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Data_Preparation.html
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Part I
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<a href="Introduction.html">Introduction</a>
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<a href="Getting_ready_to_use_R.html">Getting ready to use R</a>
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Part II
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<a href="Data_Preparation.html">Data preparation</a>
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<a href="First_Steps.html">First steps</a>
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<li>
<a href="Population_Strata.html">Population strata and clone correction</a>
</li>
<li>
<a href="Locus_Stats.html">Locus-based statistics and missing data</a>
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<li>
<a href="Genotypic_EvenRichDiv.html">Genotypic evenness, richness, and diversity</a>
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<li>
<a href="Linkage_disequilibrium.html">Linkage disequilibrium</a>
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<li>
<a href="Pop_Structure.html">Population structure</a>
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<li>
<a href="Minimum_Spanning_Networks.html">Minimum Spanning Networks</a>
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<li>
<a href="AMOVA.html">AMOVA</a>
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<a href="DAPC.html">Discriminant analysis of principal components (DAPC)</a>
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Part III
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<a href="intro_vcf.html">Population genomics and HTS</a>
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<a href="reading_vcf.html">Reading VCF data</a>
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<a href="analysis_of_genome.html">Analysis of genomic data</a>
</li>
<li>
<a href="gbs_analysis.html">Analysis of GBS data</a>
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<a href="clustering_plot.html">Clustering plot</a>
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Workshops
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<a href="workshop_icpp.html">Preparation</a>
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<a href="intro_vcf.html">Introduction</a>
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<a href="reading_vcf.html">VCF data</a>
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<a href="quality_control.html">Quality control</a>
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<a href="gbs_analysis.html">Analysis of GBS data</a>
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<a href="analysis_of_genome.html">Analysis of genome data</a>
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<a href="reading_vcf.html">VCF data</a>
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<a href="gbs_analysis.html">Analysis of GBS data</a>
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About
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<a href="Authors.html">Authors</a>
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Appendices
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<a href="intro_to_R.html">Introduction to R</a>
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<a href="funpendix.html">Function glossary</a>
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<a href="background_functions.html">Background_functions</a>
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<h1 class="title toc-ignore">Data Preparation</h1>
<h3 class="subtitle"><em>ZN Kamvar, SE Everhart and NJ Grünwald</em></h3>
</div>
<p>This section will briefly go over the basics of data import into <em>poppr</em>. For this section, we will focus on the GenAlEx format. Other formats are supported and details are given in the R help page for the <em>adegenet</em> function <code>import2genind</code>. We will show examples of haploid, diploid, and polyploid data sets and show you how you can format your data if it’s grouped into multiple stratifications.</p>
<blockquote>
<p><a href="Data_sets.html">Appendix 1</a> provides an overview and description of the data sets used in this primer.</p>
</blockquote>
<div id="importing-data" class="section level2">
<h2>Importing data</h2>
<div id="genalex-formatted-data" class="section level3">
<h3>GenAlEx formatted data</h3>
<p>To start simple, let’s examine the input file for the <code>monpop</code> data set containing 694 isolates of the plant pathogen <em>Monilinia fructicola</em> genotyped over 13 haploid microsatellite loci <span class="citation">(Everhart & Scherm, 2015)</span>. The data set is called <strong>monpop.csv</strong>.</p>
<blockquote>
<p><a class="btn btn-info btn-sm" target = "blank" href="https://github.com/grunwaldlab/Population_Genetics_in_R/blob/master/monpop.csv" role="button">view</a> or <a class="btn btn-primary btn-sm" target = "blank" href="http://grunwaldlab.github.io/Population_Genetics_in_R/monpop.csv" role="button"> download</a> monpop.csv</p>
</blockquote>
<blockquote>
<p>Note that <code>*.csv</code> stands for comma-separated-value. This can be opened in a text editor or excel. <code>*.csv</code> is a good format for portability into R.</p>
</blockquote>
<p>To import GenAlEx formatted data into <em>poppr</em>, you should use the function <code>read.genalex</code>. Below is an example using <strong>monpop.csv</strong>.</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1"></a><span class="kw">library</span>(<span class="st">"poppr"</span>)</span>
<span id="cb1-2"><a href="#cb1-2"></a>monpop <-<span class="st"> </span><span class="kw">read.genalex</span>(<span class="st">"~/Downloads/monpop.csv"</span>)</span>
<span id="cb1-3"><a href="#cb1-3"></a>monpop</span></code></pre></div>
<pre><code>##
## This is a genclone object
## -------------------------
## Genotype information:
##
## 264 multilocus genotypes
## 694 haploid individuals
## 13 codominant loci
##
## Population information:
##
## 1 stratum - Pop
## 12 populations defined -
## 7_09_BB, 26_09_BB, 26_09_FR, ..., 45_10_FR, 26_11_BB, 26_11_FR</code></pre>
<blockquote>
<p>There are other options for genalex formatted data, such as xy coordinates. We won’t cover the other options, but type <code>help("read.genalex")</code> in your R console for details.</p>
</blockquote>
</div>
<div id="other-data-formats" class="section level3">
<h3>Other data formats</h3>
<p>Given dependence of <em>poppr</em> on <em>adegenet</em>, users can import data from the following formats</p>
<ul>
<li><strong>FSTAT</strong> (file.dat)</li>
<li><strong>GENETIX</strong> (file.gtx)</li>
<li><strong>GENEPOP</strong> (file.gen)</li>
<li><strong>STRUCTURE</strong> (file.str)</li>
</ul>
<p>The <em>adegenet</em> function <code>import2genind</code> will import all of these formats. If you have sequence data, you can use the <code>read.FASTA</code> function from the <em>ape</em> package. If your data is in any other format, type <code>help("df2genind")</code> for guidance.</p>
</div>
</div>
<div id="genalex-data-format" class="section level2">
<h2>GenAlEx data format</h2>
<p><a href="http://biology-assets.anu.edu.au/GenAlEx/Welcome.html">GenAlEx</a> is a very popular add-on for Microsoft Excel. It is relatively easy to use because of its familiar, menu-driven interface. It also gives the user the option to include information on population groupings, regional groupings, and xy coordinates. The flexibility of this format made it a clear choice for import into <em>poppr</em>.</p>
<p>The data format is standard in that <em>individuals are defined in the rows</em> and <em>loci are defined in the columns</em>. The first two rows are reserved for metadata and the first two columns are reserved for the individual names and population names, respectively. The examples we will be using include haploid, diploid and polyploid data.</p>
<div id="basic-format" class="section level3">
<h3>Basic Format</h3>
<p>Below is what the <code>monpop</code> (haploid) data looks like. Highlighted in red is how missing data should be coded for SSR markers. Highlighted in blue are the parts of the metadata rows used by <em>poppr</em>. These three numbers represent:</p>
<table align="left" cellpadding="20" border="2">
<thead>
<tr>
<th class="col-xs-3">
A
</th>
<th class="col-xs-3">
B
</th>
<th class="col-xs-3">
C
</th>
</tr>
</thead>
<tbody>
<tr>
<td class="text-center">
loci
</td>
<td class="text-center">
samples
</td>
<td class="text-center">
populations
</td>
</tr>
</tbody>
</table>
<div class="figure">
<img src="images/monpop.png" alt="" />
<p class="caption">monpop</p>
</div>
<p>The columns of the metadata beyond those three rows define the number of individuals contained within each population. Since this data is redundant with the second column, it is not necessary. Notice, also, that the second column, reserved for the population assignments, has a pattern of underscores in the populations. This will be important at the end of this section. Below is a modified version of the input format that should make it easier to format.</p>
<div class="figure">
<img src="images/monpop_ideal.png" alt="" />
<p class="caption">monpop_ideal</p>
</div>
<p>Highlighted in blue is the cell that defines the number of columns highlighted in red. If we set this number to <code>1</code>, then we do not have to enter in any information in those columns. Try it for yourself.</p>
</div>
<div id="diploids" class="section level3">
<h3>Diploids</h3>
<p>Diploid data is only different in the fact that you will have two alleles per locus. This is coded such that each allele is in a separate column. Below is an example of the <code>nancycats</code> data set (from the <em>adegenet</em> package), exported like above. Highlighted in blue and red are the first and second loci, respectively.</p>
<div class="figure">
<img src="images/diploid.png" alt="" />
<p class="caption">diploid</p>
</div>
</div>
<div id="polyploids" class="section level3">
<h3>Polyploids</h3>
<p>GenAlEx does not handle polyploids, but since poppr can do it, we have set up a scheme to allow import of polyploids via this format. The limitation is that all of your loci have to have the same observed ploidy. Below is the example of <em>Phytophthora infestans</em> in the data set <code>Pinf</code> where some genotypes had observed tetraploid loci <span class="citation">(Goss et al., 2014)</span>.</p>
<div class="figure">
<img src="images/tetraploid.png" alt="" />
<p class="caption">tetraploid</p>
</div>
<p>Highlighted in blue is the first locus and highlighted in red are two samples at that locus, an observed diploid and observed triploid. Note the extra zeroes needed to make the genotype tetraploid.</p>
</div>
</div>
<div id="population-strata" class="section level2">
<h2>Population strata</h2>
<p>A hierarchical sampling approach is necessary to infer structure of populations in space or time. <em>Poppr</em> facilitates definition of stratified data by concatenating the different stratifications into a single column by a common separator (“_” by default). Here’s an example of the three stratifications of the <code>monpop</code> data set introduced above:</p>
<table border="2" cellpadding="20" align="center">
<tr>
<th class="col-xs3">
Tree
</th>
<th class="col-xs3">
Year
</th>
<th class="col-xs3">
Symptom
</th>
</tr>
<tr>
<td align="center">
7
</td>
<td align="center">
9
</td>
<td align="center">
BB
</td>
</tr>
<tr>
<td align="center">
7
</td>
<td align="center">
9
</td>
<td align="center">
BB
</td>
</tr>
<tr>
<td align="center">
7
</td>
<td align="center">
9
</td>
<td align="center">
BB
</td>
</tr>
<tr>
<td align="center">
7
</td>
<td align="center">
9
</td>
<td align="center">
BB
</td>
</tr>
<tr>
<td align="center">
7
</td>
<td align="center">
9
</td>
<td align="center">
BB
</td>
</tr>
<tr>
<td align="center">
7
</td>
<td align="center">
9
</td>
<td align="center">
BB
</td>
</tr>
</table>
<p>resulting in definition for use in <em>poppr</em> as:</p>
<!-- html table generated in R 3.1.2 by xtable 1.7-4 package -->
<!-- Fri Jan 9 20:12:57 2015 -->
<table border="2" cellpadding="20" align="center">
<tr>
<th class="col-xs3">
Tree_Year_Symptom
</th>
</tr>
<tr>
<td align="center">
7_9_BB
</td>
</tr>
<tr>
<td align="center">
7_9_BB
</td>
</tr>
<tr>
<td align="center">
7_9_BB
</td>
</tr>
<tr>
<td align="center">
7_9_BB
</td>
</tr>
<tr>
<td align="center">
7_9_BB
</td>
</tr>
<tr>
<td align="center">
7_9_BB
</td>
</tr>
</table>
<blockquote>
<p>If you do not want to concatenate your population factors into one row, save them as a separate csv file and use the functions <code>read.table</code> and <code>strata</code> after you’ve imported your genetic data to achieve the same result.</p>
</blockquote>
<p>The data imported this way can be accessed with the function <code>splitStrata</code>:</p>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1"></a><span class="co"># using monpop from above</span></span>
<span id="cb3-2"><a href="#cb3-2"></a>monpop <span class="co"># Before (One level, Pop)</span></span></code></pre></div>
<pre><code>##
## This is a genclone object
## -------------------------
## Genotype information:
##
## 264 multilocus genotypes
## 694 haploid individuals
## 13 codominant loci
##
## Population information:
##
## 1 stratum - Pop
## 12 populations defined -
## 7_09_BB, 26_09_BB, 26_09_FR, ..., 45_10_FR, 26_11_BB, 26_11_FR</code></pre>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1"></a><span class="kw">splitStrata</span>(monpop) <-<span class="st"> </span><span class="er">~</span>Tree<span class="op">/</span>Year<span class="op">/</span>Symptom</span>
<span id="cb5-2"><a href="#cb5-2"></a>monpop <span class="co"># After (Three distinct levels)</span></span></code></pre></div>
<pre><code>##
## This is a genclone object
## -------------------------
## Genotype information:
##
## 264 multilocus genotypes
## 694 haploid individuals
## 13 codominant loci
##
## Population information:
##
## 3 strata - Tree, Year, Symptom
## 12 populations defined -
## 7_09_BB, 26_09_BB, 26_09_FR, ..., 45_10_FR, 26_11_BB, 26_11_FR</code></pre>
<p>This is detailed in <a href="Population_Strata.html">our chapter on population strata</a>.</p>
</div>
<div id="references" class="section level2">
<h2>References</h2>
<!------>
<div id="refs" class="references">
<div id="ref-everhart2014finescale">
<p>Everhart S., Scherm H. 2015. Fine-scale genetic structure of <em>Monilinia fructicola</em> during brown rot epidemics within individual peach tree canopies. <em>Phytopathology</em> 105:542–549. Available at: <a href="https://doi.org/10.1094/PHYTO-03-14-0088-R">https://doi.org/10.1094/PHYTO-03-14-0088-R</a></p>
</div>
<div id="ref-goss2014irish">
<p>Goss EM., Tabima JF., Cooke DEL., Restrepo S., Fry WE., Forbes GA., Fieland VJ., Cardenas M., Grünwald NJ. 2014. The Irish potato famine pathogen <em>phytophthora infestans</em> originated in central mexico rather than the andes. <em>Proceedings of the National Academy of Sciences</em> 111:8791–8796. Available at: <a href="http://www.pnas.org/content/early/2014/05/29/1401884111.abstract">http://www.pnas.org/content/early/2014/05/29/1401884111.abstract</a></p>
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