emellb_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#input files\n",
    "infilepath=\"/home/megan/Desktop/emel_lb1234/3.reformat/\"\n",
    "genomatrix=\"genos_matrix.csv\"\n",
    "fstpopmatrix=\"fst_pop.csv\"\n",
    "siteallelematrix=\"pop_alleles_gdm.csv\"\n",
    "\n",
    "#samples \"database\"\n",
    "samplefile=\"/home/megan/megan/research/eucalyptus/eucalyptus_data/Emelliodora_PlantsSamples.csv\"\n",
    "accessionfile=\"/home/megan/megan/research/eucalyptus/eucalyptus_data/Emelliodora_Accessions.csv\"\n",
    "\n",
    "#geographic extent polygon\n",
    "extentpoly=\"/home/megan/Desktop/emel_lb1234/4.analysis/gdm/emel_dist_polygon/emel_alasuboccur_20k.shp\"\n",
    "\n",
    "#covariate to color by\n",
    "cov2color=\"Latitude\"\n",
    "#cov2color=\"Longitude\"\n",
    "#cov2color=\"CleanedFileSize\"\n",
    "#cov2color=\"missing\"\n",
    "#cov2color=\"PlateName\"\n",
    "#cov2color=\"GradientOrder\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Checking rgeos availability: TRUE\n"
     ]
    }
   ],
   "source": [
    "library(adegenet)\n",
    "library(fossil)\n",
    "library(ecodist)\n",
    "library(raster)\n",
    "library(rgdal)\n",
    "library(gdm)\n",
    "library(maptools)\n",
    "options(jupyter.plot_mimetypes = \"image/png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>275</li>\n",
       "\t<li>9378</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 275\n",
       "\\item 9378\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 275\n",
       "2. 9378\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1]  275 9378"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>Chr01.14319</th><th scope=col>Chr01.22984</th><th scope=col>Chr01.79312</th><th scope=col>Chr01.79661</th><th scope=col>Chr01.384722</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>S45001</th><td>NA</td><td>NA</td><td>0/0</td><td>0/0</td><td>NA</td></tr>\n",
       "\t<tr><th scope=row>S45002</th><td>NA</td><td>0/0</td><td>NA</td><td>0/0</td><td>NA</td></tr>\n",
       "\t<tr><th scope=row>S45003</th><td>0/0</td><td>NA</td><td>0/0</td><td>0/0</td><td>0/0</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllll}\n",
       "  & Chr01.14319 & Chr01.22984 & Chr01.79312 & Chr01.79661 & Chr01.384722\\\\\n",
       "\\hline\n",
       "\tS45001 & NA & NA & 0/0 & 0/0 & NA\\\\\n",
       "\tS45002 & NA & 0/0 & NA & 0/0 & NA\\\\\n",
       "\tS45003 & 0/0 & NA & 0/0 & 0/0 & 0/0\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "       Chr01.14319 Chr01.22984 Chr01.79312 Chr01.79661 Chr01.384722\n",
       "S45001        <NA>        <NA>         0/0         0/0         <NA>\n",
       "S45002        <NA>         0/0        <NA>         0/0         <NA>\n",
       "S45003         0/0        <NA>         0/0         0/0          0/0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#read in genotype matrix\n",
    "genos=read.delim(paste(infilepath,genomatrix,sep=\"\"), sep=\",\", header=T)     #read in file\n",
    "rownames(genos)=genos[,1]                           #change column 1 to row names\n",
    "#rownames(genos)=substr(genos[,1],9,14)             #get just sample ID from stacks names\n",
    "genos=genos[,-1]\n",
    "dim(genos)\n",
    "genos[1:3,1:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>36</li>\n",
       "\t<li>36</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 36\n",
       "\\item 36\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 36\n",
       "2. 36\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 36 36"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>LB.1</th><th scope=col>LB.10</th><th scope=col>LB.11</th><th scope=col>LB.13</th><th scope=col>LB.14</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>LB.1</th><td>NA</td><td>0.04295878</td><td>0.02719413</td><td>0.03671601</td><td>0.03389006</td></tr>\n",
       "\t<tr><th scope=row>LB.10</th><td>0.04295878</td><td>NA</td><td>0.04727133</td><td>0.04692548</td><td>0.06488105</td></tr>\n",
       "\t<tr><th scope=row>LB.11</th><td>0.02719413</td><td>0.04727133</td><td>NA</td><td>0.0487242</td><td>0.05395956</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllll}\n",
       "  & LB.1 & LB.10 & LB.11 & LB.13 & LB.14\\\\\n",
       "\\hline\n",
       "\tLB.1 & NA & 0.04295878 & 0.02719413 & 0.03671601 & 0.03389006\\\\\n",
       "\tLB.10 & 0.04295878 & NA & 0.04727133 & 0.04692548 & 0.06488105\\\\\n",
       "\tLB.11 & 0.02719413 & 0.04727133 & NA & 0.0487242 & 0.05395956\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "            LB.1      LB.10      LB.11      LB.13      LB.14\n",
       "LB.1          NA 0.04295878 0.02719413 0.03671601 0.03389006\n",
       "LB.10 0.04295878         NA 0.04727133 0.04692548 0.06488105\n",
       "LB.11 0.02719413 0.04727133         NA 0.04872420 0.05395956"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#read in fst matrix\n",
    "fst=read.delim(paste(infilepath,fstpopmatrix,sep=\"\"), sep=\",\", header=T)     #read in file\n",
    "fst_pop_names=fst[,1]                               #get pop names before mangled\n",
    "fst=fst[,-1]                                        #fix row names\n",
    "rownames(fst)=colnames(fst)                         \n",
    "dim(fst)\n",
    "fst.dist=as.dist(fst)\n",
    "fst[1:3,1:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "/// GENIND OBJECT /////////\n",
       "\n",
       " // 275 individuals; 9,378 loci; 18,756 alleles; size: 24.3 Mb\n",
       "\n",
       " // Basic content\n",
       "   @tab:  275 x 18756 matrix of allele counts\n",
       "   @loc.n.all: number of alleles per locus (range: 2-2)\n",
       "   @loc.fac: locus factor for the 18756 columns of @tab\n",
       "   @all.names: list of allele names for each locus\n",
       "   @ploidy: ploidy of each individual  (range: 2-2)\n",
       "   @type:  codom\n",
       "   @call: df2genind(X = genos.filt, sep = \"/\", NA.char = NA)\n",
       "\n",
       " // Optional content\n",
       "   - empty -"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#format for input into genid object\n",
    "genos.filt=genos\n",
    "#fix loci names so happy\n",
    "colnames(genos.filt)=gsub(\".\",\"_\",colnames(genos.filt),fixed=T)\n",
    "genos.filt[genos.filt == \"./.\"] <- NA\n",
    "#convert to genid object\n",
    "genos.gi <- df2genind(genos.filt,sep=\"/\",NA.char=NA)\n",
    "genos.gi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#add sample metadata\n",
    "sampleinfo=read.csv(samplefile, header=TRUE)  #read in sample information with populations \n",
    "samplematches=match(sampleinfo$SampleID, indNames(genos.gi))  #subset for just samples in this analysis\n",
    "samples=sampleinfo[!is.na(samplematches),][order(na.omit(samplematches)),]\n",
    "\n",
    "accessioninfo=read.csv(accessionfile, header=T)  #read in accession information\n",
    "accessionmatches=match(samples$AccessionID, accessioninfo$AccessionID)\n",
    "samples=cbind(samples,accessioninfo$Latitude[accessionmatches],accessioninfo$Longitude[accessionmatches],\n",
    "              accessioninfo$PopulationName[accessionmatches],accessioninfo$Altitude[accessionmatches],\n",
    "              accessioninfo$GradientOrder[accessionmatches])\n",
    "names(samples)[names(samples)==\"accessioninfo$Latitude[accessionmatches]\"]=\"Latitude\"\n",
    "names(samples)[names(samples)==\"accessioninfo$Longitude[accessionmatches]\"]=\"Longitude\"\n",
    "names(samples)[names(samples)==\"accessioninfo$PopulationName[accessionmatches]\"]=\"PopulationName\"\n",
    "names(samples)[names(samples)==\"accessioninfo$Altitude[accessionmatches]\"]=\"Altitude\"\n",
    "names(samples)[names(samples)==\"accessioninfo$GradientOrder[accessionmatches]\"]=\"GradientOrder\"\n",
    "samples$PopulationName=droplevels(samples$PopulationName) #fix annoying extra level with no assignment\n",
    "\n",
    "#add missing data metric\n",
    "samples$missing=rowSums(is.na(genos.filt))/dim(genos.filt)[2]*100\n",
    "\n",
    "#get color based on ordering of covariate\n",
    "cov=samples[!duplicated(samples[[cov2color]]), ] #remove duplicates of the covariate\n",
    "cov=cov[order(cov[[cov2color]]), ]               #sort \n",
    "cov$color=rev(rainbow(n=dim(cov)[1],start=0, end=.8))       #add color, rev to invert since neg lats\n",
    "#cov$color=rainbow(n=dim(cov)[1],start=0, end=.8)\n",
    "\n",
    "#add color to sample df\n",
    "colormatches=match(samples[[cov2color]], cov[[cov2color]])  #match color to samples by covariate\n",
    "samples=cbind(samples, cov$color[colormatches])\n",
    "names(samples)[names(samples)==\"cov$color[colormatches]\"]=\"cov_color\"\n",
    "samples$cov_color=as.character(samples$cov_color)\n",
    "\n",
    "#resort covariate df so it matches sorted order of populations (for dapc)\n",
    "poporder=samples[!duplicated(samples$PopulationName), ]\n",
    "poporder=poporder[order(poporder$PopulationName), ]   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#read in environmental data\n",
    "clim_vars = getData('worldclim', var='bio', res=2.5, path=\"/home/megan/megan/research/gis_data\")    \n",
    "#useful bioclim names\n",
    "names=c(#\"Latitude\",\"Longitude\",\"Altitude\",\n",
    "       \"Annual Mean Temperature\",\"Mean Diurnal Range\",\"Isothermality\",\"Temperature Seasonality\",\n",
    "       \"Max Temperature of Warmest Month\",\"Min Temperature of Coldest Month\",\n",
    "       \"Temperature Annual Range\",\"Mean Temperature of Wettest Quarter\",\"Mean Temperature of Driest Quarter\",\n",
    "       \"Mean Temperature of Warmest Quarter\",\"Mean Temperature of Coldest Quarter\",\"Annual Precipitation\",\n",
    "       \"Precipitation of Wettest Month\",\"Precipitation of Driest Month\",\"Precipitation Seasonality\",\n",
    "       \"Precipitation of Wettest Quarter\",\"Precipitation of Driest Quarter\",\n",
    "       \"Precipitation of Warmest Quarter\",\"Precipitation of Coldest Quarter\",\n",
    "       \"Koppen Class\",\"Temperature/Humidity Zone\", \"Altitude\")\n",
    "#restrict for our dataset and clean up\n",
    "range=extent(min(samples$Longitude)-1,max(samples$Longitude)+1,min(samples$Latitude)-1,max(samples$Latitude)+1)\n",
    "clim_vars_range=crop(clim_vars, range)\n",
    "rm(clim_vars)\n",
    "\n",
    "#read in altitude data\n",
    "#alt=getData('alt', download=T, country='AUS') \n",
    "    #altitude (elevation); the data were aggregated from SRTM 90 m resolution data between -60 and 60 latitude\n",
    "alt=getData('alt', country='AUS', path=\"/home/megan/megan/research/gis_data/oz_alt\")\n",
    "#alt_range=crop(alt, range)\n",
    "names(alt)=\"altitude\"  #rename\n",
    "\n",
    "#read in koppen data\n",
    "koppen_raw=read.asciigrid(\"/home/megan/megan/research/gis_data/oz_koppen_classes/kpnall.txt\")\n",
    "koppen_rb=as(koppen_raw, 'RasterBrick')\n",
    "names(koppen_rb)=\"koppen\"\n",
    "\n",
    "#read in temp/humidity zone data\n",
    "tmphu_raw=read.asciigrid(\"/home/megan/megan/research/gis_data/oz_tmphumid_zones/clim-zones.txt\")\n",
    "tmphu_rb=as(tmphu_raw, 'RasterBrick')\n",
    "names(tmphu_rb)=\"tmphu\"\n",
    "\n",
    "#add to samples\n",
    "altitude=extract(alt, cbind(samples$Longitude, samples$Latitude))\n",
    "samples=cbind(samples, extract(clim_vars_range, cbind(samples$Longitude, samples$Latitude)),\n",
    "                       extract(koppen_rb, cbind(samples$Longitude, samples$Latitude)),\n",
    "                       extract(tmphu_rb, cbind(samples$Longitude, samples$Latitude)),\n",
    "                       altitude)\n",
    "#names(samples)[names(samples)==\"X.home.megan.megan.research.gis_data.oz_koppen_classes.kpnall.txt\"]=\"koppen\"\n",
    "#names(samples)[names(samples)==\"X.home.megan.megan.research.gis_data.oz_tmphumid_zones.clim.zones.txt\"]=\"tmphu\"\n",
    "\n",
    "#resample to make same extent and add to raster stack\n",
    "koppen_resamp=resample(koppen_rb,clim_vars_range,\"bilinear\")\n",
    "clim_vars_range=stack(clim_vars_range,koppen_resamp)\n",
    "tmphu_resamp=resample(tmphu_rb,clim_vars_range,\"bilinear\")\n",
    "clim_vars_range=stack(clim_vars_range,tmphu_resamp)\n",
    "alt_resamp=resample(alt,clim_vars_range,\"bilinear\")\n",
    "clim_vars_range=stack(clim_vars_range,alt_resamp)\n",
    "\n",
    "#plot(clim_vars_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "In matrix(values, nrow = ncell(x), ncol = nlayers(x)): data length [1692548] is not a sub-multiple or multiple of the number of columns [19]Warning message:\n",
      "In matrix(values, nrow = ncell(x), ncol = nlayers(x)): data length [1692548] is not a sub-multiple or multiple of the number of columns [19]"
     ]
    }
   ],
   "source": [
    "## read in future climate rasters\n",
    "\n",
    "#2050\n",
    "bc50mc85=stack()\n",
    "for (i in 1:19)\n",
    "    {\n",
    "        temp_tif=raster(paste(\"/home/megan/megan/research/gis_data/mc85bi50/mc85bi50\",i,\".tif\",sep=\"\"))\n",
    "                          # crs=\"+proj=longlat +datum=WGS84\")\n",
    "        #temp_proj=projectRaster(temp_tif, crs=\"+proj=longlat +datum=WGS84\")\n",
    "        temp_rb=as(temp_tif, 'RasterBrick')\n",
    "        ##resample to make same extent and add to raster stack\n",
    "        temp_resamp=resample(temp_rb,clim_vars_range,\"bilinear\")\n",
    "        names(temp_resamp)=paste(\"bio\",i,sep=\"\")\n",
    "        #stack bricks\n",
    "        bc50mc85=stack(bc50mc85,temp_resamp)\n",
    "    }\n",
    "#change so NAs match the present climate variables\n",
    "#bc50mc85=bc50mc85[is.na(clim_vars_range[])]=NA\n",
    "tmp_names=names(bc50mc85)\n",
    "values(bc50mc85)[is.na(clim_vars_range[])]=NA\n",
    "names(bc50mc85)=tmp_names\n",
    "\n",
    "#2070\n",
    "bc70mc85=stack()\n",
    "for (i in 1:19)\n",
    "    {\n",
    "        temp_tif=raster(paste(\"/home/megan/megan/research/gis_data/mc85bi70/mc85bi70\",i,\".tif\",sep=\"\"))\n",
    "        temp_rb=as(temp_tif, 'RasterBrick')\n",
    "        ##resample to make same extent and add to raster stack\n",
    "        temp_resamp=resample(temp_rb,clim_vars_range,\"bilinear\")\n",
    "        names(temp_resamp)=paste(\"bio\",i,sep=\"\")\n",
    "        #stack bricks\n",
    "        bc70mc85=stack(bc70mc85,temp_resamp)\n",
    "    }\n",
    "#change so NAs match the present climate variables\n",
    "tmp_names=names(bc70mc85)\n",
    "values(bc70mc85)[is.na(clim_vars_range[])]=NA\n",
    "names(bc70mc85)=tmp_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAYAAAD958/bAAAACXBIWXMAABJ0AAASdAHeZh94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\nAAAAAAAAAAAAAACAM3wBGkpvhL6/cjYAAAAASUVORK5CYII="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(oz)\n",
    "samples_pops=unique(samples[,c(19,20,21,25)])\n",
    "chambers_pops=subset(samples_pops, PopulationName==\"LB-11\" | PopulationName==\"LB-B\" | PopulationName==\"LB-D\" | \n",
    "                     PopulationName==\"LB-G\" | PopulationName==\"LB-T1\" | PopulationName==\"LB-T3\")\n",
    "\n",
    "#read in extent and visualize\n",
    "ext=readShapeSpatial(extentpoly)\n",
    "OzCoast=shapefile(\"/home/megan/megan/research/gis_data/oz_coast/aus5m_coast_no_norfolk.shp\")\n",
    "plot(ext,col=\"lightgrey\")\n",
    "#points(units$Longitude, units$Latitude)\n",
    "plot(OzCoast, add=T)\n",
    "\n",
    "#pdf(file=\"map.pdf\")\n",
    "#oz(ylim=c(-40,-27),xlim=c(148,149.7),new=F)\n",
    "#plot(samples$longitude,samples$latitude, labels=samples$Population)\n",
    "text(samples_pops$Longitude,samples_pops$Latitude, \n",
    "     label=substring(samples_pops$PopulationName, 4), cex = .6, col=samples_pops$cov_color) #add names and colors\n",
    "text(chambers_pops$Longitude, chambers_pops$Latitude,\n",
    "     label=\"   *\")\n",
    "\n",
    "\n",
    "\n",
    "#dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "In dudi.pco(genos.dist, scannf = F, nf = 3): Non euclidean distance"
     ]
    }
   ],
   "source": [
    "#pca of genetic distance by individual\n",
    "genos.dist=dist(genos.gi)\n",
    "pcoa.genos=dudi.pco(genos.dist,scannf = F, nf = 3)\n",
    "percent_var=round(100*pcoa.genos$eig/sum(pcoa.genos$eig),1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAYAAAD958/bAAAACXBIWXMAABJ0AAASdAHeZh94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\nSZIkSZIkSZIkSZIkSePzNZ5mnD28DauQAAAAAElFTkSuQmCC"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#pdf(\"emellb_pca_275.pdf\")\n",
    "plot(pcoa.genos$li[,2:1], pch=\"\",\n",
    "    xlab = paste(\"PCOA axis 2 (\", percent_var[2],\"%)\"), ylab = paste(\"PCOA axis 1 (\", percent_var[1],\"%)\"),\n",
    "    ylim=c(8,-17))\n",
    "  #  xlim=c(-10,10),ylim=c(-15,10))\n",
    "text(pcoa.genos$li[,2:1], \n",
    "     label=samples$SampleName, cex = .6, col=samples$cov_color) #add names and colors\n",
    "#dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "In dudi.pco(fst.dist, scannf = F, nf = 3): Non euclidean distance"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAYAAAD958/bAAAACXBIWXMAABJ0AAASdAHeZh94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\nAAAAAAAAAAAAAAAAAAAAAAAAAADcAv7Fd4KTh7DcAAAAAElFTkSuQmCC"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#pca of genetic distance by population fst\n",
    "#pdf(file=\"pca.pdf\")\n",
    "pcoa.fst=dudi.pco(fst.dist,scannf = F, nf = 3)\n",
    "percent_var_site=round(100*pcoa.fst$eig/sum(pcoa.fst$eig),1)\n",
    "#pdf(\"emellb_pca_275.pdf\")\n",
    "plot(pcoa.fst$li[,1:2], pch=\"\",\n",
    "    xlab = paste(\"PCOA axis 1 (\", percent_var_site[1],\"%)\"), ylab = paste(\"PCOA axis 2 (\", percent_var_site[2],\"%)\"))\n",
    "text(pcoa.fst$li[,1:2], \n",
    "     label=substring(rownames(fst),4), cex = .8, col=poporder$cov_color) #add names and colors\n",
    "#dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>pdf:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{pdf:} 2"
      ],
      "text/markdown": [
       "**pdf:** 2"
      ],
      "text/plain": [
       "pdf \n",
       "  2 "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#make map/pca figure\n",
    "pdf(\"emellb_map_pcax2.pdf\", height=6, width=17)\n",
    "par(mfrow=c(1,3))#, mar=c(5,4,1,1))\n",
    "par(mar=c(1,1,1,0))\n",
    "\n",
    "#map\n",
    "#read in extent and visualize\n",
    "ext=readShapeSpatial(extentpoly)\n",
    "OzCoast=shapefile(\"/home/megan/megan/research/gis_data/oz_coast/aus5m_coast_no_norfolk.shp\")\n",
    "plot(ext,col=\"grey95\",ylim=c(-38.8,-24),xlim=c(148,148.1))\n",
    "#points(units$Longitude, units$Latitude)\n",
    "plot(OzCoast, add=T)\n",
    "#oz(ylim=c(-40,-25),xlim=c(148,149.7))\n",
    "text(samples$Longitude,samples$Latitude, \n",
    "     label=substring(samples$PopulationName, 4), cex = .6, col=samples$cov_color) #add names and colors\n",
    "text(chambers_pops$Longitude, chambers_pops$Latitude,\n",
    "     label=\"   *\")  #mark chamber populations\n",
    "mtext(\"A\",3, line=-2, at=142, outer=F, cex=2)\n",
    "mtext(\"sampling sites\", 3, line=-2, at=146, outer=F, cex=1)\n",
    "\n",
    "#indiv pca\n",
    "par(mar=c(5,4,1,1))\n",
    "plot(pcoa.genos$li[,2:1], pch=\"\",\n",
    "    xlab = paste(\"PCA axis 2 (\", percent_var[2],\"%)\"), ylab = paste(\"PCA axis 1 (\", percent_var[1],\"%)\"),\n",
    "    ylim=c(8,-17))\n",
    "  #  xlim=c(-10,10),ylim=c(-15,10))\n",
    "text(pcoa.genos$li[,2:1], \n",
    "     label=samples$SampleName, cex = .6, col=samples$cov_color) #add names and colors\n",
    "mtext(\"B\",3, line=-2, at=-9.5, outer=F, cex=2)\n",
    "mtext(\"individual PCA\",3, line=-2, at=20, outer=F, cex=1)\n",
    "\n",
    "#site pcs\n",
    "par(mar=c(5,6,1,1))\n",
    "plot(pcoa.fst$li[,2:1], pch=\"\",\n",
    "    xlab = paste(\"PCOA axis 2 (\", percent_var_site[2],\"%)\"), ylab = paste(\"PCOA axis 1 (\", percent_var_site[1],\"%)\"))\n",
    "text(pcoa.fst$li[,2:1], \n",
    "     label=substring(rownames(fst),4), cex = .8, col=poporder$cov_color) #add names and colors\n",
    "mtext(\"C\", 3, line=-2, at=-0.031, outer=F, cex=2)\n",
    "mtext(\"site PCA\", 3, line=-2, at=0.027, outer=F, cex=1)\n",
    "\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#unstable results\n",
    "#DAPC inferred clusters\n",
    "\n",
    "#uncomment to determine number of clusters\n",
    "#grp_infer=find.clusters(genos.gi) \n",
    "#grp_infer=find.clusters(genos.gi, n.pca=300)\n",
    "    #keep all components, unless computation constraint, retain 300\n",
    "    #clusters=minimum number in BIC graph\n",
    "        #for this case, didn't give any useful information\n",
    "\n",
    "#determine assignemnts for selected number of clusters\n",
    "grp_infer=find.clusters(genos.gi, n.pca=300, n.clust=4) #selected number of clusters, keep all pcas\n",
    "table(samples$PopulationName, grp_infer$grp)\n",
    "\n",
    "#dapc\n",
    "dapc_infer=dapc(genos.gi,grp_infer$grp,n.pca=50, n.da=10) \n",
    "    #remove n.pca and n.da to see graphs  \n",
    "    #if graphs suggest want keep max, find max without warning or lower for more stability\n",
    "\n",
    "#graph\n",
    "#pdf(\"dapcA_inf.pdf\")\n",
    "scatter(dapc_infer, scree.da=F,pch=0:20, cstar=F, solid=1, cex=.7, clab=F,\n",
    "        col=c(\"darkred\",\"darkgreen\",\"darkblue\",\"darkmagenta\",\"darkorange\",\"darkgoldenrod1\"),\n",
    "        leg=F,cell=T, posi.pca=\"bottomright\") #mstree=TRUE, \n",
    "#dev.off()\n",
    "\n",
    "#update population info\n",
    "#pdf(\"dapcB_inf.pdf\")\n",
    "dapc_infer$grp=samples$PopulationName\n",
    "scatter(dapc_infer, scree.da=F,pch=0:20, cstar=F, col=poporder$cov_color, solid=1, cex=.7, clab=F,\n",
    "        leg=F,cell=F, posi.pca=\"bottomright\")\n",
    "#dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#DAPC assigned clusters\n",
    "\n",
    "#dapc\n",
    "dapc_assign=dapc(genos.gi,samples$PopulationName,n.pca=72, n.da=5) \n",
    "    #remove n.pca and n.da to see graphs to determine values\n",
    "    #if graphs suggest want keep max, find max without warning\n",
    "\n",
    "#graph\n",
    "#pdf(\"dapc_assign.pdf\")\n",
    "scatter(dapc_assign, scree.da=F,pch=0:20, cstar=F, solid=1, cex=.7, clab=F,\n",
    "        col=poporder$cov_color, legend=T, cleg=.95,\n",
    "        cell=T, posi.pca=\"bottomright\") #mstree=TRUE, \n",
    "#dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#get geographic distance matrix for individuals\n",
    "geog_long_lat=samples[,c(\"Longitude\",\"Latitude\")]\n",
    "row.names(geog_long_lat)=samples$SampleID\n",
    "geo.dist=earth.dist(geog_long_lat, dist=T)\n",
    "\n",
    "#get geographic distance matrix for populations (by name)\n",
    "#in order of fst matrix\n",
    "populations=samples[!duplicated(samples$PopulationName), ]\n",
    "populations=populations[match(fst_pop_names, populations$PopulationName),]\n",
    "pop_geog_long_lat=populations[,c(\"Longitude\",\"Latitude\")]\n",
    "row.names(pop_geog_long_lat)=populations$PopulationName\n",
    "geo.pop.dist=earth.dist(pop_geog_long_lat, dist=T)\n",
    "\n",
    "#get ln of geographic distances\n",
    "log_geo.dist=log(geo.dist+1)\n",
    "log_geo.pop.dist=log(geo.pop.dist+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#IBD by individual\n",
    "#mantel test\n",
    "man=mantel(genos.dist ~ geo.dist, nperm=10000)\n",
    "print(\"% variation explained\")\n",
    "man[1]^2*100 #rsquared\n",
    "print(\"one-tailed p-value (null hypothesis r<=0)\")\n",
    "man[2]\n",
    "print(\"full results\")\n",
    "man\n",
    "#mantel correlogram\n",
    "gen_geo <- mgram(genos.dist, geo.dist)\n",
    "gen_geo$mgram[,4] #print pvalues\n",
    "plot(gen_geo,xlab=\"Distance (km)\") \n",
    "\n",
    "#IBD by individual\n",
    "#ln geographic distance\n",
    "man=mantel(genos.dist ~ log_geo.dist, nperm=10000)\n",
    "print(\"% variation explained\")\n",
    "man[1]^2*100 #rsquared\n",
    "print(\"one-tailed p-value (null hypothesis r<=0)\")\n",
    "man[2]\n",
    "print(\"full results\")\n",
    "man\n",
    "#mantel correlogram\n",
    "gen_geo <- mgram(genos.dist, log_geo.dist)\n",
    "gen_geo$mgram[,4] #print pvalues\n",
    "plot(gen_geo,xlab=\"log Distance (km)+1\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#IBD by population\n",
    "#mantel test\n",
    "man=mantel(fst.dist ~ geo.pop.dist, nperm=10000)\n",
    "print(\"% variation explained\")\n",
    "man[1]^2*100 #rsquared\n",
    "print(\"one-tailed p-value (null hypothesis r<=0)\")\n",
    "man[2]\n",
    "print(\"full results\")\n",
    "man\n",
    "#mantel correlogram\n",
    "gen_geo <- mgram(fst.dist, geo.pop.dist)\n",
    "gen_geo$mgram[,4] #print pvalues\n",
    "plot(gen_geo,xlab=\"Distance (km)\")\n",
    "\n",
    "#ln of geographic distance and fst/(1-fst)\n",
    "man=mantel((fst.dist/(1-fst.dist)) ~ log_geo.pop.dist, nperm=10000)\n",
    "print(\"% variation explained\")\n",
    "man[1]^2*100 #rsquared\n",
    "print(\"one-tailed p-value (null hypothesis r<=0)\")\n",
    "man[2]\n",
    "print(\"full results\")\n",
    "man\n",
    "#mantel correlogram\n",
    "gen_geo <- mgram((fst.dist/(1-fst.dist)), log_geo.pop.dist)\n",
    "gen_geo$mgram[,4] #print pvalues\n",
    "plot(gen_geo,xlab=\"log Distance (km)+1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#IBE by individual\n",
    "#loop over enviro vars\n",
    "ibe.out=data.frame(matrix(nrow=length(names), ncol=4))\n",
    "colnames(ibe.out)=c(\"enviro_var\",\"model R2(%)\",\"geog_pval\",\"enviro_pval\")\n",
    "x=1\n",
    "for (i in which(colnames(samples)==\"bio1\"):(which(colnames(samples)==\"bio1\")+length(names)-1))\n",
    "     {\n",
    "        ibe.out[x,1]=names[x]\n",
    "        env.dist=dist(samples[,i])  #get enviro distance\n",
    "        env.mrm=MRM(genos.dist ~ env.dist + geo.dist)  #do the work\n",
    "        ibe.out[x,2]=env.mrm$r.squared[1]*100\n",
    "        ibe.out[x,3]=env.mrm$coef[3,2]\n",
    "        ibe.out[x,4]=env.mrm$coef[2,2]\n",
    "        x=x+1  \n",
    "     }\n",
    "\n",
    "#just geography\n",
    "env.mrm=MRM(genos.dist ~ geo.dist)\n",
    "        ibe.out[x,1]=\"Geography alone\"\n",
    "        ibe.out[x,2]=env.mrm$r.squared[1]*100\n",
    "        ibe.out[x,3]=env.mrm$coef[2,2]\n",
    "\n",
    "#print results\n",
    "print(ibe.out, digits=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#IBE by population\n",
    "#loop over enviro vars\n",
    "ibe.out=data.frame(matrix(nrow=length(names), ncol=4))\n",
    "colnames(ibe.out)=c(\"enviro_var\",\"model R2(%)\",\"geog_pval\",\"enviro_pval\")\n",
    "x=1\n",
    "for (i in which(colnames(populations)==\"bio1\"):(which(colnames(populations)==\"bio1\")+length(names)-1))\n",
    "     {\n",
    "        ibe.out[x,1]=names[x]\n",
    "        env.dist=dist(populations[,i])  #get enviro distance\n",
    "        env.mrm=MRM(fst.dist ~ env.dist + geo.pop.dist)  #do the work\n",
    "        ibe.out[x,2]=env.mrm$r.squared[1]*100\n",
    "        ibe.out[x,3]=env.mrm$coef[3,2]\n",
    "        ibe.out[x,4]=env.mrm$coef[2,2]\n",
    "        x=x+1  \n",
    "     }\n",
    "\n",
    "#just geography\n",
    "env.mrm=MRM(fst.dist ~ geo.pop.dist)\n",
    "        ibe.out[x,1]=\"Geography alone\"\n",
    "        ibe.out[x,2]=env.mrm$r.squared[1]*100\n",
    "        ibe.out[x,3]=env.mrm$coef[2,2]\n",
    "\n",
    "#print results\n",
    "print(ibe.out, digits=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###############################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "##identify \"clones\" for removal for individual based gdm analysis\n",
    "#x=hclust(genos.dist)\n",
    "##pdf(\"sample_dend.pdf\", width=20, height=6)\n",
    "#plot(x,cex=.5)\n",
    "##dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "##remove clones\n",
    "#genos.dist.noclones=as.matrix(genos.dist)\n",
    "#which(colnames(genos.dist.noclones)==\"S45174\")\n",
    "#which(colnames(genos.dist.noclones)==\"S45255\")\n",
    "#which(colnames(genos.dist.noclones)==\"S45304\")\n",
    "#which(colnames(genos.dist.noclones)==\"S45180\")\n",
    "#which(colnames(genos.dist.noclones)==\"S45184\")\n",
    "#which(colnames(genos.dist.noclones)==\"S45181\")\n",
    "#which(colnames(genos.dist.noclones)==\"S45183\")\n",
    "#which(colnames(genos.dist.noclones)==\"S45083\")\n",
    "#which(colnames(genos.dist.noclones)==\"S45024\")\n",
    "#ind=c(135,197,232,140,143,141,142,67,21)\n",
    "#genos.dist.noclones=genos.dist.noclones[-ind,-ind]\n",
    "#genos.dist.noclones=dist(genos.dist.noclones)\n",
    "\n",
    "#samples.noclones=subset(samples, SampleID!=\"S45174\" & SampleID!=\"S45255\" & SampleID!=\"S45304\"\n",
    "#                        & SampleID!=\"S45180\" & SampleID!=\"S45184\" & SampleID!=\"S45181\" & SampleID!=\"S45183\"\n",
    "#                        & SampleID!=\"S45083\" & SampleID!=\"S45024\" )"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.1.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}