seed-phenotype-statistics.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# STEP 1: Import package code\n",
    "library(ggplot2)\n",
    "# Change the default plot size\n",
    "options(repr.plot.width=4, repr.plot.height=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>genotype</th><th scope=col>HEADER_SHAPES</th><th scope=col>area</th><th scope=col>hull.area</th><th scope=col>solidity</th><th scope=col>perimeter</th><th scope=col>width</th><th scope=col>height</th><th scope=col>longest_axis</th><th scope=col>center.of.mass.x</th><th scope=col>center.of.mass.y</th><th scope=col>hull_vertices</th><th scope=col>in_bounds</th><th scope=col>ellipse_center_x</th><th scope=col>ellipse_center_y</th><th scope=col>ellipse_major_axis</th><th scope=col>ellipse_minor_axis</th><th scope=col>ellipse_angle</th><th scope=col>ellipse_eccentricity</th><th scope=col>marker_area</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>Cs002      </td><td>SHAPES_DATA</td><td>1655       </td><td>1618.5     </td><td>1.0225517  </td><td>166.6102   </td><td>40         </td><td>62         </td><td>62         </td><td>2154.579   </td><td>2451.401   </td><td>26         </td><td>True       </td><td>2154.481   </td><td>2451.103   </td><td>66.75710   </td><td>30.41299   </td><td>156.244919 </td><td>0.8901964  </td><td>207801     </td></tr>\n",
       "\t<tr><td>Cs002      </td><td>SHAPES_DATA</td><td>1657       </td><td>1633.0     </td><td>1.0146969  </td><td>163.6812   </td><td>55         </td><td>47         </td><td>49         </td><td>2524.626   </td><td>2384.845   </td><td>25         </td><td>True       </td><td>2524.716   </td><td>2384.752   </td><td>63.24441   </td><td>32.10065   </td><td> 53.203114 </td><td>0.8616134  </td><td>207801     </td></tr>\n",
       "\t<tr><td>Cs002      </td><td>SHAPES_DATA</td><td> 979       </td><td> 937.0     </td><td>1.0448239  </td><td>125.5980   </td><td>49         </td><td>24         </td><td>48         </td><td>1623.824   </td><td>2319.848   </td><td>19         </td><td>True       </td><td>1624.177   </td><td>2319.813   </td><td>51.35154   </td><td>23.20172   </td><td> 81.575706 </td><td>0.8921085  </td><td>207801     </td></tr>\n",
       "\t<tr><td>Cs002      </td><td>SHAPES_DATA</td><td> 953       </td><td> 925.0     </td><td>1.0302703  </td><td>130.0833   </td><td>24         </td><td>52         </td><td>52         </td><td>1683.022   </td><td>2302.978   </td><td>18         </td><td>True       </td><td>1683.099   </td><td>2302.573   </td><td>53.56529   </td><td>21.67561   </td><td>  9.000335 </td><td>0.9144682  </td><td>207801     </td></tr>\n",
       "\t<tr><td>Cs002      </td><td>SHAPES_DATA</td><td> 927       </td><td> 907.5     </td><td>1.0214876  </td><td>131.8822   </td><td>32         </td><td>50         </td><td>48         </td><td>2119.455   </td><td>2269.763   </td><td>24         </td><td>True       </td><td>2119.288   </td><td>2269.406   </td><td>54.65794   </td><td>20.67560   </td><td>156.044724 </td><td>0.9256943  </td><td>207801     </td></tr>\n",
       "\t<tr><td>Cs002      </td><td>SHAPES_DATA</td><td>2200       </td><td>2764.5     </td><td>0.7958039  </td><td>252.6518   </td><td>61         </td><td>69         </td><td>58         </td><td>1753.735   </td><td>2240.008   </td><td>22         </td><td>True       </td><td>1755.812   </td><td>2238.624   </td><td>69.78819   </td><td>50.83885   </td><td>149.902786 </td><td>0.6850740  </td><td>207801     </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllllllllllll}\n",
       " genotype & HEADER\\_SHAPES & area & hull.area & solidity & perimeter & width & height & longest\\_axis & center.of.mass.x & center.of.mass.y & hull\\_vertices & in\\_bounds & ellipse\\_center\\_x & ellipse\\_center\\_y & ellipse\\_major\\_axis & ellipse\\_minor\\_axis & ellipse\\_angle & ellipse\\_eccentricity & marker\\_area\\\\\n",
       "\\hline\n",
       "\t Cs002         & SHAPES\\_DATA & 1655          & 1618.5        & 1.0225517     & 166.6102      & 40            & 62            & 62            & 2154.579      & 2451.401      & 26            & True          & 2154.481      & 2451.103      & 66.75710      & 30.41299      & 156.244919    & 0.8901964     & 207801       \\\\\n",
       "\t Cs002         & SHAPES\\_DATA & 1657          & 1633.0        & 1.0146969     & 163.6812      & 55            & 47            & 49            & 2524.626      & 2384.845      & 25            & True          & 2524.716      & 2384.752      & 63.24441      & 32.10065      &  53.203114    & 0.8616134     & 207801       \\\\\n",
       "\t Cs002         & SHAPES\\_DATA &  979          &  937.0        & 1.0448239     & 125.5980      & 49            & 24            & 48            & 1623.824      & 2319.848      & 19            & True          & 1624.177      & 2319.813      & 51.35154      & 23.20172      &  81.575706    & 0.8921085     & 207801       \\\\\n",
       "\t Cs002         & SHAPES\\_DATA &  953          &  925.0        & 1.0302703     & 130.0833      & 24            & 52            & 52            & 1683.022      & 2302.978      & 18            & True          & 1683.099      & 2302.573      & 53.56529      & 21.67561      &   9.000335    & 0.9144682     & 207801       \\\\\n",
       "\t Cs002         & SHAPES\\_DATA &  927          &  907.5        & 1.0214876     & 131.8822      & 32            & 50            & 48            & 2119.455      & 2269.763      & 24            & True          & 2119.288      & 2269.406      & 54.65794      & 20.67560      & 156.044724    & 0.9256943     & 207801       \\\\\n",
       "\t Cs002         & SHAPES\\_DATA & 2200          & 2764.5        & 0.7958039     & 252.6518      & 61            & 69            & 58            & 1753.735      & 2240.008      & 22            & True          & 1755.812      & 2238.624      & 69.78819      & 50.83885      & 149.902786    & 0.6850740     & 207801       \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "  genotype HEADER_SHAPES area hull.area solidity  perimeter width height\n",
       "1 Cs002    SHAPES_DATA   1655 1618.5    1.0225517 166.6102  40    62    \n",
       "2 Cs002    SHAPES_DATA   1657 1633.0    1.0146969 163.6812  55    47    \n",
       "3 Cs002    SHAPES_DATA    979  937.0    1.0448239 125.5980  49    24    \n",
       "4 Cs002    SHAPES_DATA    953  925.0    1.0302703 130.0833  24    52    \n",
       "5 Cs002    SHAPES_DATA    927  907.5    1.0214876 131.8822  32    50    \n",
       "6 Cs002    SHAPES_DATA   2200 2764.5    0.7958039 252.6518  61    69    \n",
       "  longest_axis center.of.mass.x center.of.mass.y hull_vertices in_bounds\n",
       "1 62           2154.579         2451.401         26            True     \n",
       "2 49           2524.626         2384.845         25            True     \n",
       "3 48           1623.824         2319.848         19            True     \n",
       "4 52           1683.022         2302.978         18            True     \n",
       "5 48           2119.455         2269.763         24            True     \n",
       "6 58           1753.735         2240.008         22            True     \n",
       "  ellipse_center_x ellipse_center_y ellipse_major_axis ellipse_minor_axis\n",
       "1 2154.481         2451.103         66.75710           30.41299          \n",
       "2 2524.716         2384.752         63.24441           32.10065          \n",
       "3 1624.177         2319.813         51.35154           23.20172          \n",
       "4 1683.099         2302.573         53.56529           21.67561          \n",
       "5 2119.288         2269.406         54.65794           20.67560          \n",
       "6 1755.812         2238.624         69.78819           50.83885          \n",
       "  ellipse_angle ellipse_eccentricity marker_area\n",
       "1 156.244919    0.8901964            207801     \n",
       "2  53.203114    0.8616134            207801     \n",
       "3  81.575706    0.8921085            207801     \n",
       "4   9.000335    0.9144682            207801     \n",
       "5 156.044724    0.9256943            207801     \n",
       "6 149.902786    0.6850740            207801     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# STEP 2: Read in the seed phenotype data from a file\n",
    "# read.table reads data from a text file into an R data frame\n",
    "seed.pheno = read.table(file = \"./examples/plantcv_seed_phenotypes.table\", sep = \"\\t\", header = TRUE)\n",
    "head(seed.pheno) # Previews the top of the data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>genotype</th><th scope=col>HEADER_SHAPES</th><th scope=col>area</th><th scope=col>hull.area</th><th scope=col>solidity</th><th scope=col>perimeter</th><th scope=col>width</th><th scope=col>height</th><th scope=col>longest_axis</th><th scope=col>center.of.mass.x</th><th scope=col>⋯</th><th scope=col>hull_vertices</th><th scope=col>in_bounds</th><th scope=col>ellipse_center_x</th><th scope=col>ellipse_center_y</th><th scope=col>ellipse_major_axis</th><th scope=col>ellipse_minor_axis</th><th scope=col>ellipse_angle</th><th scope=col>ellipse_eccentricity</th><th scope=col>marker_area</th><th scope=col>norm_area</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>Cs002      </td><td>SHAPES_DATA</td><td>1655       </td><td>1618.5     </td><td>1.0225517  </td><td>166.6102   </td><td>40         </td><td>62         </td><td>62         </td><td>2154.579   </td><td>⋯          </td><td>26         </td><td>True       </td><td>2154.481   </td><td>2451.103   </td><td>66.75710   </td><td>30.41299   </td><td>156.244919 </td><td>0.8901964  </td><td>207801     </td><td>0.007964351</td></tr>\n",
       "\t<tr><td>Cs002      </td><td>SHAPES_DATA</td><td>1657       </td><td>1633.0     </td><td>1.0146969  </td><td>163.6812   </td><td>55         </td><td>47         </td><td>49         </td><td>2524.626   </td><td>⋯          </td><td>25         </td><td>True       </td><td>2524.716   </td><td>2384.752   </td><td>63.24441   </td><td>32.10065   </td><td> 53.203114 </td><td>0.8616134  </td><td>207801     </td><td>0.007973975</td></tr>\n",
       "\t<tr><td>Cs002      </td><td>SHAPES_DATA</td><td> 979       </td><td> 937.0     </td><td>1.0448239  </td><td>125.5980   </td><td>49         </td><td>24         </td><td>48         </td><td>1623.824   </td><td>⋯          </td><td>19         </td><td>True       </td><td>1624.177   </td><td>2319.813   </td><td>51.35154   </td><td>23.20172   </td><td> 81.575706 </td><td>0.8921085  </td><td>207801     </td><td>0.004711238</td></tr>\n",
       "\t<tr><td>Cs002      </td><td>SHAPES_DATA</td><td> 953       </td><td> 925.0     </td><td>1.0302703  </td><td>130.0833   </td><td>24         </td><td>52         </td><td>52         </td><td>1683.022   </td><td>⋯          </td><td>18         </td><td>True       </td><td>1683.099   </td><td>2302.573   </td><td>53.56529   </td><td>21.67561   </td><td>  9.000335 </td><td>0.9144682  </td><td>207801     </td><td>0.004586118</td></tr>\n",
       "\t<tr><td>Cs002      </td><td>SHAPES_DATA</td><td> 927       </td><td> 907.5     </td><td>1.0214876  </td><td>131.8822   </td><td>32         </td><td>50         </td><td>48         </td><td>2119.455   </td><td>⋯          </td><td>24         </td><td>True       </td><td>2119.288   </td><td>2269.406   </td><td>54.65794   </td><td>20.67560   </td><td>156.044724 </td><td>0.9256943  </td><td>207801     </td><td>0.004460999</td></tr>\n",
       "\t<tr><td>Cs002      </td><td>SHAPES_DATA</td><td>2200       </td><td>2764.5     </td><td>0.7958039  </td><td>252.6518   </td><td>61         </td><td>69         </td><td>58         </td><td>1753.735   </td><td>⋯          </td><td>22         </td><td>True       </td><td>1755.812   </td><td>2238.624   </td><td>69.78819   </td><td>50.83885   </td><td>149.902786 </td><td>0.6850740  </td><td>207801     </td><td>0.010587052</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllllllllllllllll}\n",
       " genotype & HEADER\\_SHAPES & area & hull.area & solidity & perimeter & width & height & longest\\_axis & center.of.mass.x & ⋯ & hull\\_vertices & in\\_bounds & ellipse\\_center\\_x & ellipse\\_center\\_y & ellipse\\_major\\_axis & ellipse\\_minor\\_axis & ellipse\\_angle & ellipse\\_eccentricity & marker\\_area & norm\\_area\\\\\n",
       "\\hline\n",
       "\t Cs002         & SHAPES\\_DATA & 1655          & 1618.5        & 1.0225517     & 166.6102      & 40            & 62            & 62            & 2154.579      & ⋯             & 26            & True          & 2154.481      & 2451.103      & 66.75710      & 30.41299      & 156.244919    & 0.8901964     & 207801        & 0.007964351  \\\\\n",
       "\t Cs002         & SHAPES\\_DATA & 1657          & 1633.0        & 1.0146969     & 163.6812      & 55            & 47            & 49            & 2524.626      & ⋯             & 25            & True          & 2524.716      & 2384.752      & 63.24441      & 32.10065      &  53.203114    & 0.8616134     & 207801        & 0.007973975  \\\\\n",
       "\t Cs002         & SHAPES\\_DATA &  979          &  937.0        & 1.0448239     & 125.5980      & 49            & 24            & 48            & 1623.824      & ⋯             & 19            & True          & 1624.177      & 2319.813      & 51.35154      & 23.20172      &  81.575706    & 0.8921085     & 207801        & 0.004711238  \\\\\n",
       "\t Cs002         & SHAPES\\_DATA &  953          &  925.0        & 1.0302703     & 130.0833      & 24            & 52            & 52            & 1683.022      & ⋯             & 18            & True          & 1683.099      & 2302.573      & 53.56529      & 21.67561      &   9.000335    & 0.9144682     & 207801        & 0.004586118  \\\\\n",
       "\t Cs002         & SHAPES\\_DATA &  927          &  907.5        & 1.0214876     & 131.8822      & 32            & 50            & 48            & 2119.455      & ⋯             & 24            & True          & 2119.288      & 2269.406      & 54.65794      & 20.67560      & 156.044724    & 0.9256943     & 207801        & 0.004460999  \\\\\n",
       "\t Cs002         & SHAPES\\_DATA & 2200          & 2764.5        & 0.7958039     & 252.6518      & 61            & 69            & 58            & 1753.735      & ⋯             & 22            & True          & 1755.812      & 2238.624      & 69.78819      & 50.83885      & 149.902786    & 0.6850740     & 207801        & 0.010587052  \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "  genotype HEADER_SHAPES area hull.area solidity  perimeter width height\n",
       "1 Cs002    SHAPES_DATA   1655 1618.5    1.0225517 166.6102  40    62    \n",
       "2 Cs002    SHAPES_DATA   1657 1633.0    1.0146969 163.6812  55    47    \n",
       "3 Cs002    SHAPES_DATA    979  937.0    1.0448239 125.5980  49    24    \n",
       "4 Cs002    SHAPES_DATA    953  925.0    1.0302703 130.0833  24    52    \n",
       "5 Cs002    SHAPES_DATA    927  907.5    1.0214876 131.8822  32    50    \n",
       "6 Cs002    SHAPES_DATA   2200 2764.5    0.7958039 252.6518  61    69    \n",
       "  longest_axis center.of.mass.x ⋯ hull_vertices in_bounds ellipse_center_x\n",
       "1 62           2154.579         ⋯ 26            True      2154.481        \n",
       "2 49           2524.626         ⋯ 25            True      2524.716        \n",
       "3 48           1623.824         ⋯ 19            True      1624.177        \n",
       "4 52           1683.022         ⋯ 18            True      1683.099        \n",
       "5 48           2119.455         ⋯ 24            True      2119.288        \n",
       "6 58           1753.735         ⋯ 22            True      1755.812        \n",
       "  ellipse_center_y ellipse_major_axis ellipse_minor_axis ellipse_angle\n",
       "1 2451.103         66.75710           30.41299           156.244919   \n",
       "2 2384.752         63.24441           32.10065            53.203114   \n",
       "3 2319.813         51.35154           23.20172            81.575706   \n",
       "4 2302.573         53.56529           21.67561             9.000335   \n",
       "5 2269.406         54.65794           20.67560           156.044724   \n",
       "6 2238.624         69.78819           50.83885           149.902786   \n",
       "  ellipse_eccentricity marker_area norm_area  \n",
       "1 0.8901964            207801      0.007964351\n",
       "2 0.8616134            207801      0.007973975\n",
       "3 0.8921085            207801      0.004711238\n",
       "4 0.9144682            207801      0.004586118\n",
       "5 0.9256943            207801      0.004460999\n",
       "6 0.6850740            207801      0.010587052"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# STEP 3: Normalize the seed area by the marker area to account for differences between cameras\n",
    "# Add a new column (norm_area) to the data frame\n",
    "# In R, mathmatical operations can be done on vectors, in this case dividing the values of the area column\n",
    "# by the values in the marker_area column\n",
    "# Scroll to the right in the output to see the new column\n",
    "seed.pheno$norm_area = seed.pheno$area / seed.pheno$marker_area\n",
    "head(seed.pheno)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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UHQFAzD7O3tqQcCrp0yZQrnOLxLly5N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\nTgfUycvLq7i4uLS0tLKy0s7Obvr06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w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\ngg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# STEP 4: Plot the distribution of seed size by genotype\n",
    "# Provide ggplot with the data frame, and use aes to map the genotype and norm_area columns to the x and y variables\n",
    "seed.boxplot = ggplot(seed.pheno, aes(x = genotype, y = norm_area)) + \n",
    "               # Use the boxplot plotting feature\n",
    "               geom_boxplot() +\n",
    "               # Add the y values as a scatterplot and \"jitter\" the points along the x-axis\n",
    "               geom_jitter(width = 0.2) +\n",
    "               # Provide a better x-axis label\n",
    "               scale_x_discrete(\"Camelina genotype\") +\n",
    "               # Provide a better y-axis label\n",
    "               scale_y_continuous(\"Seed size (px/marker px)\") +\n",
    "               # Change the plotting theme (black and white theme)\n",
    "               theme_bw() +\n",
    "               # Adjust the theme to bold the axis titles\n",
    "               theme(axis.title.x = element_text(face = 'bold'), \n",
    "                     axis.title.y = element_text(face = 'bold'))\n",
    "print(seed.boxplot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Call:\n",
      "   aov(formula = norm_area ~ genotype, data = seed.pheno)\n",
      "\n",
      "Terms:\n",
      "                    genotype    Residuals\n",
      "Sum of Squares  0.0000006864 0.0002490316\n",
      "Deg. of Freedom            1           63\n",
      "\n",
      "Residual standard error: 0.001988186\n",
      "Estimated effects may be unbalanced\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "            Df   Sum Sq   Mean Sq F value Pr(>F)\n",
       "genotype     1 6.90e-07 6.860e-07   0.174  0.678\n",
       "Residuals   63 2.49e-04 3.953e-06               "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# STEP 5: Is there a significant difference between genotypes?\n",
    "# The aov function does an analysis of variance test on the linear model norm_area varies by genotype\n",
    "seed.aov = aov(norm_area ~ genotype, data = seed.pheno)\n",
    "print(seed.aov)\n",
    "summary(seed.aov)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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