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drcjar/asbestosis.ipynb

Created March 25, 2021 15:08
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first adventures in open omics - despite known asbestos sensing by nlrp3 pathway which is thought to lead to increased muc5b expression (in alevolar macrophages), muc5b expression not affected much in epithelial tissue by asbestos exposure in vitro?
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asbestosis.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# https://pubmed.ncbi.nlm.nih.gov/17331233/\n",
"# https://www.ncbi.nlm.nih.gov/sites/GDSbrowser?acc=GDS2604\n",
"# https://geoparse.readthedocs.io/en/latest/usage.html#working-with-soft-files"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [],
"source": [
"import GEOparse\n",
"import pandas as pd\n",
"% matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"25-Mar-2021 14:00:39 INFO GEOparse - Parsing GDS2604_full.soft: \n",
"25-Mar-2021 14:00:39 DEBUG GEOparse - DATABASE: Geo\n",
"25-Mar-2021 14:00:39 DEBUG GEOparse - DATASET: GDS2604\n",
"25-Mar-2021 14:00:39 DEBUG GEOparse - SUBSET: GDS2604_1\n",
"25-Mar-2021 14:00:39 DEBUG GEOparse - SUBSET: GDS2604_2\n",
"25-Mar-2021 14:00:39 DEBUG GEOparse - SUBSET: GDS2604_3\n",
"25-Mar-2021 14:00:39 DEBUG GEOparse - SUBSET: GDS2604_4\n",
"25-Mar-2021 14:00:39 DEBUG GEOparse - SUBSET: GDS2604_5\n",
"25-Mar-2021 14:00:39 DEBUG GEOparse - SUBSET: GDS2604_6\n",
"25-Mar-2021 14:00:39 DEBUG GEOparse - SUBSET: GDS2604_7\n",
"25-Mar-2021 14:00:39 DEBUG GEOparse - SUBSET: GDS2604_8\n",
"25-Mar-2021 14:00:39 DEBUG GEOparse - SUBSET: GDS2604_9\n",
"25-Mar-2021 14:00:39 DEBUG GEOparse - SUBSET: GDS2604_10\n",
"25-Mar-2021 14:00:39 DEBUG GEOparse - SUBSET: GDS2604_11\n",
"25-Mar-2021 14:00:39 DEBUG GEOparse - SUBSET: GDS2604_12\n",
"25-Mar-2021 14:00:39 DEBUG GEOparse - SUBSET: GDS2604_13\n",
"25-Mar-2021 14:00:39 DEBUG GEOparse - ANNOTATION: \n",
"25-Mar-2021 14:00:39 ERROR GEOparse - Cannot recognize type Annotation\n",
"25-Mar-2021 14:00:39 DEBUG GEOparse - DATASET: GDS2604\n",
"/home/drcjar/anaconda3/envs/ipfjes/lib/python3.5/site-packages/ipykernel_launcher.py:1: DtypeWarning: Columns (40) have mixed types. Specify dtype option on import or set low_memory=False.\n",
" \"\"\"Entry point for launching an IPython kernel.\n"
]
}
],
"source": [
"gse = GEOparse.get_GEO(filepath=\"GDS2604_full.soft\")"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"lookup = gse.columns.reset_index()"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
"muc5b = gse.table[gse.table.IDENTIFIER == \"MUC5B\"].transpose().reset_index()"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"df = pd.merge(lookup, muc5b, on='index')"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['index', 'description', 'agent', 'cell line', 'cell type', 'time',\n",
" 22733, 31547],\n",
" dtype='object')"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [],
"source": [
"df[22733] = df[22733].astype(float)\n",
"df[31547] = df[31547].astype(float)"
]
},
{
"cell_type": "code",
"execution_count": 60,
"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>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" <tr>\n",
" <th>agent</th>\n",
" <th></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",
" <th>asbestos</th>\n",
" <td>14.0</td>\n",
" <td>13.407143</td>\n",
" <td>26.293417</td>\n",
" <td>1.2</td>\n",
" <td>1.6</td>\n",
" <td>3.1</td>\n",
" <td>4.95</td>\n",
" <td>77.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>control</th>\n",
" <td>13.0</td>\n",
" <td>29.776923</td>\n",
" <td>69.359849</td>\n",
" <td>1.5</td>\n",
" <td>2.5</td>\n",
" <td>2.8</td>\n",
" <td>4.30</td>\n",
" <td>245.6</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" count mean std min 25% 50% 75% max\n",
"agent \n",
"asbestos 14.0 13.407143 26.293417 1.2 1.6 3.1 4.95 77.2\n",
"control 13.0 29.776923 69.359849 1.5 2.5 2.8 4.30 245.6"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.groupby('agent')[22733].describe()"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" <tr>\n",
" <th>agent</th>\n",
" <th></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",
" <th>asbestos</th>\n",
" <td>14.0</td>\n",
" <td>2.035714</td>\n",
" <td>0.779158</td>\n",
" <td>0.9</td>\n",
" <td>1.425</td>\n",
" <td>1.95</td>\n",
" <td>2.6</td>\n",
" <td>3.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>control</th>\n",
" <td>13.0</td>\n",
" <td>2.730769</td>\n",
" <td>2.280491</td>\n",
" <td>0.9</td>\n",
" <td>1.200</td>\n",
" <td>1.70</td>\n",
" <td>4.3</td>\n",
" <td>8.5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" count mean std min 25% 50% 75% max\n",
"agent \n",
"asbestos 14.0 2.035714 0.779158 0.9 1.425 1.95 2.6 3.3\n",
"control 13.0 2.730769 2.280491 0.9 1.200 1.70 4.3 8.5"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.groupby('agent')[31547].describe()"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" <tr>\n",
" <th>agent</th>\n",
" <th>cell line</th>\n",
" <th>cell type</th>\n",
" <th>time</th>\n",
" <th></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",
" <th rowspan=\"11\" valign=\"top\">asbestos</th>\n",
" <th rowspan=\"5\" valign=\"top\">A549</th>\n",
" <th rowspan=\"5\" valign=\"top\">epithelial</th>\n",
" <th>1 h</th>\n",
" <td>1.0</td>\n",
" <td>1.60</td>\n",
" <td>NaN</td>\n",
" <td>1.6</td>\n",
" <td>1.600</td>\n",
" <td>1.60</td>\n",
" <td>1.600</td>\n",
" <td>1.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24 h</th>\n",
" <td>1.0</td>\n",
" <td>3.10</td>\n",
" <td>NaN</td>\n",
" <td>3.1</td>\n",
" <td>3.100</td>\n",
" <td>3.10</td>\n",
" <td>3.100</td>\n",
" <td>3.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48 h</th>\n",
" <td>2.0</td>\n",
" <td>3.10</td>\n",
" <td>0.282843</td>\n",
" <td>2.9</td>\n",
" <td>3.000</td>\n",
" <td>3.10</td>\n",
" <td>3.200</td>\n",
" <td>3.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6 h</th>\n",
" <td>1.0</td>\n",
" <td>2.10</td>\n",
" <td>NaN</td>\n",
" <td>2.1</td>\n",
" <td>2.100</td>\n",
" <td>2.10</td>\n",
" <td>2.100</td>\n",
" <td>2.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7 days</th>\n",
" <td>1.0</td>\n",
" <td>2.60</td>\n",
" <td>NaN</td>\n",
" <td>2.6</td>\n",
" <td>2.600</td>\n",
" <td>2.60</td>\n",
" <td>2.600</td>\n",
" <td>2.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">Beas2B</th>\n",
" <th rowspan=\"4\" valign=\"top\">epithelial</th>\n",
" <th>1 h</th>\n",
" <td>1.0</td>\n",
" <td>1.40</td>\n",
" <td>NaN</td>\n",
" <td>1.4</td>\n",
" <td>1.400</td>\n",
" <td>1.40</td>\n",
" <td>1.400</td>\n",
" <td>1.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24 h</th>\n",
" <td>2.0</td>\n",
" <td>1.20</td>\n",
" <td>0.282843</td>\n",
" <td>1.0</td>\n",
" <td>1.100</td>\n",
" <td>1.20</td>\n",
" <td>1.300</td>\n",
" <td>1.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48 h</th>\n",
" <td>1.0</td>\n",
" <td>0.90</td>\n",
" <td>NaN</td>\n",
" <td>0.9</td>\n",
" <td>0.900</td>\n",
" <td>0.90</td>\n",
" <td>0.900</td>\n",
" <td>0.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6 h</th>\n",
" <td>1.0</td>\n",
" <td>1.80</td>\n",
" <td>NaN</td>\n",
" <td>1.8</td>\n",
" <td>1.800</td>\n",
" <td>1.80</td>\n",
" <td>1.800</td>\n",
" <td>1.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">Met5A</th>\n",
" <th rowspan=\"2\" valign=\"top\">mesothelial</th>\n",
" <th>1 h</th>\n",
" <td>1.0</td>\n",
" <td>2.30</td>\n",
" <td>NaN</td>\n",
" <td>2.3</td>\n",
" <td>2.300</td>\n",
" <td>2.30</td>\n",
" <td>2.300</td>\n",
" <td>2.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48 h</th>\n",
" <td>2.0</td>\n",
" <td>2.05</td>\n",
" <td>0.777817</td>\n",
" <td>1.5</td>\n",
" <td>1.775</td>\n",
" <td>2.05</td>\n",
" <td>2.325</td>\n",
" <td>2.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"13\" valign=\"top\">control</th>\n",
" <th rowspan=\"6\" valign=\"top\">A549</th>\n",
" <th rowspan=\"6\" valign=\"top\">epithelial</th>\n",
" <th>0 h</th>\n",
" <td>1.0</td>\n",
" <td>1.20</td>\n",
" <td>NaN</td>\n",
" <td>1.2</td>\n",
" <td>1.200</td>\n",
" <td>1.20</td>\n",
" <td>1.200</td>\n",
" <td>1.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1 h</th>\n",
" <td>1.0</td>\n",
" <td>4.30</td>\n",
" <td>NaN</td>\n",
" <td>4.3</td>\n",
" <td>4.300</td>\n",
" <td>4.30</td>\n",
" <td>4.300</td>\n",
" <td>4.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24 h</th>\n",
" <td>1.0</td>\n",
" <td>1.10</td>\n",
" <td>NaN</td>\n",
" <td>1.1</td>\n",
" <td>1.100</td>\n",
" <td>1.10</td>\n",
" <td>1.100</td>\n",
" <td>1.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48 h</th>\n",
" <td>1.0</td>\n",
" <td>2.50</td>\n",
" <td>NaN</td>\n",
" <td>2.5</td>\n",
" <td>2.500</td>\n",
" <td>2.50</td>\n",
" <td>2.500</td>\n",
" <td>2.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6 h</th>\n",
" <td>1.0</td>\n",
" <td>0.90</td>\n",
" <td>NaN</td>\n",
" <td>0.9</td>\n",
" <td>0.900</td>\n",
" <td>0.90</td>\n",
" <td>0.900</td>\n",
" <td>0.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7 days</th>\n",
" <td>1.0</td>\n",
" <td>1.40</td>\n",
" <td>NaN</td>\n",
" <td>1.4</td>\n",
" <td>1.400</td>\n",
" <td>1.40</td>\n",
" <td>1.400</td>\n",
" <td>1.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"5\" valign=\"top\">Beas2B</th>\n",
" <th rowspan=\"5\" valign=\"top\">epithelial</th>\n",
" <th>0 h</th>\n",
" <td>1.0</td>\n",
" <td>1.80</td>\n",
" <td>NaN</td>\n",
" <td>1.8</td>\n",
" <td>1.800</td>\n",
" <td>1.80</td>\n",
" <td>1.800</td>\n",
" <td>1.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1 h</th>\n",
" <td>1.0</td>\n",
" <td>0.90</td>\n",
" <td>NaN</td>\n",
" <td>0.9</td>\n",
" <td>0.900</td>\n",
" <td>0.90</td>\n",
" <td>0.900</td>\n",
" <td>0.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24 h</th>\n",
" <td>1.0</td>\n",
" <td>1.40</td>\n",
" <td>NaN</td>\n",
" <td>1.4</td>\n",
" <td>1.400</td>\n",
" <td>1.40</td>\n",
" <td>1.400</td>\n",
" <td>1.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48 h</th>\n",
" <td>1.0</td>\n",
" <td>5.00</td>\n",
" <td>NaN</td>\n",
" <td>5.0</td>\n",
" <td>5.000</td>\n",
" <td>5.00</td>\n",
" <td>5.000</td>\n",
" <td>5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6 h</th>\n",
" <td>1.0</td>\n",
" <td>1.70</td>\n",
" <td>NaN</td>\n",
" <td>1.7</td>\n",
" <td>1.700</td>\n",
" <td>1.70</td>\n",
" <td>1.700</td>\n",
" <td>1.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">Met5A</th>\n",
" <th rowspan=\"2\" valign=\"top\">mesothelial</th>\n",
" <th>1 h</th>\n",
" <td>1.0</td>\n",
" <td>4.80</td>\n",
" <td>NaN</td>\n",
" <td>4.8</td>\n",
" <td>4.800</td>\n",
" <td>4.80</td>\n",
" <td>4.800</td>\n",
" <td>4.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48 h</th>\n",
" <td>1.0</td>\n",
" <td>8.50</td>\n",
" <td>NaN</td>\n",
" <td>8.5</td>\n",
" <td>8.500</td>\n",
" <td>8.50</td>\n",
" <td>8.500</td>\n",
" <td>8.5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" count mean std min 25% \\\n",
"agent cell line cell type time \n",
"asbestos A549 epithelial 1 h 1.0 1.60 NaN 1.6 1.600 \n",
" 24 h 1.0 3.10 NaN 3.1 3.100 \n",
" 48 h 2.0 3.10 0.282843 2.9 3.000 \n",
" 6 h 1.0 2.10 NaN 2.1 2.100 \n",
" 7 days 1.0 2.60 NaN 2.6 2.600 \n",
" Beas2B epithelial 1 h 1.0 1.40 NaN 1.4 1.400 \n",
" 24 h 2.0 1.20 0.282843 1.0 1.100 \n",
" 48 h 1.0 0.90 NaN 0.9 0.900 \n",
" 6 h 1.0 1.80 NaN 1.8 1.800 \n",
" Met5A mesothelial 1 h 1.0 2.30 NaN 2.3 2.300 \n",
" 48 h 2.0 2.05 0.777817 1.5 1.775 \n",
"control A549 epithelial 0 h 1.0 1.20 NaN 1.2 1.200 \n",
" 1 h 1.0 4.30 NaN 4.3 4.300 \n",
" 24 h 1.0 1.10 NaN 1.1 1.100 \n",
" 48 h 1.0 2.50 NaN 2.5 2.500 \n",
" 6 h 1.0 0.90 NaN 0.9 0.900 \n",
" 7 days 1.0 1.40 NaN 1.4 1.400 \n",
" Beas2B epithelial 0 h 1.0 1.80 NaN 1.8 1.800 \n",
" 1 h 1.0 0.90 NaN 0.9 0.900 \n",
" 24 h 1.0 1.40 NaN 1.4 1.400 \n",
" 48 h 1.0 5.00 NaN 5.0 5.000 \n",
" 6 h 1.0 1.70 NaN 1.7 1.700 \n",
" Met5A mesothelial 1 h 1.0 4.80 NaN 4.8 4.800 \n",
" 48 h 1.0 8.50 NaN 8.5 8.500 \n",
"\n",
" 50% 75% max \n",
"agent cell line cell type time \n",
"asbestos A549 epithelial 1 h 1.60 1.600 1.6 \n",
" 24 h 3.10 3.100 3.1 \n",
" 48 h 3.10 3.200 3.3 \n",
" 6 h 2.10 2.100 2.1 \n",
" 7 days 2.60 2.600 2.6 \n",
" Beas2B epithelial 1 h 1.40 1.400 1.4 \n",
" 24 h 1.20 1.300 1.4 \n",
" 48 h 0.90 0.900 0.9 \n",
" 6 h 1.80 1.800 1.8 \n",
" Met5A mesothelial 1 h 2.30 2.300 2.3 \n",
" 48 h 2.05 2.325 2.6 \n",
"control A549 epithelial 0 h 1.20 1.200 1.2 \n",
" 1 h 4.30 4.300 4.3 \n",
" 24 h 1.10 1.100 1.1 \n",
" 48 h 2.50 2.500 2.5 \n",
" 6 h 0.90 0.900 0.9 \n",
" 7 days 1.40 1.400 1.4 \n",
" Beas2B epithelial 0 h 1.80 1.800 1.8 \n",
" 1 h 0.90 0.900 0.9 \n",
" 24 h 1.40 1.400 1.4 \n",
" 48 h 5.00 5.000 5.0 \n",
" 6 h 1.70 1.700 1.7 \n",
" Met5A mesothelial 1 h 4.80 4.800 4.8 \n",
" 48 h 8.50 8.500 8.5 "
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.groupby(['agent', 'cell line', 'cell type', 'time'])[31547].describe()"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" 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></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" <tr>\n",
" <th>agent</th>\n",
" <th>cell line</th>\n",
" <th>cell type</th>\n",
" <th>time</th>\n",
" <th></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",
" <th rowspan=\"11\" valign=\"top\">asbestos</th>\n",
" <th rowspan=\"5\" valign=\"top\">A549</th>\n",
" <th rowspan=\"5\" valign=\"top\">epithelial</th>\n",
" <th>1 h</th>\n",
" <td>1.0</td>\n",
" <td>77.2</td>\n",
" <td>NaN</td>\n",
" <td>77.2</td>\n",
" <td>77.20</td>\n",
" <td>77.2</td>\n",
" <td>77.20</td>\n",
" <td>77.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24 h</th>\n",
" <td>1.0</td>\n",
" <td>2.3</td>\n",
" <td>NaN</td>\n",
" <td>2.3</td>\n",
" <td>2.30</td>\n",
" <td>2.3</td>\n",
" <td>2.30</td>\n",
" <td>2.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48 h</th>\n",
" <td>2.0</td>\n",
" <td>2.5</td>\n",
" <td>1.838478</td>\n",
" <td>1.2</td>\n",
" <td>1.85</td>\n",
" <td>2.5</td>\n",
" <td>3.15</td>\n",
" <td>3.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6 h</th>\n",
" <td>1.0</td>\n",
" <td>4.5</td>\n",
" <td>NaN</td>\n",
" <td>4.5</td>\n",
" <td>4.50</td>\n",
" <td>4.5</td>\n",
" <td>4.50</td>\n",
" <td>4.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7 days</th>\n",
" <td>1.0</td>\n",
" <td>73.4</td>\n",
" <td>NaN</td>\n",
" <td>73.4</td>\n",
" <td>73.40</td>\n",
" <td>73.4</td>\n",
" <td>73.40</td>\n",
" <td>73.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">Beas2B</th>\n",
" <th rowspan=\"4\" valign=\"top\">epithelial</th>\n",
" <th>1 h</th>\n",
" <td>1.0</td>\n",
" <td>7.5</td>\n",
" <td>NaN</td>\n",
" <td>7.5</td>\n",
" <td>7.50</td>\n",
" <td>7.5</td>\n",
" <td>7.50</td>\n",
" <td>7.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24 h</th>\n",
" <td>2.0</td>\n",
" <td>3.5</td>\n",
" <td>2.262742</td>\n",
" <td>1.9</td>\n",
" <td>2.70</td>\n",
" <td>3.5</td>\n",
" <td>4.30</td>\n",
" <td>5.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48 h</th>\n",
" <td>1.0</td>\n",
" <td>2.4</td>\n",
" <td>NaN</td>\n",
" <td>2.4</td>\n",
" <td>2.40</td>\n",
" <td>2.4</td>\n",
" <td>2.40</td>\n",
" <td>2.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6 h</th>\n",
" <td>1.0</td>\n",
" <td>1.5</td>\n",
" <td>NaN</td>\n",
" <td>1.5</td>\n",
" <td>1.50</td>\n",
" <td>1.5</td>\n",
" <td>1.50</td>\n",
" <td>1.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">Met5A</th>\n",
" <th rowspan=\"2\" valign=\"top\">mesothelial</th>\n",
" <th>1 h</th>\n",
" <td>1.0</td>\n",
" <td>1.3</td>\n",
" <td>NaN</td>\n",
" <td>1.3</td>\n",
" <td>1.30</td>\n",
" <td>1.3</td>\n",
" <td>1.30</td>\n",
" <td>1.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48 h</th>\n",
" <td>2.0</td>\n",
" <td>2.8</td>\n",
" <td>1.979899</td>\n",
" <td>1.4</td>\n",
" <td>2.10</td>\n",
" <td>2.8</td>\n",
" <td>3.50</td>\n",
" <td>4.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"13\" valign=\"top\">control</th>\n",
" <th rowspan=\"6\" valign=\"top\">A549</th>\n",
" <th rowspan=\"6\" valign=\"top\">epithelial</th>\n",
" <th>0 h</th>\n",
" <td>1.0</td>\n",
" <td>4.3</td>\n",
" <td>NaN</td>\n",
" <td>4.3</td>\n",
" <td>4.30</td>\n",
" <td>4.3</td>\n",
" <td>4.30</td>\n",
" <td>4.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1 h</th>\n",
" <td>1.0</td>\n",
" <td>91.4</td>\n",
" <td>NaN</td>\n",
" <td>91.4</td>\n",
" <td>91.40</td>\n",
" <td>91.4</td>\n",
" <td>91.40</td>\n",
" <td>91.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24 h</th>\n",
" <td>1.0</td>\n",
" <td>2.8</td>\n",
" <td>NaN</td>\n",
" <td>2.8</td>\n",
" <td>2.80</td>\n",
" <td>2.8</td>\n",
" <td>2.80</td>\n",
" <td>2.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48 h</th>\n",
" <td>1.0</td>\n",
" <td>22.3</td>\n",
" <td>NaN</td>\n",
" <td>22.3</td>\n",
" <td>22.30</td>\n",
" <td>22.3</td>\n",
" <td>22.30</td>\n",
" <td>22.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6 h</th>\n",
" <td>1.0</td>\n",
" <td>2.8</td>\n",
" <td>NaN</td>\n",
" <td>2.8</td>\n",
" <td>2.80</td>\n",
" <td>2.8</td>\n",
" <td>2.80</td>\n",
" <td>2.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7 days</th>\n",
" <td>1.0</td>\n",
" <td>245.6</td>\n",
" <td>NaN</td>\n",
" <td>245.6</td>\n",
" <td>245.60</td>\n",
" <td>245.6</td>\n",
" <td>245.60</td>\n",
" <td>245.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"5\" valign=\"top\">Beas2B</th>\n",
" <th rowspan=\"5\" valign=\"top\">epithelial</th>\n",
" <th>0 h</th>\n",
" <td>1.0</td>\n",
" <td>1.5</td>\n",
" <td>NaN</td>\n",
" <td>1.5</td>\n",
" <td>1.50</td>\n",
" <td>1.5</td>\n",
" <td>1.50</td>\n",
" <td>1.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1 h</th>\n",
" <td>1.0</td>\n",
" <td>3.8</td>\n",
" <td>NaN</td>\n",
" <td>3.8</td>\n",
" <td>3.80</td>\n",
" <td>3.8</td>\n",
" <td>3.80</td>\n",
" <td>3.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24 h</th>\n",
" <td>1.0</td>\n",
" <td>3.8</td>\n",
" <td>NaN</td>\n",
" <td>3.8</td>\n",
" <td>3.80</td>\n",
" <td>3.8</td>\n",
" <td>3.80</td>\n",
" <td>3.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48 h</th>\n",
" <td>1.0</td>\n",
" <td>1.5</td>\n",
" <td>NaN</td>\n",
" <td>1.5</td>\n",
" <td>1.50</td>\n",
" <td>1.5</td>\n",
" <td>1.50</td>\n",
" <td>1.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6 h</th>\n",
" <td>1.0</td>\n",
" <td>2.5</td>\n",
" <td>NaN</td>\n",
" <td>2.5</td>\n",
" <td>2.50</td>\n",
" <td>2.5</td>\n",
" <td>2.50</td>\n",
" <td>2.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">Met5A</th>\n",
" <th rowspan=\"2\" valign=\"top\">mesothelial</th>\n",
" <th>1 h</th>\n",
" <td>1.0</td>\n",
" <td>2.7</td>\n",
" <td>NaN</td>\n",
" <td>2.7</td>\n",
" <td>2.70</td>\n",
" <td>2.7</td>\n",
" <td>2.70</td>\n",
" <td>2.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48 h</th>\n",
" <td>1.0</td>\n",
" <td>2.1</td>\n",
" <td>NaN</td>\n",
" <td>2.1</td>\n",
" <td>2.10</td>\n",
" <td>2.1</td>\n",
" <td>2.10</td>\n",
" <td>2.1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" count mean std min 25% \\\n",
"agent cell line cell type time \n",
"asbestos A549 epithelial 1 h 1.0 77.2 NaN 77.2 77.20 \n",
" 24 h 1.0 2.3 NaN 2.3 2.30 \n",
" 48 h 2.0 2.5 1.838478 1.2 1.85 \n",
" 6 h 1.0 4.5 NaN 4.5 4.50 \n",
" 7 days 1.0 73.4 NaN 73.4 73.40 \n",
" Beas2B epithelial 1 h 1.0 7.5 NaN 7.5 7.50 \n",
" 24 h 2.0 3.5 2.262742 1.9 2.70 \n",
" 48 h 1.0 2.4 NaN 2.4 2.40 \n",
" 6 h 1.0 1.5 NaN 1.5 1.50 \n",
" Met5A mesothelial 1 h 1.0 1.3 NaN 1.3 1.30 \n",
" 48 h 2.0 2.8 1.979899 1.4 2.10 \n",
"control A549 epithelial 0 h 1.0 4.3 NaN 4.3 4.30 \n",
" 1 h 1.0 91.4 NaN 91.4 91.40 \n",
" 24 h 1.0 2.8 NaN 2.8 2.80 \n",
" 48 h 1.0 22.3 NaN 22.3 22.30 \n",
" 6 h 1.0 2.8 NaN 2.8 2.80 \n",
" 7 days 1.0 245.6 NaN 245.6 245.60 \n",
" Beas2B epithelial 0 h 1.0 1.5 NaN 1.5 1.50 \n",
" 1 h 1.0 3.8 NaN 3.8 3.80 \n",
" 24 h 1.0 3.8 NaN 3.8 3.80 \n",
" 48 h 1.0 1.5 NaN 1.5 1.50 \n",
" 6 h 1.0 2.5 NaN 2.5 2.50 \n",
" Met5A mesothelial 1 h 1.0 2.7 NaN 2.7 2.70 \n",
" 48 h 1.0 2.1 NaN 2.1 2.10 \n",
"\n",
" 50% 75% max \n",
"agent cell line cell type time \n",
"asbestos A549 epithelial 1 h 77.2 77.20 77.2 \n",
" 24 h 2.3 2.30 2.3 \n",
" 48 h 2.5 3.15 3.8 \n",
" 6 h 4.5 4.50 4.5 \n",
" 7 days 73.4 73.40 73.4 \n",
" Beas2B epithelial 1 h 7.5 7.50 7.5 \n",
" 24 h 3.5 4.30 5.1 \n",
" 48 h 2.4 2.40 2.4 \n",
" 6 h 1.5 1.50 1.5 \n",
" Met5A mesothelial 1 h 1.3 1.30 1.3 \n",
" 48 h 2.8 3.50 4.2 \n",
"control A549 epithelial 0 h 4.3 4.30 4.3 \n",
" 1 h 91.4 91.40 91.4 \n",
" 24 h 2.8 2.80 2.8 \n",
" 48 h 22.3 22.30 22.3 \n",
" 6 h 2.8 2.80 2.8 \n",
" 7 days 245.6 245.60 245.6 \n",
" Beas2B epithelial 0 h 1.5 1.50 1.5 \n",
" 1 h 3.8 3.80 3.8 \n",
" 24 h 3.8 3.80 3.8 \n",
" 48 h 1.5 1.50 1.5 \n",
" 6 h 2.5 2.50 2.5 \n",
" Met5A mesothelial 1 h 2.7 2.70 2.7 \n",
" 48 h 2.1 2.10 2.1 "
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.groupby(['agent', 'cell line', 'cell type', 'time'])[22733].describe()"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f8d845e80f0>"
]
},
"execution_count": 103,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df[df['cell line'] == \"A549\"].groupby(['agent', 'time'])[22733].mean().unstack().plot(kind='bar')"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f8d8458a6d8>"
]
},
"execution_count": 104,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df[df['cell line'] == \"A549\"].groupby(['agent', 'time'])[31547].mean().unstack().plot(kind='bar')"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f8d84555978>"
]
},
"execution_count": 105,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df[df['cell type'] == \"epithelial\"].groupby(['agent', 'time'])[31547].mean().unstack().plot(kind='bar')"
]
},
{
"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": "3.5.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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