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alexlenail/explore_psychencode_grn.ipynb

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explore_psychencode_grn.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Summary Statistics about the PsychENCODE Gene Regulatory Netowrk"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 0. Load GRN and reference Genome"
]
},
{
"cell_type": "code",
"execution_count": 2,
"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>Transcription_Factor</th>\n",
" <th>Target_Gene</th>\n",
" <th>Enhancer_Region</th>\n",
" <th>Edge_Weight</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>BARHL2</td>\n",
" <td>SHC1</td>\n",
" <td>chr1:154869072-154870071</td>\n",
" <td>0.284806</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>BARHL2</td>\n",
" <td>SHC1</td>\n",
" <td>chr1:154868096-154869095</td>\n",
" <td>0.284806</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>MNT</td>\n",
" <td>SHC1</td>\n",
" <td>chr1:154716711-154717710</td>\n",
" <td>0.312268</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>MNT</td>\n",
" <td>SHC1</td>\n",
" <td>chr1:154701180-154702179</td>\n",
" <td>0.312268</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>MNT</td>\n",
" <td>SHC1</td>\n",
" <td>chr1:154819188-154820187</td>\n",
" <td>0.312268</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Transcription_Factor Target_Gene Enhancer_Region Edge_Weight\n",
"0 BARHL2 SHC1 chr1:154869072-154870071 0.284806\n",
"1 BARHL2 SHC1 chr1:154868096-154869095 0.284806\n",
"2 MNT SHC1 chr1:154716711-154717710 0.312268\n",
"3 MNT SHC1 chr1:154701180-154702179 0.312268\n",
"4 MNT SHC1 chr1:154819188-154820187 0.312268"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grn = pd.read_csv('./raw/GRN/PsychENCODE/INT-14_ElasticNet_Filtered_Cutoff_0.1_GRN_2.csv')\n",
"grn.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### The psychencode GRN gives the site of regulatory elements (\"Enhancer_Region\") for enhancers, but for promoters, simply lists \"Promoter\". We will probably need to go back and add actual genomic coordinates for the promoters of each gene in this GRN. For the time being, remove all TF -> Promoter -> Target Gene edges, since they make the following analysis impossible"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"grn = grn[grn.Enhancer_Region != 'Promoter']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### The psychencode GRN chunks the genome into 1kb bins. All Regulatory regions are 1kb. \n",
"\n",
"### TODO: check that they never overlap (group by chr, sort by a, make sure a's are spaced out properly)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"grn['chr'], grn['a'], grn['b'] = zip(*grn.Enhancer_Region.str.split('\\:|\\-'))\n",
"grn['a'] = pd.to_numeric(grn['a'])\n",
"grn['b'] = pd.to_numeric(grn['b'])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" 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>Transcription_Factor</th>\n",
" <th>Target_Gene</th>\n",
" <th>Enhancer_Region</th>\n",
" <th>Edge_Weight</th>\n",
" <th>chr</th>\n",
" <th>a</th>\n",
" <th>b</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>BARHL2</td>\n",
" <td>SHC1</td>\n",
" <td>chr1:154869072-154870071</td>\n",
" <td>0.284806</td>\n",
" <td>chr1</td>\n",
" <td>154869072</td>\n",
" <td>154870071</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>BARHL2</td>\n",
" <td>SHC1</td>\n",
" <td>chr1:154868096-154869095</td>\n",
" <td>0.284806</td>\n",
" <td>chr1</td>\n",
" <td>154868096</td>\n",
" <td>154869095</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>MNT</td>\n",
" <td>SHC1</td>\n",
" <td>chr1:154716711-154717710</td>\n",
" <td>0.312268</td>\n",
" <td>chr1</td>\n",
" <td>154716711</td>\n",
" <td>154717710</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>MNT</td>\n",
" <td>SHC1</td>\n",
" <td>chr1:154701180-154702179</td>\n",
" <td>0.312268</td>\n",
" <td>chr1</td>\n",
" <td>154701180</td>\n",
" <td>154702179</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>MNT</td>\n",
" <td>SHC1</td>\n",
" <td>chr1:154819188-154820187</td>\n",
" <td>0.312268</td>\n",
" <td>chr1</td>\n",
" <td>154819188</td>\n",
" <td>154820187</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Transcription_Factor Target_Gene Enhancer_Region Edge_Weight \\\n",
"0 BARHL2 SHC1 chr1:154869072-154870071 0.284806 \n",
"1 BARHL2 SHC1 chr1:154868096-154869095 0.284806 \n",
"2 MNT SHC1 chr1:154716711-154717710 0.312268 \n",
"3 MNT SHC1 chr1:154701180-154702179 0.312268 \n",
"4 MNT SHC1 chr1:154819188-154820187 0.312268 \n",
"\n",
" chr a b \n",
"0 chr1 154869072 154870071 \n",
"1 chr1 154868096 154869095 \n",
"2 chr1 154716711 154717710 \n",
"3 chr1 154701180 154702179 \n",
"4 chr1 154819188 154820187 "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grn.head()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"RR_TFs = grn.groupby(['Target_Gene', 'Enhancer_Region']).size().to_frame().reset_index()\n",
"\n",
"unique_RRs_per_Target_Gene = RR_TFs.groupby('Target_Gene')['Enhancer_Region'].size()\n",
"\n",
"TG_TFs = grn.groupby('Target_Gene')['Transcription_Factor'].value_counts().to_frame().rename(columns={'Transcription_Factor': 'count'}).reset_index()\n",
"\n",
"unique_TFs_per_Target_Gene = TG_TFs.groupby('Target_Gene')['Transcription_Factor'].size()\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
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" 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>name</th>\n",
" <th>chrom</th>\n",
" <th>strand</th>\n",
" <th>txStart</th>\n",
" <th>txEnd</th>\n",
" <th>cdsStart</th>\n",
" <th>cdsEnd</th>\n",
" <th>exonCount</th>\n",
" <th>exonStarts</th>\n",
" <th>exonEnds</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>DDX11L1</td>\n",
" <td>chr1</td>\n",
" <td>1</td>\n",
" <td>11873</td>\n",
" <td>14409</td>\n",
" <td>14409</td>\n",
" <td>14409</td>\n",
" <td>3</td>\n",
" <td>11873,12612,13220,</td>\n",
" <td>12227,12721,14409,</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>WASH7P</td>\n",
" <td>chr1</td>\n",
" <td>-1</td>\n",
" <td>14361</td>\n",
" <td>29370</td>\n",
" <td>29370</td>\n",
" <td>29370</td>\n",
" <td>11</td>\n",
" <td>14361,14969,15795,16606,16857,17232,17605,1791...</td>\n",
" <td>14829,15038,15947,16765,17055,17368,17742,1806...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>MIR6859-1</td>\n",
" <td>chr1</td>\n",
" <td>-1</td>\n",
" <td>17368</td>\n",
" <td>17436</td>\n",
" <td>17436</td>\n",
" <td>17436</td>\n",
" <td>1</td>\n",
" <td>17368,</td>\n",
" <td>17436,</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>MIR1302-2</td>\n",
" <td>chr1</td>\n",
" <td>1</td>\n",
" <td>30365</td>\n",
" <td>30503</td>\n",
" <td>30503</td>\n",
" <td>30503</td>\n",
" <td>1</td>\n",
" <td>30365,</td>\n",
" <td>30503,</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>FAM138A</td>\n",
" <td>chr1</td>\n",
" <td>-1</td>\n",
" <td>34610</td>\n",
" <td>36081</td>\n",
" <td>36081</td>\n",
" <td>36081</td>\n",
" <td>3</td>\n",
" <td>34610,35276,35720,</td>\n",
" <td>35174,35481,36081,</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" name chrom strand txStart txEnd cdsStart cdsEnd exonCount \\\n",
"0 DDX11L1 chr1 1 11873 14409 14409 14409 3 \n",
"1 WASH7P chr1 -1 14361 29370 29370 29370 11 \n",
"2 MIR6859-1 chr1 -1 17368 17436 17436 17436 1 \n",
"3 MIR1302-2 chr1 1 30365 30503 30503 30503 1 \n",
"4 FAM138A chr1 -1 34610 36081 36081 36081 3 \n",
"\n",
" exonStarts \\\n",
"0 11873,12612,13220, \n",
"1 14361,14969,15795,16606,16857,17232,17605,1791... \n",
"2 17368, \n",
"3 30365, \n",
"4 34610,35276,35720, \n",
"\n",
" exonEnds \n",
"0 12227,12721,14409, \n",
"1 14829,15038,15947,16765,17055,17368,17742,1806... \n",
"2 17436, \n",
"3 30503, \n",
"4 35174,35481,36081, "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"refseq = pd.read_csv('./processed/refseq.tsv', sep='\\t')\n",
"refseq.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# I. Min, max, mean, median stats for each of the links"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Num TFs per RR (per gene)"
]
},
{
"cell_type": "code",
"execution_count": 136,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"min: 1 max: 438 mean: 7.735816181866382 median: 5.0\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1440x640 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(18, 8), dpi=80, facecolor='w', edgecolor='k')\n",
"df = grn.groupby(['Target_Gene', 'Enhancer_Region'])['Transcription_Factor'].count()\n",
"df.plot.hist(bins=300, logy=True)\n",
"print('min:', df.min(), 'max:', df.max(), 'mean:', df.mean(), 'median:', df.median())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Num TFs per RR"
]
},
{
"cell_type": "code",
"execution_count": 137,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"min: 1 max: 1241 mean: 57.09462505848492 median: 33.0\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x164a50c88>"
]
},
"execution_count": 137,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1440x640 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(18, 8), dpi=80, facecolor='w', edgecolor='k')\n",
"df = grn.groupby(['Enhancer_Region'])['Transcription_Factor'].count()\n",
"print('min:', df.min(), 'max:', df.max(), 'mean:', df.mean(), 'median:', df.median())\n",
"df.plot.hist(bins=300, logy=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Num RRs per target Gene"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"min: 1 max: 384 mean: 39.10735481932237 median: 24.0\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x172d199e8>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1440x640 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(18, 8), dpi=80, facecolor='w', edgecolor='k')\n",
"df = unique_RRs_per_Target_Gene\n",
"print('min:', df.min(), 'max:', df.max(), 'mean:', df.mean(), 'median:', df.median())\n",
"df.plot.hist(bins=300, logy=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# II. Min, Max, mean, median genomic span of RRs per target Gene, and num other genes within that span\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### length of the entire regulatory region\n",
"\n",
"### Note: logx/logy histograms are difficult to reason about. Inspect carefully..."
]
},
{
"cell_type": "code",
"execution_count": 139,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"min: 1000 max: 92461592 mean: 675255.9217188791 median: 556600.0\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x163f45cf8>"
]
},
"execution_count": 139,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1440x640 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(18, 8), dpi=80, facecolor='w', edgecolor='k')\n",
"df = grn.groupby(['Target_Gene'])['a'].agg(np.ptp) + 1000\n",
"print('min:', df.min(), 'max:', df.max(), 'mean:', df.mean(), 'median:', df.median())\n",
"df.plot.hist(bins=4000, logx=True, logy=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Enhancer landscape for typical genes (distance from gene)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x168c85f28>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 2400x720 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(10, 3), dpi=240, facecolor='w', edgecolor='k')\n",
"\n",
"gene = grn[grn.Target_Gene == 'CLDN5']\n",
"(gene.a - gene.a.min()).plot.hist(bins=100)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x168b11748>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 2400x720 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(10, 3), dpi=240, facecolor='w', edgecolor='k')\n",
"\n",
"gene = grn[grn.Target_Gene == 'ABCD1']\n",
"(gene.a - gene.a.min()).plot.hist(bins=100)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x16d71ea58>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 2400x720 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(10, 3), dpi=240, facecolor='w', edgecolor='k')\n",
"\n",
"gene = grn[grn.Target_Gene == 'TGFB1']\n",
"(gene.a - gene.a.min()).plot.hist(bins=100)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# III. Verify all RRs are in cis with their target gene, or if not, the prevalence of trans links and max number of distinct chromosomes seen for a single target"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"chrs = grn.groupby('Target_Gene')['chr'].agg(np.unique).to_frame()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"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>chr</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Target_Gene</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>CKS1B</th>\n",
" <td>[chr1, chr5]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LSP1</th>\n",
" <td>[chr11, chr13]</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" chr\n",
"Target_Gene \n",
"CKS1B [chr1, chr5]\n",
"LSP1 [chr11, chr13]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chrs[~chrs.chr.isin(['chr1', 'chr2', 'chr3', 'chr4', 'chr5', 'chr6', 'chr7', 'chr8', 'chr9', 'chr10', \n",
" 'chr11', 'chr12', 'chr13', 'chr14', 'chr15', 'chr16', 'chr17', 'chr18', 'chr19', 'chr20', \n",
" 'chr21', 'chr22', 'chrX', 'chrY'])]"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"chr\n",
"chr1 1688\n",
"chr5 48\n",
"Name: Target_Gene, dtype: int64"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grn[grn.Target_Gene == 'CKS1B'].groupby('chr')['Target_Gene'].count()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"chr\n",
"chr11 366\n",
"chr13 651\n",
"Name: Target_Gene, dtype: int64"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grn[grn.Target_Gene == 'LSP1'].groupby('chr')['Target_Gene'].count()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# IV. Number of isoforms per gene"
]
},
{
"cell_type": "code",
"execution_count": 140,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"min: 1 max: 102 mean: 2.6052547146269296 median: 1.0\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1611330f0>"
]
},
"execution_count": 140,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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ea+zYsUX5ueeey7fffnvQtt9++23mzJlzwL4AAOVs4sSJqa+vb9HfzJkzi34/+clPSu5NnDixZNzG8VJza1+ff/55Xn311QP2BQAod+PGjSvKL774YrPtG7c50DtBcRYAQINevXoV5ddee61Fffa+Y0ySs846q+ReRUVFxowZU9Sbi7UWLVqUjz/+OEnDCSVXXnlli+YAAHAsaYu1qsbXa2pqsnr16ibHbfy71r/g8JEcB7TIzTffXJRnzZqVlStXHrDdtm3bMn369KI+ZcqUVFZWtvn8AACONnv27Ml1112XefPmJUkqKyszZ86cDBs27JDGGzNmTPH1x02bNuWhhx46aNsHHnggmzZtStKwEXz06NGH9JsAAMeLCRMmFKec/Oc//8mTTz550LZ333139uzZkyS56KKLMmDAgB9kjgAAP4Sbbrop7du3T5K88847eemllw7adunSpXnhhReK+r4fIEjEWQAAezV+R/jRRx9l9uzZTbZfsGBB/vnPfxb1ESNG7NfmN7/5Tdq1a9gC+vLLL5e0b6yuri533XVXUb/66qvTrVu3Vs0fAOBY0BZrVQMHDszAgQOTNOwXmzp16kHH/NOf/lR8sODkk0/OjTfeeEjPAexPchzQIqNGjcqll16aJNmxY0dGjx6dFStWlLTZuHFjxo4dm//+979JGk6Nu/vuu3/wuQIAHGn19fWZPHly5s6dmyRp165dZs+eXfL1xtaqqqrKfffdV9QffvjhzJgxI3V1dcW1urq6zJgxI7/73e+Ka/fff39OPPHEQ/5dAIDjQffu3XPHHXcU9dtuu63kJN6k4VTgqVOn5plnnimuPfzwwz/YHAEAfgh9+/Yt+WjmddddV5IAt9frr7+e0aNHFxuEBg0adMC1L3EWAECDUaNG5ZxzzinqU6ZMyRNPPFHEU3vV19dnzpw5JSf69uzZM9dee+1+Y55//vm5/vrri/r48eOzcOHCkjbffPNNJk6cmLfeeitJcuKJJ+aBBx44HI8EAFB22mqtqvH9p59+OlOnTs2uXbtK2syZMye33357Ub/zzjvTtWvXQ3oOYH8V9fX19Ud6EkB5WL16dX7+85+ntrY2SVJRUZFf/OIX6du3bzZs2JCampps27YtScPJKPPnz88VV1xxJKcMAHBEPP7447nllluK+tlnn51f/vKXLe7/6KOPHvTejTfeWPIlyb59+2bQoEFJksWLF2fVqlXFvUmTJuWpp55qzdQBAI4Zs2bNyqRJk5I0nKb7ySefNNl+165d+dWvfpUFCxYU184///wMGDAg27dvzxtvvFGsiyXJfffdl+nTp7fJ3AEAjqQdO3Zk+PDhefPNN4tr/fv3z8CBA3PCCSdkxYoVWbZsWXGvuro6S5YsSc+ePQ84njgLAKDBkiVLMnTo0GJ/VdIQSw0ePDhdu3bN5s2bs3jx4pJ1rKqqqtTU1OSSSy454JhbtmzJ4MGDs3LlyuLahRdemPPOOy9btmzJggUL8tVXXxX3Zs2alQkTJhz+hwMAaCMjR47M2rVrS66tW7cu69evT5J07NgxZ5111n795s2blzPOOGO/6221VjVt2rQ8+OCDRf2MM87IpZdemg4dOmTZsmX54IMPinvDhw/PvHnzUllZ2ey4QMtIjgNa5aOPPsr48ePz/vvvH7RNt27dMnPmzIwaNeoHnBkAwNHj3nvvLTnlrbWa+m/azp0789vf/jaPPfbYQdtVVFTk1ltvze9///u0b9/+kOcBAFDOWpsclySbN2/OlClT9vtCZGPt27fPvffem3vuuedwTRUA4KizefPm3HTTTSVfyD6QCy+8MH/9618PmhjXeDxxFgBAsnTp0txwww35+OOPm23bp0+fzJ49OxdffHGT7dauXZsbbrihZIP3vjp16pQZM2YU62UAAOWid+/e+fTTT1vd73//+1969+59wHttsVZVX1+fhx56KPfff/9+p8Y1du211+aPf/xjTjnllBaNC7SM5Dig1Xbu3Jlnn302zzzzTFauXJn169enc+fO+elPf5px48Zl0qRJjnkFAI5rbZkct9fSpUvz1FNPZeHChVmzZk2S5Mwzz8zll1+eyZMnZ+DAgYf8+wAAx4JDSY7bq6amJn/+85+zaNGi1NbWpn379unZs2dGjBiRyZMnp3///m00awCAo8sbb7yRv/zlL3nrrbeyZs2a7NmzJz169MigQYNyzTXXZOzYsamoqGjxeOIsAIBk9+7deemll/Liiy/m3Xffzdq1a/P111+nY8eO6dGjRy644IKMGTMmV111VYs/hFlfX5+//e1vefrpp7N8+fLU1tamU6dO6dWrV6688spMnjw5vXr1auMnAwA4/NoiOW6vtlir+vDDD/Pkk0/mlVdeyeeff55du3aluro6F110USZMmJBhw4a1ekygeZLjAAAAAAAAAAAAAAAAACg77Y70BAAAAAAAAAAAAAAAAACgtSTHAQAAAAAAAAAAAAAAAFB2JMcBAAAAAAAAAAAAAAAAUHYkxwEAAAAAAAAAAAAAAABQdiTHAQAAAAAAAAAAAAAAAFB2JMcBAAAAAAAAAAAAAAAAUHYkxwEAAAAAAAAAAAAAAABQdiTHAQAAAAAAAAAAAAAAAFB2JMcBAAAAAAAAAAAAAAAAUHYkxwEAAAAAAAAAAAAAAABQdiTHAQAAAAAAAAAAAAAAAFB2JMcBAAAAAAAAAAAAAAAAUHYkxwEAAAAAAAAAAAAAAABQdiTHAQAAAAAAAAAAAAAAAFB2JMcBAAAAAAAAAAAAAAAAUHYkxwEAAAAAAAAAAAAAAABQdiTHAQAAAAAAAAAAAAAAAFB2JMcBAAAAAAAAAAAAAAAAUHYkxwEAAAAAAAAAAAAAAABQdiTHAQAAAAAAAAAAAAAAAFB2JMcBAAAAAAAAAAAAAAAAUHYkxwEAAAAAAAAAAAAAAABQdiTHAQAAAAAAAAAAAAAAAFB2JMcBAAAAAAAAAAAAAAAAUHYkxwEAAAAAAAAAAAAAAABQdiTHAQAAAAAAAAAAAAAAAFB2JMcBAAAAAAAAAAAAAAAAUHYkxwEAAAAAAAAAAAAAAABQdiTHAQAAAAAAAAAAAAAAAFB2JMcBAAAAAAAAAAAAAAAAUHb+D74tFQy0pcmHAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 4320x1920 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(18, 8), dpi=240, facecolor='w', edgecolor='k')\n",
"df = refseq.groupby('name').size()\n",
"print('min:', df.min(), 'max:', df.max(), 'mean:', df.mean(), 'median:', df.median())\n",
"df.plot.hist(bins=100, logy=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## V. Number of TFs and RRs for gene containing the largest number of RRs"
]
},
{
"cell_type": "code",
"execution_count": 123,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"AACS has 384 regulatory regions associated with 70 unique TFs comprising altogether 8756 TF-RR links\n"
]
}
],
"source": [
"\n",
"print(unique_RRs_per_Target_Gene.idxmax(), 'has', unique_RRs_per_Target_Gene.max(), 'regulatory regions associated with', unique_TFs_per_Target_Gene['AACS'], 'unique TFs comprising altogether', RR_TFs[RR_TFs.Target_Gene == 'AACS'][0].sum(), 'TF-RR links') \n"
]
},
{
"cell_type": "code",
"execution_count": 129,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"NCOR2 has 12052 TF-RR links distributed across 298 regulatory regions and 85 unique TFs\n"
]
}
],
"source": [
"print(RR_TFs.groupby('Target_Gene')[0].sum().idxmax(), 'has', RR_TFs.groupby('Target_Gene')[0].sum().max(), 'TF-RR links distributed across', unique_RRs_per_Target_Gene['NCOR2'], 'regulatory regions and', unique_TFs_per_Target_Gene['NCOR2'], 'unique TFs')\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# VI. Number of TFs and RRs for gene containing the largest number of distinct TFs\n"
]
},
{
"cell_type": "code",
"execution_count": 131,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MBP has 198 unique TFs which bind across 66 unique RRs for 8326 TF-RR links\n"
]
}
],
"source": [
"print(unique_TFs_per_Target_Gene.idxmax(), 'has', unique_TFs_per_Target_Gene.max(), 'unique TFs which bind across', unique_RRs_per_Target_Gene['MBP'], 'unique RRs for', RR_TFs[RR_TFs.Target_Gene == 'MBP'][0].sum(), 'TF-RR links')\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.7.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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