All_Human_Data.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "e8a1f9bf-4d01-4da9-bb0b-56c7d372b788",
   "metadata": {},
   "source": [
    "### Create multifaceted plots to display human diversity in the Arivale and the Human Gut cohort"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7c3c9a5e-9d20-4107-8cba-5965227e5ddb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns \n",
    "import matplotlib.pyplot as plt\n",
    "from statannot import add_stat_annotation\n",
    "import matplotlib\n",
    "import warnings\n",
    "%matplotlib inline\n",
    "warnings.simplefilter('ignore')\n",
    "from arivale_data_interface import *\n",
    "frozen_ss_path='/proj/arivale/snapshots/arivale_snapshot_ISB_2020-03-16_2156'\n",
    "sn=list_snapshot_contents() #list all content\n",
    "def get_frozen_snapshot(ss_name, ss_path=frozen_ss_path): #collect data\n",
    "    return get_snapshot(ss_name, path=ss_path)\n",
    "from statsmodels.stats.anova import anova_lm\n",
    "import statsmodels.formula.api as smf\n",
    "import scipy.stats\n",
    "warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "540de6c0-d794-49f6-b784-9da415d11c81",
   "metadata": {},
   "outputs": [],
   "source": [
    "genpath = '/proj/gibbons/kramos/github/IBT-and-the-Gut-Microbiome'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "80e24cd3-597a-4890-9c94-cdb1a111e294",
   "metadata": {},
   "outputs": [
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       "      <td>10317.000059045</td>\n",
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       "      <td>80.491449</td>\n",
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       "<p>9511 rows × 4 columns</p>\n",
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       "      Unnamed: 0.1       Unnamed: 0   simpson  inv_simpson\n",
       "4422          4422  10317.000001626  0.039547     1.041175\n",
       "398            398  10317.000003200  0.068887     1.073984\n",
       "2055          2055  10317.000001334  0.075268     1.081394\n",
       "5729          5729  10317.000037953  0.087410     1.095782\n",
       "5219          5219  10317.000014173  0.124823     1.142626\n",
       "...            ...              ...       ...          ...\n",
       "8246          8246  10317.000017202  0.984888    66.173979\n",
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       "\n",
       "[9511 rows x 4 columns]"
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "amgut=pd.read_csv(genpath+'/american_gut_cohort/alpha-diversity.csv')\n",
    "amgut.sort_values(by='inv_simpson')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "24d02195-19e6-42b6-8417-a6b2a0ef3240",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sample_name</th>\n",
       "      <th>sex</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>age</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>bowel_movement_frequency</th>\n",
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       "      <th>Unnamed: 0.1</th>\n",
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       "      <th>0</th>\n",
       "      <td>10317.000001000</td>\n",
       "      <td>F</td>\n",
       "      <td>154.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5</td>\n",
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       "      <td>0.845933</td>\n",
       "      <td>6.490674</td>\n",
       "      <td>60.629921</td>\n",
       "      <td>114.660</td>\n",
       "      <td>21.927709</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10317.000001031</td>\n",
       "      <td>M</td>\n",
       "      <td>178.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>10317.000001041</td>\n",
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       "      <td>162.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>63.779528</td>\n",
       "      <td>134.505</td>\n",
       "      <td>23.245086</td>\n",
       "      <td>high</td>\n",
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       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>10317.000001166</td>\n",
       "      <td>F</td>\n",
       "      <td>172.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>0.909821</td>\n",
       "      <td>11.089110</td>\n",
       "      <td>67.716535</td>\n",
       "      <td>163.170</td>\n",
       "      <td>25.015327</td>\n",
       "      <td>low</td>\n",
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       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>10317.000001171</td>\n",
       "      <td>F</td>\n",
       "      <td>167.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5</td>\n",
       "      <td>4597</td>\n",
       "      <td>0.948946</td>\n",
       "      <td>19.587073</td>\n",
       "      <td>65.748031</td>\n",
       "      <td>149.940</td>\n",
       "      <td>24.384133</td>\n",
       "      <td>high</td>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "        sample_name sex  height_cm   age  weight_kg  bowel_movement_frequency  \\\n",
       "0   10317.000001000   F      154.0  64.0       52.0                       3.0   \n",
       "11  10317.000001031   M      178.0  64.0       56.0                       3.0   \n",
       "14  10317.000001041   F      162.0  59.0       61.0                       2.0   \n",
       "60  10317.000001166   F      172.0  47.0       74.0                       4.0   \n",
       "62  10317.000001171   F      167.0  68.0       68.0                       5.0   \n",
       "\n",
       "    vegetable_frequency  Unnamed: 0.1   simpson  inv_simpson  height_in  \\\n",
       "0                     5          4350  0.845933     6.490674  60.629921   \n",
       "11                    4          7313  0.814207     5.382326  70.078740   \n",
       "14                    5          9216  0.872256     7.828156  63.779528   \n",
       "60                    3          5257  0.909821    11.089110  67.716535   \n",
       "62                    5          4597  0.948946    19.587073  65.748031   \n",
       "\n",
       "    weight_lb   BMI_CALC b_vegetable_frequency  \n",
       "0     114.660  21.927709                  high  \n",
       "11    123.480  17.675812                   low  \n",
       "14    134.505  23.245086                  high  \n",
       "60    163.170  25.015327                   low  \n",
       "62    149.940  24.384133                  high  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "######### Import, clean, and format American Gut dataset #########\n",
    "\n",
    "# Get amgut metadata, filter, and merge with amgut data\n",
    "metadata = pd.read_csv(genpath+'/american_gut_cohort/american_gut/metadata.tsv', sep = '\\t', low_memory = False)\\\n",
    "[['sample_name','sex','height_cm', 'age_years', 'weight_kg', 'bowel_movement_frequency', 'vegetable_frequency']]\n",
    "\n",
    "amgut=pd.read_csv(genpath+'/american_gut_cohort/alpha-diversity.csv')\n",
    "amgut=amgut.rename(columns = {'Unnamed: 0':'sample_name'})\n",
    "amgut=pd.merge(metadata, amgut, on ='sample_name')\n",
    "\n",
    "#Converting values to numeric\n",
    "amgut['height_cm'] = pd.to_numeric(amgut['height_cm'], errors = 'coerce')\n",
    "amgut['weight_kg'] = pd.to_numeric(amgut['weight_kg'], errors='coerce')\n",
    "amgut['age_years'] =  pd.to_numeric(amgut['age_years'], errors='coerce')\n",
    "\n",
    "#Filter\n",
    "amgut=amgut[amgut['age_years'] >= 18].rename(columns = {'age_years':'age'})\n",
    "amgut=amgut[(amgut['height_cm'] <= 244) & (amgut['height_cm'] >= 122)]\n",
    "amgut=amgut[amgut['weight_kg']<300]\n",
    "        \n",
    "# # Convert BMF to numeric for regressions\n",
    "norm_bmf=list()\n",
    "for i in range(0,len(amgut)):\n",
    "    check = amgut.iloc[i,5]\n",
    "    if check == 'Less than one':\n",
    "        norm_bmf.append(1)\n",
    "    elif check == 'One':\n",
    "        norm_bmf.append(2)\n",
    "    elif check == 'Two':\n",
    "        norm_bmf.append(3)\n",
    "    elif check == 'Three':\n",
    "        norm_bmf.append(4)\n",
    "    elif check == 'Four':\n",
    "        norm_bmf.append(5)\n",
    "    elif check=='Five or more':\n",
    "        norm_bmf.append(6)\n",
    "    else:\n",
    "        norm_bmf.append(float('nan'))\n",
    "        \n",
    "amgut['bowel_movement_frequency']=norm_bmf\n",
    "\n",
    "# Convert height from cm to inches so I can calculate the BMI \n",
    "amgut['height_in']=amgut['height_cm'].apply(lambda x: x/2.54)\n",
    "amgut['weight_lb']=amgut['weight_kg'].apply(lambda x: x*2.205)\n",
    "amgut['BMI_CALC']= amgut['weight_lb']/(amgut['height_in']**2)*703\n",
    "\n",
    "# Convert sex info to binomial M and F\n",
    "sexes=['female', 'male']\n",
    "amgut=amgut[amgut['sex'].isin(sexes)]\n",
    "amgut['sex']=amgut['sex'].apply(lambda x: 'F' if x=='female' else 'M')\n",
    "\n",
    "# Drop individuals who don't have info on veggie frequency data\n",
    "amgut=amgut[amgut['vegetable_frequency']!= 'Not provided']\n",
    "\n",
    "high = ['Daily']\n",
    "# Define veggie consumption on a binary \n",
    "amgut['b_vegetable_frequency'] = amgut['vegetable_frequency'].apply(lambda x: 'high' if x in high else 'low')\n",
    "\n",
    "# Convert vegetable frequency to numeric for regressions \n",
    "vegetable_frequency=list()\n",
    "for i in range(0,len(amgut)):\n",
    "    check = amgut.iloc[i,6]\n",
    "    if check == 'Never':\n",
    "        vegetable_frequency.append(1) # start at 0 like the Arivale dataset\n",
    "    elif check == 'Rarely (less than once/week)':\n",
    "        vegetable_frequency.append(2)\n",
    "    elif check == 'Occasionally (1-2 times/week)':\n",
    "        vegetable_frequency.append(3)\n",
    "    elif check == 'Regularly (3-5 times/week)':\n",
    "        vegetable_frequency.append(4)\n",
    "    elif check == 'Daily':\n",
    "        vegetable_frequency.append(5)\n",
    "    else:\n",
    "        vegetable_frequency.append(float('nan'))\n",
    "amgut['vegetable_frequency'] = vegetable_frequency\n",
    "\n",
    "\n",
    "amgut.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "170d1d85-3efa-4152-b1bf-266f3b8418fc",
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       "      <th>assessment:digestion:bowel-movements:enum</th>\n",
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       "      <th>0</th>\n",
       "      <td>5216</td>\n",
       "      <td>AV15-4372|AKE035</td>\n",
       "      <td>0.973573</td>\n",
       "      <td>37.839776</td>\n",
       "      <td>01602320</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>M</td>\n",
       "      <td>47.0</td>\n",
       "      <td>37.229730</td>\n",
       "      <td>74.0</td>\n",
       "      <td>187.96</td>\n",
       "      <td>(3) 1-3 times daily</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>low</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5081</td>\n",
       "      <td>AV15-3716|AKE025</td>\n",
       "      <td>0.977155</td>\n",
       "      <td>43.774041</td>\n",
       "      <td>01228476</td>\n",
       "      <td>0.0</td>\n",
       "      <td>F</td>\n",
       "      <td>50.0</td>\n",
       "      <td>22.462722</td>\n",
       "      <td>65.0</td>\n",
       "      <td>165.10</td>\n",
       "      <td>(3) 1-3 times daily</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4956</td>\n",
       "      <td>AV15-3489|AKE025</td>\n",
       "      <td>0.874632</td>\n",
       "      <td>7.976489</td>\n",
       "      <td>01826852</td>\n",
       "      <td>1.0</td>\n",
       "      <td>F</td>\n",
       "      <td>35.0</td>\n",
       "      <td>24.541709</td>\n",
       "      <td>61.0</td>\n",
       "      <td>154.94</td>\n",
       "      <td>(3) 1-3 times daily</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5150</td>\n",
       "      <td>AV15-3840|AKE025</td>\n",
       "      <td>0.957654</td>\n",
       "      <td>23.615008</td>\n",
       "      <td>01602518</td>\n",
       "      <td>1.0</td>\n",
       "      <td>F</td>\n",
       "      <td>49.0</td>\n",
       "      <td>34.154541</td>\n",
       "      <td>64.0</td>\n",
       "      <td>162.56</td>\n",
       "      <td>(3) 1-3 times daily</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5020</td>\n",
       "      <td>AV15-3636|AKE025</td>\n",
       "      <td>0.981430</td>\n",
       "      <td>53.849198</td>\n",
       "      <td>01447446</td>\n",
       "      <td>1.0</td>\n",
       "      <td>F</td>\n",
       "      <td>56.0</td>\n",
       "      <td>21.950816</td>\n",
       "      <td>70.0</td>\n",
       "      <td>177.80</td>\n",
       "      <td>(2) 3-6 times per week</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0.1                id   simpson  inv_simpson sample_id  \\\n",
       "0          5216  AV15-4372|AKE035  0.973573    37.839776  01602320   \n",
       "1          5081  AV15-3716|AKE025  0.977155    43.774041  01228476   \n",
       "2          4956  AV15-3489|AKE025  0.874632     7.976489  01826852   \n",
       "3          5150  AV15-3840|AKE025  0.957654    23.615008  01602518   \n",
       "4          5020  AV15-3636|AKE025  0.981430    53.849198  01447446   \n",
       "\n",
       "   days_in_program sex   age   BMI_CALC  HEIGHT_CALC  height_cm  \\\n",
       "0             -2.0   M  47.0  37.229730         74.0     187.96   \n",
       "1              0.0   F  50.0  22.462722         65.0     165.10   \n",
       "2              1.0   F  35.0  24.541709         61.0     154.94   \n",
       "3              1.0   F  49.0  34.154541         64.0     162.56   \n",
       "4              1.0   F  56.0  21.950816         70.0     177.80   \n",
       "\n",
       "  assessment:digestion:bowel-movements:enum  bowel_movement_frequency  \\\n",
       "0                       (3) 1-3 times daily                       3.0   \n",
       "1                       (3) 1-3 times daily                       3.0   \n",
       "2                       (3) 1-3 times daily                       3.0   \n",
       "3                       (3) 1-3 times daily                       3.0   \n",
       "4                    (2) 3-6 times per week                       2.0   \n",
       "\n",
       "   vegetable_frequency b_vegetable_frequency  \n",
       "0                  1.0                   low  \n",
       "1                  4.0                  high  \n",
       "2                  4.0                  high  \n",
       "3                  2.0                   low  \n",
       "4                  3.0                  high  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "######### Import, clean, and format Arivale dataset #########\n",
    "\n",
    "arivale=pd.read_csv(genpath+'/arivale_cohort/alpha-diversity.csv').rename(columns={'Unnamed: 0':'id'})\n",
    "\n",
    "# Get sample metadata and merge\n",
    "metadata = pd.read_csv('/proj/arivale/microbiome/16S_processed/metadata.csv')[['public_client_id', 'id', 'days_in_program','sex', 'age']]\\\n",
    "                        .rename(columns = {'public_client_id':'sample_id'})\n",
    "\n",
    "\n",
    "arivale = pd.merge(arivale, metadata, on = 'id')\n",
    "\n",
    "# Get weight data and merge\n",
    "weights = get_frozen_snapshot('weight')[['public_client_id', 'days_in_program', 'BMI_CALC', 'HEIGHT_CALC']]\\\n",
    "                     .dropna().rename(columns = {'public_client_id':'sample_id'})\n",
    "\n",
    "arivale = pd.merge_asof(\n",
    "    arivale[arivale.sample_id.isin(weights.sample_id)].sort_values(by='days_in_program'),\n",
    "    weights.sort_values(by='days_in_program'), \n",
    "    by = 'sample_id', on = 'days_in_program', direction='nearest')\n",
    "\n",
    "# convert height from in to cm \n",
    "arivale['height_cm']=arivale['HEIGHT_CALC'].apply(lambda x: x*2.54)\n",
    "\n",
    "# Finally, I need bowel movement frequency \n",
    "digestion = get_frozen_snapshot('assessments_digestive_health')[['public_client_id', 'days_in_program', 'assessment:digestion:bowel-movements:enum']].dropna().rename(columns = {'public_client_id':'sample_id'})\n",
    "\n",
    "# Get the bowel movement number \n",
    "digestion['bowel_movement_frequency'] = digestion['assessment:digestion:bowel-movements:enum'].astype(str).apply(lambda x: x[1])\n",
    "digestion['bowel_movement_frequency'] = digestion['bowel_movement_frequency'].astype(int)\n",
    "\n",
    "arivale = pd.merge_asof(arivale.sort_values(by='days_in_program'),\n",
    "    # arivale[arivale.sample_id.isin(arivale.sample_id)].sort_values(by='days_in_program'),\n",
    "    digestion.sort_values(by='days_in_program'),\n",
    "    by='sample_id', on='days_in_program', direction='nearest')\n",
    "\n",
    "# Add in dietary data\n",
    "diet=get_frozen_snapshot('assessments')[['public_client_id', 'days_in_program', 'assessment:lifestyle:vegetables:enum']].dropna().reset_index(drop=True)\\\n",
    ".rename(columns={'assessment:lifestyle:vegetables:enum':'vegetable_frequency', 'public_client_id':'sample_id'})\n",
    "\n",
    "# Make the vegetable frequency numeric \n",
    "diet_num = list()\n",
    "for i in range(0, len(diet)): \n",
    "    check = diet.iloc[i, 2]\n",
    "    if check == '(0) Zero/less than 1 per day':\n",
    "        diet_num.append(1)\n",
    "    elif check == '(1) 1': \n",
    "        diet_num.append(2)\n",
    "    elif check == '(2) 2-3': \n",
    "        diet_num.append(3)\n",
    "    elif check == '(3) 4-5':\n",
    "        diet_num.append(4)\n",
    "    elif check == '(4) 6 or more': \n",
    "        diet_num.append(5) \n",
    "    else: \n",
    "        float('nan')\n",
    "diet['vegetable_frequency']=diet_num\n",
    "high = [5,4,3]\n",
    "diet['b_vegetable_frequency'] = diet['vegetable_frequency'].apply(lambda x: 'high' if x in high else 'low')\n",
    "# diet['vegetable_frequency']=diet['vegetable_frequency'].astype(str).apply(lambda x: x[1])\n",
    "# diet['vegetable_frequency']=diet['vegetable_frequency'].astype(int)\n",
    "\n",
    "arivale=pd.merge_asof(\n",
    "    arivale.sort_values(by='days_in_program'), \n",
    "    diet[diet.sample_id.isin(arivale.sample_id)].sort_values(by='days_in_program'), \n",
    "    by='sample_id', on='days_in_program', direction='nearest')\n",
    "\n",
    "arivale.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8974f66-7cd4-46a0-8c24-e09a255301e9",
   "metadata": {},
   "source": [
    "### Preprocessing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a2afb8e4-e803-4c52-b450-a1fadf421ddf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop duplicate data\n",
    "arivale=arivale.sort_values(by='days_in_program').drop_duplicates(subset='sample_id', keep='first')\n",
    "amgut=amgut.drop_duplicates(subset='sample_name', keep='first')\n",
    "\n",
    "# Drop nas\n",
    "amgut=amgut[['sample_name', 'age', 'sex', 'BMI_CALC', 'bowel_movement_frequency', 'height_cm', 'inv_simpson', 'vegetable_frequency', 'b_vegetable_frequency']].dropna()\n",
    "arivale=arivale[['sample_id', 'age', 'sex', 'BMI_CALC', 'bowel_movement_frequency', 'height_cm', 'inv_simpson','vegetable_frequency', 'b_vegetable_frequency']].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e9925cad-04d2-4d77-a67c-445bd636c839",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Log transform Simpson's Diversity and height\n",
    "features = ['inv_simpson', 'height_cm']\n",
    "arivale[features] = arivale[features].apply(lambda x: np.log(x))\n",
    "amgut[features] = amgut[features].apply(lambda x: np.log(x))\n",
    "\n",
    "# # Standardize data\n",
    "features=['age', 'BMI_CALC', 'height_cm', 'inv_simpson'] \n",
    "arivale[features]=arivale[features].apply(lambda x:(x-x.mean()) / x.std())\n",
    "amgut[features]=amgut[features].apply(lambda x:(x-x.mean()) / x.std())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8e5d6273-7a32-4c1d-8d7d-243ef4365dea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "American Gut n=5516 \n",
      " Arivale n=3063\n"
     ]
    }
   ],
   "source": [
    "print(f\"American Gut n={amgut['sample_name'].nunique()} \\n Arivale n={arivale['sample_id'].nunique()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8dde292c-162c-4cbb-9f22-8dd5914b872e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>BMI_CALC</th>\n",
       "      <th>bowel_movement_frequency</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>inv_simpson</th>\n",
       "      <th>vegetable_frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5.516000e+03</td>\n",
       "      <td>5.516000e+03</td>\n",
       "      <td>5516.000000</td>\n",
       "      <td>5.516000e+03</td>\n",
       "      <td>5.516000e+03</td>\n",
       "      <td>5516.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-3.606816e-17</td>\n",
       "      <td>3.259016e-16</td>\n",
       "      <td>2.472444</td>\n",
       "      <td>-5.245985e-15</td>\n",
       "      <td>3.284779e-16</td>\n",
       "      <td>4.363307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.946896</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.808604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-2.035654e+00</td>\n",
       "      <td>-4.725096e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-5.168769e+00</td>\n",
       "      <td>-3.847112e+00</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-7.760290e-01</td>\n",
       "      <td>-6.195311e-01</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>-6.298241e-01</td>\n",
       "      <td>-6.014392e-01</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.692614e-02</td>\n",
       "      <td>-1.736779e-01</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>-1.121675e-01</td>\n",
       "      <td>1.136347e-01</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.859062e-01</td>\n",
       "      <td>3.905475e-01</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.852240e-01</td>\n",
       "      <td>7.180870e-01</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.137207e+00</td>\n",
       "      <td>1.014882e+01</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3.960000e+00</td>\n",
       "      <td>2.552450e+00</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age      BMI_CALC  bowel_movement_frequency     height_cm  \\\n",
       "count  5.516000e+03  5.516000e+03               5516.000000  5.516000e+03   \n",
       "mean  -3.606816e-17  3.259016e-16                  2.472444 -5.245985e-15   \n",
       "std    1.000000e+00  1.000000e+00                  0.946896  1.000000e+00   \n",
       "min   -2.035654e+00 -4.725096e+00                  1.000000 -5.168769e+00   \n",
       "25%   -7.760290e-01 -6.195311e-01                  2.000000 -6.298241e-01   \n",
       "50%    4.692614e-02 -1.736779e-01                  2.000000 -1.121675e-01   \n",
       "75%    7.859062e-01  3.905475e-01                  3.000000  6.852240e-01   \n",
       "max    3.137207e+00  1.014882e+01                  6.000000  3.960000e+00   \n",
       "\n",
       "        inv_simpson  vegetable_frequency  \n",
       "count  5.516000e+03          5516.000000  \n",
       "mean   3.284779e-16             4.363307  \n",
       "std    1.000000e+00             0.808604  \n",
       "min   -3.847112e+00             1.000000  \n",
       "25%   -6.014392e-01             4.000000  \n",
       "50%    1.136347e-01             5.000000  \n",
       "75%    7.180870e-01             5.000000  \n",
       "max    2.552450e+00             5.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "amgut[['sex', 'age', 'BMI_CALC', 'bowel_movement_frequency', 'height_cm', 'inv_simpson', 'vegetable_frequency', 'b_vegetable_frequency']].describe()\n",
    "# amgut[['sex', 'age', 'BMI_CALC', 'bowel_movement_frequency', 'height_cm', 'inv_simpson']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "12efed37-05a4-43d3-a3b2-b8bcb6793883",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>BMI_CALC</th>\n",
       "      <th>bowel_movement_frequency</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>inv_simpson</th>\n",
       "      <th>vegetable_frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.063000e+03</td>\n",
       "      <td>3.063000e+03</td>\n",
       "      <td>3063.000000</td>\n",
       "      <td>3.063000e+03</td>\n",
       "      <td>3.063000e+03</td>\n",
       "      <td>3063.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.577437e-16</td>\n",
       "      <td>5.358647e-16</td>\n",
       "      <td>2.672543</td>\n",
       "      <td>-1.171479e-15</td>\n",
       "      <td>-3.734815e-16</td>\n",
       "      <td>2.767548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.553051</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.871288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-2.321232e+00</td>\n",
       "      <td>-2.210651e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-3.846851e+00</td>\n",
       "      <td>-4.410504e+00</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-7.162808e-01</td>\n",
       "      <td>-6.863102e-01</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>-8.424989e-01</td>\n",
       "      <td>-5.779619e-01</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.947174e-03</td>\n",
       "      <td>-2.266505e-01</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>-3.244152e-02</td>\n",
       "      <td>1.246068e-01</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.281752e-01</td>\n",
       "      <td>4.442128e-01</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.421272e-01</td>\n",
       "      <td>7.044238e-01</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.135602e+00</td>\n",
       "      <td>5.627377e+00</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.103384e+00</td>\n",
       "      <td>2.673010e+00</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age      BMI_CALC  bowel_movement_frequency     height_cm  \\\n",
       "count  3.063000e+03  3.063000e+03               3063.000000  3.063000e+03   \n",
       "mean   1.577437e-16  5.358647e-16                  2.672543 -1.171479e-15   \n",
       "std    1.000000e+00  1.000000e+00                  0.553051  1.000000e+00   \n",
       "min   -2.321232e+00 -2.210651e+00                  1.000000 -3.846851e+00   \n",
       "25%   -7.162808e-01 -6.863102e-01                  2.000000 -8.424989e-01   \n",
       "50%    5.947174e-03 -2.266505e-01                  3.000000 -3.244152e-02   \n",
       "75%    7.281752e-01  4.442128e-01                  3.000000  7.421272e-01   \n",
       "max    3.135602e+00  5.627377e+00                  4.000000  3.103384e+00   \n",
       "\n",
       "        inv_simpson  vegetable_frequency  \n",
       "count  3.063000e+03          3063.000000  \n",
       "mean  -3.734815e-16             2.767548  \n",
       "std    1.000000e+00             0.871288  \n",
       "min   -4.410504e+00             1.000000  \n",
       "25%   -5.779619e-01             2.000000  \n",
       "50%    1.246068e-01             3.000000  \n",
       "75%    7.044238e-01             3.000000  \n",
       "max    2.673010e+00             5.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arivale[['sex', 'age', 'BMI_CALC', 'bowel_movement_frequency', 'height_cm', 'inv_simpson', 'vegetable_frequency', 'b_vegetable_frequency']].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9da0ad9c-0042-440f-9fca-c3805147d5ff",
   "metadata": {},
   "source": [
    "### Comparing univariate versus multivariate models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d858db4e-cc80-4e01-980e-b6b5fd0fb503",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:            inv_simpson   R-squared:                       0.063\n",
      "Model:                            OLS   Adj. R-squared:                  0.061\n",
      "Method:                 Least Squares   F-statistic:                     29.23\n",
      "Date:                Mon, 24 Jun 2024   Prob (F-statistic):           2.76e-39\n",
      "Time:                        14:36:39   Log-Likelihood:                -4246.4\n",
      "No. Observations:                3063   AIC:                             8509.\n",
      "Df Residuals:                    3055   BIC:                             8557.\n",
      "Df Model:                           7                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=================================================================================================\n",
      "                                    coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------------------\n",
      "Intercept                         0.4082      0.102      4.011      0.000       0.209       0.608\n",
      "C(sex)[T.M]                      -0.1058      0.052     -2.039      0.042      -0.207      -0.004\n",
      "age                               0.0762      0.018      4.331      0.000       0.042       0.111\n",
      "BMI_CALC                         -0.1804      0.018    -10.163      0.000      -0.215      -0.146\n",
      "bowel_movement_frequency         -0.1831      0.033     -5.623      0.000      -0.247      -0.119\n",
      "vegetable_frequency               0.0440      0.021      2.138      0.033       0.004       0.084\n",
      "height_cm                         0.1670      0.061      2.734      0.006       0.047       0.287\n",
      "vegetable_frequency:height_cm    -0.0113      0.020     -0.559      0.576      -0.051       0.028\n",
      "==============================================================================\n",
      "Omnibus:                      293.032   Durbin-Watson:                   1.970\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              412.976\n",
      "Skew:                          -0.754   Prob(JB):                     2.11e-90\n",
      "Kurtosis:                       3.980   Cond. No.                         24.6\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "ari_ols=smf.ols(formula='inv_simpson ~ age + C(sex) + BMI_CALC + bowel_movement_frequency + vegetable_frequency + height_cm + vegetable_frequency:height_cm', data=arivale).fit()\n",
    "print(ari_ols.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a2015af7-2702-41bd-8aa1-104351f7989e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:            inv_simpson   R-squared:                       0.008\n",
      "Model:                            OLS   Adj. R-squared:                  0.007\n",
      "Method:                 Least Squares   F-statistic:                     23.50\n",
      "Date:                Mon, 24 Jun 2024   Prob (F-statistic):           1.31e-06\n",
      "Time:                        14:36:39   Log-Likelihood:                -4334.0\n",
      "No. Observations:                3063   AIC:                             8672.\n",
      "Df Residuals:                    3061   BIC:                             8684.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept  -3.722e-16      0.018  -2.07e-14      1.000      -0.035       0.035\n",
      "height_cm      0.0873      0.018      4.848      0.000       0.052       0.123\n",
      "==============================================================================\n",
      "Omnibus:                      294.385   Durbin-Watson:                   1.968\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              408.039\n",
      "Skew:                          -0.767   Prob(JB):                     2.49e-89\n",
      "Kurtosis:                       3.920   Cond. No.                         1.00\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "ari_ols_uni=smf.ols(formula='inv_simpson ~ height_cm', data=arivale).fit()\n",
    "print(ari_ols_uni.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "954b5907-ffa0-489d-997c-1c281b32a5e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:            inv_simpson   R-squared:                       0.025\n",
      "Model:                            OLS   Adj. R-squared:                  0.023\n",
      "Method:                 Least Squares   F-statistic:                     19.89\n",
      "Date:                Mon, 24 Jun 2024   Prob (F-statistic):           1.67e-26\n",
      "Time:                        14:36:39   Log-Likelihood:                -7757.5\n",
      "No. Observations:                5516   AIC:                         1.553e+04\n",
      "Df Residuals:                    5508   BIC:                         1.558e+04\n",
      "Df Model:                           7                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=================================================================================================\n",
      "                                    coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------------------\n",
      "Intercept                        -0.1225      0.084     -1.462      0.144      -0.287       0.042\n",
      "C(sex)[T.M]                      -0.0855      0.037     -2.295      0.022      -0.159      -0.012\n",
      "age                               0.0806      0.014      5.917      0.000       0.054       0.107\n",
      "BMI_CALC                         -0.0227      0.014     -1.650      0.099      -0.050       0.004\n",
      "bowel_movement_frequency         -0.0959      0.014     -6.764      0.000      -0.124      -0.068\n",
      "vegetable_frequency               0.0907      0.017      5.375      0.000       0.058       0.124\n",
      "height_cm                         0.2306      0.074      3.127      0.002       0.086       0.375\n",
      "vegetable_frequency:height_cm    -0.0363      0.016     -2.214      0.027      -0.068      -0.004\n",
      "==============================================================================\n",
      "Omnibus:                      375.952   Durbin-Watson:                   1.956\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              457.569\n",
      "Skew:                          -0.671   Prob(JB):                    4.37e-100\n",
      "Kurtosis:                       3.437   Cond. No.                         33.6\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# am_ols_multi=smf.ols(formula='inv_simpson ~ age + C(sex) + BMI_CALC + bowel_movement_frequency + height_cm', data=amgut).fit()\n",
    "am_ols_multi=smf.ols(formula='inv_simpson ~ age + C(sex) + BMI_CALC + bowel_movement_frequency + vegetable_frequency + height_cm + vegetable_frequency:height_cm', data=amgut).fit()\n",
    "print(am_ols_multi.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6b89d89e-5ca9-4ab9-bf08-a4a5dfdddd2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:            inv_simpson   R-squared:                       0.001\n",
      "Model:                            OLS   Adj. R-squared:                  0.001\n",
      "Method:                 Least Squares   F-statistic:                     4.651\n",
      "Date:                Mon, 24 Jun 2024   Prob (F-statistic):             0.0311\n",
      "Time:                        14:36:39   Log-Likelihood:                -7824.0\n",
      "No. Observations:                5516   AIC:                         1.565e+04\n",
      "Df Residuals:                    5514   BIC:                         1.567e+04\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept   3.053e-16      0.013   2.27e-14      1.000      -0.026       0.026\n",
      "height_cm      0.0290      0.013      2.157      0.031       0.003       0.055\n",
      "==============================================================================\n",
      "Omnibus:                      328.010   Durbin-Watson:                   1.935\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              388.462\n",
      "Skew:                          -0.626   Prob(JB):                     4.43e-85\n",
      "Kurtosis:                       3.350   Cond. No.                         1.00\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "am_ols_uni=smf.ols(formula='inv_simpson ~ height_cm', data=amgut).fit()\n",
    "print(am_ols_uni.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "699dd59c-a0fa-4c5e-9e21-bab111deec9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.251303771674972 0.2108236699380981\n"
     ]
    }
   ],
   "source": [
    "B1 = ari_ols.params['height_cm']\n",
    "B2 = ari_ols_uni.params['height_cm']\n",
    "sd1 = ari_ols.bse['height_cm']\n",
    "sd2 = ari_ols_uni.bse['height_cm']\n",
    "z_score = (B1-B2)/np.sqrt((sd1**2)+sd2**2)\n",
    "\n",
    "p_value_ari=2*(1-scipy.stats.norm.cdf(np.abs(z_score)))\n",
    "print(z_score, p_value_ari)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7a8da3d3-bd8a-4306-af9f-17b6acbc1425",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.6891803688184672 0.007162770546692343\n"
     ]
    }
   ],
   "source": [
    "B1 = am_ols_multi.params['height_cm']\n",
    "B2 = am_ols_uni.params['height_cm']\n",
    "sd1 = am_ols_multi.bse['height_cm']\n",
    "sd2 = am_ols_uni.bse['height_cm']\n",
    "z_score = (B1-B2)/np.sqrt((sd1**2)+sd2**2)\n",
    "\n",
    "p_value_amgut=2*(1-scipy.stats.norm.cdf(np.abs(z_score)))\n",
    "# p_value_sf =2*(scipy.stats.norm.sf(np.abs(z_score)))\n",
    "print(z_score, p_value_amgut)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0cd12a64-a105-494f-b90c-14fd086805c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.011318954769367747"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import combine_pvalues \n",
    "# Check if interaction effect is significant acoss datasets\n",
    "p_values = [p_value_ari, p_value_amgut]\n",
    "# Using Fisher's method to combine the p-values \n",
    "combined_p_value = combine_pvalues(p_values, method='fisher') \n",
    "combined_p_value[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0af75030-b59d-4e49-a63a-d3d0a0630ad7",
   "metadata": {},
   "source": [
    "## OLS regressions in more detail (used to generate Table 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fa99c1a7-8db2-492e-b47b-0c2f37185e5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "covars = ['C(sex)', 'age', 'BMI_CALC', 'bowel_movement_frequency', 'vegetable_frequency', 'height_cm', 'height_cm:vegetable_frequency']\n",
    "d_formula=dict()\n",
    "d_formula[0] = {\n",
    "    'formula':'inv_simpson ~ age + C(sex) + BMI_CALC + bowel_movement_frequency + vegetable_frequency + height_cm + height_cm:vegetable_frequency',\n",
    "    'excluded':'none',\n",
    "    # 'covariates':list(map(lambda x: x.replace('C(sex)', 'C(sex)[T.M]').replace('C(vegetable_frequency)','C(vegetable_frequency)[T.low]'), covars))}\n",
    "    'covariates':list(map(lambda x: x.replace('C(sex)', 'C(sex)[T.M]'), covars))}\n",
    "d_formula[1] = {\n",
    "    'formula':'inv_simpson ~ height_cm',\n",
    "    'excluded':'all but height',\n",
    "    'covariates':float('NaN')}\n",
    "\n",
    "for i in range(0,len(covars)):\n",
    "    excluded=covars[i]\n",
    "    temp_covars=[i for i in covars if i != excluded]\n",
    "    formula=' + '.join(temp_covars)\n",
    "    formula = 'inv_simpson ~ '+formula\n",
    "    temp_covars=list(map(lambda x: x.replace('C(sex)', 'C(sex)[T.M]'),temp_covars))\n",
    "    \n",
    "    d_formula[i+2] = {\n",
    "        'formula':formula,\n",
    "        'excluded':excluded,\n",
    "        'covariates':temp_covars}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c52823b-959a-4860-979f-6644c4587656",
   "metadata": {},
   "source": [
    "### Getting the $r^2$ of each individual covariate in my model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ac927ed1-4cba-4863-9c97-d093d61fff4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_covar_rsq(dataframe, f_dict):\n",
    "    big_squared=pd.DataFrame()\n",
    "    \n",
    "    for i in range(0, len(f_dict)): # for every formula combination in my dictionary \n",
    "        if i==0: # first entry in the dict is the full formula \n",
    "            results = smf.ols(formula=f_dict[i]['formula'], data=dataframe).fit() \n",
    "            ref_r=results.rsquared # this is going to be used to derive r_squared of indiviudal covariates\n",
    "            betas=pd.DataFrame(results.params).reset_index().rename(columns={'index':'covar', 0:'Beta'}) # get beta coefficients\n",
    "            betas['formula']=f_dict[i]['formula'] # record the formula used\n",
    "            betas['rsquared_model']=results.rsquared # get the rsquared of the model\n",
    "            ps = pd.DataFrame(results.pvalues.loc[f_dict[i]['covariates']]).reset_index().rename(columns={0:'p_covar', 'index':'covar'}) # get pvalues for each covar\n",
    "            betas=pd.merge(betas, ps, on = 'covar') #merge ps with beta coefficients for each covar\n",
    "            \n",
    "            ### F-test to compare model with and without height as a covar\n",
    "            model_reduced = smf.ols(formula = 'inv_simpson ~ age + C(sex) + BMI_CALC + bowel_movement_frequency + vegetable_frequency', data=dataframe).fit()\n",
    "            F_test = anova_lm(model_reduced, results)\n",
    "            betas['F']=F_test.loc[1,'F']\n",
    "            betas['F_pval']=F_test.loc[1,'Pr(>F)']\n",
    "\n",
    "        \n",
    "        elif i==1: # the second entry in my dict is just simpson vs height\n",
    "            # like before, get beta coefficient and r^2 values\n",
    "            results=smf.ols(formula=f_dict[i]['formula'], data=dataframe).fit()\n",
    "            temp=pd.DataFrame(results.params).reset_index().rename(columns={'index':'covar', 0:'Beta'})\n",
    "            temp['formula']=f_dict[i]['formula']\n",
    "            temp['rsquared_model']=results.rsquared\n",
    "            temp['p_covar']=results.pvalues['height_cm']\n",
    "            \n",
    "            F_test = anova_lm(results)\n",
    "            temp['F']=F_test.loc['height_cm','F']\n",
    "            temp['F_pval']=F_test.loc['height_cm','PR(>F)']\n",
    "            betas=pd.concat([betas, temp])\n",
    "        \n",
    "        else: # for every other formula, we excluded one covar so we can derive the r^2 for indiviudal covars\n",
    "            results = smf.ols(formula=f_dict[i]['formula'], data=dataframe).fit() #ols with the formula[i]\n",
    "            squared_df=pd.DataFrame(columns=['covar', 'rsquared_covar', 'formula', 'rsquared_model'])\n",
    "            squared_df.loc[i,'covar']=f_dict[i]['excluded'] # the excluded covar is the one we are getting an r^2 value for\n",
    "            squared_df.loc[i,'rsquared_covar']=ref_r-results.rsquared\n",
    "            squared_df.loc[i,'formula']=f_dict[i]['formula']\n",
    "            squared_df.loc[i,'rsquared_model']=ref_r\n",
    "            big_squared=pd.concat([big_squared, squared_df])\n",
    "    \n",
    "    #Now we have everything we need to make one big df with my regression info \n",
    "    big_squared.loc[2,'covar']='C(sex)[T.M]' #so I can merge my dfs on covars\n",
    "    # big_squared.loc[6,'covar']='C(vegetable_frequency)[T.low]'\n",
    "    complete=pd.merge(big_squared[['covar', 'rsquared_covar', 'rsquared_model']], \\\n",
    "                      betas[['covar', 'Beta', 'formula', 'rsquared_model', 'p_covar', 'F', 'F_pval']], \\\n",
    "                      on=['covar', 'rsquared_model'], how='right')\n",
    "    return(complete, F_test)\n",
    "\n",
    "arivale_results, arivale_F= find_covar_rsq(arivale, d_formula)\n",
    "amgut_results, amgut_F = find_covar_rsq(amgut, d_formula)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4cb5183d-0215-4125-aec1-e4263f9c7080",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cohort</th>\n",
       "      <th>covar</th>\n",
       "      <th>rsquared_covar</th>\n",
       "      <th>p_covar</th>\n",
       "      <th>Beta</th>\n",
       "      <th>formula</th>\n",
       "      <th>rsquared_model</th>\n",
       "      <th>F</th>\n",
       "      <th>F_pval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Arivale</td>\n",
       "      <td>C(sex)[T.M]</td>\n",
       "      <td>0.001276</td>\n",
       "      <td>4.150382e-02</td>\n",
       "      <td>-0.105785</td>\n",
       "      <td>inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...</td>\n",
       "      <td>0.062769</td>\n",
       "      <td>15.121112</td>\n",
       "      <td>2.919280e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Arivale</td>\n",
       "      <td>age</td>\n",
       "      <td>0.005754</td>\n",
       "      <td>1.534000e-05</td>\n",
       "      <td>0.076237</td>\n",
       "      <td>inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...</td>\n",
       "      <td>0.062769</td>\n",
       "      <td>15.121112</td>\n",
       "      <td>2.919280e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arivale</td>\n",
       "      <td>BMI_CALC</td>\n",
       "      <td>0.031686</td>\n",
       "      <td>6.936063e-24</td>\n",
       "      <td>-0.180406</td>\n",
       "      <td>inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...</td>\n",
       "      <td>0.062769</td>\n",
       "      <td>15.121112</td>\n",
       "      <td>2.919280e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arivale</td>\n",
       "      <td>bowel_movement_frequency</td>\n",
       "      <td>0.0097</td>\n",
       "      <td>2.046390e-08</td>\n",
       "      <td>-0.183141</td>\n",
       "      <td>inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...</td>\n",
       "      <td>0.062769</td>\n",
       "      <td>15.121112</td>\n",
       "      <td>2.919280e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Arivale</td>\n",
       "      <td>vegetable_frequency</td>\n",
       "      <td>0.001402</td>\n",
       "      <td>3.262911e-02</td>\n",
       "      <td>0.044044</td>\n",
       "      <td>inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...</td>\n",
       "      <td>0.062769</td>\n",
       "      <td>15.121112</td>\n",
       "      <td>2.919280e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Arivale</td>\n",
       "      <td>height_cm</td>\n",
       "      <td>0.002292</td>\n",
       "      <td>6.300646e-03</td>\n",
       "      <td>0.166973</td>\n",
       "      <td>inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...</td>\n",
       "      <td>0.062769</td>\n",
       "      <td>15.121112</td>\n",
       "      <td>2.919280e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Arivale</td>\n",
       "      <td>height_cm:vegetable_frequency</td>\n",
       "      <td>0.000096</td>\n",
       "      <td>5.764059e-01</td>\n",
       "      <td>-0.011350</td>\n",
       "      <td>inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...</td>\n",
       "      <td>0.062769</td>\n",
       "      <td>15.121112</td>\n",
       "      <td>2.919280e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Arivale</td>\n",
       "      <td>height_cm</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.309907e-06</td>\n",
       "      <td>0.087289</td>\n",
       "      <td>inv_simpson ~ height_cm</td>\n",
       "      <td>0.007619</td>\n",
       "      <td>23.502200</td>\n",
       "      <td>1.309907e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>American Gut</td>\n",
       "      <td>C(sex)[T.M]</td>\n",
       "      <td>0.000933</td>\n",
       "      <td>2.176496e-02</td>\n",
       "      <td>-0.085489</td>\n",
       "      <td>inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...</td>\n",
       "      <td>0.02465</td>\n",
       "      <td>10.320239</td>\n",
       "      <td>3.360115e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>American Gut</td>\n",
       "      <td>age</td>\n",
       "      <td>0.0062</td>\n",
       "      <td>3.473446e-09</td>\n",
       "      <td>0.080599</td>\n",
       "      <td>inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...</td>\n",
       "      <td>0.02465</td>\n",
       "      <td>10.320239</td>\n",
       "      <td>3.360115e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>American Gut</td>\n",
       "      <td>BMI_CALC</td>\n",
       "      <td>0.000482</td>\n",
       "      <td>9.905642e-02</td>\n",
       "      <td>-0.022677</td>\n",
       "      <td>inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...</td>\n",
       "      <td>0.02465</td>\n",
       "      <td>10.320239</td>\n",
       "      <td>3.360115e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>American Gut</td>\n",
       "      <td>bowel_movement_frequency</td>\n",
       "      <td>0.008102</td>\n",
       "      <td>1.478061e-11</td>\n",
       "      <td>-0.095905</td>\n",
       "      <td>inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...</td>\n",
       "      <td>0.02465</td>\n",
       "      <td>10.320239</td>\n",
       "      <td>3.360115e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>American Gut</td>\n",
       "      <td>vegetable_frequency</td>\n",
       "      <td>0.005117</td>\n",
       "      <td>7.953747e-08</td>\n",
       "      <td>0.090667</td>\n",
       "      <td>inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...</td>\n",
       "      <td>0.02465</td>\n",
       "      <td>10.320239</td>\n",
       "      <td>3.360115e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>American Gut</td>\n",
       "      <td>height_cm</td>\n",
       "      <td>0.001732</td>\n",
       "      <td>1.772796e-03</td>\n",
       "      <td>0.230578</td>\n",
       "      <td>inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...</td>\n",
       "      <td>0.02465</td>\n",
       "      <td>10.320239</td>\n",
       "      <td>3.360115e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>American Gut</td>\n",
       "      <td>height_cm:vegetable_frequency</td>\n",
       "      <td>0.000868</td>\n",
       "      <td>2.683591e-02</td>\n",
       "      <td>-0.036310</td>\n",
       "      <td>inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...</td>\n",
       "      <td>0.02465</td>\n",
       "      <td>10.320239</td>\n",
       "      <td>3.360115e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>American Gut</td>\n",
       "      <td>height_cm</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.107323e-02</td>\n",
       "      <td>0.029032</td>\n",
       "      <td>inv_simpson ~ height_cm</td>\n",
       "      <td>0.000843</td>\n",
       "      <td>4.651294</td>\n",
       "      <td>3.107323e-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          cohort                          covar rsquared_covar       p_covar  \\\n",
       "0        Arivale                    C(sex)[T.M]       0.001276  4.150382e-02   \n",
       "1        Arivale                            age       0.005754  1.534000e-05   \n",
       "2        Arivale                       BMI_CALC       0.031686  6.936063e-24   \n",
       "3        Arivale       bowel_movement_frequency         0.0097  2.046390e-08   \n",
       "4        Arivale            vegetable_frequency       0.001402  3.262911e-02   \n",
       "5        Arivale                      height_cm       0.002292  6.300646e-03   \n",
       "6        Arivale  height_cm:vegetable_frequency       0.000096  5.764059e-01   \n",
       "7        Arivale                      height_cm            NaN  1.309907e-06   \n",
       "8   American Gut                    C(sex)[T.M]       0.000933  2.176496e-02   \n",
       "9   American Gut                            age         0.0062  3.473446e-09   \n",
       "10  American Gut                       BMI_CALC       0.000482  9.905642e-02   \n",
       "11  American Gut       bowel_movement_frequency       0.008102  1.478061e-11   \n",
       "12  American Gut            vegetable_frequency       0.005117  7.953747e-08   \n",
       "13  American Gut                      height_cm       0.001732  1.772796e-03   \n",
       "14  American Gut  height_cm:vegetable_frequency       0.000868  2.683591e-02   \n",
       "15  American Gut                      height_cm            NaN  3.107323e-02   \n",
       "\n",
       "        Beta                                            formula  \\\n",
       "0  -0.105785  inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...   \n",
       "1   0.076237  inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...   \n",
       "2  -0.180406  inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...   \n",
       "3  -0.183141  inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...   \n",
       "4   0.044044  inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...   \n",
       "5   0.166973  inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...   \n",
       "6  -0.011350  inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...   \n",
       "7   0.087289                            inv_simpson ~ height_cm   \n",
       "8  -0.085489  inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...   \n",
       "9   0.080599  inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...   \n",
       "10 -0.022677  inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...   \n",
       "11 -0.095905  inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...   \n",
       "12  0.090667  inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...   \n",
       "13  0.230578  inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...   \n",
       "14 -0.036310  inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...   \n",
       "15  0.029032                            inv_simpson ~ height_cm   \n",
       "\n",
       "   rsquared_model          F        F_pval  \n",
       "0        0.062769  15.121112  2.919280e-07  \n",
       "1        0.062769  15.121112  2.919280e-07  \n",
       "2        0.062769  15.121112  2.919280e-07  \n",
       "3        0.062769  15.121112  2.919280e-07  \n",
       "4        0.062769  15.121112  2.919280e-07  \n",
       "5        0.062769  15.121112  2.919280e-07  \n",
       "6        0.062769  15.121112  2.919280e-07  \n",
       "7        0.007619  23.502200  1.309907e-06  \n",
       "8         0.02465  10.320239  3.360115e-05  \n",
       "9         0.02465  10.320239  3.360115e-05  \n",
       "10        0.02465  10.320239  3.360115e-05  \n",
       "11        0.02465  10.320239  3.360115e-05  \n",
       "12        0.02465  10.320239  3.360115e-05  \n",
       "13        0.02465  10.320239  3.360115e-05  \n",
       "14        0.02465  10.320239  3.360115e-05  \n",
       "15       0.000843   4.651294  3.107323e-02  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arivale_results['cohort']='Arivale' # add cohort info so I can merge results\n",
    "amgut_results['cohort']='American Gut'\n",
    "\n",
    "complete=pd.concat([arivale_results, amgut_results])\n",
    "complete=complete[complete['covar']!='Intercept'] # I don't want the intercept\n",
    "complete=complete[['cohort','covar', 'rsquared_covar', 'p_covar', 'Beta', 'formula', 'rsquared_model', 'F', 'F_pval']]\n",
    "\n",
    "complete=complete.reset_index(drop=True)\n",
    "interaction_results = complete.copy()\n",
    "interaction_results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fbd1e0e-24a2-468b-9c1d-6bb3f3a0cd16",
   "metadata": {},
   "source": [
    "### Again without the interaction term"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a408d0c9-a14d-494b-a015-74fb3f328b9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "covars = ['C(sex)', 'age', 'BMI_CALC', 'bowel_movement_frequency', 'vegetable_frequency', 'height_cm']\n",
    "d_formula=dict()\n",
    "d_formula[0] = {\n",
    "    'formula':'inv_simpson ~ age + C(sex) + BMI_CALC + bowel_movement_frequency + vegetable_frequency + height_cm',\n",
    "    'excluded':'none',\n",
    "    # 'covariates':list(map(lambda x: x.replace('C(sex)', 'C(sex)[T.M]').replace('C(vegetable_frequency)','C(vegetable_frequency)[T.low]'), covars))}\n",
    "    'covariates':list(map(lambda x: x.replace('C(sex)', 'C(sex)[T.M]'), covars))}\n",
    "d_formula[1] = {\n",
    "    'formula':'inv_simpson ~ height_cm',\n",
    "    'excluded':'all but height',\n",
    "    'covariates':float('NaN')}\n",
    "\n",
    "for i in range(0,len(covars)):\n",
    "    excluded=covars[i]\n",
    "    temp_covars=[i for i in covars if i != excluded]\n",
    "    formula=' + '.join(temp_covars)\n",
    "    formula = 'inv_simpson ~ '+formula\n",
    "    temp_covars=list(map(lambda x: x.replace('C(sex)', 'C(sex)[T.M]'),temp_covars))\n",
    "    \n",
    "    d_formula[i+2] = {\n",
    "        'formula':formula,\n",
    "        'excluded':excluded,\n",
    "        'covariates':temp_covars}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "aa1cc75e-5520-4fad-a328-67a007f9def4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cohort</th>\n",
       "      <th>covar</th>\n",
       "      <th>rsquared_covar</th>\n",
       "      <th>p_covar</th>\n",
       "      <th>Beta</th>\n",
       "      <th>formula</th>\n",
       "      <th>rsquared_model</th>\n",
       "      <th>F</th>\n",
       "      <th>F_pval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Arivale</td>\n",
       "      <td>C(sex)[T.M]</td>\n",
       "      <td>0.001273</td>\n",
       "      <td>4.171192e-02</td>\n",
       "      <td>-0.105664</td>\n",
       "      <td>inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...</td>\n",
       "      <td>0.062673</td>\n",
       "      <td>29.936814</td>\n",
       "      <td>4.823979e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Arivale</td>\n",
       "      <td>age</td>\n",
       "      <td>0.005754</td>\n",
       "      <td>1.530991e-05</td>\n",
       "      <td>0.076237</td>\n",
       "      <td>inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...</td>\n",
       "      <td>0.062673</td>\n",
       "      <td>29.936814</td>\n",
       "      <td>4.823979e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arivale</td>\n",
       "      <td>BMI_CALC</td>\n",
       "      <td>0.031791</td>\n",
       "      <td>5.802374e-24</td>\n",
       "      <td>-0.180650</td>\n",
       "      <td>inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...</td>\n",
       "      <td>0.062673</td>\n",
       "      <td>29.936814</td>\n",
       "      <td>4.823979e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arivale</td>\n",
       "      <td>bowel_movement_frequency</td>\n",
       "      <td>0.009628</td>\n",
       "      <td>2.298816e-08</td>\n",
       "      <td>-0.182229</td>\n",
       "      <td>inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...</td>\n",
       "      <td>0.062673</td>\n",
       "      <td>29.936814</td>\n",
       "      <td>4.823979e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Arivale</td>\n",
       "      <td>vegetable_frequency</td>\n",
       "      <td>0.001397</td>\n",
       "      <td>3.288895e-02</td>\n",
       "      <td>0.043972</td>\n",
       "      <td>inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...</td>\n",
       "      <td>0.062673</td>\n",
       "      <td>29.936814</td>\n",
       "      <td>4.823979e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Arivale</td>\n",
       "      <td>height_cm</td>\n",
       "      <td>0.009182</td>\n",
       "      <td>4.823979e-08</td>\n",
       "      <td>0.135791</td>\n",
       "      <td>inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...</td>\n",
       "      <td>0.062673</td>\n",
       "      <td>29.936814</td>\n",
       "      <td>4.823979e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Arivale</td>\n",
       "      <td>height_cm</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.309907e-06</td>\n",
       "      <td>0.087289</td>\n",
       "      <td>inv_simpson ~ height_cm</td>\n",
       "      <td>0.007619</td>\n",
       "      <td>23.502200</td>\n",
       "      <td>1.309907e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>American Gut</td>\n",
       "      <td>C(sex)[T.M]</td>\n",
       "      <td>0.000886</td>\n",
       "      <td>2.536114e-02</td>\n",
       "      <td>-0.083305</td>\n",
       "      <td>inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...</td>\n",
       "      <td>0.023781</td>\n",
       "      <td>15.725370</td>\n",
       "      <td>7.416360e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>American Gut</td>\n",
       "      <td>age</td>\n",
       "      <td>0.006206</td>\n",
       "      <td>3.459716e-09</td>\n",
       "      <td>0.080636</td>\n",
       "      <td>inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...</td>\n",
       "      <td>0.023781</td>\n",
       "      <td>15.725370</td>\n",
       "      <td>7.416360e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>American Gut</td>\n",
       "      <td>BMI_CALC</td>\n",
       "      <td>0.000517</td>\n",
       "      <td>8.776210e-02</td>\n",
       "      <td>-0.023473</td>\n",
       "      <td>inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...</td>\n",
       "      <td>0.023781</td>\n",
       "      <td>15.725370</td>\n",
       "      <td>7.416360e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>American Gut</td>\n",
       "      <td>bowel_movement_frequency</td>\n",
       "      <td>0.007963</td>\n",
       "      <td>2.236052e-11</td>\n",
       "      <td>-0.095042</td>\n",
       "      <td>inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...</td>\n",
       "      <td>0.023781</td>\n",
       "      <td>15.725370</td>\n",
       "      <td>7.416360e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>American Gut</td>\n",
       "      <td>vegetable_frequency</td>\n",
       "      <td>0.005018</td>\n",
       "      <td>1.071261e-07</td>\n",
       "      <td>0.089758</td>\n",
       "      <td>inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...</td>\n",
       "      <td>0.023781</td>\n",
       "      <td>15.725370</td>\n",
       "      <td>7.416360e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>American Gut</td>\n",
       "      <td>height_cm</td>\n",
       "      <td>0.002787</td>\n",
       "      <td>7.416360e-05</td>\n",
       "      <td>0.072387</td>\n",
       "      <td>inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...</td>\n",
       "      <td>0.023781</td>\n",
       "      <td>15.725370</td>\n",
       "      <td>7.416360e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>American Gut</td>\n",
       "      <td>height_cm</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.107323e-02</td>\n",
       "      <td>0.029032</td>\n",
       "      <td>inv_simpson ~ height_cm</td>\n",
       "      <td>0.000843</td>\n",
       "      <td>4.651294</td>\n",
       "      <td>3.107323e-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          cohort                     covar rsquared_covar       p_covar  \\\n",
       "0        Arivale               C(sex)[T.M]       0.001273  4.171192e-02   \n",
       "1        Arivale                       age       0.005754  1.530991e-05   \n",
       "2        Arivale                  BMI_CALC       0.031791  5.802374e-24   \n",
       "3        Arivale  bowel_movement_frequency       0.009628  2.298816e-08   \n",
       "4        Arivale       vegetable_frequency       0.001397  3.288895e-02   \n",
       "5        Arivale                 height_cm       0.009182  4.823979e-08   \n",
       "6        Arivale                 height_cm            NaN  1.309907e-06   \n",
       "7   American Gut               C(sex)[T.M]       0.000886  2.536114e-02   \n",
       "8   American Gut                       age       0.006206  3.459716e-09   \n",
       "9   American Gut                  BMI_CALC       0.000517  8.776210e-02   \n",
       "10  American Gut  bowel_movement_frequency       0.007963  2.236052e-11   \n",
       "11  American Gut       vegetable_frequency       0.005018  1.071261e-07   \n",
       "12  American Gut                 height_cm       0.002787  7.416360e-05   \n",
       "13  American Gut                 height_cm            NaN  3.107323e-02   \n",
       "\n",
       "        Beta                                            formula  \\\n",
       "0  -0.105664  inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...   \n",
       "1   0.076237  inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...   \n",
       "2  -0.180650  inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...   \n",
       "3  -0.182229  inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...   \n",
       "4   0.043972  inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...   \n",
       "5   0.135791  inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...   \n",
       "6   0.087289                            inv_simpson ~ height_cm   \n",
       "7  -0.083305  inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...   \n",
       "8   0.080636  inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...   \n",
       "9  -0.023473  inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...   \n",
       "10 -0.095042  inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...   \n",
       "11  0.089758  inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...   \n",
       "12  0.072387  inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...   \n",
       "13  0.029032                            inv_simpson ~ height_cm   \n",
       "\n",
       "   rsquared_model          F        F_pval  \n",
       "0        0.062673  29.936814  4.823979e-08  \n",
       "1        0.062673  29.936814  4.823979e-08  \n",
       "2        0.062673  29.936814  4.823979e-08  \n",
       "3        0.062673  29.936814  4.823979e-08  \n",
       "4        0.062673  29.936814  4.823979e-08  \n",
       "5        0.062673  29.936814  4.823979e-08  \n",
       "6        0.007619  23.502200  1.309907e-06  \n",
       "7        0.023781  15.725370  7.416360e-05  \n",
       "8        0.023781  15.725370  7.416360e-05  \n",
       "9        0.023781  15.725370  7.416360e-05  \n",
       "10       0.023781  15.725370  7.416360e-05  \n",
       "11       0.023781  15.725370  7.416360e-05  \n",
       "12       0.023781  15.725370  7.416360e-05  \n",
       "13       0.000843   4.651294  3.107323e-02  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arivale_results, arivale_F= find_covar_rsq(arivale, d_formula)\n",
    "amgut_results, amgut_F = find_covar_rsq(amgut, d_formula)\n",
    "\n",
    "arivale_results['cohort']='Arivale' # add cohort info so I can merge results\n",
    "amgut_results['cohort']='American Gut'\n",
    "\n",
    "complete=pd.concat([arivale_results, amgut_results])\n",
    "complete=complete[complete['covar']!='Intercept'] # I don't want the intercept\n",
    "complete=complete[['cohort','covar', 'rsquared_covar', 'p_covar', 'Beta', 'formula', 'rsquared_model', 'F', 'F_pval']]\n",
    "complete=complete.reset_index(drop=True)\n",
    "\n",
    "non_interaction_results = complete.copy()\n",
    "non_interaction_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "15b3cd02-929f-4a76-91f3-088b4c55210e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cohort</th>\n",
       "      <th>covar</th>\n",
       "      <th>rsquared_covar</th>\n",
       "      <th>p_covar</th>\n",
       "      <th>Beta</th>\n",
       "      <th>formula</th>\n",
       "      <th>rsquared_model</th>\n",
       "      <th>F</th>\n",
       "      <th>F_pval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Arivale</td>\n",
       "      <td>C(sex)[T.M]</td>\n",
       "      <td>0.001276</td>\n",
       "      <td>4.150382e-02</td>\n",
       "      <td>-0.105785</td>\n",
       "      <td>inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...</td>\n",
       "      <td>0.062769</td>\n",
       "      <td>15.121112</td>\n",
       "      <td>2.919280e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Arivale</td>\n",
       "      <td>age</td>\n",
       "      <td>0.005754</td>\n",
       "      <td>1.534000e-05</td>\n",
       "      <td>0.076237</td>\n",
       "      <td>inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...</td>\n",
       "      <td>0.062769</td>\n",
       "      <td>15.121112</td>\n",
       "      <td>2.919280e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arivale</td>\n",
       "      <td>BMI_CALC</td>\n",
       "      <td>0.031686</td>\n",
       "      <td>6.936063e-24</td>\n",
       "      <td>-0.180406</td>\n",
       "      <td>inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...</td>\n",
       "      <td>0.062769</td>\n",
       "      <td>15.121112</td>\n",
       "      <td>2.919280e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arivale</td>\n",
       "      <td>bowel_movement_frequency</td>\n",
       "      <td>0.0097</td>\n",
       "      <td>2.046390e-08</td>\n",
       "      <td>-0.183141</td>\n",
       "      <td>inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...</td>\n",
       "      <td>0.062769</td>\n",
       "      <td>15.121112</td>\n",
       "      <td>2.919280e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Arivale</td>\n",
       "      <td>vegetable_frequency</td>\n",
       "      <td>0.001402</td>\n",
       "      <td>3.262911e-02</td>\n",
       "      <td>0.044044</td>\n",
       "      <td>inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...</td>\n",
       "      <td>0.062769</td>\n",
       "      <td>15.121112</td>\n",
       "      <td>2.919280e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Arivale</td>\n",
       "      <td>height_cm</td>\n",
       "      <td>0.002292</td>\n",
       "      <td>6.300646e-03</td>\n",
       "      <td>0.166973</td>\n",
       "      <td>inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...</td>\n",
       "      <td>0.062769</td>\n",
       "      <td>15.121112</td>\n",
       "      <td>2.919280e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Arivale</td>\n",
       "      <td>height_cm:vegetable_frequency</td>\n",
       "      <td>0.000096</td>\n",
       "      <td>5.764059e-01</td>\n",
       "      <td>-0.011350</td>\n",
       "      <td>inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...</td>\n",
       "      <td>0.062769</td>\n",
       "      <td>15.121112</td>\n",
       "      <td>2.919280e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Arivale</td>\n",
       "      <td>height_cm</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.309907e-06</td>\n",
       "      <td>0.087289</td>\n",
       "      <td>inv_simpson ~ height_cm</td>\n",
       "      <td>0.007619</td>\n",
       "      <td>23.502200</td>\n",
       "      <td>1.309907e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>American Gut</td>\n",
       "      <td>C(sex)[T.M]</td>\n",
       "      <td>0.000933</td>\n",
       "      <td>2.176496e-02</td>\n",
       "      <td>-0.085489</td>\n",
       "      <td>inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...</td>\n",
       "      <td>0.02465</td>\n",
       "      <td>10.320239</td>\n",
       "      <td>3.360115e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>American Gut</td>\n",
       "      <td>age</td>\n",
       "      <td>0.0062</td>\n",
       "      <td>3.473446e-09</td>\n",
       "      <td>0.080599</td>\n",
       "      <td>inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...</td>\n",
       "      <td>0.02465</td>\n",
       "      <td>10.320239</td>\n",
       "      <td>3.360115e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>American Gut</td>\n",
       "      <td>BMI_CALC</td>\n",
       "      <td>0.000482</td>\n",
       "      <td>9.905642e-02</td>\n",
       "      <td>-0.022677</td>\n",
       "      <td>inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...</td>\n",
       "      <td>0.02465</td>\n",
       "      <td>10.320239</td>\n",
       "      <td>3.360115e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>American Gut</td>\n",
       "      <td>bowel_movement_frequency</td>\n",
       "      <td>0.008102</td>\n",
       "      <td>1.478061e-11</td>\n",
       "      <td>-0.095905</td>\n",
       "      <td>inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...</td>\n",
       "      <td>0.02465</td>\n",
       "      <td>10.320239</td>\n",
       "      <td>3.360115e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>American Gut</td>\n",
       "      <td>vegetable_frequency</td>\n",
       "      <td>0.005117</td>\n",
       "      <td>7.953747e-08</td>\n",
       "      <td>0.090667</td>\n",
       "      <td>inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...</td>\n",
       "      <td>0.02465</td>\n",
       "      <td>10.320239</td>\n",
       "      <td>3.360115e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>American Gut</td>\n",
       "      <td>height_cm</td>\n",
       "      <td>0.001732</td>\n",
       "      <td>1.772796e-03</td>\n",
       "      <td>0.230578</td>\n",
       "      <td>inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...</td>\n",
       "      <td>0.02465</td>\n",
       "      <td>10.320239</td>\n",
       "      <td>3.360115e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>American Gut</td>\n",
       "      <td>height_cm:vegetable_frequency</td>\n",
       "      <td>0.000868</td>\n",
       "      <td>2.683591e-02</td>\n",
       "      <td>-0.036310</td>\n",
       "      <td>inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...</td>\n",
       "      <td>0.02465</td>\n",
       "      <td>10.320239</td>\n",
       "      <td>3.360115e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>American Gut</td>\n",
       "      <td>height_cm</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.107323e-02</td>\n",
       "      <td>0.029032</td>\n",
       "      <td>inv_simpson ~ height_cm</td>\n",
       "      <td>0.000843</td>\n",
       "      <td>4.651294</td>\n",
       "      <td>3.107323e-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          cohort                          covar rsquared_covar       p_covar  \\\n",
       "0        Arivale                    C(sex)[T.M]       0.001276  4.150382e-02   \n",
       "1        Arivale                            age       0.005754  1.534000e-05   \n",
       "2        Arivale                       BMI_CALC       0.031686  6.936063e-24   \n",
       "3        Arivale       bowel_movement_frequency         0.0097  2.046390e-08   \n",
       "4        Arivale            vegetable_frequency       0.001402  3.262911e-02   \n",
       "5        Arivale                      height_cm       0.002292  6.300646e-03   \n",
       "6        Arivale  height_cm:vegetable_frequency       0.000096  5.764059e-01   \n",
       "7        Arivale                      height_cm            NaN  1.309907e-06   \n",
       "8   American Gut                    C(sex)[T.M]       0.000933  2.176496e-02   \n",
       "9   American Gut                            age         0.0062  3.473446e-09   \n",
       "10  American Gut                       BMI_CALC       0.000482  9.905642e-02   \n",
       "11  American Gut       bowel_movement_frequency       0.008102  1.478061e-11   \n",
       "12  American Gut            vegetable_frequency       0.005117  7.953747e-08   \n",
       "13  American Gut                      height_cm       0.001732  1.772796e-03   \n",
       "14  American Gut  height_cm:vegetable_frequency       0.000868  2.683591e-02   \n",
       "15  American Gut                      height_cm            NaN  3.107323e-02   \n",
       "\n",
       "        Beta                                            formula  \\\n",
       "0  -0.105785  inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...   \n",
       "1   0.076237  inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...   \n",
       "2  -0.180406  inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...   \n",
       "3  -0.183141  inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...   \n",
       "4   0.044044  inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...   \n",
       "5   0.166973  inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...   \n",
       "6  -0.011350  inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...   \n",
       "7   0.087289                            inv_simpson ~ height_cm   \n",
       "8  -0.085489  inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...   \n",
       "9   0.080599  inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...   \n",
       "10 -0.022677  inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...   \n",
       "11 -0.095905  inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...   \n",
       "12  0.090667  inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...   \n",
       "13  0.230578  inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...   \n",
       "14 -0.036310  inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...   \n",
       "15  0.029032                            inv_simpson ~ height_cm   \n",
       "\n",
       "   rsquared_model          F        F_pval  \n",
       "0        0.062769  15.121112  2.919280e-07  \n",
       "1        0.062769  15.121112  2.919280e-07  \n",
       "2        0.062769  15.121112  2.919280e-07  \n",
       "3        0.062769  15.121112  2.919280e-07  \n",
       "4        0.062769  15.121112  2.919280e-07  \n",
       "5        0.062769  15.121112  2.919280e-07  \n",
       "6        0.062769  15.121112  2.919280e-07  \n",
       "7        0.007619  23.502200  1.309907e-06  \n",
       "8         0.02465  10.320239  3.360115e-05  \n",
       "9         0.02465  10.320239  3.360115e-05  \n",
       "10        0.02465  10.320239  3.360115e-05  \n",
       "11        0.02465  10.320239  3.360115e-05  \n",
       "12        0.02465  10.320239  3.360115e-05  \n",
       "13        0.02465  10.320239  3.360115e-05  \n",
       "14        0.02465  10.320239  3.360115e-05  \n",
       "15       0.000843   4.651294  3.107323e-02  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interaction_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6b1c3e2c-b962-41c2-ab7e-d79b5aa3d860",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "arivale_height_rsq, arivale_height_p = non_interaction_results.loc[5, 'rsquared_covar'], non_interaction_results.loc[5, 'F_pval']\n",
    "amgut_height_rsq, amgut_height_p = non_interaction_results.loc[12, 'rsquared_covar'], non_interaction_results.loc[12, 'F_pval']\n",
    "\n",
    "arivale_interaction_rsq, arivale_interaction_p = interaction_results.loc[6, 'rsquared_covar'], interaction_results.loc[6, 'p_covar']\n",
    "amgut_interaction_rsq, amgut_interaction_p = interaction_results.loc[14, 'rsquared_covar'], interaction_results.loc[14, 'p_covar'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "bb15cc06-d269-40a9-9623-4230f09d2767",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.07995538211857002"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import combine_pvalues \n",
    "# Check if interaction effect is significant acoss datasets\n",
    "p_values = [arivale_interaction_p, amgut_interaction_p] \n",
    "# Using Fisher's method to combine the p-values \n",
    "combined_p_value = combine_pvalues(p_values, method='fisher') \n",
    "combined_p_value[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "996b5deb-f9c6-4c6b-bd4d-68bc7b81aec7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Arivale cumulative rsquared values: 0.05902442389475171\n",
      "American gut cumulative rsquared values: 0.022490079503634464\n"
     ]
    }
   ],
   "source": [
    "# complete.loc[:6, 'rsquared_covar'].sum()\n",
    "print(f\"Arivale cumulative rsquared values: {complete.loc[:6, 'rsquared_covar'].sum()}\")\n",
    "print(f\"American gut cumulative rsquared values: {complete.loc[8:14, 'rsquared_covar'].sum()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a698fbc9-6e57-4a35-bc62-f42a72d554e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cohort</th>\n",
       "      <th>covariate</th>\n",
       "      <th>$R^2$</th>\n",
       "      <th>p_covar</th>\n",
       "      <th>Beta</th>\n",
       "      <th>model</th>\n",
       "      <th>rsquared_model</th>\n",
       "      <th>F</th>\n",
       "      <th>F_pval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Arivale</td>\n",
       "      <td>C(sex)[T.M]</td>\n",
       "      <td>0.001273</td>\n",
       "      <td>4.171192e-02</td>\n",
       "      <td>-0.105664</td>\n",
       "      <td>inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...</td>\n",
       "      <td>0.062673</td>\n",
       "      <td>29.936814</td>\n",
       "      <td>4.823979e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Arivale</td>\n",
       "      <td>age</td>\n",
       "      <td>0.005754</td>\n",
       "      <td>1.530991e-05</td>\n",
       "      <td>0.076237</td>\n",
       "      <td>inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...</td>\n",
       "      <td>0.062673</td>\n",
       "      <td>29.936814</td>\n",
       "      <td>4.823979e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arivale</td>\n",
       "      <td>BMI_CALC</td>\n",
       "      <td>0.031791</td>\n",
       "      <td>5.802374e-24</td>\n",
       "      <td>-0.180650</td>\n",
       "      <td>inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...</td>\n",
       "      <td>0.062673</td>\n",
       "      <td>29.936814</td>\n",
       "      <td>4.823979e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arivale</td>\n",
       "      <td>bowel_movement_frequency</td>\n",
       "      <td>0.009628</td>\n",
       "      <td>2.298816e-08</td>\n",
       "      <td>-0.182229</td>\n",
       "      <td>inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...</td>\n",
       "      <td>0.062673</td>\n",
       "      <td>29.936814</td>\n",
       "      <td>4.823979e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Arivale</td>\n",
       "      <td>vegetable_frequency</td>\n",
       "      <td>0.001397</td>\n",
       "      <td>3.288895e-02</td>\n",
       "      <td>0.043972</td>\n",
       "      <td>inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...</td>\n",
       "      <td>0.062673</td>\n",
       "      <td>29.936814</td>\n",
       "      <td>4.823979e-08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    cohort                 covariate     $R^2$       p_covar      Beta  \\\n",
       "0  Arivale               C(sex)[T.M]  0.001273  4.171192e-02 -0.105664   \n",
       "1  Arivale                       age  0.005754  1.530991e-05  0.076237   \n",
       "2  Arivale                  BMI_CALC  0.031791  5.802374e-24 -0.180650   \n",
       "3  Arivale  bowel_movement_frequency  0.009628  2.298816e-08 -0.182229   \n",
       "4  Arivale       vegetable_frequency  0.001397  3.288895e-02  0.043972   \n",
       "\n",
       "                                               model rsquared_model  \\\n",
       "0  inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...       0.062673   \n",
       "1  inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...       0.062673   \n",
       "2  inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...       0.062673   \n",
       "3  inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...       0.062673   \n",
       "4  inv_simpson ~ age + C(sex) + BMI_CALC + bowel_...       0.062673   \n",
       "\n",
       "           F        F_pval  \n",
       "0  29.936814  4.823979e-08  \n",
       "1  29.936814  4.823979e-08  \n",
       "2  29.936814  4.823979e-08  \n",
       "3  29.936814  4.823979e-08  \n",
       "4  29.936814  4.823979e-08  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = complete.copy().rename(columns = {'formula':'model', 'covar':'covariate', 'rsquared_covar':'$R^2$'})\n",
    "df['model'] = df['model'].replace('inv_simpson ~ age + C(sex) + BMI_CALC + bowel_movement_frequency + vegetable_frequency + height_cm + height_cm:vegetable_frequency','multivariate').replace('inv_simpson ~ height_cm', 'univariate')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e73f01ec-4522-475a-abf9-82fbb4d959e1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Save to csv so I can make an excel table with it\n",
    "interaction_results.to_csv(genpath+'/output/regression_results.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0324efdc-765b-483c-95f3-dcd2e6f6044d",
   "metadata": {},
   "source": [
    "### Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0ded6526-6bbe-4bad-a1f2-d25c4876a3e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add color column so I can assign them when I plot\n",
    "amgut['color'] = amgut.sex.apply(lambda x: '#AA4499' if x == 'F' else '#88CCEE')\n",
    "arivale['color'] = arivale.sex.apply(lambda x: '#AA4499' if x == 'F' else '#88CCEE')\n",
    "\n",
    "# Order values by sex so that color assignment is correct\n",
    "amgut = amgut.sort_values(by='sex', ascending=False)\n",
    "arivale = arivale.sort_values(by='sex', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "131e1280-89f7-4674-89d9-2149386b0f14",
   "metadata": {},
   "outputs": [],
   "source": [
    "formula = 'inv_simpson ~ age + C(sex) + BMI_CALC + bowel_movement_frequency + vegetable_frequency + height_cm + vegetable_frequency:height_cm'\n",
    "amgut_model = smf.ols(formula = formula, data=amgut).fit()\n",
    "int_amgut_pval = amgut_model.pvalues['vegetable_frequency:height_cm']\n",
    "arivale_model = smf.ols(formula = formula, data=arivale).fit()\n",
    "int_arivale_pval = arivale_model.pvalues['vegetable_frequency:height_cm']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "cfe9eea0-1677-4c95-962d-2f7bb37889fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, 'Height (log[cm])'), Text(0, 0.5, 'Simpson (log[1/D])')]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "params= {'font.family':'sans-serif',\n",
    "         'font.sans-serif':'Arial'\n",
    "         }\n",
    "# plt.rcParams.update(params)\n",
    "sns.set_theme(style='whitegrid', rc=params, font_scale=1.2)\n",
    "fig, axs = plt.subplots(2,2, figsize = (10,10), tight_layout=True)\n",
    "\n",
    "x = 'height_cm'\n",
    "y = 'inv_simpson'\n",
    "\n",
    "color_label = {'#88CCEE':'Male', '#AA4499':'Female'}\n",
    "colors = ['#88CCEE','#AA4499']\n",
    "labels = [color_label[x] for x in arivale['color'].unique()]\n",
    "\n",
    "sns.regplot(data = arivale, ax = axs[0,0],\n",
    "            x=x, y=y,scatter_kws={'c':arivale['color'], 'color':None, 'alpha':0.5, 'zorder':1},\\\n",
    "            line_kws={'color':'#332288', 'alpha':0.9})\n",
    "\n",
    "index =0\n",
    "for i, grp in arivale.groupby(['color']):\n",
    "    grp.plot(kind = 'scatter', x=x, y=y, c=i, ax = axs[0,0], zorder=0, alpha = 0.5, label=labels[index])\n",
    "    index +=1\n",
    "\n",
    "labels = [color_label[x] for x in amgut['color'].unique()]\n",
    "\n",
    "sns.regplot(data = amgut, ax = axs[0,1],\n",
    "            x=x, y=y,scatter_kws={'c':amgut['color'], 'color':None, 'alpha':0.5, 'zorder':1},\\\n",
    "            line_kws={'color':'#332288', 'alpha':0.9})\n",
    "\n",
    "index =0\n",
    "for i, grp in amgut.groupby(['color']):\n",
    "    grp.plot(kind = 'scatter', x=x, y=y, c=i, ax = axs[0,1], zorder=0, alpha = 0.5, label = labels[index])\n",
    "    index +=1\n",
    "\n",
    "axs[0,0].set(xlabel='Height (log[cm])',ylabel='Simpson (log[1/D])')\n",
    "axs[0,0].text(-4, 2.6, 'Height term, controlling for diet', fontsize=12) \n",
    "axs[0,0].text(-4,2.2, f'$P$={arivale_height_p:.2E}', fontsize=12)\n",
    "axs[0,0].text(-4,1.8,f'$R^2$={arivale_height_rsq:.2E}', fontsize=12)\n",
    "axs[0,0].set_title('a', loc='left', fontsize=14, fontweight='bold')\n",
    "\n",
    "axs[0,1].set(xlabel='Height (log[cm])', ylabel='Simpson (log[1/D])')\n",
    "# axs[1].set_yticks([-4, -3, -2, -1, 0, 1, 2, 3])\n",
    "axs[0,1].text(-5.3, 2.6, 'Height term, controlling for diet', fontsize=12)\n",
    "axs[0,1].text(-5.3,2.25, f'$P$={amgut_height_p:.2E}', fontsize=12)\n",
    "axs[0,1].text(-5.3,1.9,f'$R^2$={amgut_height_rsq:.2E}', fontsize=12)\n",
    "axs[0,1].set_title('b', loc='left', fontsize=14, fontweight='bold')\n",
    "\n",
    "x='height_cm'\n",
    "y='inv_simpson'\n",
    "\n",
    "sns.regplot(data=arivale[arivale['b_vegetable_frequency']=='low'], x=x, y=y, ax=axs[1,0], label='Low vegetable consumption',\\\n",
    "           scatter_kws={'alpha':0.5})\n",
    "sns.regplot(data=arivale[arivale['b_vegetable_frequency']=='high'], x=x, y=y, ax=axs[1,0], label='High vegetable consumption',\\\n",
    "           scatter_kws={'alpha':0.5})\n",
    "\n",
    "\n",
    "sns.regplot(data=amgut[amgut['b_vegetable_frequency']=='low'], x=x, y=y, ax=axs[1,1], label='Low vegetable consumption',\\\n",
    "            scatter_kws={'alpha':0.5})\n",
    "sns.regplot(data=amgut[amgut['b_vegetable_frequency']=='high'], x=x, y=y, ax=axs[1,1], label='High vegetable consumption',\\\n",
    "           scatter_kws={'alpha':0.5}) \n",
    "\n",
    "axs[1,0].text(-3.9, 2.6, 'Interaction term (height:diet)', fontsize=12)\n",
    "axs[1,0].text(-3.9,2.2, f'$P$={arivale_interaction_p:.2E}', fontsize=12)\n",
    "axs[1,0].text(-3.9, 1.8, f'$R^2$={arivale_interaction_rsq:.2E}', fontsize=12)\n",
    "\n",
    "axs[1,1].text(-5.3, 2.5, 'Interaction term (height:diet)', fontsize=12)\n",
    "axs[1,1].text(-5.3,2.15, f'$P$={amgut_interaction_p:.2E}', fontsize=12)\n",
    "axs[1,1].text(-5.3,1.8, f'$R^2$={amgut_interaction_rsq:.2E}', fontsize=12)\n",
    "axs[1,0].set_title('c', loc='left', fontsize=14, fontweight='bold')\n",
    "axs[1,1].set_title('d', loc='left', fontsize=14, fontweight='bold')\n",
    "\n",
    "axs[0,0].legend(loc='lower left')\n",
    "axs[1,0].legend(loc='lower left')\n",
    "# axs[1,0].set(xlabel='Height(log[cm])', ylabel='Simpson(log[1/D])', ylim=(-6, 3))\n",
    "axs[1,0].set(xlabel='Height (log[cm])', ylabel='Simpson (log[1/D])')\n",
    "axs[1,1].legend(loc='lower left')\n",
    "# axs[1,1].set(xlabel='Height(log[cm])', ylabel='Simpson(log[1/D])', ylim=(-6, 3))\n",
    "axs[1,1].set(xlabel='Height (log[cm])', ylabel='Simpson (log[1/D])')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "dd038a1c-b4c0-4d91-966e-1af67e2b4601",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.savefig(genpath+'/output/all_human_cohort_diversity.pdf')\n",
    "fig.savefig(genpath+'/output/all_human_cohort_diversity.png')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.19"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}