{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# Inline graphing\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Import 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",
"from matplotlib_venn import venn3, venn2"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Set working directory to dNef 1 folder\n",
"os.chdir('Path/To/Data')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# Reading in full data for first dNef experiment\n",
"ua1 = pd.read_csv('UnA/20190612_UnA_SVCidentified_postiter_threshold.csv', index_col=0)\n",
"ua1.drop(columns=['3K', '5.4K', '12.2K', '24K', '78.4K', '110K', '195.5K'], inplace=True)\n",
"ua1.columns = ['Gene_ua1', 'ProteinInfo_ua1', 'Compartment_ua1', 'Probability_ua1']\n",
"\n",
"ub1 = pd.read_csv('UnB/20190612_UnB_SVCidentified_postiter_threshold.csv', index_col=0)\n",
"ub1.drop(columns=['3K', '5.4K', '12.2K', '24K', '78.4K', '110K', '195.5K'], inplace=True)\n",
"ub1.columns = ['Gene_ub1', 'ProteinInfo_ub1', 'Compartment_ub1', 'Probability_ub1']\n",
"\n",
"uc1 = pd.read_csv('UnC/20190612_UnC_SVCidentified_postiter_threshold.csv', index_col=0)\n",
"uc1.drop(columns=['3K', '5.4K', '12.2K', '24K', '78.4K', '110K', '195.5K'], inplace=True)\n",
"uc1.columns = ['Gene_uc1', 'ProteinInfo_uc1', 'Compartment_uc1', 'Probability_uc1']\n",
"\n",
"ia1 = pd.read_csv('IndA/20190612_IndA_SVCidentified_postiter_threshold.csv', index_col=0)\n",
"ia1.drop(columns=['3K', '5.4K', '12.2K', '24K', '78.4K', '110K', '195.5K'], inplace=True)\n",
"ia1.columns = ['Gene_ia1', 'ProteinInfo_ia1', 'Compartment_ia1', 'Probability_ia1']\n",
"\n",
"ib1 = pd.read_csv('IndB/20190612_IndB_SVCidentified_postiter_threshold.csv', index_col=0)\n",
"ib1.drop(columns=['3K', '5.4K', '12.2K', '24K', '78.4K', '110K', '195.5K'], inplace=True)\n",
"ib1.columns = ['Gene_ib1', 'ProteinInfo_ib1', 'Compartment_ib1', 'Probability_ib1']\n",
"\n",
"ic1 = pd.read_csv('IndC/20190612_IndC_SVCidentified_postiter_threshold.csv', index_col=0)\n",
"ic1.drop(columns=['3K', '5.4K', '12.2K', '24K', '78.4K', '110K', '195.5K'], inplace=True)\n",
"ic1.columns = ['Gene_ic1', 'ProteinInfo_ic1', 'Compartment_ic1', 'Probability_ic1']"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# Set working directory to dNef 2 folder\n",
"os.chdir('Path/To/Data')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# Reading in full data for second dNef experiment\n",
"ua2 = pd.read_csv('UnA/20191122_UnA_SVCidentified_postiter_threshold.csv', index_col=0)\n",
"ua2.drop(columns=['3K', '5.4K', '12.2K', '24K', '78.4K', '110K', '195.5K'], inplace=True)\n",
"ua2.columns = ['Gene_ua2', 'ProteinInfo_ua2', 'Compartment_ua2', 'Probability_ua2']\n",
"\n",
"ub2 = pd.read_csv('UnB/20191122_UnB_SVCidentified_postiter_threshold.csv', index_col=0)\n",
"ub2.drop(columns=['3K', '5.4K', '12.2K', '24K', '78.4K', '110K', '195.5K'], inplace=True)\n",
"ub2.columns = ['Gene_ub2', 'ProteinInfo_ub2', 'Compartment_ub2', 'Probability_ub2']\n",
"\n",
"uc2 = pd.read_csv('UnC/20191122_UnC_SVCidentified_postiter_threshold.csv', index_col=0)\n",
"uc2.drop(columns=['3K', '5.4K', '12.2K', '24K', '78.4K', '110K', '195.5K'], inplace=True)\n",
"uc2.columns = ['Gene_uc2', 'ProteinInfo_uc2', 'Compartment_uc2', 'Probability_uc2']\n",
"\n",
"ia2 = pd.read_csv('IndA/20191122_IndA_SVCidentified_postiter_threshold.csv', index_col=0)\n",
"ia2.drop(columns=['3K', '5.4K', '12.2K', '24K', '78.4K', '110K', '195.5K'], inplace=True)\n",
"ia2.columns = ['Gene_ia2', 'ProteinInfo_ia2', 'Compartment_ia2', 'Probability_ia2']\n",
"\n",
"ib2 = pd.read_csv('IndB/20191122_IndB_SVCidentified_postiter_threshold.csv', index_col=0)\n",
"ib2.drop(columns=['3K', '5.4K', '12.2K', '24K', '78.4K', '110K', '195.5K'], inplace=True)\n",
"ib2.columns = ['Gene_ib2', 'ProteinInfo_ib2', 'Compartment_ib2', 'Probability_ib2']\n",
"\n",
"ic2 = pd.read_csv('IndC/20191122_IndC_SVCidentified_postiter_threshold.csv', index_col=0)\n",
"ic2.drop(columns=['3K', '5.4K', '12.2K', '24K', '78.4K', '110K', '195.5K'], inplace=True)\n",
"ic2.columns = ['Gene_ic2', 'ProteinInfo_ic2', 'Compartment_ic2', 'Probability_ic2']"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"# Set working directory to folder comparing biological replicates\n",
"os.chdir('Path/To/Data')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# Defining commonly detected protein dataframes for uninduced\n",
"unlist = [ua1, ub1, uc1, ua2, ub2, uc2]\n",
"\n",
"un = pd.concat(unlist, axis=1, join='outer', sort='True')\n",
"\n",
"for item in un.index:\n",
" if un.at[item, 'ProteinInfo_ua1'] is np.nan:\n",
" if un.at[item, 'ProteinInfo_ub1'] is not np.nan:\n",
" un.at[item, 'ProteinInfo_ua1'] = un.at[item, 'ProteinInfo_ub1']\n",
" elif un.at[item, 'ProteinInfo_uc1'] is not np.nan:\n",
" un.at[item, 'ProteinInfo_ua1'] = un.at[item, 'ProteinInfo_uc1']\n",
" elif un.at[item, 'ProteinInfo_ua2'] is not np.nan:\n",
" un.at[item, 'ProteinInfo_ua1'] = un.at[item, 'ProteinInfo_ua2']\n",
" elif un.at[item, 'ProteinInfo_ub2'] is not np.nan:\n",
" un.at[item, 'ProteinInfo_ua1'] = un.at[item, 'ProteinInfo_ub2']\n",
" elif un.at[item, 'ProteinInfo_uc2'] is not np.nan:\n",
" un.at[item, 'ProteinInfo_ua1'] = un.at[item, 'ProteinInfo_uc2']\n",
" \n",
"for item in un.index:\n",
" if un.at[item, 'Gene_ua1'] is np.nan:\n",
" if un.at[item, 'Gene_ub1'] is not np.nan:\n",
" un.at[item, 'Gene_ua1'] = un.at[item, 'Gene_ub1']\n",
" elif un.at[item, 'Gene_uc1'] is not np.nan:\n",
" un.at[item, 'Gene_ua1'] = un.at[item, 'Gene_uc1']\n",
" elif un.at[item, 'Gene_ua2'] is not np.nan:\n",
" un.at[item, 'Gene_ua1'] = un.at[item, 'Gene_ua2']\n",
" elif un.at[item, 'Gene_ub2'] is not np.nan:\n",
" un.at[item, 'Gene_ua1'] = un.at[item, 'Gene_ub2']\n",
" elif un.at[item, 'Gene_uc2'] is not np.nan:\n",
" un.at[item, 'Gene_ua1'] = un.at[item, 'Gene_uc2']\n",
"\n",
"un.drop(columns=['ProteinInfo_ub1', 'ProteinInfo_uc1', 'ProteinInfo_ua2', 'ProteinInfo_ub2', 'ProteinInfo_uc2', \n",
" 'Gene_ub1', 'Gene_uc1', 'Gene_ua2', 'Gene_ub2', 'Gene_uc2'], inplace=True)\n",
"un.rename({'Gene_ua1':'Gene', 'ProteinInfo_ua1':'ProteinInfo'}, axis='columns', inplace=True)\n",
"un.loc[:, 'Compartment_ua1':'Probability_uc2'] = un.loc[:, 'Compartment_ua1':'Probability_uc2'].fillna('ND')\n",
"ndset = ['ND']\n",
"un['NDcount'] = (un.isin(ndset).sum(1))/2"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"# Defining commonly detected protein dataframes for induced\n",
"indlist = [ia1, ib1, ic1, ia2, ib2, ic2]\n",
"\n",
"ind = pd.concat(indlist, axis=1, join='outer', sort='True')\n",
"\n",
"for item in ind.index:\n",
" if ind.at[item, 'ProteinInfo_ia1'] is np.nan:\n",
" if ind.at[item, 'ProteinInfo_ib1'] is not np.nan:\n",
" ind.at[item, 'ProteinInfo_ia1'] = ind.at[item, 'ProteinInfo_ib1']\n",
" elif ind.at[item, 'ProteinInfo_ic1'] is not np.nan:\n",
" ind.at[item, 'ProteinInfo_ia1'] = ind.at[item, 'ProteinInfo_ic1']\n",
" elif ind.at[item, 'ProteinInfo_ia2'] is not np.nan:\n",
" ind.at[item, 'ProteinInfo_ia1'] = ind.at[item, 'ProteinInfo_ia2']\n",
" elif ind.at[item, 'ProteinInfo_ib2'] is not np.nan:\n",
" ind.at[item, 'ProteinInfo_ia1'] = ind.at[item, 'ProteinInfo_ib2']\n",
" elif ind.at[item, 'ProteinInfo_ic2'] is not np.nan:\n",
" ind.at[item, 'ProteinInfo_ia1'] = ind.at[item, 'ProteinInfo_ic2']\n",
" \n",
"for item in ind.index:\n",
" if ind.at[item, 'Gene_ia1'] is np.nan:\n",
" if ind.at[item, 'Gene_ib1'] is not np.nan:\n",
" ind.at[item, 'Gene_ia1'] = ind.at[item, 'Gene_ib1']\n",
" elif ind.at[item, 'Gene_ic1'] is not np.nan:\n",
" ind.at[item, 'Gene_ia1'] = ind.at[item, 'Gene_ic1']\n",
" elif ind.at[item, 'Gene_ia2'] is not np.nan:\n",
" ind.at[item, 'Gene_ia1'] = ind.at[item, 'Gene_ia2']\n",
" elif ind.at[item, 'Gene_ib2'] is not np.nan:\n",
" ind.at[item, 'Gene_ia1'] = ind.at[item, 'Gene_ib2']\n",
" elif ind.at[item, 'Gene_ic2'] is not np.nan:\n",
" ind.at[item, 'Gene_ia1'] = ind.at[item, 'Gene_ic2']\n",
"\n",
"ind.drop(columns=['ProteinInfo_ib1', 'ProteinInfo_ic1', 'ProteinInfo_ia2', 'ProteinInfo_ib2', 'ProteinInfo_ic2', \n",
" 'Gene_ib1', 'Gene_ic1', 'Gene_ia2', 'Gene_ib2', 'Gene_ic2'], inplace=True)\n",
"ind.rename({'Gene_ia1':'Gene', 'ProteinInfo_ia1':'ProteinInfo'}, axis='columns', inplace=True)\n",
"ind.loc[:, 'Compartment_ia1':'Probability_ic2'] = ind.loc[:, 'Compartment_ia1':'Probability_ic2'].fillna('ND')\n",
"ndset = ['ND']\n",
"ind['NDcount'] = (ind.isin(ndset).sum(1))/2"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"# Pulling out value counts for ND columns (Not Detected)\n",
"uncount = un.NDcount.value_counts().to_frame()\n",
"uncount.sort_index(inplace=True)\n",
"\n",
"indcount = ind.NDcount.value_counts().to_frame()\n",
"indcount.sort_index(inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 1440x1440 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Pie plots of protein replicate counts\n",
"fig = plt.figure(figsize=(20, 20))\n",
"ax1 = plt.subplot2grid((2, 2), (0, 0))\n",
"ax2 = plt.subplot2grid((2, 2), (0, 1))\n",
"ax3 = plt.subplot2grid((2, 2), (1, 0), colspan=2)\n",
"\n",
"cmap = plt.get_cmap('Blues')\n",
"color = cmap(np.array([175, 150, 125, 100, 75, 50]))\n",
"ax1.pie(uncount.NDcount, labels=['6 of 6 replicates\\n({:d} proteins)'.format(uncount.at[0.0, 'NDcount']), \n",
" '5 of 6 replicates\\n({:d} proteins)'.format(uncount.at[1.0, 'NDcount']), \n",
" '4 of 6 replicates\\n({:d} proteins)'.format(uncount.at[2.0, 'NDcount']), \n",
" '3 of 6 replicates\\n({:d} proteins)'.format(uncount.at[3.0, 'NDcount']), \n",
" '2 of 6 replicates\\n({:d} proteins)'.format(uncount.at[4.0, 'NDcount']), \n",
" '1 of 6 replicates\\n({:d} proteins)'.format(uncount.at[5.0, 'NDcount'])], \n",
" autopct='%1.2f%%', colors=color, \n",
" explode=(0.0, 0.025, 0.1, 0.1, 0.1, 0.1), pctdistance=0.8, textprops={'fontsize': 15})\n",
"ax1.set_title('Uninduced Replicates', fontsize=25, y=0.95)\n",
"\n",
"cmap = plt.get_cmap('Reds')\n",
"color = cmap(np.array([175, 150, 125, 100, 75, 50]))\n",
"ax2.pie(indcount.NDcount, labels=['6 of 6 replicates\\n({:d} proteins)'.format(indcount.at[0.0, 'NDcount']), \n",
" '5 of 6 replicates\\n({:d} proteins)'.format(indcount.at[1.0, 'NDcount']), \n",
" '4 of 6 replicates\\n({:d} proteins)'.format(indcount.at[2.0, 'NDcount']), \n",
" '3 of 6 replicates\\n({:d} proteins)'.format(indcount.at[3.0, 'NDcount']), \n",
" '2 of 6 replicates\\n({:d} proteins)'.format(indcount.at[4.0, 'NDcount']), \n",
" '1 of 6 replicates\\n({:d} proteins)'.format(indcount.at[5.0, 'NDcount'])], \n",
" autopct='%1.2f%%', colors=color, \n",
" explode=(0.0, 0.025, 0.1, 0.1, 0.1, 0.1), pctdistance=0.8, textprops={'fontsize': 15})\n",
"ax2.set_title('Induced Replicates', fontsize=25, y=0.95)\n",
"\n",
"v1 = venn2((set(un.index), set(ind.index)), set_labels=('Uninduced', 'Induced'), ax=ax3)\n",
"for t in v1.subset_labels:\n",
" t.set_fontsize(18)\n",
"for t in v1.set_labels:\n",
" t.set_fontsize(18)\n",
"v1.get_patch_by_id('10').set_color('blue')\n",
"v1.get_patch_by_id('01').set_color('red')\n",
"v1.get_patch_by_id('11').set_color('magenta')\n",
"ax3.set_title('Comparing proteins found in \\nat least 1 replicate per condition', fontsize=25, y=0.95)\n",
"\n",
"plt.suptitle('Breakdown of Protein Detection Across dNef Biological Replicates', fontsize=35, y=0.93, x=0.5)\n",
"plt.savefig('ProteinDetectionComparisons.png', dpi=300, bbox_inches = \"tight\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(198, 15)\n",
"(287, 15)\n"
]
}
],
"source": [
"# Commonly detected proteins only in uninduced and induced, respectively\n",
"un_onlylist = np.setdiff1d((un.index), (ind.index))\n",
"un_only = un.loc[un_onlylist, :]\n",
"print(un_only.shape)\n",
"\n",
"ind_onlylist = np.setdiff1d((ind.index), (un.index))\n",
"ind_only = ind.loc[ind_onlylist, :]\n",
"print(ind_only.shape)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"# Saving dataframes un_only and ind_only\n",
"un_only.to_csv('20191122_UninducedDetectedOnly_1ormore.csv', sep=',')\n",
"\n",
"ind_only.to_csv('20191122_InducedDetectedOnly_1ormore.csv', sep=',')"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"# Generating a further refined list for each condition that strictly looks for proteins present in all replicates of one condition and\n",
"# not at all present in the other condition\n",
"un6 = un_only[un_only.NDcount == 0]\n",
"un6 = un6.loc[:, 'Gene':'Probability_uc2']\n",
"un6.to_csv('20191122_UninducedDetectedOnly6of6.csv', sep=',')\n",
"\n",
"ind6 = ind_only[ind_only.NDcount == 0]\n",
"ind6.loc[:, 'Gene':'Probability_ic2']\n",
"ind6.to_csv('20191122_InducedDetectedOnly6of6.csv', sep=',')"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"# Saving just UniProt IDs from each condition for STRING analysis\n",
"un = un_only.reset_index()\n",
"un = un.rename({'index':'ProteinID'}, axis='columns')\n",
"un = un['ProteinID']\n",
"un.to_csv('20191122_UninducedIDs.csv', index=False, header=False)\n",
"\n",
"ind = ind_only.reset_index()\n",
"ind = ind.rename({'index':'ProteinID'}, axis='columns')\n",
"ind = ind['ProteinID']\n",
"ind.to_csv('20191122_InducedIDs.csv', index=False, header=False)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"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>ProteinID</th>\n",
" <th>Gene</th>\n",
" <th>ProteinInfo</th>\n",
" <th>Compartment_ua1</th>\n",
" <th>Probability_ua1</th>\n",
" <th>Compartment_ub1</th>\n",
" <th>Probability_ub1</th>\n",
" <th>Compartment_uc1</th>\n",
" <th>Probability_uc1</th>\n",
" <th>Compartment_ua2</th>\n",
" <th>Probability_ua2</th>\n",
" <th>Compartment_ub2</th>\n",
" <th>Probability_ub2</th>\n",
" <th>Compartment_uc2</th>\n",
" <th>Probability_uc2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: [ProteinID, Gene, ProteinInfo, Compartment_ua1, Probability_ua1, Compartment_ub1, Probability_ub1, Compartment_uc1, Probability_uc1, Compartment_ua2, Probability_ua2, Compartment_ub2, Probability_ub2, Compartment_uc2, Probability_uc2]\n",
"Index: []"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"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>ProteinID</th>\n",
" <th>Gene</th>\n",
" <th>ProteinInfo</th>\n",
" <th>Compartment_ia1</th>\n",
" <th>Probability_ia1</th>\n",
" <th>Compartment_ib1</th>\n",
" <th>Probability_ib1</th>\n",
" <th>Compartment_ic1</th>\n",
" <th>Probability_ic1</th>\n",
" <th>Compartment_ia2</th>\n",
" <th>Probability_ia2</th>\n",
" <th>Compartment_ib2</th>\n",
" <th>Probability_ib2</th>\n",
" <th>Compartment_ic2</th>\n",
" <th>Probability_ic2</th>\n",
" <th>NDcount</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: [ProteinID, Gene, ProteinInfo, Compartment_ia1, Probability_ia1, Compartment_ib1, Probability_ib1, Compartment_ic1, Probability_ic1, Compartment_ia2, Probability_ia2, Compartment_ib2, Probability_ib2, Compartment_ic2, Probability_ic2, NDcount]\n",
"Index: []"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Saving just UniProt IDs from each condition for STRING analysis on more stringent un6 and ind6\n",
"un = un6.reset_index()\n",
"un = un.rename({'index':'ProteinID'}, axis='columns')\n",
"display(un)\n",
"un = un['ProteinID']\n",
"un.to_csv('20191122_UninducedIDs6of6.csv', index=False, header=False)\n",
"\n",
"ind = ind6.reset_index()\n",
"ind = ind.rename({'index':'ProteinID'}, axis='columns')\n",
"display(ind)\n",
"ind = ind['ProteinID']\n",
"ind.to_csv('20191122_InducedIDs6of6.csv', index=False, header=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Links to STRING Analyses for each:\n",
"##### Note: HIV proteins will not appear here as they are not in the STRING database \n",
"[Uninduced Replicates Only (detected in 1+ replicate)](https://version-11-0.string-db.org/cgi/network.pl?networkId=FU6dESy1k84C)<br>\n",
"[Induced Replicates Only (detected in 1+ replicate)](https://version-11-0.string-db.org/cgi/network.pl?networkId=UYOw1cHsqraq)<br>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Comparing Detection (Partials Included)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Set working directory to dNef 1 folder\n",
"os.chdir('Path/To/Data')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# Reading in full data for first dNef experiment\n",
"ua1 = pd.read_csv('UnA/20190612_UnA_raw_partials.csv', index_col=0)\n",
"ua1.drop(columns=['Protein description', '3K', '5.4K', '12.2K', '24K', '78.4K', '110K', '195.5K'], inplace=True)\n",
"ua1.columns = ['Present']\n",
"ua1.Present = 'Y'\n",
"\n",
"ub1 = pd.read_csv('UnB/20190612_UnB_raw_partials.csv', index_col=0)\n",
"ub1.drop(columns=['Protein description', '3K', '5.4K', '12.2K', '24K', '78.4K', '110K', '195.5K'], inplace=True)\n",
"ub1.columns = ['Present']\n",
"ub1.Present = 'Y'\n",
"\n",
"uc1 = pd.read_csv('UnC/20190612_UnC_raw_partials.csv', index_col=0)\n",
"uc1.drop(columns=['Protein description', '3K', '5.4K', '12.2K', '24K', '78.4K', '110K', '195.5K'], inplace=True)\n",
"uc1.columns = ['Present']\n",
"uc1.Present = 'Y'\n",
"\n",
"ia1 = pd.read_csv('IndA/20190612_IndA_raw_partials.csv', index_col=0)\n",
"ia1.drop(columns=['Protein description', '3K', '5.4K', '12.2K', '24K', '78.4K', '110K', '195.5K'], inplace=True)\n",
"ia1.columns = ['Present']\n",
"ia1.Present = 'Y'\n",
"\n",
"ib1 = pd.read_csv('IndB/20190612_IndB_raw_partials.csv', index_col=0)\n",
"ib1.drop(columns=['Protein description', '3K', '5.4K', '12.2K', '24K', '78.4K', '110K', '195.5K'], inplace=True)\n",
"ib1.columns = ['Present']\n",
"ib1.Present = 'Y'\n",
"\n",
"ic1 = pd.read_csv('IndC/20190612_IndC_raw_partials.csv', index_col=0)\n",
"ic1.drop(columns=['Protein description', '3K', '5.4K', '12.2K', '24K', '78.4K', '110K', '195.5K'], inplace=True)\n",
"ic1.columns = ['Present']\n",
"ic1.Present = 'Y'"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# Set working directory to dNef 2 folder\n",
"os.chdir('Path/To/Data')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"# Reading in full data for second dNef experiment\n",
"ua2 = pd.read_csv('UnA/20191122_UnA_raw_partials.csv', index_col=0)\n",
"ua2.drop(columns=['Protein', '3K', '5.4K', '12.2K', '24K', '78.4K', '110K', '195.5K'], inplace=True)\n",
"ua2.columns = ['Present']\n",
"ua2.Present = 'Y'\n",
"\n",
"ub2 = pd.read_csv('UnB/20191122_UnB_raw_partials.csv', index_col=0)\n",
"ub2.drop(columns=['Protein', '3K', '5.4K', '12.2K', '24K', '78.4K', '110K', '195.5K'], inplace=True)\n",
"ub2.columns = ['Present']\n",
"ub2.Present = 'Y'\n",
"\n",
"uc2 = pd.read_csv('UnC/20191122_UnC_raw_partials.csv', index_col=0)\n",
"uc2.drop(columns=['Protein', '3K', '5.4K', '12.2K', '24K', '78.4K', '110K', '195.5K'], inplace=True)\n",
"uc2.columns = ['Present']\n",
"uc2.Present = 'Y'\n",
"\n",
"ia2 = pd.read_csv('IndA/20191122_IndA_raw_partials.csv', index_col=0)\n",
"ia2.drop(columns=['Protein', '3K', '5.4K', '12.2K', '24K', '78.4K', '110K', '195.5K'], inplace=True)\n",
"ia2.columns = ['Present']\n",
"ia2.Present = 'Y'\n",
"\n",
"ib2 = pd.read_csv('IndB/20191122_IndB_raw_partials.csv', index_col=0)\n",
"ib2.drop(columns=['Protein', '3K', '5.4K', '12.2K', '24K', '78.4K', '110K', '195.5K'], inplace=True)\n",
"ib2.columns = ['Present']\n",
"ib2.Present = 'Y'\n",
"\n",
"ic2 = pd.read_csv('IndC/20191122_IndC_raw_partials.csv', index_col=0)\n",
"ic2.drop(columns=['Protein', '3K', '5.4K', '12.2K', '24K', '78.4K', '110K', '195.5K'], inplace=True)\n",
"ic2.columns = ['Present']\n",
"ic2.Present = 'Y'"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# Set working directory to folder comparing biological replicates\n",
"os.chdir('Path/To/Data')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"# Defining commonly detected protein dataframes for uninduced\n",
"unlist = [ua1, ub1, uc1, ua2, ub2, uc2]\n",
"\n",
"un = pd.concat(unlist, axis=1, join='outer', sort='True')\n",
"\n",
"un.fillna('ND', inplace=True)\n",
"ndset = ['ND']\n",
"un['NDcount'] = (un.isin(ndset).sum(1))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"# Defining commonly detected protein dataframes for induced\n",
"indlist = [ia1, ib1, ic1, ia2, ib2, ic2]\n",
"\n",
"ind = pd.concat(indlist, axis=1, join='outer', sort='True')\n",
"\n",
"ind.fillna('ND', inplace=True)\n",
"ndset = ['ND']\n",
"ind['NDcount'] = (ind.isin(ndset).sum(1))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(140, 7)\n",
"(209, 7)\n"
]
}
],
"source": [
"# Commonly detected proteins only in uninduced and induced, respectively\n",
"un_onlylist = np.setdiff1d((un.index), (ind.index))\n",
"un_only = un.loc[un_onlylist, :]\n",
"print(un_only.shape)\n",
"\n",
"ind_onlylist = np.setdiff1d((ind.index), (un.index))\n",
"ind_only = ind.loc[ind_onlylist, :]\n",
"print(ind_only.shape)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"# Saving just UniProt IDs from each condition for STRING analysis\n",
"un = un_only.reset_index()\n",
"un = un.rename({'index':'UniProtID'}, axis='columns')\n",
"un = un['UniProtID']\n",
"un.to_csv('20191126_Partials_UninducedIDs.csv', index=False, header=False)\n",
"\n",
"ind = ind_only.reset_index()\n",
"ind = ind.rename({'index':'UniProtID'}, axis='columns')\n",
"ind = ind['UniProtID']\n",
"ind.to_csv('20191126_Partials_InducedIDs.csv', index=False, header=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### STRING diagrams for partials\n",
"[Uninduced only detected in 1+ replicates](https://version-11-0.string-db.org/cgi/network.pl?networkId=mr6AjzGNE5KS)<br>\n",
"[Induced only detected in 1+ replicates](https://version-11-0.string-db.org/cgi/network.pl?networkId=QdAceDL8PXY2)<br>"
]
}
],
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"kernelspec": {
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"name": "python3"
},
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"file_extension": ".py",
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