date.ipynb

rats_biochemistry.ipynb

rats_masses.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Анализ масс"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Updating HTML index of packages in '.Library'\n",
      "Making 'packages.html' ... done\n"
     ]
    }
   ],
   "source": [
    "install.packages(c(\"gridExtra\", \"ggsignif\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "library(gridExtra)\n",
    "library(ggplot2)\n",
    "library(ggsignif)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# графики"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "masses <- read.csv(\"masses.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "title <- 'Относительные массы внутренних органов, %'\n",
    "x_label <- 'Группы'\n",
    "y_label <- '%'\n",
    "group_labels <- c(\"Спирт\", \"Интактная\", \"Парацетамол\", \"Водка\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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SqVavXq1YsWLZoyZcoPP/xQU01Lvrxs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qla5cuXLmzJn+/v53797dvXt3ZGRkZGSk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\nADTi/wO6VkFSMZcFPgAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(masses, aes(group, value, fill = group)) +\n",
    "    geom_boxplot() +\n",
    "    stat_boxplot(geom = \"errorbar\", width = 0.5) +\n",
    "    scale_x_discrete() +\n",
    "    scale_fill_brewer(palette = \"Set3\",\n",
    "                      name = x_label,\n",
    "                      labels = group_labels) +\n",
    "    theme_bw() +\n",
    "    theme(axis.text.x = element_blank(),\n",
    "          axis.text.y = element_text(angle = 60, hjust = 0.5, size = 8)) +\n",
    "    labs(title = title,\n",
    "         x = element_blank(),\n",
    "         y = y_label) +\n",
    "    facet_wrap(~ organ, scales = 'free_y', ncol = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ggsave(\"masses.png\", scale = 1, width = 25, height = 10, units = \"cm\", dpi = 300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Корреляционный анализ\n",
    "\n",
    "Критерий Манна-Уитни-Вилкоксона\n",
    "\n",
    "## Сравнение парацетамол ~ интактная группа\n",
    "\n",
    "### Печень (Liver)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tWilcoxon rank sum test\n",
       "\n",
       "data:  value by group\n",
       "W = 0, p-value = 1.083e-05\n",
       "alternative hypothesis: true location shift is not equal to 0\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wilcox.test(value ~ group, subset = masses$organ %in% 'liver' & masses$group %in% c('intact', 'paracetamol'),\n",
    "            data = masses, paired = FALSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Селезенка (Spleen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tWilcoxon rank sum test\n",
       "\n",
       "data:  value by group\n",
       "W = 61, p-value = 0.4359\n",
       "alternative hypothesis: true location shift is not equal to 0\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wilcox.test(value ~ group, subset = masses$organ %in% 'spleen' & masses$group %in% c('intact', 'paracetamol'),\n",
    "            data = masses, paired = FALSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Почки (Buds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in wilcox.test.default(x = c(0.43, 0.56, 0.52, 0.51, 0.52, 0.66, :\n",
      "“cannot compute exact p-value with ties”"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tWilcoxon rank sum test with continuity correction\n",
       "\n",
       "data:  value by group\n",
       "W = 27, p-value = 0.08885\n",
       "alternative hypothesis: true location shift is not equal to 0\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wilcox.test(value ~ group, subset = masses$organ %in% 'buds' & masses$group %in% c('intact', 'paracetamol'),\n",
    "            data = masses, paired = FALSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Надпочечники (Adrenal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in wilcox.test.default(x = c(0.015, 0.02, 0.017, 0.012, 0.021, 0.015, :\n",
      "“cannot compute exact p-value with ties”"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tWilcoxon rank sum test with continuity correction\n",
       "\n",
       "data:  value by group\n",
       "W = 46, p-value = 0.791\n",
       "alternative hypothesis: true location shift is not equal to 0\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wilcox.test(value ~ group, subset = masses$organ %in% 'adrenal' & masses$group %in% c('intact', 'paracetamol'),\n",
    "            data = masses, paired = FALSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Слепая кишка (Blind gut)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tWilcoxon rank sum test\n",
       "\n",
       "data:  value by group\n",
       "W = 21, p-value = 0.02881\n",
       "alternative hypothesis: true location shift is not equal to 0\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wilcox.test(value ~ group, subset = masses$organ %in% 'blindgut' & masses$group %in% c('intact', 'paracetamol'),\n",
    "            data = masses, paired = FALSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Сравнение спирт ~ интактная группа\n",
    "\n",
    "### Печень (Liver)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tWilcoxon rank sum test\n",
       "\n",
       "data:  value by group\n",
       "W = 91, p-value = 0.00105\n",
       "alternative hypothesis: true location shift is not equal to 0\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wilcox.test(value ~ group, subset = masses$organ %in% 'liver' & masses$group %in% c('intact', 'alcohol'),\n",
    "            data = masses, paired = FALSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Селезенка (Spleen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tWilcoxon rank sum test\n",
       "\n",
       "data:  value by group\n",
       "W = 67, p-value = 0.2176\n",
       "alternative hypothesis: true location shift is not equal to 0\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wilcox.test(value ~ group, subset = masses$organ %in% 'spleen' & masses$group %in% c('intact', 'alcohol'),\n",
    "            data = masses, paired = FALSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Почки (Buds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in wilcox.test.default(x = c(0.513736264, 0.574938575, 0.579787234, :\n",
      "“cannot compute exact p-value with ties”"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tWilcoxon rank sum test with continuity correction\n",
       "\n",
       "data:  value by group\n",
       "W = 48, p-value = 0.9097\n",
       "alternative hypothesis: true location shift is not equal to 0\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wilcox.test(value ~ group, subset = masses$organ %in% 'buds' & masses$group %in% c('intact', 'alcohol'),\n",
    "            data = masses, paired = FALSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Надпочечники (Adrenal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in wilcox.test.default(x = c(0.018131868, 0.021621622, 0.015957447, :\n",
      "“cannot compute exact p-value with ties”"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tWilcoxon rank sum test with continuity correction\n",
       "\n",
       "data:  value by group\n",
       "W = 51, p-value = 0.9698\n",
       "alternative hypothesis: true location shift is not equal to 0\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wilcox.test(value ~ group, subset = masses$organ %in% 'adrenal' & masses$group %in% c('intact', 'alcohol'),\n",
    "            data = masses, paired = FALSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Слепая кишка (Blind gut)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tWilcoxon rank sum test\n",
       "\n",
       "data:  value by group\n",
       "W = 86, p-value = 0.005196\n",
       "alternative hypothesis: true location shift is not equal to 0\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wilcox.test(value ~ group, subset = masses$organ %in% 'blindgut' & masses$group %in% c('intact', 'alcohol'),\n",
    "            data = masses, paired = FALSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Сравнение водка ~ интактная группа\n",
    "\n",
    "### Печень (Liver)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tWilcoxon rank sum test\n",
       "\n",
       "data:  value by group\n",
       "W = 6, p-value = 0.0003248\n",
       "alternative hypothesis: true location shift is not equal to 0\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wilcox.test(value ~ group, subset = masses$organ %in% 'liver' & masses$group %in% c('intact', 'vodka'),\n",
    "            data = masses, paired = FALSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Селезенка (Spleen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tWilcoxon rank sum test\n",
       "\n",
       "data:  value by group\n",
       "W = 39, p-value = 0.4359\n",
       "alternative hypothesis: true location shift is not equal to 0\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wilcox.test(value ~ group, subset = masses$organ %in% 'spleen' & masses$group %in% c('intact', 'vodka'),\n",
    "            data = masses, paired = FALSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Почки (Buds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in wilcox.test.default(x = c(0.43, 0.56, 0.52, 0.51, 0.52, 0.66, :\n",
      "“cannot compute exact p-value with ties”"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tWilcoxon rank sum test with continuity correction\n",
       "\n",
       "data:  value by group\n",
       "W = 45, p-value = 0.7336\n",
       "alternative hypothesis: true location shift is not equal to 0\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wilcox.test(value ~ group, subset = masses$organ %in% 'buds' & masses$group %in% c('intact', 'vodka'),\n",
    "            data = masses, paired = FALSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Надпочечники (Adrenal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in wilcox.test.default(x = c(0.015, 0.02, 0.017, 0.012, 0.021, 0.015, :\n",
      "“cannot compute exact p-value with ties”"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tWilcoxon rank sum test with continuity correction\n",
       "\n",
       "data:  value by group\n",
       "W = 55, p-value = 0.7332\n",
       "alternative hypothesis: true location shift is not equal to 0\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wilcox.test(value ~ group, subset = masses$organ %in% 'adrenal' & masses$group %in% c('intact', 'vodka'),\n",
    "            data = masses, paired = FALSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Слепая кишка (Blind gut)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tWilcoxon rank sum test\n",
       "\n",
       "data:  value by group\n",
       "W = 2, p-value = 4.33e-05\n",
       "alternative hypothesis: true location shift is not equal to 0\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wilcox.test(value ~ group, subset = masses$organ %in% 'blindgut' & masses$group %in% c('intact', 'vodka'),\n",
    "            data = masses, paired = FALSE)"
   ]
  }
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rats_microbiology.ipynb