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<h1 class="title toc-ignore">Creating a phylogeny from the 1000 Genome Database</h1>
<h4 class="author">JReceveur</h4>
<h4 class="date">Aug 6, 2020</h4>
</div>
<div id="creating-a-dendrogram-from-a-downloaded-vcf-file" class="section level2">
<h2>Creating a Dendrogram from a downloaded VCF file</h2>
<p>This document provides an example of creating a dendrogram from a VCF file. The VCF file can be altered using VCFtools to look at a specific area of the genome. The lines starting with ## are what you would expect to see for the output</p>
<p>For a more in depth tutorial of SNPRelate see <a href="http://corearray.sourceforge.net/tutorials/SNPRelate/" class="uri">http://corearray.sourceforge.net/tutorials/SNPRelate/</a></p>
<p>This document was created using Rmarkdown <a href="http://rmarkdown.rstudio.com" class="uri">http://rmarkdown.rstudio.com</a>.</p>
<p>**Thanks to Thomas Quiroz Monnens for catching an error in a previous version of this tutorial!</p>
<p>```</p>
</div>
<div id="installing-packages" class="section level1">
<h1>Installing Packages</h1>
<pre class="r"><code>#if (!requireNamespace("BiocManager", quietly = TRUE))
# install.packages("BiocManager")
#BiocManager::install("SNPRelate")</code></pre>
</div>
<div id="loading-packages" class="section level1">
<h1>Loading Packages</h1>
<p>The packages will have to be loaded each time you open R or Rstudio.</p>
<pre class="r"><code>library(gdsfmt)
library(SNPRelate)</code></pre>
<pre><code>## SNPRelate -- supported by Streaming SIMD Extensions 2 (SSE2)</code></pre>
<pre class="r"><code>library(ggplot2)</code></pre>
<pre><code>## Warning: package 'ggplot2' was built under R version 4.0.2</code></pre>
</div>
<div id="loading-vcf-file" class="section level1">
<h1>Loading VCF file</h1>
<p>There are two ways that the VCF file can be loaded into Rstudio if it already on your computer. The first is directly specifying the file path and name, the second is using the file.choose option which will open a browser so you can search for the file. The second option is commented out with # (putting a # before any code means it will be treated as text, to run it, just remove the #).</p>
<p>If you want to follow along with the example data, the data (Approx 1.4 MB) is available for download <a href="https://github.com/BenbowLab/BenbowLab.github.io/blob/master/VCFExampleData.vcf" class="uri">https://github.com/BenbowLab/BenbowLab.github.io/blob/master/VCFExampleData.vcf</a></p>
<pre class="r"><code>vcf.fn <- "VCFExampleData.vcf"
#vcf.fn<- file.choose()</code></pre>
</div>
<div id="parsing" class="section level1">
<h1>Parsing</h1>
<p>The next command will turn the VCF file into a less data intensive form (GDS) for easier computing. If you have loaded an entire genome, expect this command to take an hour or more.</p>
<pre class="r"><code>snpgdsVCF2GDS(vcf.fn,"data.gds",method ="biallelic.only")</code></pre>
<pre><code>## Start file conversion from VCF to SNP GDS ...
## Method: exacting biallelic SNPs
## Number of samples: 16
## Parsing "VCFExampleData.vcf" ...
## import 2148 variants.
## + genotype { Bit2 16x2148, 8.4K } *
## Optimize the access efficiency ...
## Clean up the fragments of GDS file:
## open the file 'data.gds' (22.4K)
## # of fragments: 46
## save to 'data.gds.tmp'
## rename 'data.gds.tmp' (22.1K, reduced: 312B)
## # of fragments: 20</code></pre>
</div>
<div id="formatting-for-ibs-matrix" class="section level1">
<h1>Formatting for IBS matrix</h1>
<p>These commands prepare the data so it is formatted correctly to create a Identity by State matrix (fraction of identity by state for each pair of samples).</p>
<pre class="r"><code>genofile<-snpgdsOpen("data.gds")
set.seed(100)
ibs.hc<-snpgdsHCluster(snpgdsIBS(genofile,num.thread=2, autosome.only=FALSE))</code></pre>
<pre><code>## Identity-By-State (IBS) analysis on genotypes:
## Excluding 46 SNPs (monomorphic: TRUE, MAF: NaN, missing rate: NaN)
## # of samples: 16
## # of SNPs: 2,102
## using 2 threads
## IBS: the sum of all selected genotypes (0,1,2) = 48494
## Fri Aug 07 18:26:24 2020 (internal increment: 65536)
##
[..................................................] 0%, ETC: ---
[==================================================] 100%, completed, 0s
## Fri Aug 07 18:26:24 2020 Done.</code></pre>
</div>
<div id="turn-the-clustering-into-a-tree-file-and-plotting-tree" class="section level1">
<h1>Turn the clustering into a tree file and plotting tree</h1>
<p>This step takes the clustering results from before and turns the numerical values into a dendrogram</p>
<pre class="r"><code>rv <- snpgdsCutTree(ibs.hc)</code></pre>
<pre><code>## Determine groups by permutation (Z threshold: 15, outlier threshold: 5):
## Create 1 groups.</code></pre>
<pre class="r"><code>plot(rv$dendrogram,main="Dendrogram based on IBS")</code></pre>
<p><img src="Phylogeny_files/figure-html/unnamed-chunk-3-1.png" width="672" /></p>
</div>
<div id="dissimilarity-matrices" class="section level1">
<h1>Dissimilarity matrices</h1>
<p>This command creates an dissimilarity matrix between all the samples. If you are looking at the X chromosome, make sure the autosome.only= code is changed to autosome.only=False.</p>
<p>*Note: A previous version of this example incorrectly used the snpgdsIBS command here instead of snpgdsDiss. We apologize for any confusion.</p>
<pre class="r"><code>dissMatrix = snpgdsDiss(genofile , sample.id=NULL, autosome.only=TRUE,remove.monosnp=TRUE, maf=NaN, missing.rate=NaN, num.thread=2, verbose=TRUE)</code></pre>
<pre><code>## Individual dissimilarity analysis on genotypes:
## Excluding 0 SNP on non-autosomes
## Excluding 46 SNPs (monomorphic: TRUE, MAF: NaN, missing rate: NaN)
## # of samples: 16
## # of SNPs: 2,102
## using 2 threads
## Dissimilarity: the sum of all selected genotypes (0,1,2) = 48494
## Dissimilarity: Fri Aug 07 18:26:24 2020 0%
## Dissimilarity: Fri Aug 07 18:26:24 2020 100%</code></pre>
</div>
<div id="clustering-analysis" class="section level1">
<h1>Clustering Analysis</h1>
<p>This step performs a clustering analysis similar to the IBS analysis above but using dissimilarity. The next line creates a tree file based on dissimilarity rather than relatedness.</p>
<pre class="r"><code>snpHCluster = snpgdsHCluster(dissMatrix, sample.id=NULL, need.mat=TRUE, hang=0.01)
cutTree = snpgdsCutTree(snpHCluster, z.threshold=15, outlier.n=5, n.perm = 5000, samp.group=NULL,
col.outlier="red", col.list=NULL, pch.outlier=4, pch.list=NULL,label.H=FALSE, label.Z=TRUE,
verbose=TRUE)</code></pre>
<pre><code>## Determine groups by permutation (Z threshold: 15, outlier threshold: 5):
## Create 1 groups.</code></pre>
<pre class="r"><code>cutTree</code></pre>
<pre><code>## $sample.id
## [1] "AA0035-C" "AA0036-C" "AA0037-C" "AA0038-C" "AB0286-C" "AB0290-C"
## [7] "AB0292-C" "AX0001-C" "AX0002-C" "AY0001-C" "AY0002-C" "BY0001-C"
## [13] "BY0002-C" "BZ0001-C" "BZ0003-C" "BZ0004-C"
##
## $z.threshold
## [1] 15
##
## $outlier.n
## [1] 5
##
## $samp.order
## [1] 9 13 8 12 15 11 7 16 5 10 6 14 1 3 2 4
##
## $samp.group
## [1] G001 G001 G001 G001 G001 G001 G001 G001 G001 G001 G001 G001 G001 G001 G001
## [16] G001
## Levels: G001
##
## $dmat
## G001
## G001 1.039722
##
## $dendrogram
## 'dendrogram' with 2 branches and 16 members total, at height 1.262116
##
## $merge
## z n1 n2
## 1 0.0000000 1 1
## 2 1.3832952 1 2
## 3 1.7783401 1 3
## 4 1.9990924 1 4
## 5 1.5520638 1 5
## 6 0.0000000 1 1
## 7 0.0000000 1 1
## 8 1.8316610 1 6
## 9 1.2139331 1 2
## 10 0.7770847 1 2
## 11 1.4885736 1 7
## 12 2.9061354 3 8
## 13 1.6057339 1 3
## 14 1.0663228 1 11
## 15 4.6598993 4 12
##
## $clust.count
## G001
## 16</code></pre>
<pre class="r"><code>snpgdsClose(genofile)</code></pre>
</div>
<div id="displaying-a-tree-based-on-dissimilarity" class="section level1">
<h1>Displaying a tree based on dissimilarity</h1>
<pre class="r"><code>snpgdsDrawTree(cutTree, main = "Dendrogram based on dissimilarity",edgePar=list(col=rgb(0.5,0.5,0.5,0.75),t.col="black"),
y.label.kinship=T,leaflab="perpendicular",yaxis.kinship=F)</code></pre>
<p><img src="Phylogeny_files/figure-html/unnamed-chunk-6-1.png" width="672" /></p>
</div>
<div id="session-info" class="section level1">
<h1>Session Info</h1>
<pre class="r"><code>sessionInfo()</code></pre>
<pre><code>## R version 4.0.1 (2020-06-06)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 18362)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=English_United States.1252
## [2] LC_CTYPE=English_United States.1252
## [3] LC_MONETARY=English_United States.1252
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.1252
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggplot2_3.3.2 SNPRelate_1.22.0 gdsfmt_1.24.1
##
## loaded via a namespace (and not attached):
## [1] knitr_1.29 magrittr_1.5 tidyselect_1.1.0 munsell_0.5.0
## [5] colorspace_1.4-1 R6_2.4.1 rlang_0.4.7 dplyr_1.0.1
## [9] stringr_1.4.0 tools_4.0.1 grid_4.0.1 gtable_0.3.0
## [13] xfun_0.16 withr_2.2.0 htmltools_0.5.0 ellipsis_0.3.1
## [17] yaml_2.2.1 digest_0.6.25 tibble_3.0.3 lifecycle_0.2.0
## [21] crayon_1.3.4 purrr_0.3.4 vctrs_0.3.2 glue_1.4.1
## [25] evaluate_0.14 rmarkdown_2.3 stringi_1.4.6 compiler_4.0.1
## [29] pillar_1.4.6 generics_0.0.2 scales_1.1.1 pkgconfig_2.0.3</code></pre>
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