VanTineEtAl_Fig5_VolcanoPlot_sensitivePatients_PreVsPost.ipynb.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6a4d0ab2-1e3a-41df-ac4f-4ffe333fa5f1",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### For volcano plot (pre vs. post tratment in drug sensitive sarcoma patients) in Figure 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "435f3409-f31f-43ae-93ba-660601f44d31",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import *\n",
    "from matplotlib import colors\n",
    "import os\n",
    "from numpy import ma\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "from scipy import stats\n",
    "from scipy.stats import zscore\n",
    "from sklearn.decomposition import PCA\n",
    "from natsort import natsorted\n",
    "import ipywidgets\n",
    "from glob import glob\n",
    "import logging\n",
    "import sys\n",
    "import pingouin as pg\n",
    "from statsmodels.stats.weightstats import ttest_ind\n",
    "from statistics import mean\n",
    "from scipy.stats import t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "5cda038e-4da9-44d6-be09-37fa2d9abfd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# set working directory\n",
    "\n",
    "os.chdir('/Users/jasonheld/Manuscripts/2022_Sarcoma-Exosomes/Figure4 data_SenstiveResistant')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "fc173927-ba85-438b-a577-52b789635fa7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import data from github\n",
    "\n",
    "dfSarcoma = pd.read_excel('/Users/jasonheld/Manuscripts/2022_Sarcoma-Exosomes/vanTine_001_FinalDataWithControls/MQ/vanTine_001_90_samples/Protein/Model/Protein_pVals_Final_WithMetaData.xlsx', sheet_name='Sheet1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "f883ed74-9d0f-4743-b3dd-f103b0cf6d3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "parser = 'group'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "1958aad4-6ec3-41d5-8617-6086964f0008",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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>timing</th>\n",
       "      <th>dataset</th>\n",
       "      <th>race</th>\n",
       "      <th>sensitivity</th>\n",
       "      <th>gender</th>\n",
       "      <th>sample</th>\n",
       "      <th>patient</th>\n",
       "      <th>cancer</th>\n",
       "      <th>cancerType</th>\n",
       "      <th>dataType</th>\n",
       "      <th>age</th>\n",
       "      <th>group</th>\n",
       "      <th>protAcc</th>\n",
       "      <th>area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Pre</td>\n",
       "      <td>sarcoma</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>S</td>\n",
       "      <td>M</td>\n",
       "      <td>BJW.S.Pre</td>\n",
       "      <td>BJW</td>\n",
       "      <td>c</td>\n",
       "      <td>EPI</td>\n",
       "      <td>NormalizedIntensity_Log2</td>\n",
       "      <td>30-39</td>\n",
       "      <td>S.Pre</td>\n",
       "      <td>A0A075B6H7</td>\n",
       "      <td>1.639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Post</td>\n",
       "      <td>sarcoma</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>S</td>\n",
       "      <td>F</td>\n",
       "      <td>BNN.S.Post</td>\n",
       "      <td>BNN</td>\n",
       "      <td>c</td>\n",
       "      <td>MPNST</td>\n",
       "      <td>NormalizedIntensity_Log2</td>\n",
       "      <td>70-79</td>\n",
       "      <td>S.Post</td>\n",
       "      <td>A0A075B6H7</td>\n",
       "      <td>-1.775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Post</td>\n",
       "      <td>sarcoma</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>S</td>\n",
       "      <td>M</td>\n",
       "      <td>BPE.S.Post</td>\n",
       "      <td>BPE</td>\n",
       "      <td>c</td>\n",
       "      <td>LS</td>\n",
       "      <td>NormalizedIntensity_Log2</td>\n",
       "      <td>60-69</td>\n",
       "      <td>S.Post</td>\n",
       "      <td>A0A075B6H7</td>\n",
       "      <td>-0.856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Pre</td>\n",
       "      <td>sarcoma</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>S</td>\n",
       "      <td>M</td>\n",
       "      <td>BPE.S.Pre</td>\n",
       "      <td>BPE</td>\n",
       "      <td>c</td>\n",
       "      <td>LS</td>\n",
       "      <td>NormalizedIntensity_Log2</td>\n",
       "      <td>60-69</td>\n",
       "      <td>S.Pre</td>\n",
       "      <td>A0A075B6H7</td>\n",
       "      <td>0.830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Post</td>\n",
       "      <td>sarcoma</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>S</td>\n",
       "      <td>F</td>\n",
       "      <td>BPV.S.Post</td>\n",
       "      <td>BPV</td>\n",
       "      <td>c</td>\n",
       "      <td>LS</td>\n",
       "      <td>NormalizedIntensity_Log2</td>\n",
       "      <td>50-59</td>\n",
       "      <td>S.Post</td>\n",
       "      <td>A0A075B6H7</td>\n",
       "      <td>0.643</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   timing  dataset   race sensitivity gender      sample patient cancer  \\\n",
       "11    Pre  sarcoma  WHITE           S      M   BJW.S.Pre     BJW      c   \n",
       "20   Post  sarcoma  WHITE           S      F  BNN.S.Post     BNN      c   \n",
       "26   Post  sarcoma  WHITE           S      M  BPE.S.Post     BPE      c   \n",
       "27    Pre  sarcoma  WHITE           S      M   BPE.S.Pre     BPE      c   \n",
       "28   Post  sarcoma  WHITE           S      F  BPV.S.Post     BPV      c   \n",
       "\n",
       "   cancerType                  dataType    age   group     protAcc   area  \n",
       "11        EPI  NormalizedIntensity_Log2  30-39   S.Pre  A0A075B6H7  1.639  \n",
       "20      MPNST  NormalizedIntensity_Log2  70-79  S.Post  A0A075B6H7 -1.775  \n",
       "26         LS  NormalizedIntensity_Log2  60-69  S.Post  A0A075B6H7 -0.856  \n",
       "27         LS  NormalizedIntensity_Log2  60-69   S.Pre  A0A075B6H7  0.830  \n",
       "28         LS  NormalizedIntensity_Log2  50-59  S.Post  A0A075B6H7  0.643  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter for the analysis\n",
    "\n",
    "df = dfSarcoma\n",
    "dataTypeFilter = 'NormalizedIntensity_Log2'\n",
    "    \n",
    "df = df.loc[:,~df.columns.str.contains('CON_')] # remove contaminants\n",
    "df = df.query(\"dataType == @dataTypeFilter\")\n",
    "\n",
    "df = df.query(\"timing != 'Control' and sensitivity == 'S'\") # sensitive\n",
    "\n",
    "totalCols = df.columns\n",
    "numCols = df._get_numeric_data().columns\n",
    "catCols = list(set(totalCols)-set(numCols))\n",
    "\n",
    "df = pd.melt(df,\n",
    "    id_vars = catCols,\n",
    "    value_vars = numCols,\n",
    "    var_name = 'protAcc',\n",
    "    value_name=\"area\",\n",
    "    ignore_index=False,\n",
    "    )\n",
    "\n",
    "#remove rows without areas & duplicates\n",
    "df.dropna(subset=['area'], inplace=True)\n",
    "df.drop_duplicates(inplace = True)\n",
    "\n",
    "# add a column specifying the dataset\n",
    "dataset = 'sarcoma'\n",
    "df.insert(loc=1, column='dataset', value=dataset)\n",
    "\n",
    "dfProtein = df\n",
    "dfProtein.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9175dd1b-a145-4467-b92c-90c25251d425",
   "metadata": {},
   "source": [
    "#### annotate merged long/tidy data with uniprot IDs (or other metadata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "1e32108e-dbc1-44fd-8ba2-c92714a15133",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jasonheld/Python/env_221209_3-10/lib/python3.10/site-packages/openpyxl/styles/stylesheet.py:226: UserWarning: Workbook contains no default style, apply openpyxl's default\n",
      "  warn(\"Workbook contains no default style, apply openpyxl's default\")\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\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>protAcc</th>\n",
       "      <th>Reviewed</th>\n",
       "      <th>Protein names</th>\n",
       "      <th>geneNamePrimary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A0A075B6G3</td>\n",
       "      <td>unreviewed</td>\n",
       "      <td>Dystrophin</td>\n",
       "      <td>DMD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A0A087WV00</td>\n",
       "      <td>unreviewed</td>\n",
       "      <td>Diacylglycerol kinase (DAG kinase) (EC 2.7.1.107)</td>\n",
       "      <td>DGKI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A0A087WZT3</td>\n",
       "      <td>unreviewed</td>\n",
       "      <td>BOLA2-SMG1P6 readthrough</td>\n",
       "      <td>BOLA2-SMG1P6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A0A087X1C5</td>\n",
       "      <td>reviewed</td>\n",
       "      <td>Putative cytochrome P450 2D7 (EC 1.14.14.1)</td>\n",
       "      <td>CYP2D7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A0A0A0MQU7</td>\n",
       "      <td>unreviewed</td>\n",
       "      <td>HNF1 homeobox A</td>\n",
       "      <td>HNF1A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81786</th>\n",
       "      <td>X6RLU5</td>\n",
       "      <td>unreviewed</td>\n",
       "      <td>Calcium voltage-gated channel auxiliary subuni...</td>\n",
       "      <td>CACNA2D4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81787</th>\n",
       "      <td>X6RLV5</td>\n",
       "      <td>unreviewed</td>\n",
       "      <td>DEAD-box helicase 5</td>\n",
       "      <td>DDX5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81788</th>\n",
       "      <td>X6RLY7</td>\n",
       "      <td>unreviewed</td>\n",
       "      <td>Calcium voltage-gated channel auxiliary subuni...</td>\n",
       "      <td>CACNA2D4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81789</th>\n",
       "      <td>X6RM00</td>\n",
       "      <td>unreviewed</td>\n",
       "      <td>ELKS/RAB6-interacting/CAST family member 1</td>\n",
       "      <td>ERC1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81790</th>\n",
       "      <td>X6RM24</td>\n",
       "      <td>unreviewed</td>\n",
       "      <td>Enolase superfamily member 1</td>\n",
       "      <td>ENOSF1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>81791 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          protAcc    Reviewed  \\\n",
       "0      A0A075B6G3  unreviewed   \n",
       "1      A0A087WV00  unreviewed   \n",
       "2      A0A087WZT3  unreviewed   \n",
       "3      A0A087X1C5    reviewed   \n",
       "4      A0A0A0MQU7  unreviewed   \n",
       "...           ...         ...   \n",
       "81786      X6RLU5  unreviewed   \n",
       "81787      X6RLV5  unreviewed   \n",
       "81788      X6RLY7  unreviewed   \n",
       "81789      X6RM00  unreviewed   \n",
       "81790      X6RM24  unreviewed   \n",
       "\n",
       "                                           Protein names geneNamePrimary  \n",
       "0                                             Dystrophin             DMD  \n",
       "1      Diacylglycerol kinase (DAG kinase) (EC 2.7.1.107)            DGKI  \n",
       "2                               BOLA2-SMG1P6 readthrough    BOLA2-SMG1P6  \n",
       "3            Putative cytochrome P450 2D7 (EC 1.14.14.1)          CYP2D7  \n",
       "4                                        HNF1 homeobox A           HNF1A  \n",
       "...                                                  ...             ...  \n",
       "81786  Calcium voltage-gated channel auxiliary subuni...        CACNA2D4  \n",
       "81787                                DEAD-box helicase 5            DDX5  \n",
       "81788  Calcium voltage-gated channel auxiliary subuni...        CACNA2D4  \n",
       "81789         ELKS/RAB6-interacting/CAST family member 1            ERC1  \n",
       "81790                       Enolase superfamily member 1          ENOSF1  \n",
       "\n",
       "[81791 rows x 4 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make dfUniprot for annotation, this excel file is on the associated GitHub page\n",
    "\n",
    "dfUniprot = pd.read_excel('/Users/jasonheld/Manuscripts/2022_Sarcoma-Exosomes/Figure2_Data_CancerVsNormal/uniprot-download_true_fields_accession_2Creviewed_2Cid_2Cprotein_nam-2023.03.17-19.33.16.44.xlsx')\n",
    "dfUniprot = dfUniprot.rename(columns={'Entry' : 'protAcc'})\n",
    "dfUniprot = dfUniprot.rename(columns={'Gene Names (primary)' : 'geneNamePrimary'})\n",
    "dfUniprot = dfUniprot.drop(columns=['Length','Entry Name','Organism','Gene Names'])\n",
    "dfUniprot['protAcc'] = dfUniprot['protAcc'].astype(str)\n",
    "dfUniprot.drop_duplicates(subset=['protAcc'], inplace = True)\n",
    "dfUniprot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "00f85887-51d4-47cb-be86-d87f15122509",
   "metadata": {},
   "outputs": [],
   "source": [
    "# annotate uniprot info\n",
    "dfMerged = dfProtein.merge(dfUniprot, on='protAcc', how='left')\n",
    "\n",
    "totalCols = dfMerged.columns\n",
    "numCols = dfMerged._get_numeric_data().columns\n",
    "catCols = list(set(totalCols)-set(numCols))\n",
    "\n",
    "dfMerged['area'] = dfMerged['area'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "d3dc887d-30cc-4ef7-9a58-5a7cdb6d42fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove Remove 'crapome' proteins\n",
    "\n",
    "crapome =['KRT1', 'KRT2', 'KRT3', 'KRT4', 'KRT5', 'KRT6', 'KRT7', 'KRT8', 'KRT9', 'KRT10', 'KRT11', 'KRT12', 'KRT13', 'KRT14', 'KRT15', 'KRT16', 'KRT17', 'KRT18', 'KRT19', 'KRT20', 'KRT21', 'KRT22', 'KRT23', 'KRT24']\n",
    "\n",
    "dfMerged = dfMerged.loc[~dfMerged['geneNamePrimary'].str.contains('|'.join(crapome))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "d7ff7d7a-01ce-4066-8ebe-4c02ea311cf3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# count protein IDs\n",
    "\n",
    "dfTemp = dfMerged\n",
    "\n",
    "nSamplesFilter = 10\n",
    "nSamplesControlOnlyFilter = 0\n",
    "    \n",
    "#drop 0 values\n",
    "dfTemp.dropna(subset = ['area'], inplace = True)\n",
    "\n",
    "# count\n",
    "dfTemp['nSamples'] = dfTemp.groupby('protAcc')['protAcc'].transform('count')\n",
    "nSamplesControlOnly = dfTemp.query('cancer == \"n\"').groupby('protAcc')['protAcc'].agg('count').to_frame()\n",
    "nSamplesControlOnly.rename(columns = {'protAcc' : 'nSamplesControlOnly'}, inplace = True)\n",
    "dfTemp = dfTemp.join(nSamplesControlOnly, on = 'protAcc')\n",
    "dfTemp['nSamplesControlOnly'] = dfTemp['nSamplesControlOnly'].fillna(0)\n",
    "\n",
    "# filter by N detections\n",
    "dfTemp = dfTemp.query('nSamples >= @nSamplesFilter')\n",
    "dfTemp = dfTemp.query('nSamplesControlOnly >= @nSamplesControlOnlyFilter')\n",
    "\n",
    "dfFiltered = dfTemp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "aada51ce-f8ab-454f-b1df-0fb26b26b40e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/39/4j31xtfn0k1gbr7xx3tcxqgw0000gr/T/ipykernel_55137/3059717704.py:22: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  dfStatsPre[col].fillna(value = dfStatsPre[col].min(), inplace = True)\n"
     ]
    }
   ],
   "source": [
    "# find and select data/feature columns\n",
    "\n",
    "totalCols = dfFiltered.columns\n",
    "numCols = dfFiltered._get_numeric_data().columns\n",
    "catCols = list(set(totalCols)-set(numCols))\n",
    "\n",
    "# pivot \n",
    "dfStatsPre = dfFiltered.pivot_table(\n",
    "    values = 'area',\n",
    "    index = [parser,'sample'],\n",
    "    columns = ['geneNamePrimary'],\n",
    ").reset_index().rename_axis(None, axis=1)\n",
    "dfStatsPre.set_index(parser, inplace = True)\n",
    "\n",
    "dfStatsPre.reset_index(inplace = True)\n",
    "sampleNames = dfStatsPre['sample']\n",
    "dfStatsPre = dfStatsPre.drop(columns = ['sample'])\n",
    "\n",
    "# impute nan to min value in column\n",
    "\n",
    "for col in dfStatsPre.columns:\n",
    "    dfStatsPre[col].fillna(value = dfStatsPre[col].min(), inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "77ef0aa5-9f34-4c7a-b241-e09aa0a1ad0d",
   "metadata": {},
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   "source": [
    "# melt\n",
    "dfStatsPre = dfStatsPre.melt(\n",
    "    id_vars = parser,\n",
    "    value_name = 'area',\n",
    "    var_name = 'geneNamePrimary',\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "9aafeac1-439b-4ca4-8f1d-87d837eaa578",
   "metadata": {},
   "outputs": [],
   "source": [
    "# setup groups\n",
    "\n",
    "RPre = dfStatsPre.query('group == \"S.Post\"')\n",
    "SPre = dfStatsPre.query('group == \"S.Pre\"')"
   ]
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   "source": [
    "# make dfStats with pvals / qvals and other stats\n",
    "\n",
    "colName = 'geneNamePrimary'\n",
    "valueCol = 'area'\n",
    "\n",
    "# make a list of all genes\n",
    "allGenes = dfStatsPre['geneNamePrimary'].unique()\n",
    "\n",
    "# to make new dfStats\n",
    "dfStats = pd.DataFrame()\n",
    "geneNamePrimaryList = []\n",
    "group1List = []\n",
    "group2List = []\n",
    "oneGroupDetectionList = []\n",
    "group2DetectionOnlyList = []\n",
    "group1MeanList = []\n",
    "group2MeanList = []\n",
    "pvalScipyList = []\n",
    "pvalPingouinList = []\n",
    "pvalstatsmodelList = []\n",
    "\n",
    "for i in allGenes:\n",
    "    group1 = RPre.query(\"{0} == '{1}'\".format(colName, i))\n",
    "    group2 = SPre.query(\"{0} == '{1}'\".format(colName, i))\n",
    "    geneNamePrimaryList.append(i)\n",
    "\n",
    "    group1List.append(group1)\n",
    "    group2List.append(group2)\n",
    "    \n",
    "    if len(group1) < 2 or len(group2) < 2:\n",
    "        if len(group1) < 3:\n",
    "            oneGroupDetectionList.append('DetectedOnceOrLessInGroup1')\n",
    "            manualPvalList.append('')\n",
    "            pvalScipyList.append('')\n",
    "            group1MeanList.append('')\n",
    "            group2MeanList.append('')\n",
    "        elif len(group2) < 3:\n",
    "            oneGroupDetectionList.append('DetectedOnceOrLessInGroup2')\n",
    "            manualPvalList.append('')\n",
    "            pvalScipyList.append('')\n",
    "            group1MeanList.append('')\n",
    "            group2MeanList.append('')\n",
    "        continue\n",
    "    else:\n",
    "        oneGroupDetectionList.append('')\n",
    "        \n",
    "        mean1, mean2 = mean(group1[valueCol]), mean(group2[valueCol]) # calculate means\n",
    "        group1MeanList.append(mean1)\n",
    "        group2MeanList.append(mean2)\n",
    "\n",
    "        # scipy t-test\n",
    "        tstat, p = stats.ttest_ind(group1[valueCol], group2[valueCol], equal_var=False)\n",
    "        pvalScipyList.append(p)\n",
    "        display(p)\n",
    "\n",
    "dfStats['geneNamePrimary'] = geneNamePrimaryList\n",
    "dfStats['RPre'] = group1List\n",
    "dfStats['SPre'] = group2List\n",
    "dfStats['meanRPre']= group1MeanList\n",
    "dfStats['meanSPre']= group2MeanList\n",
    "dfStats['oneGroupDetection'] = oneGroupDetectionList\n",
    "dfStats['pvalScipy'] = pvalScipyList\n",
    "\n",
    "# define direction change\n",
    "dfStats['directionChange'] = np.where((dfStats['meanRPre'] > dfStats['meanSPre']), 'up', 'down')\n",
    "dfStats['directionChange'] = np.where(dfStats['oneGroupDetection'] == 'DetectedOnceOrLessInGroup1', 'down', dfStats['directionChange'])\n",
    "dfStats['directionChange'] = np.where(dfStats['oneGroupDetection'] == 'DetectedOnceOrLessInGroup2', 'up', dfStats['directionChange'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "48acc8b2-7737-4f34-a30f-0aaf0797de0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop missing values\n",
    "dfStats.dropna(subset=['pvalScipy'], inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "738711e5-b7d5-4b7e-9672-dbd9b9523d76",
   "metadata": {
    "tags": []
   },
   "source": [
    "### BH FDR correction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "89a72f1e-9513-47a8-91c3-a368ae02bb3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Benjmani-hochburg correction\n",
    "from statsmodels.stats.multitest import multipletests\n",
    "\n",
    "# BH FDR correction with stats models\n",
    "scipyQvals = multipletests(dfStats.query(\"pvalScipy != ''\")['pvalScipy'], alpha=0.05, method=\"fdr_bh\")\n",
    "\n",
    "# rename column\n",
    "dfStats.loc[dfStats['pvalScipy'] != '', 'qvalScipy'] = scipyQvals[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "8508a402-7beb-4439-9e2e-648d40b850a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# select significant proteins (FDR)\n",
    "\n",
    "dfStats['sigFDR'] = 'notSig'\n",
    "dfStats.loc[pd.isnull(dfStats['qvalScipy']), 'sigFDR'] = 'sig'\n",
    "dfStats.loc[dfStats['qvalScipy'] < 0.05, 'sigFDR'] = 'sig'\n",
    "\n",
    "# list of all significant genes\n",
    "geneNamePrimarySigAll = dfStats.query('sigFDR == \"sig\"')['geneNamePrimary'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "22d58614-1448-4386-9a6b-bfc5bef2d200",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jasonheld/Python/env_221209_3-10/lib/python3.10/site-packages/pandas/core/arraylike.py:399: RuntimeWarning: invalid value encountered in log2\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "# some calulations needed for the volcano plot\n",
    "\n",
    "# calc ratios\n",
    "dfStats['FC_RPre:SPre'] = dfStats['meanRPre'] / dfStats['meanSPre']\n",
    "dfStats['log2FC_RPre:SPre'] = np.log2(dfStats['FC_RPre:SPre'])\n",
    "\n",
    "dfStats['FC_RPre:SPre'] = dfStats['meanRPre'] - dfStats['meanSPre']\n",
    "dfStats['log2FC_RPre:SPre'] = dfStats['FC_RPre:SPre']\n",
    "\n",
    "# -log10 q values\n",
    "dfStats['-log10qvalScipy'] = np.log10(dfStats['qvalScipy']).mul(-1)\n",
    "dfStats['-log10qvalScipyRank'] = dfStats['-log10qvalScipy'].rank(method = 'first', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "c6d1aae8-c8f9-414a-a061-c1a8be26d149",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jasonheld/Python/env_221209_3-10/lib/python3.10/site-packages/matplotlib/text.py:1437: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
      "  x = float(self.convert_xunits(x))\n",
      "/Users/jasonheld/Python/env_221209_3-10/lib/python3.10/site-packages/matplotlib/text.py:1439: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
      "  y = float(self.convert_yunits(y))\n",
      "/Users/jasonheld/Python/env_221209_3-10/lib/python3.10/site-packages/matplotlib/text.py:692: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
      "  posx = float(self.convert_xunits(self._x))\n",
      "/Users/jasonheld/Python/env_221209_3-10/lib/python3.10/site-packages/matplotlib/text.py:693: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
      "  posy = float(self.convert_yunits(self._y))\n",
      "/Users/jasonheld/Python/env_221209_3-10/lib/python3.10/site-packages/matplotlib/text.py:832: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
      "  x = float(self.convert_xunits(self._x))\n",
      "/Users/jasonheld/Python/env_221209_3-10/lib/python3.10/site-packages/matplotlib/text.py:833: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
      "  y = float(self.convert_yunits(self._y))\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make annotated volcano plot\n",
    "\n",
    "diffExprGeneList = dfStats[dfStats['-log10qvalScipyRank'] < 1.301]['geneNamePrimary'].to_list()\n",
    "diffExprGeneList.extend(dfStats[dfStats['log2FC_RPre:SPre'] < -1.5]['geneNamePrimary'].to_list())\n",
    "diffExprGeneList.extend(dfStats[dfStats['log2FC_RPre:SPre'] > 2.5]['geneNamePrimary'].to_list())\n",
    "\n",
    "mask = dfStats['geneNamePrimary'].isin(diffExprGeneList)\n",
    "annotations = diffExprGeneList\n",
    "\n",
    "diffExprGeneListAlt = dfStats[dfStats['-log10qvalScipyRank'] < 15]['geneNamePrimary'].to_list()\n",
    "diffExprGeneListAlt.extend(dfStats[dfStats['log2FC_RPre:SPre'] < -5]['geneNamePrimary'].to_list())\n",
    "diffExprGeneListAlt.extend(dfStats[dfStats['log2FC_RPre:SPre'] > 5]['geneNamePrimary'].to_list())\n",
    "\n",
    "if len(diffExprGeneList) > 40:\n",
    "    mask = dfStats['geneNamePrimary'].isin(diffExprGeneListAlt)\n",
    "    annotations = diffExprGeneListAlt\n",
    "\n",
    "plt.figure(figsize=(4,4))\n",
    "\n",
    "sns.scatterplot(data = dfStats.loc[mask],\n",
    "    x = 'log2FC_RPre:SPre',\n",
    "    y = '-log10qvalScipy',\n",
    "    size=5,\n",
    "    facecolor = 'red',\n",
    "    edgecolor = 'black',)\n",
    "\n",
    "for i in annotations:\n",
    "    plt.annotate(i,\n",
    "        (dfStats[dfStats.geneNamePrimary == i]['log2FC_RPre:SPre'],\n",
    "        dfStats[dfStats.geneNamePrimary == i]['-log10qvalScipy'],\n",
    "        )\n",
    "    ) \n",
    "\n",
    "sns.scatterplot(\n",
    "    data=dfStats,\n",
    "    x=\"log2FC_RPre:SPre\",\n",
    "    y=\"-log10qvalScipy\",\n",
    "    hue = 'sigFDR',\n",
    "    ).axhline(\n",
    "    1.3, color = 'darkgrey', linestyle = '--', linewidth=1)\n",
    "\n",
    "plt.legend(bbox_to_anchor=(1.5, .5), loc='center right', borderaxespad=0)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bbd6493d-f1aa-478b-962a-76d6cc5b7646",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c6f1aee2-60d0-42a6-99d2-612223973a49",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dacdcf87-bc49-483e-a8c5-093a78a7f05a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e33ecba3-ee82-475e-9397-44076f17a440",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60250196-90c3-4469-901d-deb23e612a2d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "88ab04ed-0f2f-446c-bc6f-7ba22fca9cfd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}