{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"#Setting matplotlib display setting as inline plots\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"#Importing necessary packages\n",
"import os\n",
"import pandas as pd\n",
"import numpy as np\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"plt.rcParams['font.family'] = 'Arial'\n",
"import matplotlib\n",
"import matplotlib.image as mpimg\n",
"from scipy import stats\n",
"import matplotlib.ticker as ticker\n",
"import matplotlib.patches as mpatches\n",
"from collections import OrderedDict"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# WT Uninduced"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"#Set working directory to location of raw data. This is where output will be put as well unless file paths are specified.\n",
"os.getcwd()\n",
"os.chdir('C:/Users/ooma1/OneDrive - UC San Diego/Guatelli Lab/Cell Fractionation/20190308 Jurkat TREHIV WT fract MS/UnA/rowsum')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Large Protein Complex 140\n",
"Mitochondrion 99\n",
"Endosome 38\n",
"ER 27\n",
"Plasma membrane 20\n",
"Lysosome 13\n",
"Golgi 12\n",
"Peroxisome 12\n",
"Name: Compartment, dtype: int64\n"
]
}
],
"source": [
"#Read in the csv data file, using the ProteinID column as the index_col\n",
"data = pd.read_csv('20190516_UnA_SVCidentified_postiter.csv', index_col = 0)\n",
"data = data[data['Probability'] == 0]\n",
"print(data.Compartment.value_counts())"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"una1 = data.groupby('Compartment').mean()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"#Set working directory to location of raw data. This is where output will be put as well unless file paths are specified.\n",
"os.getcwd()\n",
"os.chdir('C:/Users/ooma1/OneDrive - UC San Diego/Guatelli Lab/Cell Fractionation/20190308 Jurkat TREHIV WT fract MS/UnB/rowsum')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Large Protein Complex 140\n",
"Mitochondrion 99\n",
"Endosome 38\n",
"ER 27\n",
"Plasma membrane 20\n",
"Lysosome 13\n",
"Golgi 12\n",
"Peroxisome 12\n",
"Name: Compartment, dtype: int64\n"
]
}
],
"source": [
"#Read in the csv data file, using the ProteinID column as the index_col\n",
"data = pd.read_csv('20190516_UnB_SVCidentified_postiter.csv', index_col = 0)\n",
"data = data[data['Probability'] == 0]\n",
"print(data.Compartment.value_counts())"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"unb1 = data.groupby('Compartment').mean()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"#Set working directory to location of raw data. This is where output will be put as well unless file paths are specified.\n",
"os.getcwd()\n",
"os.chdir('C:/Users/ooma1/OneDrive - UC San Diego/Guatelli Lab/Cell Fractionation/20190308 Jurkat TREHIV WT fract MS/UnC/rowsum')"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Large Protein Complex 140\n",
"Mitochondrion 99\n",
"Endosome 38\n",
"ER 27\n",
"Plasma membrane 20\n",
"Lysosome 13\n",
"Golgi 12\n",
"Peroxisome 12\n",
"Name: Compartment, dtype: int64\n"
]
}
],
"source": [
"#Read in the csv data file, using the ProteinID column as the index_col\n",
"data = pd.read_csv('20190516_UnC_SVCidentified_postiter.csv', index_col = 0)\n",
"data = data[data['Probability'] == 0]\n",
"print(data.Compartment.value_counts())"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"unc1 = data.groupby('Compartment').mean()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"#Set working directory to location of raw data. This is where output will be put as well unless file paths are specified.\n",
"os.getcwd()\n",
"os.chdir('C:/Users/ooma1/OneDrive - UC San Diego/Guatelli Lab/Cell Fractionation/20190517 Jurkat TREHIV WT fract MS/UnA')"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Large Protein Complex 131\n",
"Endosome 41\n",
"ER 30\n",
"Plasma membrane 21\n",
"Golgi 18\n",
"Mitochondrion 17\n",
"Peroxisome 14\n",
"Lysosome 11\n",
"Name: Compartment, dtype: int64\n"
]
}
],
"source": [
"#Read in the csv data file, using the ProteinID column as the index_col\n",
"data = pd.read_csv('20190518_UnA_SVCidentified_postiter.csv', index_col = 0)\n",
"data = data[data['Probability'] == 0]\n",
"print(data.Compartment.value_counts())"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"una2 = data.groupby('Compartment').mean()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"#Set working directory to location of raw data. This is where output will be put as well unless file paths are specified.\n",
"os.getcwd()\n",
"os.chdir('C:/Users/ooma1/OneDrive - UC San Diego/Guatelli Lab/Cell Fractionation/20190517 Jurkat TREHIV WT fract MS/UnB')"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Large Protein Complex 131\n",
"Endosome 41\n",
"ER 30\n",
"Plasma membrane 21\n",
"Golgi 18\n",
"Mitochondrion 17\n",
"Peroxisome 14\n",
"Lysosome 11\n",
"Name: Compartment, dtype: int64\n"
]
}
],
"source": [
"#Read in the csv data file, using the ProteinID column as the index_col\n",
"data = pd.read_csv('20190518_UnB_SVCidentified_postiter.csv', index_col = 0)\n",
"data = data[data['Probability'] == 0]\n",
"print(data.Compartment.value_counts())"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"unb2 = data.groupby('Compartment').mean()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"#Set working directory to location of raw data. This is where output will be put as well unless file paths are specified.\n",
"os.getcwd()\n",
"os.chdir('C:/Users/ooma1/OneDrive - UC San Diego/Guatelli Lab/Cell Fractionation/20190517 Jurkat TREHIV WT fract MS/UnC')"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Large Protein Complex 131\n",
"Endosome 41\n",
"ER 30\n",
"Plasma membrane 21\n",
"Golgi 18\n",
"Mitochondrion 17\n",
"Peroxisome 14\n",
"Lysosome 11\n",
"Name: Compartment, dtype: int64\n"
]
}
],
"source": [
"#Read in the csv data file, using the ProteinID column as the index_col\n",
"data = pd.read_csv('20190518_UnC_SVCidentified_postiter.csv', index_col = 0)\n",
"data = data[data['Probability'] == 0]\n",
"print(data.Compartment.value_counts())"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"unc2 = data.groupby('Compartment').mean()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"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>3K</th>\n",
" <th>5.4K</th>\n",
" <th>12.2K</th>\n",
" <th>24K</th>\n",
" <th>78.4K</th>\n",
" <th>110K</th>\n",
" <th>195.5K</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Compartment</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>ER</td>\n",
" <td>0.126374</td>\n",
" <td>0.222143</td>\n",
" <td>0.629719</td>\n",
" <td>0.758687</td>\n",
" <td>0.822371</td>\n",
" <td>0.285259</td>\n",
" <td>0.102011</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Endosome</td>\n",
" <td>0.129746</td>\n",
" <td>0.198549</td>\n",
" <td>0.471019</td>\n",
" <td>0.547866</td>\n",
" <td>0.626779</td>\n",
" <td>0.471520</td>\n",
" <td>0.385702</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Golgi</td>\n",
" <td>0.171673</td>\n",
" <td>0.368142</td>\n",
" <td>0.860256</td>\n",
" <td>0.607252</td>\n",
" <td>0.455986</td>\n",
" <td>0.298173</td>\n",
" <td>0.191515</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Large Protein Complex</td>\n",
" <td>0.158224</td>\n",
" <td>0.186947</td>\n",
" <td>0.328029</td>\n",
" <td>0.351469</td>\n",
" <td>0.486257</td>\n",
" <td>0.589570</td>\n",
" <td>0.621087</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Lysosome</td>\n",
" <td>0.070848</td>\n",
" <td>0.176393</td>\n",
" <td>0.712256</td>\n",
" <td>0.920900</td>\n",
" <td>0.782967</td>\n",
" <td>0.248574</td>\n",
" <td>0.107674</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Mitochondrion</td>\n",
" <td>0.885522</td>\n",
" <td>0.875615</td>\n",
" <td>0.640594</td>\n",
" <td>0.047590</td>\n",
" <td>0.035805</td>\n",
" <td>0.032395</td>\n",
" <td>0.030444</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Peroxisome</td>\n",
" <td>0.192849</td>\n",
" <td>0.359672</td>\n",
" <td>0.874663</td>\n",
" <td>0.747646</td>\n",
" <td>0.576210</td>\n",
" <td>0.168411</td>\n",
" <td>0.071884</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Plasma membrane</td>\n",
" <td>0.165106</td>\n",
" <td>0.315364</td>\n",
" <td>0.813079</td>\n",
" <td>0.745969</td>\n",
" <td>0.617753</td>\n",
" <td>0.216457</td>\n",
" <td>0.105006</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 3K 5.4K 12.2K 24K 78.4K \\\n",
"Compartment \n",
"ER 0.126374 0.222143 0.629719 0.758687 0.822371 \n",
"Endosome 0.129746 0.198549 0.471019 0.547866 0.626779 \n",
"Golgi 0.171673 0.368142 0.860256 0.607252 0.455986 \n",
"Large Protein Complex 0.158224 0.186947 0.328029 0.351469 0.486257 \n",
"Lysosome 0.070848 0.176393 0.712256 0.920900 0.782967 \n",
"Mitochondrion 0.885522 0.875615 0.640594 0.047590 0.035805 \n",
"Peroxisome 0.192849 0.359672 0.874663 0.747646 0.576210 \n",
"Plasma membrane 0.165106 0.315364 0.813079 0.745969 0.617753 \n",
"\n",
" 110K 195.5K \n",
"Compartment \n",
"ER 0.285259 0.102011 \n",
"Endosome 0.471520 0.385702 \n",
"Golgi 0.298173 0.191515 \n",
"Large Protein Complex 0.589570 0.621087 \n",
"Lysosome 0.248574 0.107674 \n",
"Mitochondrion 0.032395 0.030444 \n",
"Peroxisome 0.168411 0.071884 \n",
"Plasma membrane 0.216457 0.105006 "
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wtun = una1 + unb1 + unc1 + una2 + unb2 + unc2\n",
"wtun = wtun/6\n",
"wtun = wtun.loc[:, '3K':'195.5K']\n",
"wtun"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# WT Induced"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"#Set working directory to location of raw data. This is where output will be put as well unless file paths are specified.\n",
"os.getcwd()\n",
"os.chdir('C:/Users/ooma1/OneDrive - UC San Diego/Guatelli Lab/Cell Fractionation/20190308 Jurkat TREHIV WT fract MS/IndA/rowsum')"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Large Protein Complex 140\n",
"Mitochondrion 99\n",
"Endosome 38\n",
"ER 27\n",
"Plasma membrane 20\n",
"Lysosome 13\n",
"Golgi 12\n",
"Peroxisome 12\n",
"Name: Compartment, dtype: int64\n"
]
}
],
"source": [
"#Read in the csv data file, using the ProteinID column as the index_col\n",
"data = pd.read_csv('20190516_IndA_SVCidentified_postiter.csv', index_col = 0)\n",
"data = data[data['Probability'] == 0]\n",
"print(data.Compartment.value_counts())"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"IndA1 = data.groupby('Compartment').mean()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"#Set working directory to location of raw data. This is where output will be put as well unless file paths are specified.\n",
"os.getcwd()\n",
"os.chdir('C:/Users/ooma1/OneDrive - UC San Diego/Guatelli Lab/Cell Fractionation/20190308 Jurkat TREHIV WT fract MS/IndB/rowsum')"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Large Protein Complex 140\n",
"Mitochondrion 99\n",
"Endosome 38\n",
"ER 27\n",
"Plasma membrane 20\n",
"Lysosome 13\n",
"Golgi 12\n",
"Peroxisome 12\n",
"Name: Compartment, dtype: int64\n"
]
}
],
"source": [
"#Read in the csv data file, using the ProteinID column as the index_col\n",
"data = pd.read_csv('20190516_IndB_SVCidentified_postiter.csv', index_col = 0)\n",
"data = data[data['Probability'] == 0]\n",
"print(data.Compartment.value_counts())"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"IndB1 = data.groupby('Compartment').mean()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"#Set working directory to location of raw data. This is where output will be put as well unless file paths are specified.\n",
"os.getcwd()\n",
"os.chdir('C:/Users/ooma1/OneDrive - UC San Diego/Guatelli Lab/Cell Fractionation/20190308 Jurkat TREHIV WT fract MS/IndC/rowsum')"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Large Protein Complex 140\n",
"Mitochondrion 99\n",
"Endosome 38\n",
"ER 27\n",
"Plasma membrane 20\n",
"Lysosome 13\n",
"Golgi 12\n",
"Peroxisome 12\n",
"Name: Compartment, dtype: int64\n"
]
}
],
"source": [
"#Read in the csv data file, using the ProteinID column as the index_col\n",
"data = pd.read_csv('20190516_IndC_SVCidentified_postiter.csv', index_col = 0)\n",
"data = data[data['Probability'] == 0]\n",
"print(data.Compartment.value_counts())"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"IndC1 = data.groupby('Compartment').mean()"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"#Set working directory to location of raw data. This is where output will be put as well unless file paths are specified.\n",
"os.getcwd()\n",
"os.chdir('C:/Users/ooma1/OneDrive - UC San Diego/Guatelli Lab/Cell Fractionation/20190517 Jurkat TREHIV WT fract MS/IndA')"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Large Protein Complex 131\n",
"Endosome 41\n",
"ER 30\n",
"Plasma membrane 21\n",
"Golgi 18\n",
"Mitochondrion 17\n",
"Peroxisome 14\n",
"Lysosome 11\n",
"Name: Compartment, dtype: int64\n"
]
}
],
"source": [
"#Read in the csv data file, using the ProteinID column as the index_col\n",
"data = pd.read_csv('20190518_IndA_SVCidentified_postiter.csv', index_col = 0)\n",
"data = data[data['Probability'] == 0]\n",
"print(data.Compartment.value_counts())"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"IndA2 = data.groupby('Compartment').mean()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"#Set working directory to location of raw data. This is where output will be put as well unless file paths are specified.\n",
"os.getcwd()\n",
"os.chdir('C:/Users/ooma1/OneDrive - UC San Diego/Guatelli Lab/Cell Fractionation/20190517 Jurkat TREHIV WT fract MS/IndB')"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Large Protein Complex 131\n",
"Endosome 41\n",
"ER 30\n",
"Plasma membrane 21\n",
"Golgi 18\n",
"Mitochondrion 17\n",
"Peroxisome 14\n",
"Lysosome 11\n",
"Name: Compartment, dtype: int64\n"
]
}
],
"source": [
"#Read in the csv data file, using the ProteinID column as the index_col\n",
"data = pd.read_csv('20190518_IndB_SVCidentified_postiter.csv', index_col = 0)\n",
"data = data[data['Probability'] == 0]\n",
"print(data.Compartment.value_counts())"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"IndB2 = data.groupby('Compartment').mean()"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"#Set working directory to location of raw data. This is where output will be put as well unless file paths are specified.\n",
"os.getcwd()\n",
"os.chdir('C:/Users/ooma1/OneDrive - UC San Diego/Guatelli Lab/Cell Fractionation/20190517 Jurkat TREHIV WT fract MS/IndC')"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Large Protein Complex 131\n",
"Endosome 41\n",
"ER 30\n",
"Plasma membrane 21\n",
"Golgi 18\n",
"Mitochondrion 17\n",
"Peroxisome 14\n",
"Lysosome 11\n",
"Name: Compartment, dtype: int64\n"
]
}
],
"source": [
"#Read in the csv data file, using the ProteinID column as the index_col\n",
"data = pd.read_csv('20190518_IndC_SVCidentified_postiter.csv', index_col = 0)\n",
"data = data[data['Probability'] == 0]\n",
"print(data.Compartment.value_counts())"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
"IndC2 = data.groupby('Compartment').mean()"
]
},
{
"cell_type": "code",
"execution_count": 40,
"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>3K</th>\n",
" <th>5.4K</th>\n",
" <th>12.2K</th>\n",
" <th>24K</th>\n",
" <th>78.4K</th>\n",
" <th>110K</th>\n",
" <th>195.5K</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Compartment</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>ER</td>\n",
" <td>0.080446</td>\n",
" <td>0.204630</td>\n",
" <td>0.631232</td>\n",
" <td>0.747960</td>\n",
" <td>0.872273</td>\n",
" <td>0.302175</td>\n",
" <td>0.072423</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Endosome</td>\n",
" <td>0.100437</td>\n",
" <td>0.191817</td>\n",
" <td>0.498335</td>\n",
" <td>0.566060</td>\n",
" <td>0.635377</td>\n",
" <td>0.422014</td>\n",
" <td>0.363544</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Golgi</td>\n",
" <td>0.097482</td>\n",
" <td>0.284181</td>\n",
" <td>0.779995</td>\n",
" <td>0.632179</td>\n",
" <td>0.551668</td>\n",
" <td>0.357343</td>\n",
" <td>0.194642</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Large Protein Complex</td>\n",
" <td>0.129843</td>\n",
" <td>0.173515</td>\n",
" <td>0.343541</td>\n",
" <td>0.384383</td>\n",
" <td>0.485607</td>\n",
" <td>0.518886</td>\n",
" <td>0.608713</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Lysosome</td>\n",
" <td>0.041334</td>\n",
" <td>0.190763</td>\n",
" <td>0.788796</td>\n",
" <td>0.906023</td>\n",
" <td>0.808148</td>\n",
" <td>0.204866</td>\n",
" <td>0.073344</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Mitochondrion</td>\n",
" <td>0.900459</td>\n",
" <td>0.873670</td>\n",
" <td>0.398394</td>\n",
" <td>0.047985</td>\n",
" <td>0.044057</td>\n",
" <td>0.032597</td>\n",
" <td>0.039087</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Peroxisome</td>\n",
" <td>0.087997</td>\n",
" <td>0.229932</td>\n",
" <td>0.674062</td>\n",
" <td>0.784761</td>\n",
" <td>0.874761</td>\n",
" <td>0.237764</td>\n",
" <td>0.049002</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Plasma membrane</td>\n",
" <td>0.082264</td>\n",
" <td>0.278171</td>\n",
" <td>0.826792</td>\n",
" <td>0.777959</td>\n",
" <td>0.671343</td>\n",
" <td>0.232853</td>\n",
" <td>0.102336</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 3K 5.4K 12.2K 24K 78.4K \\\n",
"Compartment \n",
"ER 0.080446 0.204630 0.631232 0.747960 0.872273 \n",
"Endosome 0.100437 0.191817 0.498335 0.566060 0.635377 \n",
"Golgi 0.097482 0.284181 0.779995 0.632179 0.551668 \n",
"Large Protein Complex 0.129843 0.173515 0.343541 0.384383 0.485607 \n",
"Lysosome 0.041334 0.190763 0.788796 0.906023 0.808148 \n",
"Mitochondrion 0.900459 0.873670 0.398394 0.047985 0.044057 \n",
"Peroxisome 0.087997 0.229932 0.674062 0.784761 0.874761 \n",
"Plasma membrane 0.082264 0.278171 0.826792 0.777959 0.671343 \n",
"\n",
" 110K 195.5K \n",
"Compartment \n",
"ER 0.302175 0.072423 \n",
"Endosome 0.422014 0.363544 \n",
"Golgi 0.357343 0.194642 \n",
"Large Protein Complex 0.518886 0.608713 \n",
"Lysosome 0.204866 0.073344 \n",
"Mitochondrion 0.032597 0.039087 \n",
"Peroxisome 0.237764 0.049002 \n",
"Plasma membrane 0.232853 0.102336 "
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wtind = IndA1 + IndB1 + IndC1 + IndA2 + IndB2 + IndC2\n",
"wtind = wtind/6\n",
"wtind = wtind.loc[:, '3K':'195.5K']\n",
"wtind"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# dNef Uninduced"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
"#Set working directory to location of raw data. This is where output will be put as well unless file paths are specified.\n",
"os.getcwd()\n",
"os.chdir('C:/Users/ooma1/OneDrive - UC San Diego/Guatelli Lab/Cell Fractionation/20190612 Jurkat TREHIV dNef fract MS/UnA')"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Large Protein Complex 160\n",
"Endosome 44\n",
"ER 33\n",
"Plasma membrane 25\n",
"Golgi 19\n",
"Mitochondrion 15\n",
"Lysosome 13\n",
"Peroxisome 11\n",
"Name: Compartment, dtype: int64\n"
]
}
],
"source": [
"#Read in the csv data file, using the ProteinID column as the index_col\n",
"data = pd.read_csv('20190612_UnA_SVCidentified_postiter.csv', index_col = 0)\n",
"data = data[data['Probability'] == 0]\n",
"print(data.Compartment.value_counts())"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"una1 = data.groupby('Compartment').mean()"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"#Set working directory to location of raw data. This is where output will be put as well unless file paths are specified.\n",
"os.getcwd()\n",
"os.chdir('C:/Users/ooma1/OneDrive - UC San Diego/Guatelli Lab/Cell Fractionation/20190612 Jurkat TREHIV dNef fract MS/UnB')"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Large Protein Complex 160\n",
"Endosome 44\n",
"ER 33\n",
"Plasma membrane 25\n",
"Golgi 19\n",
"Mitochondrion 15\n",
"Lysosome 13\n",
"Peroxisome 11\n",
"Name: Compartment, dtype: int64\n"
]
}
],
"source": [
"#Read in the csv data file, using the ProteinID column as the index_col\n",
"data = pd.read_csv('20190612_UnB_SVCidentified_postiter.csv', index_col = 0)\n",
"data = data[data['Probability'] == 0]\n",
"print(data.Compartment.value_counts())"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [],
"source": [
"unb1 = data.groupby('Compartment').mean()"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
"#Set working directory to location of raw data. This is where output will be put as well unless file paths are specified.\n",
"os.getcwd()\n",
"os.chdir('C:/Users/ooma1/OneDrive - UC San Diego/Guatelli Lab/Cell Fractionation/20190612 Jurkat TREHIV dNef fract MS/UnC')"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Large Protein Complex 160\n",
"Endosome 44\n",
"ER 33\n",
"Plasma membrane 25\n",
"Golgi 19\n",
"Mitochondrion 15\n",
"Lysosome 13\n",
"Peroxisome 11\n",
"Name: Compartment, dtype: int64\n"
]
}
],
"source": [
"#Read in the csv data file, using the ProteinID column as the index_col\n",
"data = pd.read_csv('20190612_UnC_SVCidentified_postiter.csv', index_col = 0)\n",
"data = data[data['Probability'] == 0]\n",
"print(data.Compartment.value_counts())"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [],
"source": [
"unc1 = data.groupby('Compartment').mean()"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"#Set working directory to location of raw data. This is where output will be put as well unless file paths are specified.\n",
"os.getcwd()\n",
"os.chdir('C:/Users/ooma1/OneDrive - UC San Diego/Guatelli Lab/Cell Fractionation/20191122 Jurkat TREHIV dNef fract MS/UnA')"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Large Protein Complex 179\n",
"Endosome 43\n",
"ER 42\n",
"Plasma membrane 22\n",
"Mitochondrion 17\n",
"Peroxisome 15\n",
"Lysosome 15\n",
"Golgi 12\n",
"Name: Compartment, dtype: int64\n"
]
}
],
"source": [
"#Read in the csv data file, using the ProteinID column as the index_col\n",
"data = pd.read_csv('20191122_UnA_SVCidentified_postiter.csv', index_col = 0)\n",
"data = data[data['Probability'] == 0]\n",
"print(data.Compartment.value_counts())"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [],
"source": [
"una2 = data.groupby('Compartment').mean()"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [],
"source": [
"#Set working directory to location of raw data. This is where output will be put as well unless file paths are specified.\n",
"os.getcwd()\n",
"os.chdir('C:/Users/ooma1/OneDrive - UC San Diego/Guatelli Lab/Cell Fractionation/20191122 Jurkat TREHIV dNef fract MS/UnB')"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Large Protein Complex 179\n",
"Endosome 43\n",
"ER 42\n",
"Plasma membrane 22\n",
"Mitochondrion 17\n",
"Peroxisome 15\n",
"Lysosome 15\n",
"Golgi 12\n",
"Name: Compartment, dtype: int64\n"
]
}
],
"source": [
"#Read in the csv data file, using the ProteinID column as the index_col\n",
"data = pd.read_csv('20191122_UnB_SVCidentified_postiter.csv', index_col = 0)\n",
"data = data[data['Probability'] == 0]\n",
"print(data.Compartment.value_counts())"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [],
"source": [
"unb2 = data.groupby('Compartment').mean()"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [],
"source": [
"#Set working directory to location of raw data. This is where output will be put as well unless file paths are specified.\n",
"os.getcwd()\n",
"os.chdir('C:/Users/ooma1/OneDrive - UC San Diego/Guatelli Lab/Cell Fractionation/20191122 Jurkat TREHIV dNef fract MS/UnC')"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Large Protein Complex 179\n",
"Endosome 43\n",
"ER 42\n",
"Plasma membrane 22\n",
"Mitochondrion 17\n",
"Peroxisome 15\n",
"Lysosome 15\n",
"Golgi 12\n",
"Name: Compartment, dtype: int64\n"
]
}
],
"source": [
"#Read in the csv data file, using the ProteinID column as the index_col\n",
"data = pd.read_csv('20191122_UnC_SVCidentified_postiter.csv', index_col = 0)\n",
"data = data[data['Probability'] == 0]\n",
"print(data.Compartment.value_counts())"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [],
"source": [
"unc2 = data.groupby('Compartment').mean()"
]
},
{
"cell_type": "code",
"execution_count": 59,
"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>3K</th>\n",
" <th>5.4K</th>\n",
" <th>12.2K</th>\n",
" <th>24K</th>\n",
" <th>78.4K</th>\n",
" <th>110K</th>\n",
" <th>195.5K</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Compartment</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>ER</td>\n",
" <td>0.164431</td>\n",
" <td>0.269401</td>\n",
" <td>0.628846</td>\n",
" <td>0.667375</td>\n",
" <td>0.787927</td>\n",
" <td>0.308165</td>\n",
" <td>0.094533</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Endosome</td>\n",
" <td>0.167829</td>\n",
" <td>0.194931</td>\n",
" <td>0.445695</td>\n",
" <td>0.524594</td>\n",
" <td>0.654421</td>\n",
" <td>0.462966</td>\n",
" <td>0.385201</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Golgi</td>\n",
" <td>0.216396</td>\n",
" <td>0.369476</td>\n",
" <td>0.709280</td>\n",
" <td>0.551092</td>\n",
" <td>0.503733</td>\n",
" <td>0.306302</td>\n",
" <td>0.210152</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Large Protein Complex</td>\n",
" <td>0.207367</td>\n",
" <td>0.181057</td>\n",
" <td>0.311503</td>\n",
" <td>0.373354</td>\n",
" <td>0.517465</td>\n",
" <td>0.562989</td>\n",
" <td>0.587309</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Lysosome</td>\n",
" <td>0.097688</td>\n",
" <td>0.230298</td>\n",
" <td>0.793632</td>\n",
" <td>0.854492</td>\n",
" <td>0.751609</td>\n",
" <td>0.230860</td>\n",
" <td>0.072274</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Mitochondrion</td>\n",
" <td>0.863889</td>\n",
" <td>0.891014</td>\n",
" <td>0.509259</td>\n",
" <td>0.058592</td>\n",
" <td>0.044433</td>\n",
" <td>0.035784</td>\n",
" <td>0.029962</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Peroxisome</td>\n",
" <td>0.171804</td>\n",
" <td>0.328234</td>\n",
" <td>0.822079</td>\n",
" <td>0.785931</td>\n",
" <td>0.665653</td>\n",
" <td>0.158147</td>\n",
" <td>0.051735</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Plasma membrane</td>\n",
" <td>0.152352</td>\n",
" <td>0.304668</td>\n",
" <td>0.729786</td>\n",
" <td>0.688524</td>\n",
" <td>0.697427</td>\n",
" <td>0.258393</td>\n",
" <td>0.120621</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 3K 5.4K 12.2K 24K 78.4K \\\n",
"Compartment \n",
"ER 0.164431 0.269401 0.628846 0.667375 0.787927 \n",
"Endosome 0.167829 0.194931 0.445695 0.524594 0.654421 \n",
"Golgi 0.216396 0.369476 0.709280 0.551092 0.503733 \n",
"Large Protein Complex 0.207367 0.181057 0.311503 0.373354 0.517465 \n",
"Lysosome 0.097688 0.230298 0.793632 0.854492 0.751609 \n",
"Mitochondrion 0.863889 0.891014 0.509259 0.058592 0.044433 \n",
"Peroxisome 0.171804 0.328234 0.822079 0.785931 0.665653 \n",
"Plasma membrane 0.152352 0.304668 0.729786 0.688524 0.697427 \n",
"\n",
" 110K 195.5K \n",
"Compartment \n",
"ER 0.308165 0.094533 \n",
"Endosome 0.462966 0.385201 \n",
"Golgi 0.306302 0.210152 \n",
"Large Protein Complex 0.562989 0.587309 \n",
"Lysosome 0.230860 0.072274 \n",
"Mitochondrion 0.035784 0.029962 \n",
"Peroxisome 0.158147 0.051735 \n",
"Plasma membrane 0.258393 0.120621 "
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dnefun = una1 + unb1 + unc1 + una2 + unb2 + unc2\n",
"dnefun = dnefun/6\n",
"dnefun = dnefun.loc[:, '3K':'195.5K']\n",
"dnefun"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# dNef Induced"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [],
"source": [
"#Set working directory to location of raw data. This is where output will be put as well unless file paths are specified.\n",
"os.getcwd()\n",
"os.chdir('C:/Users/ooma1/OneDrive - UC San Diego/Guatelli Lab/Cell Fractionation/20190612 Jurkat TREHIV dNef fract MS/IndA')"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Large Protein Complex 160\n",
"Endosome 44\n",
"ER 33\n",
"Plasma membrane 25\n",
"Golgi 19\n",
"Mitochondrion 15\n",
"Lysosome 13\n",
"Peroxisome 11\n",
"Name: Compartment, dtype: int64\n"
]
}
],
"source": [
"#Read in the csv data file, using the ProteinID column as the index_col\n",
"data = pd.read_csv('20190612_IndA_SVCidentified_postiter.csv', index_col = 0)\n",
"data = data[data['Probability'] == 0]\n",
"print(data.Compartment.value_counts())"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [],
"source": [
"IndA1 = data.groupby('Compartment').mean()"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [],
"source": [
"#Set working directory to location of raw data. This is where output will be put as well unless file paths are specified.\n",
"os.getcwd()\n",
"os.chdir('C:/Users/ooma1/OneDrive - UC San Diego/Guatelli Lab/Cell Fractionation/20190612 Jurkat TREHIV dNef fract MS/IndB')"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Large Protein Complex 160\n",
"Endosome 44\n",
"ER 33\n",
"Plasma membrane 25\n",
"Golgi 19\n",
"Mitochondrion 15\n",
"Lysosome 13\n",
"Peroxisome 11\n",
"Name: Compartment, dtype: int64\n"
]
}
],
"source": [
"#Read in the csv data file, using the ProteinID column as the index_col\n",
"data = pd.read_csv('20190612_IndB_SVCidentified_postiter.csv', index_col = 0)\n",
"data = data[data['Probability'] == 0]\n",
"print(data.Compartment.value_counts())"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [],
"source": [
"IndB1 = data.groupby('Compartment').mean()"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [],
"source": [
"#Set working directory to location of raw data. This is where output will be put as well unless file paths are specified.\n",
"os.getcwd()\n",
"os.chdir('C:/Users/ooma1/OneDrive - UC San Diego/Guatelli Lab/Cell Fractionation/20190612 Jurkat TREHIV dNef fract MS/IndC')"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Large Protein Complex 160\n",
"Endosome 44\n",
"ER 33\n",
"Plasma membrane 25\n",
"Golgi 19\n",
"Mitochondrion 15\n",
"Lysosome 13\n",
"Peroxisome 11\n",
"Name: Compartment, dtype: int64\n"
]
}
],
"source": [
"#Read in the csv data file, using the ProteinID column as the index_col\n",
"data = pd.read_csv('20190612_IndC_SVCidentified_postiter.csv', index_col = 0)\n",
"data = data[data['Probability'] == 0]\n",
"print(data.Compartment.value_counts())"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [],
"source": [
"IndC1 = data.groupby('Compartment').mean()"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [],
"source": [
"#Set working directory to location of raw data. This is where output will be put as well unless file paths are specified.\n",
"os.getcwd()\n",
"os.chdir('C:/Users/ooma1/OneDrive - UC San Diego/Guatelli Lab/Cell Fractionation/20191122 Jurkat TREHIV dNef fract MS/IndA')"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Large Protein Complex 179\n",
"Endosome 43\n",
"ER 42\n",
"Plasma membrane 22\n",
"Mitochondrion 17\n",
"Peroxisome 15\n",
"Lysosome 15\n",
"Golgi 12\n",
"Name: Compartment, dtype: int64\n"
]
}
],
"source": [
"#Read in the csv data file, using the ProteinID column as the index_col\n",
"data = pd.read_csv('20191122_IndA_SVCidentified_postiter.csv', index_col = 0)\n",
"data = data[data['Probability'] == 0]\n",
"print(data.Compartment.value_counts())"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [],
"source": [
"IndA2 = data.groupby('Compartment').mean()"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [],
"source": [
"#Set working directory to location of raw data. This is where output will be put as well unless file paths are specified.\n",
"os.getcwd()\n",
"os.chdir('C:/Users/ooma1/OneDrive - UC San Diego/Guatelli Lab/Cell Fractionation/20191122 Jurkat TREHIV dNef fract MS/IndB')"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Large Protein Complex 179\n",
"Endosome 43\n",
"ER 42\n",
"Plasma membrane 22\n",
"Mitochondrion 17\n",
"Peroxisome 15\n",
"Lysosome 15\n",
"Golgi 12\n",
"Name: Compartment, dtype: int64\n"
]
}
],
"source": [
"#Read in the csv data file, using the ProteinID column as the index_col\n",
"data = pd.read_csv('20191122_IndB_SVCidentified_postiter.csv', index_col = 0)\n",
"data = data[data['Probability'] == 0]\n",
"print(data.Compartment.value_counts())"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [],
"source": [
"IndB2 = data.groupby('Compartment').mean()"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [],
"source": [
"#Set working directory to location of raw data. This is where output will be put as well unless file paths are specified.\n",
"os.getcwd()\n",
"os.chdir('C:/Users/ooma1/OneDrive - UC San Diego/Guatelli Lab/Cell Fractionation/20191122 Jurkat TREHIV dNef fract MS/IndC')"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Large Protein Complex 179\n",
"Endosome 43\n",
"ER 42\n",
"Plasma membrane 22\n",
"Mitochondrion 17\n",
"Peroxisome 15\n",
"Lysosome 15\n",
"Golgi 12\n",
"Name: Compartment, dtype: int64\n"
]
}
],
"source": [
"#Read in the csv data file, using the ProteinID column as the index_col\n",
"data = pd.read_csv('20191122_IndC_SVCidentified_postiter.csv', index_col = 0)\n",
"data = data[data['Probability'] == 0]\n",
"print(data.Compartment.value_counts())"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [],
"source": [
"IndC2 = data.groupby('Compartment').mean()"
]
},
{
"cell_type": "code",
"execution_count": 78,
"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>3K</th>\n",
" <th>5.4K</th>\n",
" <th>12.2K</th>\n",
" <th>24K</th>\n",
" <th>78.4K</th>\n",
" <th>110K</th>\n",
" <th>195.5K</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Compartment</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>ER</td>\n",
" <td>0.141365</td>\n",
" <td>0.273227</td>\n",
" <td>0.596172</td>\n",
" <td>0.691554</td>\n",
" <td>0.845100</td>\n",
" <td>0.317655</td>\n",
" <td>0.091627</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Endosome</td>\n",
" <td>0.150730</td>\n",
" <td>0.225240</td>\n",
" <td>0.448095</td>\n",
" <td>0.517516</td>\n",
" <td>0.654215</td>\n",
" <td>0.483977</td>\n",
" <td>0.360956</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Golgi</td>\n",
" <td>0.161871</td>\n",
" <td>0.381648</td>\n",
" <td>0.708779</td>\n",
" <td>0.580401</td>\n",
" <td>0.572367</td>\n",
" <td>0.310275</td>\n",
" <td>0.195877</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Large Protein Complex</td>\n",
" <td>0.176328</td>\n",
" <td>0.209902</td>\n",
" <td>0.334409</td>\n",
" <td>0.397833</td>\n",
" <td>0.516337</td>\n",
" <td>0.581357</td>\n",
" <td>0.532515</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Lysosome</td>\n",
" <td>0.084639</td>\n",
" <td>0.248109</td>\n",
" <td>0.759588</td>\n",
" <td>0.849096</td>\n",
" <td>0.788754</td>\n",
" <td>0.239705</td>\n",
" <td>0.082431</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Mitochondrion</td>\n",
" <td>0.892598</td>\n",
" <td>0.879657</td>\n",
" <td>0.345092</td>\n",
" <td>0.054311</td>\n",
" <td>0.044118</td>\n",
" <td>0.036538</td>\n",
" <td>0.034723</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Peroxisome</td>\n",
" <td>0.140079</td>\n",
" <td>0.293249</td>\n",
" <td>0.728581</td>\n",
" <td>0.791642</td>\n",
" <td>0.819419</td>\n",
" <td>0.191716</td>\n",
" <td>0.047615</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Plasma membrane</td>\n",
" <td>0.125115</td>\n",
" <td>0.298090</td>\n",
" <td>0.701921</td>\n",
" <td>0.703225</td>\n",
" <td>0.744500</td>\n",
" <td>0.277011</td>\n",
" <td>0.127003</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 3K 5.4K 12.2K 24K 78.4K \\\n",
"Compartment \n",
"ER 0.141365 0.273227 0.596172 0.691554 0.845100 \n",
"Endosome 0.150730 0.225240 0.448095 0.517516 0.654215 \n",
"Golgi 0.161871 0.381648 0.708779 0.580401 0.572367 \n",
"Large Protein Complex 0.176328 0.209902 0.334409 0.397833 0.516337 \n",
"Lysosome 0.084639 0.248109 0.759588 0.849096 0.788754 \n",
"Mitochondrion 0.892598 0.879657 0.345092 0.054311 0.044118 \n",
"Peroxisome 0.140079 0.293249 0.728581 0.791642 0.819419 \n",
"Plasma membrane 0.125115 0.298090 0.701921 0.703225 0.744500 \n",
"\n",
" 110K 195.5K \n",
"Compartment \n",
"ER 0.317655 0.091627 \n",
"Endosome 0.483977 0.360956 \n",
"Golgi 0.310275 0.195877 \n",
"Large Protein Complex 0.581357 0.532515 \n",
"Lysosome 0.239705 0.082431 \n",
"Mitochondrion 0.036538 0.034723 \n",
"Peroxisome 0.191716 0.047615 \n",
"Plasma membrane 0.277011 0.127003 "
]
},
"execution_count": 78,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dnefind = IndA1 + IndB1 + IndC1 + IndA2 + IndB2 + IndC2\n",
"dnefind = dnefind/6\n",
"dnefind = dnefind.loc[:, '3K':'195.5K']\n",
"compartment_list = list(dnefind.index)\n",
"dnefind"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [],
"source": [
"#Setting working folder\n",
"os.chdir('C:/Users/ooma1/OneDrive - UC San Diego/Guatelli Lab/Cell Fractionation/FractPaperFigures/')"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 612x792 with 8 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Generating figure\n",
"fig = plt.figure(figsize=(8.5, 11))\n",
"ax1 = plt.subplot2grid((5, 6), (0, 1), rowspan=1, colspan=2)\n",
"ax2 = plt.subplot2grid((5, 6), (0, 3), rowspan=1, colspan=2)\n",
"ax3 = plt.subplot2grid((4, 6), (1, 1), rowspan=1, colspan=2)\n",
"ax4 = plt.subplot2grid((4, 6), (1, 3), rowspan=1, colspan=2)\n",
"ax5 = plt.subplot2grid((4, 6), (2, 0), rowspan=1, colspan=3)\n",
"ax6 = plt.subplot2grid((4, 6), (2, 3), rowspan=1, colspan=3)\n",
"ax7 = plt.subplot2grid((4, 6), (3, 0), rowspan=1, colspan=3)\n",
"ax8 = plt.subplot2grid((4, 6), (3, 3), rowspan=1, colspan=3)\n",
"\n",
"bca = pd.read_csv('Fig2_BCA.csv', index_col=0)\n",
"wt2_westerns = mpimg.imread('Fig2_WT2_Westerns.png')\n",
"dnef1_westerns = mpimg.imread('Fig2_dNef1_Westerns.png')\n",
"\n",
"# Uninduced BCA assays\n",
"bca_unind = bca[bca['Condition'] == 'Uninduced'].copy()\n",
"bca_unind.mean(axis=1).plot(kind='bar', by='Condition', yerr=bca_unind.std(axis=1), ax=ax1, color='grey', \n",
" edgecolor='black', error_kw=dict(ecolor='black', lw=2, capsize=3, capthick=2))\n",
"ax1.set_title('Uninduced Fractions Protein Yield', fontsize=12)\n",
"ax1.set_xlabel('')\n",
"ax1.set_xticklabels(bca_unind.index, rotation=-45, ha='left', rotation_mode='anchor')\n",
"ax1.set_ylabel('% of Total Cellular Protein', fontsize=9)\n",
"ax1.set_ylim((0, 3.5))\n",
"ax1.annotate('A)', (-0.25, 1.175), xycoords='axes fraction', fontsize=14)\n",
"\n",
"pos1 = ax1.get_position()\n",
"points1 = pos1.get_points()\n",
"new_points = points1 - [[0.05, 0], [0.05, 0]]\n",
"pos1.set_points(new_points)\n",
"ax1.set_position(pos1)\n",
"\n",
"# Induced BCA assays\n",
"bca_ind = bca[bca['Condition'] == 'Induced'].copy()\n",
"bca_ind.mean(axis=1).plot(kind='bar', by='Condition', yerr=bca_ind.std(axis=1), ax=ax2, color='white', \n",
" edgecolor='black', error_kw=dict(ecolor='black', lw=2, capsize=3, capthick=2))\n",
"ax2.set_title('Induced Fractions Protein Yield', fontsize=12)\n",
"ax2.set_xlabel('')\n",
"ax2.set_xticklabels(bca_ind.index, rotation=-45, ha='left', rotation_mode='anchor')\n",
"ax2.set_ylabel('% of Total Cellular Protein', fontsize=9)\n",
"ax2.set_ylim((0, 3.5))\n",
"\n",
"pos2 = ax2.get_position()\n",
"points2 = pos2.get_points()\n",
"new_points = points2 + [[0.05, 0], [0.05, 0]]\n",
"pos2.set_points(new_points)\n",
"ax2.set_position(pos2)\n",
"\n",
"# Wild-type westerns\n",
"ax3.imshow(wt2_westerns)\n",
"ax3.axis('off')\n",
"ax3.annotate('B)', (-0.25, 1.05), xycoords='axes fraction', fontsize=14)\n",
"\n",
"ax3.annotate('3K', (0.15, 1.03), xycoords='axes fraction', horizontalalignment='center', verticalalignment='center', fontsize=10)\n",
"ax3.annotate('195.5K', (0.2, 1.03), xytext=(0.89, 1.03), xycoords='axes fraction', \n",
" horizontalalignment='center', verticalalignment='center', fontsize=10, \n",
" arrowprops={'arrowstyle':'<-'})\n",
"\n",
"ax3.annotate('gp160', (1.165, 0.945), xycoords='axes fraction', horizontalalignment='center', fontsize=10)\n",
"ax3.annotate('gp41', (1.165, 0.545), xycoords='axes fraction', horizontalalignment='center', fontsize=10)\n",
"ax3.annotate('p55', (1.16, 0.385), xycoords='axes fraction', horizontalalignment='center', fontsize=10)\n",
"ax3.annotate('Vpu', (1.155, 0.24), xycoords='axes fraction', horizontalalignment='center', fontsize=10)\n",
"ax3.annotate('Nef', (1.15, 0.08), xycoords='axes fraction', horizontalalignment='center', fontsize=10)\n",
"\n",
"# dNef westerns\n",
"ax4.imshow(dnef1_westerns)\n",
"ax4.axis('off')\n",
"\n",
"ax4.annotate('3K', (0.18, 1.035), xycoords='axes fraction', horizontalalignment='center', verticalalignment='center', fontsize=10)\n",
"ax4.annotate('195.5K', (0.23, 1.035), xytext=(0.89, 1.035), xycoords='axes fraction', \n",
" horizontalalignment='center', verticalalignment='center', fontsize=10, \n",
" arrowprops={'arrowstyle':'<-'})\n",
"\n",
"pos4 = ax4.get_position()\n",
"points4 = pos4.get_points()\n",
"new_points = points4 - [[0.02, 0], [0.02, 0]]\n",
"pos4.set_points(new_points)\n",
"ax4.set_position(pos4)\n",
"\n",
"# WT Unind parallel coords\n",
"color_list = plt.cm.tab20(range(0,20))\n",
"colorlist = [color_list[0], color_list[2], color_list[4], color_list[6], color_list[8], color_list[10], \n",
" color_list[12], color_list[5]]\n",
"\n",
"ax5.annotate('C)', (-0.18, 1.075), xycoords='axes fraction', fontsize=14)\n",
"\n",
"wtun.reset_index(inplace=True)\n",
"pd.plotting.parallel_coordinates(wtun, 'Compartment', cols=['3K', '5.4K', '12.2K', '24K', '78.4K', '110K', '195.5K'], \n",
" color=[color_list[0], color_list[2], color_list[4], color_list[6], color_list[8], color_list[10], \n",
" color_list[12], color_list[5]], ax=ax5)\n",
"ax5.legend().remove()\n",
"ax5.set_title('Wild-type Uninduced')\n",
"ax5.tick_params(top=False, bottom=False, labelbottom=False, left=False, labelleft=False)\n",
"\n",
"# WT Ind parallel coords\n",
"wtind.reset_index(inplace=True)\n",
"pd.plotting.parallel_coordinates(wtind, 'Compartment', cols=['3K', '5.4K', '12.2K', '24K', '78.4K', '110K', '195.5K'], \n",
" color=[color_list[0], color_list[2], color_list[4], color_list[6], color_list[8], color_list[10], \n",
" color_list[12], color_list[5]], ax=ax6)\n",
"ax6.legend().remove()\n",
"ax6.set_title('Wild-type Induced')\n",
"ax6.tick_params(top=False, bottom=False, labelbottom=False, left=False, right=False, labelleft=False)\n",
"\n",
"pos6 = ax6.get_position()\n",
"points6 = pos6.get_points()\n",
"new_points = points6 + [[0.025, 0], [0.025, 0]]\n",
"pos6.set_points(new_points)\n",
"ax6.set_position(pos6)\n",
"\n",
"# dNef Unind parallel coords\n",
"dnefun.reset_index(inplace=True)\n",
"pd.plotting.parallel_coordinates(dnefun, 'Compartment', cols=['3K', '5.4K', '12.2K', '24K', '78.4K', '110K', '195.5K'], \n",
" color=[color_list[0], color_list[2], color_list[4], color_list[6], color_list[8], color_list[10], \n",
" color_list[12], color_list[5]], ax=ax7)\n",
"\n",
"ax7.set_xlabel('Fraction')\n",
"ax7.set_ylabel('Scaled Average Abundance')\n",
"ax7.set_title('dNef Uninduced')\n",
"\n",
"ax7.legend(loc=(0.025, -0.5), ncol=4)\n",
"\n",
"# dNef Ind parallel coords\n",
"dnefind.reset_index(inplace=True)\n",
"pd.plotting.parallel_coordinates(dnefind, 'Compartment', cols=['3K', '5.4K', '12.2K', '24K', '78.4K', '110K', '195.5K'], \n",
" color=[color_list[0], color_list[2], color_list[4], color_list[6], color_list[8], color_list[10], \n",
" color_list[12], color_list[5]], ax=ax8)\n",
"ax8.legend().remove()\n",
"ax8.set_title('dNef Induced')\n",
"ax8.tick_params(left=False, right=False, labelleft=False, bottom=False, labelbottom=False)\n",
"\n",
"pos8 = ax8.get_position()\n",
"points8 = pos8.get_points()\n",
"new_points = points8 + [[0.025, 0], [0.025, 0]]\n",
"pos8.set_points(new_points)\n",
"ax8.set_position(pos8)\n",
"\n",
"fig.suptitle('Figure 2', x=0, y=1, fontsize=12)\n",
"\n",
"plt.savefig('Figure2.pdf', dpi=500, bbox_inches='tight')\n",
"plt.show()"
]
}
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