve for each population in your data set giving the number of expected MLGs for a given sample size. For the sake of this example, instead of drawing a curve for each of the 37 countries represented in this sample, let"s set the hierarchical level to year.
The minimum value from the base function rowSums of the table represents the minimum common sample size of all populations defined in the table.
Alone, the different functionalities are neat. Combined, we can create interesting data sets. All we have to do is use the sublist flag in the function:. OK, the output tells us that there are three MLGs that are crossing between these populations, but we do not know how many are in each. We can easily find that out if we subset our original table, v.
Now we can see that Norway has a higher incidence of nearly all of these MLGs. We can investigate the incidence of these MLGs throughout our data set. One thing that the genclone object keeps track of is a single vector defining the unique multilocus genotypes within the data. These are represented as integers and can be accessed with mlg. This is useful for finding MLGs that correspond to certain individuals or populations. The integers produced are the MLG assignment of each individual in the same order as the data set.
This means that the first two individuals have the exact same set of alleles at each locus, so they have the same MLG: If we look at the number of unique integers in the vector, it corresponds to the number of observed multilocus genotypes:. We can use this vector to show us the 22 individuals. Note that there is an alternative way to list individuals matching specific MLGs using the function mlg. This function will return a list where each element represents a unique MLG. You can use this data to find out which individuals correspond to specific MLGs.
Each element in the list is named with the MLG, but the index does not necessarily match up, so it is important to convert your query MLGs to strings:. We can also use the vector of MLGs to subset mlg. Poppr utilizes ggplot2 to produce many of its graphs.
One advantage it gives the user is the ability to manipulate these graphs. With base R graphs, the only manipulation that can be performed is by adding elements to the graph. It is a static image. The ggplot graphs are actually represented as objects in your R environment. One common need is to change the title. We can easily do that with the function ggtitle. We would use ggtitle Aphanomyces euteiches multilocus genotype distribution. Unfortunately, we need italics for a latin binomial.
One way to acheive this is by using the expression function and declaring which text needs to be italicized. Let"s say we wanted to remove the grey background. The x axis labels are now horizontal when they should be vertical.
We can remove them with axis. And, if for some bizarre reason, you liked the color gradient in poppr version 1, you can get that back by adding the fill aesthetic:. This allows you to produce publication quality graphs directly in R. R has the ability to produce nice graphics from most any type of data, but to get these graphics into a report, presentation, or manuscript can be a bit challenging.
It"s no secret that the R Documentation pages are a little difficult to interpret, so I will give the reader here a short example on how to export graphics from R. Note that any code here that will produce images will also be present in other places in this vignette. Before you export graphics, you have to ask yourself what they will be used for. If you want to use the graphic for a website, you might want to opt for a low-resolution image so that it can load quickly.
With printing, you"ll want to make sure that you have a scalable or at least a very high resolution image. Here, I will give some general guidelines for graphics note that these are merely suggestions, not defined rules. What you see is not always what you get I have often seen presentations where the colors were too light or posters with painfully pixellated graphs.
Think about what you are going to be using a graphic for and how it will appear to the intended audience given the media type. For simple black and white line images, dpi is better. This will leave you with crisp, professional looking images.
If possible, save to SVG, then rasterize Raster images bmp, png, jpg, etc… are based off of the number of pixels or dots per inch it takes to render the image. This means that the raster image is more or less a very fine mosaic. Vector images SVG are built upon several interconnected polygons, arcs, and lines that scale relative to one another to create your graphic.
With vector graphics, you can produce a plot and scale it to the size of a building if you wanted to. Before saving, make sure the units and dimensions are correct Unless you really wanted to save a graph that"s over 6 feet wide. Often times, fine details such as labels on networks need to be tweaked by hand. Luckily, there are a wide variety of programs that can help you do that. Here is a short list of image editors both free and for a price that you can use to edit your graphics.
Saving a plot with ggplot2 is performed with one command after your plot has rendered:. Note that you can name the file anything, and ggsave will save it in that format for you. The details are in the documentation and you can access it by typing help ggsave in your R console.
The important things to note are that you can set a width , height , and unit. The only downside to this function is that you can only save one plot at a time. If you want to be able to save multiple plots, read on to the next section. Some of the functions that poppr offers will give you multiple plots, and if you want to save them all, using ggsave will require a lot of tedious typing and clicking. Luckily, R has Functions that will save any plot you generate in nearly any image format you want.
You can save in raster images such as png, bpm, and jpeg. You can also save in vector based images such as svg, pdf, and postscript. For raster images and svg files, you can only save your plots in multiple files, but pdf and postscript plots can be saved in one file as multiple pages. All of these functions have the same basic form. You call the function to specify the file type you want eg. Let"s give an example saving to pdf and png files.
If you wanted to do the same thing, but place them all in one file, you should use the pdf option. Remember, it is important not to forget to type dev. Note that I did not have to specify a resolution for this image since it is based off of vector graphics.
Agapow, Paul-Michael, and Austin Burt. Brown, A. Feldman, and E. Bruvo, Ruzica, Nicolaas K. Michiels, Thomas G. Everhart, Sydney, and Harald Scherm. Goss, Erica M. Tabima, David E. Cooke, Silvia Restrepo, William E. Fry, Gregory A. Forbes, Valerie J. Fieland, Martha Cardenas, and Niklaus J. Goodwin, Michael G. Milgroom, and William E. Heck, Kenneth L. Hurlbert, S H. Jombart, Thibaut. Kamvar, Zhian N. Tabima, and Niklaus J. Ludwig, J. The modified EMMA source code can be downloaded here.
Integration of genomic prediction was implemented through CMLM to improve prediction accuracy. Genomic prediction is the terminology used for prediction of disease risk in humans. In plant and animal breeding, the genomic prediction is known as genomic selection. GAPIT estimates genomic breeding values as well as their prediction accuracy.
GAPIT implements additional strategies to handle large genotypic data sets. By subdividing the genotypic data into multiple smaller files, the memory requirement of GAPIT remains constant.
GAPIT reports detailed results in a series of tables and graphs e. Poppr version 2. What is poppr? Additionally, if you use any following functionalities: minimum spanning networks with reticulation collapsing multilocus genotypes into multilocus lineages with mlg.
Contributing Please note that this project is released with a Contributor Code of Conduct. Community Contributing guide Code of conduct.
Citation Citing poppr. Developers Zhian N. Kamvar Maintainer, author Javier F.
0コメント