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.ipynb
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.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import sys\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"sns.set()\n",
"import itertools\n",
"from matplotlib import rcParams\n",
"from collections import defaultdict\n",
"rcParams['font.family'] = 'sans-serif'\n",
"rcParams['font.sans-serif'] = ['Arial']\n",
"rcParams['pdf.fonttype'] = 42\n",
"rcParams['axes.formatter.useoffset'] = False"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Loading datasets:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"fiveeight = pd.read_csv(\"../../Analysis/diffBUM-HMM/mature_rRNA_5.8S_control_identical_conditions_diff_BUM_HMM.txt\",\\\n",
" sep=\"\\t\",\\\n",
" index_col=0,\n",
" header=0)\n",
"five = pd.read_csv(\"../../Analysis/diffBUM-HMM/mature_rRNA_5S_control_identical_conditions_diff_BUM_HMM.txt\",\\\n",
" sep=\"\\t\",\\\n",
" index_col=0,\n",
" header=0)\n",
"twentyfive = pd.read_csv(\"../../Analysis/diffBUM-HMM/mature_rRNA_25S_control_identical_conditions_diff_BUM_HMM.txt\",\\\n",
" sep=\"\\t\",\\\n",
" index_col=0,\n",
" header=0)\n",
"eighteen = pd.read_csv(\"../../Analysis/diffBUM-HMM/mature_rRNA_18S_control_identical_conditions_diff_BUM_HMM.txt\",\\\n",
" sep=\"\\t\",\\\n",
" index_col=0,\n",
" header=0)\n",
"\n",
"eighteennew = pd.read_csv(\"../../Analysis/diffBUM-HMM/mature_rRNA_18S_control_identical_conditions_NEWanalysis_diff_BUM_HMM.txt\",\\\n",
" sep=\"\\t\",\\\n",
" index_col=0,\n",
" header=0)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Dropping positions with insufficient coverage:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"position = fiveeight[fiveeight == -999].dropna().index\n",
"fiveeight.drop(position,inplace=True)\n",
"position = five[five == -999].dropna().index\n",
"five.drop(position,inplace=True)\n",
"position = eighteen[eighteen == -999].dropna().index\n",
"eighteen.drop(position,inplace=True)\n",
"\n",
"position = eighteennew[eighteennew== -999].dropna().index\n",
"eighteennew.drop(position,inplace=True)\n",
"\n",
"position = twentyfive[twentyfive == -999].dropna().index\n",
"twentyfive.drop(position,inplace=True)\n",
"\n",
"### Only the last position had a -999 in the datasets."
]
},
{
"cell_type": "code",
"execution_count": 4,
"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>UU</th>\n",
" <th>UM</th>\n",
" <th>MU</th>\n",
" <th>MM</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>1.0</td>\n",
" <td>1.694943e-63</td>\n",
" <td>1.694943e-63</td>\n",
" <td>2.872833e-126</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>1.0</td>\n",
" <td>3.862743e-12</td>\n",
" <td>3.862743e-12</td>\n",
" <td>1.492079e-23</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>1.0</td>\n",
" <td>5.676103e-24</td>\n",
" <td>5.676103e-24</td>\n",
" <td>3.221815e-47</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>1.0</td>\n",
" <td>7.203460e-16</td>\n",
" <td>7.203460e-16</td>\n",
" <td>5.188983e-31</td>\n",
" </tr>\n",
" <tr>\n",
" <td>5</td>\n",
" <td>1.0</td>\n",
" <td>5.886530e-29</td>\n",
" <td>5.886530e-29</td>\n",
" <td>3.465124e-57</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" UU UM MU MM\n",
"1 1.0 1.694943e-63 1.694943e-63 2.872833e-126\n",
"2 1.0 3.862743e-12 3.862743e-12 1.492079e-23\n",
"3 1.0 5.676103e-24 5.676103e-24 3.221815e-47\n",
"4 1.0 7.203460e-16 7.203460e-16 5.188983e-31\n",
"5 1.0 5.886530e-29 5.886530e-29 3.465124e-57"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"eighteennew.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Making violinplots:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Gathering all the data from the UM and MU columns that have the posteriors for being differentially modified:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"_fiveeight = list(itertools.chain.from_iterable(fiveeight[[\"UM\",\"MU\"]].values))\n",
"_five = list(itertools.chain.from_iterable(five[[\"UM\",\"MU\"]].values))\n",
"_twentyfive = list(itertools.chain.from_iterable(twentyfive[[\"UM\",\"MU\"]].values))\n",
"_eighteen = list(itertools.chain.from_iterable(eighteen[[\"UM\",\"MU\"]].values))\n",
"\n",
"_eighteennew = list(itertools.chain.from_iterable(eighteennew[[\"UM\",\"MU\"]].values))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"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>UU</th>\n",
" <th>UM</th>\n",
" <th>MU</th>\n",
" <th>MM</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>1.000000</td>\n",
" <td>2.699683e-11</td>\n",
" <td>2.699683e-11</td>\n",
" <td>7.288288e-22</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>1.000000</td>\n",
" <td>1.647115e-22</td>\n",
" <td>1.647115e-22</td>\n",
" <td>2.712988e-44</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>1.000000</td>\n",
" <td>1.879406e-32</td>\n",
" <td>1.879406e-32</td>\n",
" <td>3.532169e-64</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>1.000000</td>\n",
" <td>4.654247e-24</td>\n",
" <td>4.654247e-24</td>\n",
" <td>2.166201e-47</td>\n",
" </tr>\n",
" <tr>\n",
" <td>5</td>\n",
" <td>1.000000</td>\n",
" <td>2.401017e-14</td>\n",
" <td>2.401017e-14</td>\n",
" <td>5.764884e-28</td>\n",
" </tr>\n",
" <tr>\n",
" <td>6</td>\n",
" <td>0.999999</td>\n",
" <td>3.115782e-07</td>\n",
" <td>3.115782e-07</td>\n",
" <td>9.708104e-14</td>\n",
" </tr>\n",
" <tr>\n",
" <td>7</td>\n",
" <td>1.000000</td>\n",
" <td>3.736168e-12</td>\n",
" <td>3.736168e-12</td>\n",
" <td>1.395895e-23</td>\n",
" </tr>\n",
" <tr>\n",
" <td>8</td>\n",
" <td>1.000000</td>\n",
" <td>4.472711e-14</td>\n",
" <td>4.472711e-14</td>\n",
" <td>2.000514e-27</td>\n",
" </tr>\n",
" <tr>\n",
" <td>9</td>\n",
" <td>1.000000</td>\n",
" <td>1.333472e-22</td>\n",
" <td>1.333472e-22</td>\n",
" <td>1.778149e-44</td>\n",
" </tr>\n",
" <tr>\n",
" <td>10</td>\n",
" <td>1.000000</td>\n",
" <td>1.276043e-30</td>\n",
" <td>1.276043e-30</td>\n",
" <td>1.628285e-60</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" UU UM MU MM\n",
"1 1.000000 2.699683e-11 2.699683e-11 7.288288e-22\n",
"2 1.000000 1.647115e-22 1.647115e-22 2.712988e-44\n",
"3 1.000000 1.879406e-32 1.879406e-32 3.532169e-64\n",
"4 1.000000 4.654247e-24 4.654247e-24 2.166201e-47\n",
"5 1.000000 2.401017e-14 2.401017e-14 5.764884e-28\n",
"6 0.999999 3.115782e-07 3.115782e-07 9.708104e-14\n",
"7 1.000000 3.736168e-12 3.736168e-12 1.395895e-23\n",
"8 1.000000 4.472711e-14 4.472711e-14 2.000514e-27\n",
"9 1.000000 1.333472e-22 1.333472e-22 1.778149e-44\n",
"10 1.000000 1.276043e-30 1.276043e-30 1.628285e-60"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fiveeight[:10]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"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>UU</th>\n",
" <th>UM</th>\n",
" <th>MU</th>\n",
" <th>MM</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>1.0</td>\n",
" <td>1.694943e-63</td>\n",
" <td>1.694943e-63</td>\n",
" <td>2.872833e-126</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>1.0</td>\n",
" <td>3.862743e-12</td>\n",
" <td>3.862743e-12</td>\n",
" <td>1.492079e-23</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>1.0</td>\n",
" <td>5.676103e-24</td>\n",
" <td>5.676103e-24</td>\n",
" <td>3.221815e-47</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>1.0</td>\n",
" <td>7.203460e-16</td>\n",
" <td>7.203460e-16</td>\n",
" <td>5.188983e-31</td>\n",
" </tr>\n",
" <tr>\n",
" <td>5</td>\n",
" <td>1.0</td>\n",
" <td>5.886530e-29</td>\n",
" <td>5.886530e-29</td>\n",
" <td>3.465124e-57</td>\n",
" </tr>\n",
" <tr>\n",
" <td>6</td>\n",
" <td>1.0</td>\n",
" <td>2.685610e-11</td>\n",
" <td>2.685610e-11</td>\n",
" <td>7.212499e-22</td>\n",
" </tr>\n",
" <tr>\n",
" <td>7</td>\n",
" <td>1.0</td>\n",
" <td>2.340133e-22</td>\n",
" <td>2.340133e-22</td>\n",
" <td>5.476222e-44</td>\n",
" </tr>\n",
" <tr>\n",
" <td>8</td>\n",
" <td>1.0</td>\n",
" <td>2.489041e-18</td>\n",
" <td>2.489041e-18</td>\n",
" <td>6.195327e-36</td>\n",
" </tr>\n",
" <tr>\n",
" <td>9</td>\n",
" <td>1.0</td>\n",
" <td>2.491819e-08</td>\n",
" <td>2.491819e-08</td>\n",
" <td>6.209161e-16</td>\n",
" </tr>\n",
" <tr>\n",
" <td>10</td>\n",
" <td>1.0</td>\n",
" <td>2.037583e-30</td>\n",
" <td>2.037583e-30</td>\n",
" <td>4.151746e-60</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" UU UM MU MM\n",
"1 1.0 1.694943e-63 1.694943e-63 2.872833e-126\n",
"2 1.0 3.862743e-12 3.862743e-12 1.492079e-23\n",
"3 1.0 5.676103e-24 5.676103e-24 3.221815e-47\n",
"4 1.0 7.203460e-16 7.203460e-16 5.188983e-31\n",
"5 1.0 5.886530e-29 5.886530e-29 3.465124e-57\n",
"6 1.0 2.685610e-11 2.685610e-11 7.212499e-22\n",
"7 1.0 2.340133e-22 2.340133e-22 5.476222e-44\n",
"8 1.0 2.489041e-18 2.489041e-18 6.195327e-36\n",
"9 1.0 2.491819e-08 2.491819e-08 6.209161e-16\n",
"10 1.0 2.037583e-30 2.037583e-30 4.151746e-60"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"eighteennew[:10]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"_fiveeightMM = list(itertools.chain.from_iterable(fiveeight[[\"MM\"]].values))\n",
"_fiveMM = list(itertools.chain.from_iterable(five[[\"MM\"]].values))\n",
"_twentyfiveMM = list(itertools.chain.from_iterable(twentyfive[[\"MM\"]].values))\n",
"_eighteenMM = list(itertools.chain.from_iterable(eighteen[[\"MM\"]].values))\n",
"\n",
"_eighteennewMM = list(itertools.chain.from_iterable(eighteennew[[\"MM\"]].values))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Putting it in a dataframe, 25S first:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"index = np.arange(0,len(_twentyfive))\n",
"values = pd.DataFrame(np.nan,index=index,columns=[\"5SMM\",\"5.8SMM\",\"18SMM\",\"18S-NEW-MM\",\"25SMM\",\"5S\",\"5.8S\",\"18S\",\"18S-NEW\",\"25S\"])"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"length = len(_five)-1\n",
"values.loc[0:length,\"5S\"] = _five\n",
"\n",
"length = len(_fiveeight)-1\n",
"values.loc[0:length,\"5.8S\"] = _fiveeight\n",
"\n",
"length = len(_eighteen)-1\n",
"values.loc[0:length,\"18S\"] = _eighteen\n",
"\n",
"length = len(_eighteennew)-1\n",
"values.loc[0:length,\"18S-NEW\"] = _eighteennew\n",
"\n",
"length = len(_twentyfive)-1\n",
"values.loc[0:length,\"25S\"] = _twentyfive\n",
"\n",
"length = len(_fiveMM)-1\n",
"values.loc[0:length,\"5SMM\"] = _fiveMM\n",
"\n",
"length = len(_fiveeightMM)-1\n",
"values.loc[0:length,\"5.8SMM\"] = _fiveeightMM\n",
"\n",
"length = len(_eighteenMM)-1\n",
"values.loc[0:length,\"18SMM\"] = _eighteenMM\n",
"\n",
"length = len(_eighteennewMM)-1\n",
"values.loc[0:length,\"18S-NEW-MM\"] = _eighteennewMM\n",
"\n",
"length = len(_twentyfiveMM)-1\n",
"values.loc[0:length,\"25SMM\"] = _twentyfiveMM"
]
},
{
"cell_type": "code",
"execution_count": 9,
"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>5SMM</th>\n",
" <th>5.8SMM</th>\n",
" <th>18SMM</th>\n",
" <th>18S-NEW-MM</th>\n",
" <th>25SMM</th>\n",
" <th>5S</th>\n",
" <th>5.8S</th>\n",
" <th>18S</th>\n",
" <th>18S-NEW</th>\n",
" <th>25S</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>9.999888e-01</td>\n",
" <td>7.288288e-22</td>\n",
" <td>1.981656e-27</td>\n",
" <td>2.872833e-126</td>\n",
" <td>0.295833</td>\n",
" <td>5.588118e-06</td>\n",
" <td>2.699683e-11</td>\n",
" <td>4.451579e-14</td>\n",
" <td>1.694943e-63</td>\n",
" <td>2.480723e-01</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>7.138806e-03</td>\n",
" <td>2.712988e-44</td>\n",
" <td>5.230114e-43</td>\n",
" <td>1.492079e-23</td>\n",
" <td>0.988544</td>\n",
" <td>5.588118e-06</td>\n",
" <td>2.699683e-11</td>\n",
" <td>4.451579e-14</td>\n",
" <td>1.694943e-63</td>\n",
" <td>2.480723e-01</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>4.629931e-34</td>\n",
" <td>3.532169e-64</td>\n",
" <td>1.970948e-33</td>\n",
" <td>3.221815e-47</td>\n",
" <td>1.000000</td>\n",
" <td>7.735265e-02</td>\n",
" <td>1.647115e-22</td>\n",
" <td>7.231953e-22</td>\n",
" <td>3.862743e-12</td>\n",
" <td>5.711572e-03</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>3.819163e-59</td>\n",
" <td>2.166201e-47</td>\n",
" <td>3.534559e-76</td>\n",
" <td>5.188983e-31</td>\n",
" <td>1.000000</td>\n",
" <td>7.735265e-02</td>\n",
" <td>1.647115e-22</td>\n",
" <td>7.231953e-22</td>\n",
" <td>3.862743e-12</td>\n",
" <td>5.711572e-03</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>1.592421e-98</td>\n",
" <td>5.764884e-28</td>\n",
" <td>3.745262e-22</td>\n",
" <td>3.465124e-57</td>\n",
" <td>1.000000</td>\n",
" <td>2.151727e-17</td>\n",
" <td>1.879406e-32</td>\n",
" <td>4.439537e-17</td>\n",
" <td>5.676103e-24</td>\n",
" <td>1.258842e-11</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 5SMM 5.8SMM 18SMM 18S-NEW-MM 25SMM \\\n",
"0 9.999888e-01 7.288288e-22 1.981656e-27 2.872833e-126 0.295833 \n",
"1 7.138806e-03 2.712988e-44 5.230114e-43 1.492079e-23 0.988544 \n",
"2 4.629931e-34 3.532169e-64 1.970948e-33 3.221815e-47 1.000000 \n",
"3 3.819163e-59 2.166201e-47 3.534559e-76 5.188983e-31 1.000000 \n",
"4 1.592421e-98 5.764884e-28 3.745262e-22 3.465124e-57 1.000000 \n",
"\n",
" 5S 5.8S 18S 18S-NEW 25S \n",
"0 5.588118e-06 2.699683e-11 4.451579e-14 1.694943e-63 2.480723e-01 \n",
"1 5.588118e-06 2.699683e-11 4.451579e-14 1.694943e-63 2.480723e-01 \n",
"2 7.735265e-02 1.647115e-22 7.231953e-22 3.862743e-12 5.711572e-03 \n",
"3 7.735265e-02 1.647115e-22 7.231953e-22 3.862743e-12 5.711572e-03 \n",
"4 2.151727e-17 1.879406e-32 4.439537e-17 5.676103e-24 1.258842e-11 "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"values.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Making plots:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.arange(0.0,1.0,0.1)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig,ax = plt.subplots(figsize=(10,6))\n",
"ax = sns.boxplot(data=values)\n",
"ylabels = [0,0.2,0.4,0.6,0.8,1.0]\n",
"ax.set_yticks(ylabels)\n",
"ax.set_yticklabels(ylabels)\n",
"fig.savefig(\"diffBUM_HMM_output_control_samples.pdf\",dpi=400)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Check if anything at all was modified in 5.8S and whether it makes sense with the structure!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Making 5.8S MM output file:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"fiveeight[\"MM\"].to_csv(\"5.8S_MM_values.txt\",sep=\"\\t\",header=False)\n",
"eighteen[\"MM\"].to_csv(\"18S_MM_values.txt\",sep=\"\\t\",header=False)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"totalmodnucs = len(eighteen[eighteen.MM >= 0.95])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 253 out of the 1799 nucleotides with sufficient coverage were called modified in all three datasets:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"253"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"totalmodnucs"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"totalnucs = len(eighteen.index)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1799"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"totalnucs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plot nucleotides predicted to be modified by diffBUM_HMM on 18S structure (requires pyCRAC)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n",
"Bad key \"text.kerning_factor\" on line 4 in\n",
"/anaconda3/lib/python3.7/site-packages/matplotlib/mpl-data/stylelib/_classic_test_patch.mplstyle.\n",
"You probably need to get an updated matplotlibrc file from\n",
"http://github.com/matplotlib/matplotlib/blob/master/matplotlibrc.template\n",
"or from the matplotlib source distribution\n"
]
}
],
"source": [
"%%bash\n",
"\n",
"DIR=../../Scripts\n",
"\n",
"python $DIR/colorStructureFile.py \\\n",
"-f \"../../Analysis/rRNA_secondary_structure_diagrams/S_cerevisiae.svg\" \\\n",
"-d \"18S_MM_values.txt\" \\\n",
"-c BUM_HMM \\\n",
"-o \"18S_MM_values.svg\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### The control dataset is a DMS Structure-Seq dataset. So the expectation would be that mostly A's and C's would be called modified by difBUM_HMM. Check this!!!!:"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"refseq = \"\".join([i.strip() for i in open(\"../../Reference_sequences/18S_refseq.txt\",\"r\").readlines()])"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'TATCTGGTTG'"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"refseq[:10]"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1800"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(refseq)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Int64Index([ 7, 8, 9, 10, 13, 18, 21, 22, 23, 24,\n",
" ...\n",
" 1705, 1706, 1719, 1720, 1721, 1722, 1727, 1729, 1730, 1748],\n",
" dtype='int64', length=253)\n"
]
}
],
"source": [
"significant = eighteen[eighteen.MM >= 0.95].index\n",
"print(significant)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" UU UM MU MM\n",
"7 5.843202e-09 7.643500e-05 7.643500e-05 0.999847\n",
"8 3.881934e-19 6.230517e-10 6.230517e-10 1.000000\n",
"9 1.030573e-13 3.210253e-07 3.210253e-07 0.999999\n",
"10 4.675099e-06 2.157523e-03 2.157523e-03 0.995680\n",
"13 9.115940e-08 3.018350e-04 3.018350e-04 0.999396\n",
"... ... ... ... ...\n",
"1722 2.873284e-14 1.695076e-07 1.695076e-07 1.000000\n",
"1727 5.923253e-21 7.696267e-11 7.696267e-11 1.000000\n",
"1729 7.931163e-26 2.816232e-13 2.816232e-13 1.000000\n",
"1730 1.820529e-33 4.266766e-17 4.266766e-17 1.000000\n",
"1748 4.296382e-18 2.072772e-09 2.072772e-09 1.000000\n",
"\n",
"[253 rows x 4 columns]\n"
]
}
],
"source": [
"print(eighteen[eighteen.MM >= 0.95])"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"nucdict = defaultdict(int)\n",
"for pos in significant:\n",
" nuc = refseq[pos]\n",
" nucdict[nuc] += 1"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"defaultdict(int, {'T': 35, 'G': 17, 'A': 154, 'C': 47})"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nucdict"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {