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olgabot/014_rotate_i7_indices_for_updated_samplesheets.ipynb

Created October 16, 2017 19:05
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014_rotate_i7_indices_for_updated_samplesheets.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import glob\n",
"import os\n",
"from itertools import chain\n",
"import string\n",
"import matplotlib.pyplot as plt\n",
"\n",
"import seaborn as sns\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"DATA_FOLDER = 'data'\n",
"FIGURE_FOLDER = 'figures'\n",
"\n",
"notebook_name = '014_rotate_i7_indices_for_updated_samplesheets'\n",
"data_folder = os.path.join(DATA_FOLDER, notebook_name)\n",
"figure_folder = os.path.join(FIGURE_FOLDER, notebook_name)\n",
"\n",
"! mkdir -p $data_folder\n",
"! mkdir -p $figure_folder"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"pd.options.display.max_columns = 30"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
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" <th>letter_row</th>\n",
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" </tr>\n",
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" <td>NEXT-i5-IDT-193</td>\n",
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" <td>NEXT-i5-IDT-281</td>\n",
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" <tr>\n",
" <th>C</th>\n",
" <td>NEXT-i5-IDT-2</td>\n",
" <td>NEXT-i5-IDT-98</td>\n",
" <td>NEXT-i5-IDT-10</td>\n",
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" <td>NEXT-i5-IDT-146</td>\n",
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" <td>NEXT-i5-IDT-82</td>\n",
" <td>NEXT-i5-IDT-178</td>\n",
" <td>NEXT-i5-IDT-90</td>\n",
" <td>NEXT-i5-IDT-186</td>\n",
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" <tr>\n",
" <th>D</th>\n",
" <td>NEXT-i5-IDT-194</td>\n",
" <td>NEXT-i5-IDT-290</td>\n",
" <td>NEXT-i5-IDT-202</td>\n",
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" <td>NEXT-i5-IDT-266</td>\n",
" <td>NEXT-i5-IDT-362</td>\n",
" <td>NEXT-i5-IDT-274</td>\n",
" <td>NEXT-i5-IDT-370</td>\n",
" <td>NEXT-i5-IDT-282</td>\n",
" <td>NEXT-i5-IDT-378</td>\n",
" </tr>\n",
" <tr>\n",
" <th>E</th>\n",
" <td>NEXT-i5-IDT-3</td>\n",
" <td>NEXT-i5-IDT-99</td>\n",
" <td>NEXT-i5-IDT-11</td>\n",
" <td>NEXT-i5-IDT-107</td>\n",
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" <td>NEXT-i5-IDT-27</td>\n",
" <td>NEXT-i5-IDT-123</td>\n",
" <td>NEXT-i5-IDT-35</td>\n",
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" <td>NEXT-i5-IDT-59</td>\n",
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" <td>NEXT-i5-IDT-163</td>\n",
" <td>NEXT-i5-IDT-75</td>\n",
" <td>NEXT-i5-IDT-171</td>\n",
" <td>NEXT-i5-IDT-83</td>\n",
" <td>NEXT-i5-IDT-179</td>\n",
" <td>NEXT-i5-IDT-91</td>\n",
" <td>NEXT-i5-IDT-187</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"number_col 1 2 3 \\\n",
"letter_row \n",
"A NEXT-i5-IDT-1 NEXT-i5-IDT-97 NEXT-i5-IDT-9 \n",
"B NEXT-i5-IDT-193 NEXT-i5-IDT-289 NEXT-i5-IDT-201 \n",
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"D NEXT-i5-IDT-194 NEXT-i5-IDT-290 NEXT-i5-IDT-202 \n",
"E NEXT-i5-IDT-3 NEXT-i5-IDT-99 NEXT-i5-IDT-11 \n",
"\n",
"number_col 4 5 6 \\\n",
"letter_row \n",
"A NEXT-i5-IDT-105 NEXT-i5-IDT-17 NEXT-i5-IDT-113 \n",
"B NEXT-i5-IDT-297 NEXT-i5-IDT-209 NEXT-i5-IDT-305 \n",
"C NEXT-i5-IDT-106 NEXT-i5-IDT-18 NEXT-i5-IDT-114 \n",
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"E NEXT-i5-IDT-107 NEXT-i5-IDT-19 NEXT-i5-IDT-115 \n",
"\n",
"number_col 7 8 9 \\\n",
"letter_row \n",
"A NEXT-i5-IDT-25 NEXT-i5-IDT-121 NEXT-i5-IDT-33 \n",
"B NEXT-i5-IDT-217 NEXT-i5-IDT-313 NEXT-i5-IDT-225 \n",
"C NEXT-i5-IDT-26 NEXT-i5-IDT-122 NEXT-i5-IDT-34 \n",
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"E NEXT-i5-IDT-27 NEXT-i5-IDT-123 NEXT-i5-IDT-35 \n",
"\n",
"number_col 10 11 12 \\\n",
"letter_row \n",
"A NEXT-i5-IDT-129 NEXT-i5-IDT-41 NEXT-i5-IDT-137 \n",
"B NEXT-i5-IDT-321 NEXT-i5-IDT-233 NEXT-i5-IDT-329 \n",
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"\n",
"number_col 13 14 15 \\\n",
"letter_row \n",
"A NEXT-i5-IDT-49 NEXT-i5-IDT-145 NEXT-i5-IDT-57 \n",
"B NEXT-i5-IDT-241 NEXT-i5-IDT-337 NEXT-i5-IDT-249 \n",
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"E NEXT-i5-IDT-51 NEXT-i5-IDT-147 NEXT-i5-IDT-59 \n",
"\n",
"number_col 16 17 18 \\\n",
"letter_row \n",
"A NEXT-i5-IDT-153 NEXT-i5-IDT-65 NEXT-i5-IDT-161 \n",
"B NEXT-i5-IDT-345 NEXT-i5-IDT-257 NEXT-i5-IDT-353 \n",
"C NEXT-i5-IDT-154 NEXT-i5-IDT-66 NEXT-i5-IDT-162 \n",
"D NEXT-i5-IDT-346 NEXT-i5-IDT-258 NEXT-i5-IDT-354 \n",
"E NEXT-i5-IDT-155 NEXT-i5-IDT-67 NEXT-i5-IDT-163 \n",
"\n",
"number_col 19 20 21 \\\n",
"letter_row \n",
"A NEXT-i5-IDT-73 NEXT-i5-IDT-169 NEXT-i5-IDT-81 \n",
"B NEXT-i5-IDT-265 NEXT-i5-IDT-361 NEXT-i5-IDT-273 \n",
"C NEXT-i5-IDT-74 NEXT-i5-IDT-170 NEXT-i5-IDT-82 \n",
"D NEXT-i5-IDT-266 NEXT-i5-IDT-362 NEXT-i5-IDT-274 \n",
"E NEXT-i5-IDT-75 NEXT-i5-IDT-171 NEXT-i5-IDT-83 \n",
"\n",
"number_col 22 23 24 \n",
"letter_row \n",
"A NEXT-i5-IDT-177 NEXT-i5-IDT-89 NEXT-i5-IDT-185 \n",
"B NEXT-i5-IDT-369 NEXT-i5-IDT-281 NEXT-i5-IDT-377 \n",
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]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"i5 = pd.read_excel('/Users/olgabot/Downloads/NovaSeq and NextSeq 384-Well Index Format.xlsx',\n",
" sheetname='i5 384 Well Format')\n",
"i5 = i5.applymap(lambda x: x.replace('i7', 'i5'))\n",
"i5.index.name = 'letter_row'\n",
"i5.columns.name = 'number_col'\n",
"i5.head()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(16, 24)\n"
]
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" <td>NEXT-i7-IDT-81</td>\n",
" <td>NEXT-i7-IDT-177</td>\n",
" <td>NEXT-i7-IDT-89</td>\n",
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" <th>B</th>\n",
" <td>NEXT-i7-IDT-193</td>\n",
" <td>NEXT-i7-IDT-289</td>\n",
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" <td>NEXT-i7-IDT-313</td>\n",
" <td>NEXT-i7-IDT-225</td>\n",
" <td>NEXT-i7-IDT-321</td>\n",
" <td>NEXT-i7-IDT-233</td>\n",
" <td>NEXT-i7-IDT-329</td>\n",
" <td>NEXT-i7-IDT-241</td>\n",
" <td>NEXT-i7-IDT-337</td>\n",
" <td>NEXT-i7-IDT-249</td>\n",
" <td>NEXT-i7-IDT-345</td>\n",
" <td>NEXT-i7-IDT-257</td>\n",
" <td>NEXT-i7-IDT-353</td>\n",
" <td>NEXT-i7-IDT-265</td>\n",
" <td>NEXT-i7-IDT-361</td>\n",
" <td>NEXT-i7-IDT-273</td>\n",
" <td>NEXT-i7-IDT-369</td>\n",
" <td>NEXT-i7-IDT-281</td>\n",
" <td>NEXT-i7-IDT-377</td>\n",
" </tr>\n",
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" <th>C</th>\n",
" <td>NEXT-i7-IDT-2</td>\n",
" <td>NEXT-i7-IDT-98</td>\n",
" <td>NEXT-i7-IDT-10</td>\n",
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" <td>NEXT-i7-IDT-154</td>\n",
" <td>NEXT-i7-IDT-66</td>\n",
" <td>NEXT-i7-IDT-162</td>\n",
" <td>NEXT-i7-IDT-74</td>\n",
" <td>NEXT-i7-IDT-170</td>\n",
" <td>NEXT-i7-IDT-82</td>\n",
" <td>NEXT-i7-IDT-178</td>\n",
" <td>NEXT-i7-IDT-90</td>\n",
" <td>NEXT-i7-IDT-186</td>\n",
" </tr>\n",
" <tr>\n",
" <th>D</th>\n",
" <td>NEXT-i7-IDT-194</td>\n",
" <td>NEXT-i7-IDT-290</td>\n",
" <td>NEXT-i7-IDT-202</td>\n",
" <td>NEXT-i7-IDT-298</td>\n",
" <td>NEXT-i7-IDT-210</td>\n",
" <td>NEXT-i7-IDT-306</td>\n",
" <td>NEXT-i7-IDT-218</td>\n",
" <td>NEXT-i7-IDT-314</td>\n",
" <td>NEXT-i7-IDT-226</td>\n",
" <td>NEXT-i7-IDT-322</td>\n",
" <td>NEXT-i7-IDT-234</td>\n",
" <td>NEXT-i7-IDT-330</td>\n",
" <td>NEXT-i7-IDT-242</td>\n",
" <td>NEXT-i7-IDT-338</td>\n",
" <td>NEXT-i7-IDT-250</td>\n",
" <td>NEXT-i7-IDT-346</td>\n",
" <td>NEXT-i7-IDT-258</td>\n",
" <td>NEXT-i7-IDT-354</td>\n",
" <td>NEXT-i7-IDT-266</td>\n",
" <td>NEXT-i7-IDT-362</td>\n",
" <td>NEXT-i7-IDT-274</td>\n",
" <td>NEXT-i7-IDT-370</td>\n",
" <td>NEXT-i7-IDT-282</td>\n",
" <td>NEXT-i7-IDT-378</td>\n",
" </tr>\n",
" <tr>\n",
" <th>E</th>\n",
" <td>NEXT-i7-IDT-3</td>\n",
" <td>NEXT-i7-IDT-99</td>\n",
" <td>NEXT-i7-IDT-11</td>\n",
" <td>NEXT-i7-IDT-107</td>\n",
" <td>NEXT-i7-IDT-19</td>\n",
" <td>NEXT-i7-IDT-115</td>\n",
" <td>NEXT-i7-IDT-27</td>\n",
" <td>NEXT-i7-IDT-123</td>\n",
" <td>NEXT-i7-IDT-35</td>\n",
" <td>NEXT-i7-IDT-131</td>\n",
" <td>NEXT-i7-IDT-43</td>\n",
" <td>NEXT-i7-IDT-139</td>\n",
" <td>NEXT-i7-IDT-51</td>\n",
" <td>NEXT-i7-IDT-147</td>\n",
" <td>NEXT-i7-IDT-59</td>\n",
" <td>NEXT-i7-IDT-155</td>\n",
" <td>NEXT-i7-IDT-67</td>\n",
" <td>NEXT-i7-IDT-163</td>\n",
" <td>NEXT-i7-IDT-75</td>\n",
" <td>NEXT-i7-IDT-171</td>\n",
" <td>NEXT-i7-IDT-83</td>\n",
" <td>NEXT-i7-IDT-179</td>\n",
" <td>NEXT-i7-IDT-91</td>\n",
" <td>NEXT-i7-IDT-187</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"number_col 1 2 3 \\\n",
"letter_row \n",
"A NEXT-i7-IDT-1 NEXT-i7-IDT-97 NEXT-i7-IDT-9 \n",
"B NEXT-i7-IDT-193 NEXT-i7-IDT-289 NEXT-i7-IDT-201 \n",
"C NEXT-i7-IDT-2 NEXT-i7-IDT-98 NEXT-i7-IDT-10 \n",
"D NEXT-i7-IDT-194 NEXT-i7-IDT-290 NEXT-i7-IDT-202 \n",
"E NEXT-i7-IDT-3 NEXT-i7-IDT-99 NEXT-i7-IDT-11 \n",
"\n",
"number_col 4 5 6 \\\n",
"letter_row \n",
"A NEXT-i7-IDT-105 NEXT-i7-IDT-17 NEXT-i7-IDT-113 \n",
"B NEXT-i7-IDT-297 NEXT-i7-IDT-209 NEXT-i7-IDT-305 \n",
"C NEXT-i7-IDT-106 NEXT-i7-IDT-18 NEXT-i7-IDT-114 \n",
"D NEXT-i7-IDT-298 NEXT-i7-IDT-210 NEXT-i7-IDT-306 \n",
"E NEXT-i7-IDT-107 NEXT-i7-IDT-19 NEXT-i7-IDT-115 \n",
"\n",
"number_col 7 8 9 \\\n",
"letter_row \n",
"A NEXT-i7-IDT-25 NEXT-i7-IDT-121 NEXT-i7-IDT-33 \n",
"B NEXT-i7-IDT-217 NEXT-i7-IDT-313 NEXT-i7-IDT-225 \n",
"C NEXT-i7-IDT-26 NEXT-i7-IDT-122 NEXT-i7-IDT-34 \n",
"D NEXT-i7-IDT-218 NEXT-i7-IDT-314 NEXT-i7-IDT-226 \n",
"E NEXT-i7-IDT-27 NEXT-i7-IDT-123 NEXT-i7-IDT-35 \n",
"\n",
"number_col 10 11 12 \\\n",
"letter_row \n",
"A NEXT-i7-IDT-129 NEXT-i7-IDT-41 NEXT-i7-IDT-137 \n",
"B NEXT-i7-IDT-321 NEXT-i7-IDT-233 NEXT-i7-IDT-329 \n",
"C NEXT-i7-IDT-130 NEXT-i7-IDT-42 NEXT-i7-IDT-138 \n",
"D NEXT-i7-IDT-322 NEXT-i7-IDT-234 NEXT-i7-IDT-330 \n",
"E NEXT-i7-IDT-131 NEXT-i7-IDT-43 NEXT-i7-IDT-139 \n",
"\n",
"number_col 13 14 15 \\\n",
"letter_row \n",
"A NEXT-i7-IDT-49 NEXT-i7-IDT-145 NEXT-i7-IDT-57 \n",
"B NEXT-i7-IDT-241 NEXT-i7-IDT-337 NEXT-i7-IDT-249 \n",
"C NEXT-i7-IDT-50 NEXT-i7-IDT-146 NEXT-i7-IDT-58 \n",
"D NEXT-i7-IDT-242 NEXT-i7-IDT-338 NEXT-i7-IDT-250 \n",
"E NEXT-i7-IDT-51 NEXT-i7-IDT-147 NEXT-i7-IDT-59 \n",
"\n",
"number_col 16 17 18 \\\n",
"letter_row \n",
"A NEXT-i7-IDT-153 NEXT-i7-IDT-65 NEXT-i7-IDT-161 \n",
"B NEXT-i7-IDT-345 NEXT-i7-IDT-257 NEXT-i7-IDT-353 \n",
"C NEXT-i7-IDT-154 NEXT-i7-IDT-66 NEXT-i7-IDT-162 \n",
"D NEXT-i7-IDT-346 NEXT-i7-IDT-258 NEXT-i7-IDT-354 \n",
"E NEXT-i7-IDT-155 NEXT-i7-IDT-67 NEXT-i7-IDT-163 \n",
"\n",
"number_col 19 20 21 \\\n",
"letter_row \n",
"A NEXT-i7-IDT-73 NEXT-i7-IDT-169 NEXT-i7-IDT-81 \n",
"B NEXT-i7-IDT-265 NEXT-i7-IDT-361 NEXT-i7-IDT-273 \n",
"C NEXT-i7-IDT-74 NEXT-i7-IDT-170 NEXT-i7-IDT-82 \n",
"D NEXT-i7-IDT-266 NEXT-i7-IDT-362 NEXT-i7-IDT-274 \n",
"E NEXT-i7-IDT-75 NEXT-i7-IDT-171 NEXT-i7-IDT-83 \n",
"\n",
"number_col 22 23 24 \n",
"letter_row \n",
"A NEXT-i7-IDT-177 NEXT-i7-IDT-89 NEXT-i7-IDT-185 \n",
"B NEXT-i7-IDT-369 NEXT-i7-IDT-281 NEXT-i7-IDT-377 \n",
"C NEXT-i7-IDT-178 NEXT-i7-IDT-90 NEXT-i7-IDT-186 \n",
"D NEXT-i7-IDT-370 NEXT-i7-IDT-282 NEXT-i7-IDT-378 \n",
"E NEXT-i7-IDT-179 NEXT-i7-IDT-91 NEXT-i7-IDT-187 "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"i7 = i5.applymap(lambda x: x.replace('i5', 'i7'))\n",
"print(i7.shape)\n",
"i7.head()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
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CI71FZlWdLWknSZulQVdExAUtpJsNuLIwM2vD4LwiT010R5Guqm8CGwCnp0EHSvpARBxR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\nMDOzQv4/VzI1/MY4PxEAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x119587198>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ncol = 24\n",
"nrow = 16\n",
"data = [range(ncol) for i in range(nrow)]\n",
"original_heatmap = pd.DataFrame(data, index=i5.index, columns=i5.columns)\n",
"\n",
"fig, ax = plt.subplots()\n",
"sns.heatmap(original_heatmap)\n",
"ax.set(title='Original i5/i7 layout')\n",
"fig.tight_layout()\n",
"fig.savefig(os.path.join(figure_folder, 'original_i5-i7_layout.png'))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>number_col</th>\n",
" <th>letter_row</th>\n",
" <th>value</th>\n",
" </tr>\n",
" <tr>\n",
" <th>well_id</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>A1</th>\n",
" <td>1</td>\n",
" <td>A</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>B1</th>\n",
" <td>1</td>\n",
" <td>B</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>C1</th>\n",
" <td>1</td>\n",
" <td>C</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>D1</th>\n",
" <td>1</td>\n",
" <td>D</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>E1</th>\n",
" <td>1</td>\n",
" <td>E</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" number_col letter_row value\n",
"well_id \n",
"A1 1 A 0\n",
"B1 1 B 0\n",
"C1 1 C 0\n",
"D1 1 D 0\n",
"E1 1 E 0"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"original_heatmap_tidy = original_heatmap.unstack().reset_index()\n",
"original_heatmap_tidy = original_heatmap_tidy.rename(columns={'level_0': 'number_col', \n",
" 'level_1': 'letter_row', \n",
" 0: 'value'})\n",
"original_heatmap_tidy['well_id'] = original_heatmap_tidy['letter_row'] + original_heatmap_tidy['number_col'].astype(str)\n",
"original_heatmap_tidy = original_heatmap_tidy.set_index('well_id')\n",
"original_heatmap_tidy.head()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>index_name</th>\n",
" </tr>\n",
" <tr>\n",
" <th>well_id</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>A1</th>\n",
" <td>NEXT-i7-IDT-1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>B1</th>\n",
" <td>NEXT-i7-IDT-193</td>\n",
" </tr>\n",
" <tr>\n",
" <th>C1</th>\n",
" <td>NEXT-i7-IDT-2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>D1</th>\n",
" <td>NEXT-i7-IDT-194</td>\n",
" </tr>\n",
" <tr>\n",
" <th>E1</th>\n",
" <td>NEXT-i7-IDT-3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" index_name\n",
"well_id \n",
"A1 NEXT-i7-IDT-1\n",
"B1 NEXT-i7-IDT-193\n",
"C1 NEXT-i7-IDT-2\n",
"D1 NEXT-i7-IDT-194\n",
"E1 NEXT-i7-IDT-3"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"i7_tidy = i7.unstack().reset_index()\n",
"i7_tidy['well_id'] = i7_tidy['letter_row'] + i7_tidy['number_col'].astype(str)\n",
"i7_tidy = i7_tidy.set_index('well_id')\n",
"i7_tidy = i7_tidy.rename(columns={0: 'index_name'})\n",
"i7_tidy = i7_tidy.drop(['number_col', 'letter_row'], axis=1)\n",
"i7_tidy.head()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>number_col</th>\n",
" <th>letter_row</th>\n",
" <th>value</th>\n",
" <th>index_name</th>\n",
" </tr>\n",
" <tr>\n",
" <th>well_id</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>A1</th>\n",
" <td>1</td>\n",
" <td>A</td>\n",
" <td>0</td>\n",
" <td>NEXT-i7-IDT-1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>B1</th>\n",
" <td>1</td>\n",
" <td>B</td>\n",
" <td>0</td>\n",
" <td>NEXT-i7-IDT-193</td>\n",
" </tr>\n",
" <tr>\n",
" <th>C1</th>\n",
" <td>1</td>\n",
" <td>C</td>\n",
" <td>0</td>\n",
" <td>NEXT-i7-IDT-2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>D1</th>\n",
" <td>1</td>\n",
" <td>D</td>\n",
" <td>0</td>\n",
" <td>NEXT-i7-IDT-194</td>\n",
" </tr>\n",
" <tr>\n",
" <th>E1</th>\n",
" <td>1</td>\n",
" <td>E</td>\n",
" <td>0</td>\n",
" <td>NEXT-i7-IDT-3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" number_col letter_row value index_name\n",
"well_id \n",
"A1 1 A 0 NEXT-i7-IDT-1\n",
"B1 1 B 0 NEXT-i7-IDT-193\n",
"C1 1 C 0 NEXT-i7-IDT-2\n",
"D1 1 D 0 NEXT-i7-IDT-194\n",
"E1 1 E 0 NEXT-i7-IDT-3"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"i7_heatmap_tidy = original_heatmap_tidy.join(i7_tidy)\n",
"i7_heatmap_tidy.head()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"NEXT-i7-IDT-1 0\n",
"NEXT-i7-IDT-193 0\n",
"NEXT-i7-IDT-2 0\n",
"NEXT-i7-IDT-194 0\n",
"NEXT-i7-IDT-3 0\n",
"dtype: int64"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"i7_index_to_value = pd.Series(i7_heatmap_tidy['value'].values, index=i7_heatmap_tidy['index_name'].values)\n",
"i7_index_to_value.head() "
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" <tr style=\"text-align: right;\">\n",
" <th>number_col</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>4</th>\n",
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" <th>12</th>\n",
" <th>13</th>\n",
" <th>14</th>\n",
" <th>15</th>\n",
" <th>16</th>\n",
" <th>17</th>\n",
" <th>18</th>\n",
" <th>19</th>\n",
" <th>20</th>\n",
" <th>21</th>\n",
" <th>22</th>\n",
" <th>23</th>\n",
" <th>24</th>\n",
" </tr>\n",
" <tr>\n",
" <th>letter_row</th>\n",
" <th></th>\n",
" <th></th>\n",
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" <tr>\n",
" <th>A</th>\n",
" <td>NEXT-i7-IDT-1</td>\n",
" <td>NEXT-i7-IDT-97</td>\n",
" <td>NEXT-i7-IDT-9</td>\n",
" <td>NEXT-i7-IDT-105</td>\n",
" <td>NEXT-i7-IDT-17</td>\n",
" <td>NEXT-i7-IDT-113</td>\n",
" <td>NEXT-i7-IDT-25</td>\n",
" <td>NEXT-i7-IDT-121</td>\n",
" <td>NEXT-i7-IDT-33</td>\n",
" <td>NEXT-i7-IDT-129</td>\n",
" <td>NEXT-i7-IDT-41</td>\n",
" <td>NEXT-i7-IDT-137</td>\n",
" <td>NEXT-i7-IDT-49</td>\n",
" <td>NEXT-i7-IDT-145</td>\n",
" <td>NEXT-i7-IDT-57</td>\n",
" <td>NEXT-i7-IDT-153</td>\n",
" <td>NEXT-i7-IDT-65</td>\n",
" <td>NEXT-i7-IDT-161</td>\n",
" <td>NEXT-i7-IDT-73</td>\n",
" <td>NEXT-i7-IDT-169</td>\n",
" <td>NEXT-i7-IDT-81</td>\n",
" <td>NEXT-i7-IDT-177</td>\n",
" <td>NEXT-i7-IDT-89</td>\n",
" <td>NEXT-i7-IDT-185</td>\n",
" </tr>\n",
" <tr>\n",
" <th>B</th>\n",
" <td>NEXT-i7-IDT-193</td>\n",
" <td>NEXT-i7-IDT-289</td>\n",
" <td>NEXT-i7-IDT-201</td>\n",
" <td>NEXT-i7-IDT-297</td>\n",
" <td>NEXT-i7-IDT-209</td>\n",
" <td>NEXT-i7-IDT-305</td>\n",
" <td>NEXT-i7-IDT-217</td>\n",
" <td>NEXT-i7-IDT-313</td>\n",
" <td>NEXT-i7-IDT-225</td>\n",
" <td>NEXT-i7-IDT-321</td>\n",
" <td>NEXT-i7-IDT-233</td>\n",
" <td>NEXT-i7-IDT-329</td>\n",
" <td>NEXT-i7-IDT-241</td>\n",
" <td>NEXT-i7-IDT-337</td>\n",
" <td>NEXT-i7-IDT-249</td>\n",
" <td>NEXT-i7-IDT-345</td>\n",
" <td>NEXT-i7-IDT-257</td>\n",
" <td>NEXT-i7-IDT-353</td>\n",
" <td>NEXT-i7-IDT-265</td>\n",
" <td>NEXT-i7-IDT-361</td>\n",
" <td>NEXT-i7-IDT-273</td>\n",
" <td>NEXT-i7-IDT-369</td>\n",
" <td>NEXT-i7-IDT-281</td>\n",
" <td>NEXT-i7-IDT-377</td>\n",
" </tr>\n",
" <tr>\n",
" <th>C</th>\n",
" <td>NEXT-i7-IDT-2</td>\n",
" <td>NEXT-i7-IDT-98</td>\n",
" <td>NEXT-i7-IDT-10</td>\n",
" <td>NEXT-i7-IDT-106</td>\n",
" <td>NEXT-i7-IDT-18</td>\n",
" <td>NEXT-i7-IDT-114</td>\n",
" <td>NEXT-i7-IDT-26</td>\n",
" <td>NEXT-i7-IDT-122</td>\n",
" <td>NEXT-i7-IDT-34</td>\n",
" <td>NEXT-i7-IDT-130</td>\n",
" <td>NEXT-i7-IDT-42</td>\n",
" <td>NEXT-i7-IDT-138</td>\n",
" <td>NEXT-i7-IDT-50</td>\n",
" <td>NEXT-i7-IDT-146</td>\n",
" <td>NEXT-i7-IDT-58</td>\n",
" <td>NEXT-i7-IDT-154</td>\n",
" <td>NEXT-i7-IDT-66</td>\n",
" <td>NEXT-i7-IDT-162</td>\n",
" <td>NEXT-i7-IDT-74</td>\n",
" <td>NEXT-i7-IDT-170</td>\n",
" <td>NEXT-i7-IDT-82</td>\n",
" <td>NEXT-i7-IDT-178</td>\n",
" <td>NEXT-i7-IDT-90</td>\n",
" <td>NEXT-i7-IDT-186</td>\n",
" </tr>\n",
" <tr>\n",
" <th>D</th>\n",
" <td>NEXT-i7-IDT-194</td>\n",
" <td>NEXT-i7-IDT-290</td>\n",
" <td>NEXT-i7-IDT-202</td>\n",
" <td>NEXT-i7-IDT-298</td>\n",
" <td>NEXT-i7-IDT-210</td>\n",
" <td>NEXT-i7-IDT-306</td>\n",
" <td>NEXT-i7-IDT-218</td>\n",
" <td>NEXT-i7-IDT-314</td>\n",
" <td>NEXT-i7-IDT-226</td>\n",
" <td>NEXT-i7-IDT-322</td>\n",
" <td>NEXT-i7-IDT-234</td>\n",
" <td>NEXT-i7-IDT-330</td>\n",
" <td>NEXT-i7-IDT-242</td>\n",
" <td>NEXT-i7-IDT-338</td>\n",
" <td>NEXT-i7-IDT-250</td>\n",
" <td>NEXT-i7-IDT-346</td>\n",
" <td>NEXT-i7-IDT-258</td>\n",
" <td>NEXT-i7-IDT-354</td>\n",
" <td>NEXT-i7-IDT-266</td>\n",
" <td>NEXT-i7-IDT-362</td>\n",
" <td>NEXT-i7-IDT-274</td>\n",
" <td>NEXT-i7-IDT-370</td>\n",
" <td>NEXT-i7-IDT-282</td>\n",
" <td>NEXT-i7-IDT-378</td>\n",
" </tr>\n",
" <tr>\n",
" <th>E</th>\n",
" <td>NEXT-i7-IDT-3</td>\n",
" <td>NEXT-i7-IDT-99</td>\n",
" <td>NEXT-i7-IDT-11</td>\n",
" <td>NEXT-i7-IDT-107</td>\n",
" <td>NEXT-i7-IDT-19</td>\n",
" <td>NEXT-i7-IDT-115</td>\n",
" <td>NEXT-i7-IDT-27</td>\n",
" <td>NEXT-i7-IDT-123</td>\n",
" <td>NEXT-i7-IDT-35</td>\n",
" <td>NEXT-i7-IDT-131</td>\n",
" <td>NEXT-i7-IDT-43</td>\n",
" <td>NEXT-i7-IDT-139</td>\n",
" <td>NEXT-i7-IDT-51</td>\n",
" <td>NEXT-i7-IDT-147</td>\n",
" <td>NEXT-i7-IDT-59</td>\n",
" <td>NEXT-i7-IDT-155</td>\n",
" <td>NEXT-i7-IDT-67</td>\n",
" <td>NEXT-i7-IDT-163</td>\n",
" <td>NEXT-i7-IDT-75</td>\n",
" <td>NEXT-i7-IDT-171</td>\n",
" <td>NEXT-i7-IDT-83</td>\n",
" <td>NEXT-i7-IDT-179</td>\n",
" <td>NEXT-i7-IDT-91</td>\n",
" <td>NEXT-i7-IDT-187</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"number_col 1 2 3 \\\n",
"letter_row \n",
"A NEXT-i7-IDT-1 NEXT-i7-IDT-97 NEXT-i7-IDT-9 \n",
"B NEXT-i7-IDT-193 NEXT-i7-IDT-289 NEXT-i7-IDT-201 \n",
"C NEXT-i7-IDT-2 NEXT-i7-IDT-98 NEXT-i7-IDT-10 \n",
"D NEXT-i7-IDT-194 NEXT-i7-IDT-290 NEXT-i7-IDT-202 \n",
"E NEXT-i7-IDT-3 NEXT-i7-IDT-99 NEXT-i7-IDT-11 \n",
"\n",
"number_col 4 5 6 \\\n",
"letter_row \n",
"A NEXT-i7-IDT-105 NEXT-i7-IDT-17 NEXT-i7-IDT-113 \n",
"B NEXT-i7-IDT-297 NEXT-i7-IDT-209 NEXT-i7-IDT-305 \n",
"C NEXT-i7-IDT-106 NEXT-i7-IDT-18 NEXT-i7-IDT-114 \n",
"D NEXT-i7-IDT-298 NEXT-i7-IDT-210 NEXT-i7-IDT-306 \n",
"E NEXT-i7-IDT-107 NEXT-i7-IDT-19 NEXT-i7-IDT-115 \n",
"\n",
"number_col 7 8 9 \\\n",
"letter_row \n",
"A NEXT-i7-IDT-25 NEXT-i7-IDT-121 NEXT-i7-IDT-33 \n",
"B NEXT-i7-IDT-217 NEXT-i7-IDT-313 NEXT-i7-IDT-225 \n",
"C NEXT-i7-IDT-26 NEXT-i7-IDT-122 NEXT-i7-IDT-34 \n",
"D NEXT-i7-IDT-218 NEXT-i7-IDT-314 NEXT-i7-IDT-226 \n",
"E NEXT-i7-IDT-27 NEXT-i7-IDT-123 NEXT-i7-IDT-35 \n",
"\n",
"number_col 10 11 12 \\\n",
"letter_row \n",
"A NEXT-i7-IDT-129 NEXT-i7-IDT-41 NEXT-i7-IDT-137 \n",
"B NEXT-i7-IDT-321 NEXT-i7-IDT-233 NEXT-i7-IDT-329 \n",
"C NEXT-i7-IDT-130 NEXT-i7-IDT-42 NEXT-i7-IDT-138 \n",
"D NEXT-i7-IDT-322 NEXT-i7-IDT-234 NEXT-i7-IDT-330 \n",
"E NEXT-i7-IDT-131 NEXT-i7-IDT-43 NEXT-i7-IDT-139 \n",
"\n",
"number_col 13 14 15 \\\n",
"letter_row \n",
"A NEXT-i7-IDT-49 NEXT-i7-IDT-145 NEXT-i7-IDT-57 \n",
"B NEXT-i7-IDT-241 NEXT-i7-IDT-337 NEXT-i7-IDT-249 \n",
"C NEXT-i7-IDT-50 NEXT-i7-IDT-146 NEXT-i7-IDT-58 \n",
"D NEXT-i7-IDT-242 NEXT-i7-IDT-338 NEXT-i7-IDT-250 \n",
"E NEXT-i7-IDT-51 NEXT-i7-IDT-147 NEXT-i7-IDT-59 \n",
"\n",
"number_col 16 17 18 \\\n",
"letter_row \n",
"A NEXT-i7-IDT-153 NEXT-i7-IDT-65 NEXT-i7-IDT-161 \n",
"B NEXT-i7-IDT-345 NEXT-i7-IDT-257 NEXT-i7-IDT-353 \n",
"C NEXT-i7-IDT-154 NEXT-i7-IDT-66 NEXT-i7-IDT-162 \n",
"D NEXT-i7-IDT-346 NEXT-i7-IDT-258 NEXT-i7-IDT-354 \n",
"E NEXT-i7-IDT-155 NEXT-i7-IDT-67 NEXT-i7-IDT-163 \n",
"\n",
"number_col 19 20 21 \\\n",
"letter_row \n",
"A NEXT-i7-IDT-73 NEXT-i7-IDT-169 NEXT-i7-IDT-81 \n",
"B NEXT-i7-IDT-265 NEXT-i7-IDT-361 NEXT-i7-IDT-273 \n",
"C NEXT-i7-IDT-74 NEXT-i7-IDT-170 NEXT-i7-IDT-82 \n",
"D NEXT-i7-IDT-266 NEXT-i7-IDT-362 NEXT-i7-IDT-274 \n",
"E NEXT-i7-IDT-75 NEXT-i7-IDT-171 NEXT-i7-IDT-83 \n",
"\n",
"number_col 22 23 24 \n",
"letter_row \n",
"A NEXT-i7-IDT-177 NEXT-i7-IDT-89 NEXT-i7-IDT-185 \n",
"B NEXT-i7-IDT-369 NEXT-i7-IDT-281 NEXT-i7-IDT-377 \n",
"C NEXT-i7-IDT-178 NEXT-i7-IDT-90 NEXT-i7-IDT-186 \n",
"D NEXT-i7-IDT-370 NEXT-i7-IDT-282 NEXT-i7-IDT-378 \n",
"E NEXT-i7-IDT-179 NEXT-i7-IDT-91 NEXT-i7-IDT-187 "
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"i7 = i5.applymap(lambda x: x.replace('i5', 'i7'))\n",
"i7.head()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"dict_keys(['170907_A00111_0051_BH2HWLDMXX.csv', '170910_A00111_0053_BH2HGKDMXX.csv', '170914_A00111_57_58.csv', '170919_A00111_59_60.csv', '170921_A00111_62_63_Nova5.csv', '170925_A00111_66_67_Nova6.csv', '170928_A00111_Nova7.csv'])"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"original_novaseq_folder = 'maca_samplesheets/'\n",
"\n",
"novaseqs = {}\n",
"\n",
"for csv in sorted(glob.glob(os.path.join(original_novaseq_folder, '*.csv'))):\n",
" basename = os.path.basename(csv)\n",
" df = pd.read_csv(csv, skiprows=1)\n",
"# df['samplesheet'] = basename\n",
"# dfs.append(df)\n",
" novaseqs[basename] = df\n",
"novaseqs.keys()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(8832, 6)\n"
]
},
{
"data": {
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"\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>Sample_ID</th>\n",
" <th>Sample_Name</th>\n",
" <th>I7_Index_ID</th>\n",
" <th>index</th>\n",
" <th>I5_Index_ID</th>\n",
" <th>index2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>K12-MAA000917-3_11_M-1-1</td>\n",
" <td>K12-MAA000917-3_11_M-1-1</td>\n",
" <td>NEXT-i7-IDT-1</td>\n",
" <td>CTGATCGT</td>\n",
" <td>NEXT-i5-IDT-17</td>\n",
" <td>AGATCGTC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>K13-MAA000917-3_11_M-1-1</td>\n",
" <td>K13-MAA000917-3_11_M-1-1</td>\n",
" <td>NEXT-i7-IDT-193</td>\n",
" <td>CTTCGTTC</td>\n",
" <td>NEXT-i5-IDT-209</td>\n",
" <td>AACACTGG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>K14-MAA000917-3_11_M-1-1</td>\n",
" <td>K14-MAA000917-3_11_M-1-1</td>\n",
" <td>NEXT-i7-IDT-2</td>\n",
" <td>ACTCTCGA</td>\n",
" <td>NEXT-i5-IDT-18</td>\n",
" <td>GCTCAGTT</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>K15-MAA000917-3_11_M-1-1</td>\n",
" <td>K15-MAA000917-3_11_M-1-1</td>\n",
" <td>NEXT-i7-IDT-194</td>\n",
" <td>GTCTAGGT</td>\n",
" <td>NEXT-i5-IDT-210</td>\n",
" <td>TTGGTGCA</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>K16-MAA000917-3_11_M-1-1</td>\n",
" <td>K16-MAA000917-3_11_M-1-1</td>\n",
" <td>NEXT-i7-IDT-3</td>\n",
" <td>TGAGCTAG</td>\n",
" <td>NEXT-i5-IDT-19</td>\n",
" <td>GTCCTAAG</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sample_ID Sample_Name I7_Index_ID \\\n",
"0 K12-MAA000917-3_11_M-1-1 K12-MAA000917-3_11_M-1-1 NEXT-i7-IDT-1 \n",
"1 K13-MAA000917-3_11_M-1-1 K13-MAA000917-3_11_M-1-1 NEXT-i7-IDT-193 \n",
"2 K14-MAA000917-3_11_M-1-1 K14-MAA000917-3_11_M-1-1 NEXT-i7-IDT-2 \n",
"3 K15-MAA000917-3_11_M-1-1 K15-MAA000917-3_11_M-1-1 NEXT-i7-IDT-194 \n",
"4 K16-MAA000917-3_11_M-1-1 K16-MAA000917-3_11_M-1-1 NEXT-i7-IDT-3 \n",
"\n",
" index I5_Index_ID index2 \n",
"0 CTGATCGT NEXT-i5-IDT-17 AGATCGTC \n",
"1 CTTCGTTC NEXT-i5-IDT-209 AACACTGG \n",
"2 ACTCTCGA NEXT-i5-IDT-18 GCTCAGTT \n",
"3 GTCTAGGT NEXT-i5-IDT-210 TTGGTGCA \n",
"4 TGAGCTAG NEXT-i5-IDT-19 GTCCTAAG "
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"novaseq7 = novaseqs['170928_A00111_Nova7.csv']\n",
"print(novaseq7.shape)\n",
"novaseq7.head()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(8832,)\n",
"(384,)\n"
]
},
{
"data": {
"text/plain": [
"NEXT-i7-IDT-1 CTGATCGT\n",
"NEXT-i7-IDT-193 CTTCGTTC\n",
"NEXT-i7-IDT-2 ACTCTCGA\n",
"NEXT-i7-IDT-194 GTCTAGGT\n",
"NEXT-i7-IDT-3 TGAGCTAG\n",
"dtype: object"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"i7_to_seq = pd.Series(index=novaseq7.I7_Index_ID.values, data=novaseq7['index'].values)\n",
"print(i7_to_seq.shape)\n",
"i7_to_seq = i7_to_seq.drop_duplicates()\n",
"print(i7_to_seq.shape)\n",
"i7_to_seq.head()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(8832,)\n",
"(384,)\n"
]
},
{
"data": {
"text/plain": [
"NEXT-i5-IDT-17 AGATCGTC\n",
"NEXT-i5-IDT-209 AACACTGG\n",
"NEXT-i5-IDT-18 GCTCAGTT\n",
"NEXT-i5-IDT-210 TTGGTGCA\n",
"NEXT-i5-IDT-19 GTCCTAAG\n",
"dtype: object"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"i5_to_seq = pd.Series(index=novaseq7.I5_Index_ID.values, data=novaseq7['index2'].values)\n",
"print(i5_to_seq.shape)\n",
"i5_to_seq = i5_to_seq.drop_duplicates()\n",
"print(i5_to_seq.shape)\n",
"i5_to_seq.head()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"8832"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# samplesheet_concatenated.head()\n",
"\n",
"original_index_pairs = set(zip(novaseq7.I7_Index_ID, novaseq7.I5_Index_ID))\n",
"len(original_index_pairs)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def tidifier(indices, index_id='I5_Index_ID'):\n",
" tidy = indices.unstack().reset_index()\n",
" tidy = tidy.rename(\n",
" columns={'level_0': 'number_col', 'level_1': 'letter_row', 0: index_id},)\n",
" tidy = tidy.set_index(['letter_row', 'number_col'])\n",
" tidy = tidy.sort_index()\n",
"# tidy.index = np.arange(len(tidy))\n",
" return tidy"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(16, 24)\n",
"384\n",
"(16, 24)\n",
"384\n",
"(16, 24)\n",
"384\n",
"(16, 24)\n",
"384\n",
"(16, 24)\n",
"384\n",
"(16, 24)\n",
"384\n",
"(16, 24)\n",
"384\n",
"(16, 24)\n",
"384\n",
"(16, 24)\n",
"384\n",
"(16, 24)\n",
"384\n",
"(16, 24)\n",
"384\n",
"(16, 24)\n",
"384\n",
"(16, 24)\n",
"384\n",
"(16, 24)\n",
"384\n",
"(16, 24)\n",
"384\n",
"(16, 24)\n",
"384\n",
"(16, 24)\n",
"384\n",
"(16, 24)\n",
"384\n",
"(16, 24)\n",
"384\n",
"(16, 24)\n",
"384\n",
"(16, 24)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/olgabot/anaconda3/envs/maca-dash/lib/python3.6/site-packages/matplotlib/pyplot.py:524: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
" max_open_warning, RuntimeWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"384\n",
"(16, 24)\n",
"384\n",
"(16, 24)\n",
"384\n",
"cumulative_overlapping_indices: 8832\n",
"(8832, 8)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\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>I7_Index_ID</th>\n",
" <th>I5_Index_ID</th>\n",
" <th>index</th>\n",
" <th>index2</th>\n",
" <th>Sample_ID</th>\n",
" <th>Sample_Name</th>\n",
" <th>well_id</th>\n",
" <th>iteration</th>\n",
" </tr>\n",
" <tr>\n",
" <th>letter_row</th>\n",
" <th>number_col</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=\"5\" valign=\"top\">A</th>\n",
" <th>1</th>\n",
" <td>NEXT-i7-IDT-185</td>\n",
" <td>NEXT-i5-IDT-1</td>\n",
" <td>GCCACTTA</td>\n",
" <td>ACGATCAG</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>A1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>NEXT-i7-IDT-1</td>\n",
" <td>NEXT-i5-IDT-97</td>\n",
" <td>CTGATCGT</td>\n",
" <td>TGATAGGC</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>A2</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>NEXT-i7-IDT-97</td>\n",
" <td>NEXT-i5-IDT-9</td>\n",
" <td>GCCTATCA</td>\n",
" <td>ATTAGCCG</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>A3</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>NEXT-i7-IDT-9</td>\n",
" <td>NEXT-i5-IDT-105</td>\n",
" <td>CGGCTAAT</td>\n",
" <td>CCAAGTAG</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>A4</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>NEXT-i7-IDT-105</td>\n",
" <td>NEXT-i5-IDT-17</td>\n",
" <td>CTACTTGG</td>\n",
" <td>AGATCGTC</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>A5</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" I7_Index_ID I5_Index_ID index index2 \\\n",
"letter_row number_col \n",
"A 1 NEXT-i7-IDT-185 NEXT-i5-IDT-1 GCCACTTA ACGATCAG \n",
" 2 NEXT-i7-IDT-1 NEXT-i5-IDT-97 CTGATCGT TGATAGGC \n",
" 3 NEXT-i7-IDT-97 NEXT-i5-IDT-9 GCCTATCA ATTAGCCG \n",
" 4 NEXT-i7-IDT-9 NEXT-i5-IDT-105 CGGCTAAT CCAAGTAG \n",
" 5 NEXT-i7-IDT-105 NEXT-i5-IDT-17 CTACTTGG AGATCGTC \n",
"\n",
" Sample_ID Sample_Name well_id iteration \n",
"letter_row number_col \n",
"A 1 None None A1 1 \n",
" 2 None None A2 1 \n",
" 3 None None A3 1 \n",
" 4 None None A4 1 \n",
" 5 None None A5 1 "
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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4uVfVNTOrsk53RzXLt+qamVVZr7U8zMxs6NzyMLO2e+2JPsYs6/VNu1nMG+4S1OfKw2wE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\nN6WI6NEAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x121882a58>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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vv4+Ib9bJt/b2NgGbAVcDRMR2ddLcHBHrpe29Uhl/DXwAuCQijq05/m5gjXSH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\nrcQMAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1206a0198>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x11f56e438>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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SYAvgcrL1X34TEV9skm/j7W0C3gBcBhARuzRJc21EbJ62D0hl/CnwZuDnEXFM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\nrLT/DxOa08xqVIecAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x120b54c50>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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K2iG93kvSiZI+JGnpIdKtI+kTko6X9A1JBw+W1cx636gfMJd0QET8oCHuUOBD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\nK82Vh5mZlebKw8zMSvv/nma0pYmZbf8AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11f1f0630>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x12015a6d8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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OjohfpX1XR8TmDccfQfbAqvHABcBWwEVk67/8LiK+0CTfxtvbBLwOuBAgInZt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\n6sWVh5mZlebKw8zMSnPlYWZmpf1/kAhaLQykVZ0AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x12015a7f0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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VG+Jntjh+C0mbp+1XSfqYpLeVzPO0kse/IeXzlhb7t5Q0MW1PkPR5Sb+W9BVJ\nK7dIc4iktZvta3H8spL2k/Sm9HpvScdLOkjSMkOke5mkT0g6TtI3JR04WFYz632jfsBc0qyIOLkh\n7hDgIOBWYBPgoxHxq7Tv+ojYrOH4o8geWDUOuADYEriYbP2XP0TEl5rk23h7m4DtgIsAImLXJmmu\njogt0vYHUhl/CbwF+HVEHNtw/C3AxukOiTnA08AvgB1S/Dua5PEk8BRwJ3A68POImNd4XO74n6T3\nvTzwBLAicHbKQxGxf5M0hwA7A5cBbwNuSGnfDnw4Ii5plZ9Zr5D0koh4eKTLMWJGesZjB2ZM3tsk\nbi6wYtqeRjZN/6Pp9Q0tjh9LdgGdD0xM8ROAm1rkez3wY2BbYJv0/wNpe5sWaW7IbV8DrJq2VwDm\nNjn+1nx+DftubJUHWYvyLcCJwDzg98D+wEpNjr8p/T8OeAgYm15riPc+N3fc8sAlaXudZp9vPwTg\nJV3IY+pIv882yrwycCxwG/AY8CjZl7pjgUklz/W7FvETgWOAHwF7N+z7bos0qwPfI1skcCpwdPq9\nPhNYo8nxUxrCVOBuYDIwZaQ/55EIo6LbStJNLcJcYLUmScZExL8AIuJusgv7jpK+SXZRbLQwIhZF\nxNPAnRExP6V9Bmj1AOEZwHXAp4EnI/u2/UxEXBoRl7ZIM0bSZElTyS6+81I+TwHNnu9+s6RZafsv\nkmakz2NDcg/bahARMRAR50fE+4A1ge8CM4G7WpRpWWAlsopgsDtsPNCy24rFc4TGk7VWiIh7W6WR\ntLKkYyXdJukxSY9KujXFTRoin6Yk/a5J3ERJx0j6kaS9G/Z9t8V5Vpf0PUnfkTRV0tGS5ko6U9Ia\nLdJMaQhTgavTz3ZKk+Nn5rZXlnRi+v39qaRmv7+kz2WVtD1D0l3AVZLukbRNk+Ovl/QZSS9vdr4W\necyQdLGkH0taW9IFkp6UdI2kTVukWVHSFyTdko6dJ+lKSQe0yOZM4HFg24iYEhFTyVroj6d9jeff\nrEV4LVkvQjMnk/1dnwXsKeksSePTvq1apDkF+CtwH1kvwzNkrej/Bb7f5PhHyP7eB8O1wEvJvkRe\n2yKP3jbStVeRQPaNeBNg3YYwDbi/yfEXAZs0xI0DTgMWNTn+KmD5tD0mF78yDd/4m6RdC/g5cDxN\nWkENx95NdgH/e/p/jRS/Ik1aEin/U8i6oK4iqzDuAi4l67ZqlkfLb/6D77Eh7rB0znuAQ4ALgR+Q\nfQs7qsVG3zJKAAAD0UlEQVR5PgrclI67DZiV4lcFLmuR5g/AEcDqubjVU9z5LdJs1iK8FnigyfFn\nkX2j3Z1s1uxZwPi0r+nPkaxVdjDwyfSejiBbvuFg4Fct0gykn2E+/Hvw59rk+Otz2z8Evph+fw8D\nzmmRx9zc9sXA5ml7Q5qsp5Ty/jpwL9kjnw8D1hzm9/Fqsu7avcguou9M8TsAV7RI8yvggPR7/zHg\ns8AGwKnAl5scf/sQ+b9oH7CI7O/34ibhmRbnubHh9aeBP5G1Dlr93PO9APcOdb4U9/H0uzI9/5kP\n9fn2ehjxAhQqZNb98oYW+37aJG6t/EWqYd/WTeLGtzh2lfwvyzBl3KnZH0/BtMsD6w2xfyKwcbpo\nrjbMuTZsI/81By80wCSyxdO2GCbNq9NxryiYR6mLSIovdSHpxkUkxZe6kLBk5dFYxlZ53AqMS9tX\nNuxr1sWZz+ONZK3NB9Nn1XTl12Hee9MvIcBfGl5fk/4fA9zW5PjzgcPzv7dkvQVHAH9scvzNwAYt\n8r5viM9qTEPcAcAtwD3DvQ/gi8N9vil+8IviN8la6i/6otBPYcQL4NAfoexFJO0vdSHp1kUk7St8\nISF76M7HUqXzd9KNKmlfq3Glg9Nntj1Zf/xxZONpnwd+1OT4F1WOZON4M4GTW+RxBdnY2B5kLc/d\nU/w2tFgtFvgz6YscsCvZDSWD+5q1JCYDXyFroT5ONu5xa4p70VgB2ReS/2iR9+4t4r8KvKlJ/Ezg\nby3SfIE0LtoQvz7wi2F+l3cFrgQe7OTfyGgLI14Ah/4IDReRxxouIpNbpCl1Ien2RSQdN+yFBDiq\nIQzeKLE6cNoQ6bYFziC7CWIucB7Z8xrGNTn2Z238TDYm6078HfCKVEE9QVbZvr5FmteQdXc9DlxO\naumSdVke0iLNK4A3NX7OwMwhjt+h6PHDpNmxjTTDlovsZpqNhitXL4cRL4CDA2nMpMo0VebRcCGp\nTbm6mUerNGTjaLcD55CN+e2W29estVTq+BR/cNVp2ilXr4cRL4CDA8PcaNCJNN3Io67lGsn3Tnu3\nzRc+vltp2smj18NoXZLdRhlJN7XaRfPbrUun6UYedS1XXd87DbfNS9oW+IWkdWl+23zZ47uVpp08\neporD+uW1YC3kvWV54lsELYTabqRR13LVdf3/pCkTSLiRoCI+JeknYGTgOkdOL5badrJo6e58rBu\n+Q1Zs//Gxh2SLulQmm7kUddy1fW970fDBNiIWAjsJ+mEDhzfrTTt5NHTRv3aVmZm1n2jYnkSMzOr\nF1ceZmZWmisPMzMrzZWHmZmV9v8Bvfw43AGsbQ8AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11ff56898>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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U/HqypE9K+omkf5W0Vos0h0vaqNm2FvuvIulASa/O7/eT9BVJ75e08gjpNpH0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\nrkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11e833e48>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x11fd271d0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x121a3aac8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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AnBIRN3VyLuV+LzMzs7a528rMzEpz5WFmZqW58jAzs9L6tvKQtJWk3SWt1RA/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\nAa0fOF7eR4TnAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11eb03c88>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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7aXomsgnbHwf+BIwBjouIG9o5l1K/l5mZWcvcbWVmZoW58jAzs8JceZiZWWF9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\nMyvMlYeZmRXmysPMzAr7/zzul72FdvieAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11f443748>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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jJrIJ2x8C/giMAY6JiOvbOZdSv5eZmVlh7rYyM7PSXHmYmVlprjzMzKy0nq08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\ns9JceZiZWWmuPMzMrLT/B7PceeNz/t25AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x118fae828>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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Kc7eVmZmV5srDzMxKc+VhZmal9WzlIemlknaQtFJD/KwWx28paYu0/XJJH5X0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\nAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11f608828>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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ZmVlprjzMzKw0Vx5mZlZa31Yekl4gaRdJ0xriF7TYfztJ2+bXL5T0IUlvLJnn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\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1208bbfd0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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StidK+qSkX0r6nKR1WuQ5XNJGzfa1SL+GpAMkvSK93lfS1yW9R9LqI+TbVNKH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\nQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11ecb8be0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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bmZlZaa48zMysNFceZmZWWt9WHpI2lbSzpNUb4ue2OH4bSVvn7ZdI+oikN5bM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\nRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x120e58d68>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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cSv1eZmZmTXO3lZmZFebKw8zMCnPlYWZmhXVt5SHp5ZJ2lTSuJn5Gg+O3lbRN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\n9PLqs4UAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11f7b7f28>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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gglRZ3CCpD1gBmDNQIndbmZlVWfnzPC4EtgGQtC4wCnhisERueZiZVVkbB8wlnQNsDawg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\nVy+/91bSdEseraY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"text/plain": [
"<matplotlib.figure.Figure at 0x11afa4358>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x120cd37b8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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tphkxkU3Y/gDwa2AMcGpE3NzOuZT6vczMzApzt5WZmZXmysPMzEpz5WFmZqX1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\nMDOz0lx5mJlZaa48zMystP8D0oiXiviPcg4AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11e9f4cc0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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pd1vNqmiabsmjlTS9XK5ePvdW0nRLHq2mGTaRTdj+DPBHYBRwYkTc3MqxlPq9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\nrkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11f43b550>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"cumulative_overlapping_indices = 0\n",
"\n",
"dfs = []\n",
"\n",
"i5_tidy = tidifier(i5)\n",
"\n",
"n_iterations = len(i7.columns)\n",
"\n",
"# original_index_pairs = set(zip(i5.values.flat, i7.values.flat))\n",
"\n",
"def rotate_columns(indices_2d, i, direction='forward'):\n",
" if direction == 'forward':\n",
" rotated = pd.concat([indices_2d.iloc[:, -i:], indices_2d.iloc[:, :-i]], axis=1)\n",
" elif direction == 'backward':\n",
" rotated = pd.concat([indices_2d.iloc[:, i:], indices_2d.iloc[:, :i]], axis=1)\n",
" return rotated\n",
"\n",
"\n",
"for i in range(n_iterations):\n",
" if i == 0:\n",
" continue\n",
"# i7_rotated = pd.concat([i7.iloc[:, i:], i7.iloc[:, :i]], axis=1)\n",
"# i7_rotated = pd.concat([i7.iloc[:, -i:], i7.iloc[:, :-i]], axis=1)\n",
" i7_rotated = rotate_columns(i7, i, 'forward')\n",
" print(i7_rotated.shape)\n",
" i7_rotated.columns = np.arange(1, len(i7_rotated.columns)+1)\n",
" i7.columns.name = 'number_col'\n",
" i7.index.name = 'letter_row'\n",
"# i7_rotated.to_csv(csv)\n",
" \n",
" rotated_heatmap = i7_rotated.applymap(lambda x: i7_index_to_value[x])\n",
" fig, ax = plt.subplots()\n",
" sns.heatmap(rotated_heatmap)\n",
" ax.set(title=f'i7 layout, rotation iteration: {i}')\n",
" png = os.path.join(figure_folder, f'XT-C-{str(i).zfill(2)}.png')\n",
" fig.tight_layout()\n",
" fig.savefig(png)\n",
" \n",
" new_index_pairs = set(zip(i7_rotated.values.flat, i5.values.flat))\n",
" n_overlapping_indices = len(new_index_pairs & original_index_pairs)\n",
" print(n_overlapping_indices)\n",
" \n",
" cumulative_overlapping_indices += n_overlapping_indices\n",
" \n",
" i7_tidy = tidifier(i7_rotated, index_id='I7_Index_ID')\n",
" df = pd.concat([i7_tidy, i5_tidy], axis=1)\n",
" df['index'] = df['I7_Index_ID'].map(lambda x: i7_to_seq[x])\n",
" df['index2'] = df['I5_Index_ID'].map(lambda x: i5_to_seq[x])\n",
" df['Sample_ID'] = None\n",
" df['Sample_Name'] = None\n",
" df['well_id'] = [''.join(map(str, x)) for x in df.index.values]\n",
" \n",
" csv = os.path.join(data_folder, f'XT-C-{str(i).zfill(2)}.csv')\n",
" df.to_csv(csv, index=False)\n",
"\n",
" df['iteration'] = i\n",
"# print(df.head())\n",
" dfs.append(df)\n",
" \n",
"print(f'cumulative_overlapping_indices: {cumulative_overlapping_indices}')\n",
"samplesheets = pd.concat(dfs)\n",
"print(samplesheets.shape)\n",
"samplesheets.head()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(8832, 8)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>letter_row</th>\n",
" <th>number_col</th>\n",
" <th>i7_index_id</th>\n",
" <th>i5_index_id</th>\n",
" <th>iteration</th>\n",
" <th>well_id</th>\n",
" <th>i7_index</th>\n",
" <th>i5_index</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>A</td>\n",
" <td>1</td>\n",
" <td>NEXT-i7-IDT-1</td>\n",
" <td>NEXT-i5-IDT-97</td>\n",
" <td>1</td>\n",
" <td>A1</td>\n",
" <td>CTGATCGT</td>\n",
" <td>TGATAGGC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>A</td>\n",
" <td>2</td>\n",
" <td>NEXT-i7-IDT-97</td>\n",
" <td>NEXT-i5-IDT-9</td>\n",
" <td>1</td>\n",
" <td>A2</td>\n",
" <td>GCCTATCA</td>\n",
" <td>ATTAGCCG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>A</td>\n",
" <td>3</td>\n",
" <td>NEXT-i7-IDT-9</td>\n",
" <td>NEXT-i5-IDT-105</td>\n",
" <td>1</td>\n",
" <td>A3</td>\n",
" <td>CGGCTAAT</td>\n",
" <td>CCAAGTAG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>A</td>\n",
" <td>4</td>\n",
" <td>NEXT-i7-IDT-105</td>\n",
" <td>NEXT-i5-IDT-17</td>\n",
" <td>1</td>\n",
" <td>A4</td>\n",
" <td>CTACTTGG</td>\n",
" <td>AGATCGTC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>A</td>\n",
" <td>5</td>\n",
" <td>NEXT-i7-IDT-17</td>\n",
" <td>NEXT-i5-IDT-113</td>\n",
" <td>1</td>\n",
" <td>A5</td>\n",
" <td>GACGATCT</td>\n",
" <td>GTCAGTCA</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" letter_row number_col i7_index_id i5_index_id iteration well_id \\\n",
"0 A 1 NEXT-i7-IDT-1 NEXT-i5-IDT-97 1 A1 \n",
"1 A 2 NEXT-i7-IDT-97 NEXT-i5-IDT-9 1 A2 \n",
"2 A 3 NEXT-i7-IDT-9 NEXT-i5-IDT-105 1 A3 \n",
"3 A 4 NEXT-i7-IDT-105 NEXT-i5-IDT-17 1 A4 \n",
"4 A 5 NEXT-i7-IDT-17 NEXT-i5-IDT-113 1 A5 \n",
"\n",
" i7_index i5_index \n",
"0 CTGATCGT TGATAGGC \n",
"1 GCCTATCA ATTAGCCG \n",
"2 CGGCTAAT CCAAGTAG \n",
"3 CTACTTGG AGATCGTC \n",
"4 GACGATCT GTCAGTCA "
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"backwards_rotated = pd.read_csv('data/013_backwards_rotate_indices/backwards_rotated_samplesheets_for_real_this_time.csv')\n",
"print(backwards_rotated.shape)\n",
"backwards_rotated.head()"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"backwards_rotated_indices = set(zip(backwards_rotated['i7_index_id'], backwards_rotated['i5_index_id']))"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"8832"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"samplesheets_indices = set(zip(samplesheets['I7_Index_ID'], samplesheets['I5_Index_ID']))\n",
"len(backwards_rotated_indices & samplesheets_indices)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Sample_ID</th>\n",
" <th>Sample_Name</th>\n",
" <th>I7_Index_ID</th>\n",
" <th>index</th>\n",
" <th>I5_Index_ID</th>\n",
" <th>index2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>K12-MAA000917-3_11_M-1-1</td>\n",
" <td>K12-MAA000917-3_11_M-1-1</td>\n",
" <td>NEXT-i7-IDT-1</td>\n",
" <td>CTGATCGT</td>\n",
" <td>NEXT-i5-IDT-17</td>\n",
" <td>AGATCGTC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>K13-MAA000917-3_11_M-1-1</td>\n",
" <td>K13-MAA000917-3_11_M-1-1</td>\n",
" <td>NEXT-i7-IDT-193</td>\n",
" <td>CTTCGTTC</td>\n",
" <td>NEXT-i5-IDT-209</td>\n",
" <td>AACACTGG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>K14-MAA000917-3_11_M-1-1</td>\n",
" <td>K14-MAA000917-3_11_M-1-1</td>\n",
" <td>NEXT-i7-IDT-2</td>\n",
" <td>ACTCTCGA</td>\n",
" <td>NEXT-i5-IDT-18</td>\n",
" <td>GCTCAGTT</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>K15-MAA000917-3_11_M-1-1</td>\n",
" <td>K15-MAA000917-3_11_M-1-1</td>\n",
" <td>NEXT-i7-IDT-194</td>\n",
" <td>GTCTAGGT</td>\n",
" <td>NEXT-i5-IDT-210</td>\n",
" <td>TTGGTGCA</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>K16-MAA000917-3_11_M-1-1</td>\n",
" <td>K16-MAA000917-3_11_M-1-1</td>\n",
" <td>NEXT-i7-IDT-3</td>\n",
" <td>TGAGCTAG</td>\n",
" <td>NEXT-i5-IDT-19</td>\n",
" <td>GTCCTAAG</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sample_ID Sample_Name I7_Index_ID \\\n",
"0 K12-MAA000917-3_11_M-1-1 K12-MAA000917-3_11_M-1-1 NEXT-i7-IDT-1 \n",
"1 K13-MAA000917-3_11_M-1-1 K13-MAA000917-3_11_M-1-1 NEXT-i7-IDT-193 \n",
"2 K14-MAA000917-3_11_M-1-1 K14-MAA000917-3_11_M-1-1 NEXT-i7-IDT-2 \n",
"3 K15-MAA000917-3_11_M-1-1 K15-MAA000917-3_11_M-1-1 NEXT-i7-IDT-194 \n",
"4 K16-MAA000917-3_11_M-1-1 K16-MAA000917-3_11_M-1-1 NEXT-i7-IDT-3 \n",
"\n",
" index I5_Index_ID index2 \n",
"0 CTGATCGT NEXT-i5-IDT-17 AGATCGTC \n",
"1 CTTCGTTC NEXT-i5-IDT-209 AACACTGG \n",
"2 ACTCTCGA NEXT-i5-IDT-18 GCTCAGTT \n",
"3 GTCTAGGT NEXT-i5-IDT-210 TTGGTGCA \n",
"4 TGAGCTAG NEXT-i5-IDT-19 GTCCTAAG "
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"novaseq7.head()"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['I7_Index_ID', 'index', 'I5_Index_ID', 'index2', 'well_id', 'iteration']"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"index_cols = 'I7_Index_ID\tindex\tI5_Index_ID\tindex2'.split() + ['well_id', 'iteration']\n",
"index_cols"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>I7_Index_ID</th>\n",
" <th>I5_Index_ID</th>\n",
" <th>index</th>\n",
" <th>index2</th>\n",
" <th>Sample_ID</th>\n",
" <th>Sample_Name</th>\n",
" <th>well_id</th>\n",
" <th>iteration</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>NEXT-i7-IDT-185</td>\n",
" <td>NEXT-i5-IDT-1</td>\n",
" <td>GCCACTTA</td>\n",
" <td>ACGATCAG</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>A1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>NEXT-i7-IDT-1</td>\n",
" <td>NEXT-i5-IDT-97</td>\n",
" <td>CTGATCGT</td>\n",
" <td>TGATAGGC</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>A2</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>NEXT-i7-IDT-97</td>\n",
" <td>NEXT-i5-IDT-9</td>\n",
" <td>GCCTATCA</td>\n",
" <td>ATTAGCCG</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>A3</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>NEXT-i7-IDT-9</td>\n",
" <td>NEXT-i5-IDT-105</td>\n",
" <td>CGGCTAAT</td>\n",
" <td>CCAAGTAG</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>A4</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>NEXT-i7-IDT-105</td>\n",
" <td>NEXT-i5-IDT-17</td>\n",
" <td>CTACTTGG</td>\n",
" <td>AGATCGTC</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>A5</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" I7_Index_ID I5_Index_ID index index2 Sample_ID Sample_Name \\\n",
"0 NEXT-i7-IDT-185 NEXT-i5-IDT-1 GCCACTTA ACGATCAG None None \n",
"1 NEXT-i7-IDT-1 NEXT-i5-IDT-97 CTGATCGT TGATAGGC None None \n",
"2 NEXT-i7-IDT-97 NEXT-i5-IDT-9 GCCTATCA ATTAGCCG None None \n",
"3 NEXT-i7-IDT-9 NEXT-i5-IDT-105 CGGCTAAT CCAAGTAG None None \n",
"4 NEXT-i7-IDT-105 NEXT-i5-IDT-17 CTACTTGG AGATCGTC None None \n",
"\n",
" well_id iteration \n",
"0 A1 1 \n",
"1 A2 1 \n",
"2 A3 1 \n",
"3 A4 1 \n",
"4 A5 1 "
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"samplesheets.index = np.arange(len(samplesheets))\n",
"samplesheets.head()"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"XT-C-01.csv XT-C-05.csv XT-C-09.csv XT-C-13.csv XT-C-17.csv XT-C-21.csv\r\n",
"XT-C-02.csv XT-C-06.csv XT-C-10.csv XT-C-14.csv XT-C-18.csv XT-C-22.csv\r\n",
"XT-C-03.csv XT-C-07.csv XT-C-11.csv XT-C-15.csv XT-C-19.csv XT-C-23.csv\r\n",
"XT-C-04.csv XT-C-08.csv XT-C-12.csv XT-C-16.csv XT-C-20.csv\r\n"
]
}
],
"source": [
"ls $data_folder"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Sample_ID</th>\n",
" <th>Sample_Name</th>\n",
" <th>I7_Index_ID</th>\n",
" <th>index</th>\n",
" <th>I5_Index_ID</th>\n",
" <th>index2</th>\n",
" <th>well_id</th>\n",
" <th>iteration</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>K12-MAA000917-3_11_M-1-1</td>\n",
" <td>K12-MAA000917-3_11_M-1-1</td>\n",
" <td>NEXT-i7-IDT-185</td>\n",
" <td>GCCACTTA</td>\n",
" <td>NEXT-i5-IDT-1</td>\n",
" <td>ACGATCAG</td>\n",
" <td>A1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>K13-MAA000917-3_11_M-1-1</td>\n",
" <td>K13-MAA000917-3_11_M-1-1</td>\n",
" <td>NEXT-i7-IDT-1</td>\n",
" <td>CTGATCGT</td>\n",
" <td>NEXT-i5-IDT-97</td>\n",
" <td>TGATAGGC</td>\n",
" <td>A2</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>K14-MAA000917-3_11_M-1-1</td>\n",
" <td>K14-MAA000917-3_11_M-1-1</td>\n",
" <td>NEXT-i7-IDT-97</td>\n",
" <td>GCCTATCA</td>\n",
" <td>NEXT-i5-IDT-9</td>\n",
" <td>ATTAGCCG</td>\n",
" <td>A3</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>K15-MAA000917-3_11_M-1-1</td>\n",
" <td>K15-MAA000917-3_11_M-1-1</td>\n",
" <td>NEXT-i7-IDT-9</td>\n",
" <td>CGGCTAAT</td>\n",
" <td>NEXT-i5-IDT-105</td>\n",
" <td>CCAAGTAG</td>\n",
" <td>A4</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>K16-MAA000917-3_11_M-1-1</td>\n",
" <td>K16-MAA000917-3_11_M-1-1</td>\n",
" <td>NEXT-i7-IDT-105</td>\n",
" <td>CTACTTGG</td>\n",
" <td>NEXT-i5-IDT-17</td>\n",
" <td>AGATCGTC</td>\n",
" <td>A5</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sample_ID Sample_Name I7_Index_ID \\\n",
"0 K12-MAA000917-3_11_M-1-1 K12-MAA000917-3_11_M-1-1 NEXT-i7-IDT-185 \n",
"1 K13-MAA000917-3_11_M-1-1 K13-MAA000917-3_11_M-1-1 NEXT-i7-IDT-1 \n",
"2 K14-MAA000917-3_11_M-1-1 K14-MAA000917-3_11_M-1-1 NEXT-i7-IDT-97 \n",
"3 K15-MAA000917-3_11_M-1-1 K15-MAA000917-3_11_M-1-1 NEXT-i7-IDT-9 \n",
"4 K16-MAA000917-3_11_M-1-1 K16-MAA000917-3_11_M-1-1 NEXT-i7-IDT-105 \n",
"\n",
" index I5_Index_ID index2 well_id iteration \n",
"0 GCCACTTA NEXT-i5-IDT-1 ACGATCAG A1 1 \n",
"1 CTGATCGT NEXT-i5-IDT-97 TGATAGGC A2 1 \n",
"2 GCCTATCA NEXT-i5-IDT-9 ATTAGCCG A3 1 \n",
"3 CGGCTAAT NEXT-i5-IDT-105 CCAAGTAG A4 1 \n",
"4 CTACTTGG NEXT-i5-IDT-17 AGATCGTC A5 1 "
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"correct_nova7 = pd.read_csv('data/014_backwards_rotate_indices_for_updated_samplesheets/170928_A00111_Nova7.csv', \n",
" index_col=0)\n",
"correct_nova7.head()"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"--- Before, using old indices: ---\n",
" Sample_ID Sample_Name I7_Index_ID index \\\n",
"0 A1-D041912-3_8_M-1-1 A1-D041912-3_8_M-1-1 NEXT-i7-IDT-1 CTGATCGT \n",
"1 A2-D041912-3_8_M-1-1 A2-D041912-3_8_M-1-1 NEXT-i7-IDT-193 CTTCGTTC \n",
"2 A3-D041912-3_8_M-1-1 A3-D041912-3_8_M-1-1 NEXT-i7-IDT-2 ACTCTCGA \n",
"3 A4-D041912-3_8_M-1-1 A4-D041912-3_8_M-1-1 NEXT-i7-IDT-194 GTCTAGGT \n",
"4 A5-D041912-3_8_M-1-1 A5-D041912-3_8_M-1-1 NEXT-i7-IDT-3 TGAGCTAG \n",
"\n",
" I5_Index_ID index2 \n",
"0 NEXT-i5-IDT-185 TAAGTGGC \n",
"1 NEXT-i5-IDT-377 GAGGCATT \n",
"2 NEXT-i5-IDT-186 AGTCAGGT \n",
"3 NEXT-i5-IDT-378 ACACCTCA \n",
"4 NEXT-i5-IDT-187 GCCTTAAC \n",
"--- After changing indices: ---\n",
" Sample_ID Sample_Name I7_Index_ID index \\\n",
"0 A1-D041912-3_8_M-1-1 A1-D041912-3_8_M-1-1 NEXT-i7-IDT-185 GCCACTTA \n",
"1 A2-D041912-3_8_M-1-1 A2-D041912-3_8_M-1-1 NEXT-i7-IDT-1 CTGATCGT \n",
"2 A3-D041912-3_8_M-1-1 A3-D041912-3_8_M-1-1 NEXT-i7-IDT-97 GCCTATCA \n",
"3 A4-D041912-3_8_M-1-1 A4-D041912-3_8_M-1-1 NEXT-i7-IDT-9 CGGCTAAT \n",
"4 A5-D041912-3_8_M-1-1 A5-D041912-3_8_M-1-1 NEXT-i7-IDT-105 CTACTTGG \n",
"\n",
" I5_Index_ID index2 well_id iteration \n",
"0 NEXT-i5-IDT-1 ACGATCAG A1 1 \n",
"1 NEXT-i5-IDT-97 TGATAGGC A2 1 \n",
"2 NEXT-i5-IDT-9 ATTAGCCG A3 1 \n",
"3 NEXT-i5-IDT-105 CCAAGTAG A4 1 \n",
"4 NEXT-i5-IDT-17 AGATCGTC A5 1 \n",
"\n",
"\n",
"--- Before, using old indices: ---\n",
" Sample_ID Sample_Name I7_Index_ID \\\n",
"0 M2-MAA000377-3_9_M-1-1 M2-MAA000377-3_9_M-1-1 NEXT-i7-IDT-1 \n",
"1 M10-MAA000377-3_9_M-1-1 M10-MAA000377-3_9_M-1-1 NEXT-i7-IDT-193 \n",
"2 M15-MAA000377-3_9_M-1-1 M15-MAA000377-3_9_M-1-1 NEXT-i7-IDT-2 \n",
"3 M16-MAA000377-3_9_M-1-1 M16-MAA000377-3_9_M-1-1 NEXT-i7-IDT-194 \n",
"4 N4-MAA000377-3_9_M-1-1 N4-MAA000377-3_9_M-1-1 NEXT-i7-IDT-3 \n",
"\n",
" index I5_Index_ID index2 \n",
"0 CTGATCGT NEXT-i5-IDT-185 TAAGTGGC \n",
"1 CTTCGTTC NEXT-i5-IDT-377 GAGGCATT \n",
"2 ACTCTCGA NEXT-i5-IDT-186 AGTCAGGT \n",
"3 GTCTAGGT NEXT-i5-IDT-378 ACACCTCA \n",
"4 TGAGCTAG NEXT-i5-IDT-187 GCCTTAAC \n",
"--- After changing indices: ---\n",
" Sample_ID Sample_Name I7_Index_ID \\\n",
"0 M2-MAA000377-3_9_M-1-1 M2-MAA000377-3_9_M-1-1 NEXT-i7-IDT-185 \n",
"1 M10-MAA000377-3_9_M-1-1 M10-MAA000377-3_9_M-1-1 NEXT-i7-IDT-1 \n",
"2 M15-MAA000377-3_9_M-1-1 M15-MAA000377-3_9_M-1-1 NEXT-i7-IDT-97 \n",
"3 M16-MAA000377-3_9_M-1-1 M16-MAA000377-3_9_M-1-1 NEXT-i7-IDT-9 \n",
"4 N4-MAA000377-3_9_M-1-1 N4-MAA000377-3_9_M-1-1 NEXT-i7-IDT-105 \n",
"\n",
" index I5_Index_ID index2 well_id iteration \n",
"0 GCCACTTA NEXT-i5-IDT-1 ACGATCAG A1 1 \n",
"1 CTGATCGT NEXT-i5-IDT-97 TGATAGGC A2 1 \n",
"2 GCCTATCA NEXT-i5-IDT-9 ATTAGCCG A3 1 \n",
"3 CGGCTAAT NEXT-i5-IDT-105 CCAAGTAG A4 1 \n",
"4 CTACTTGG NEXT-i5-IDT-17 AGATCGTC A5 1 \n",
"\n",
"\n",
"--- Before, using old indices: ---\n",
" Sample_ID Sample_Name I7_Index_ID index \\\n",
"0 A1-B000127-3_38_F-1-1 A1-B000127-3_38_F-1-1 NEXT-i7-IDT-1 CTGATCGT \n",
"1 A2-B000127-3_38_F-1-1 A2-B000127-3_38_F-1-1 NEXT-i7-IDT-193 CTTCGTTC \n",
"2 A3-B000127-3_38_F-1-1 A3-B000127-3_38_F-1-1 NEXT-i7-IDT-2 ACTCTCGA \n",
"3 A4-B000127-3_38_F-1-1 A4-B000127-3_38_F-1-1 NEXT-i7-IDT-194 GTCTAGGT \n",
"4 A5-B000127-3_38_F-1-1 A5-B000127-3_38_F-1-1 NEXT-i7-IDT-3 TGAGCTAG \n",
"\n",
" I5_Index_ID index2 \n",
"0 NEXT-i5-IDT-185 TAAGTGGC \n",
"1 NEXT-i5-IDT-377 GAGGCATT \n",
"2 NEXT-i5-IDT-186 AGTCAGGT \n",
"3 NEXT-i5-IDT-378 ACACCTCA \n",
"4 NEXT-i5-IDT-187 GCCTTAAC \n",
"--- After changing indices: ---\n",
" Sample_ID Sample_Name I7_Index_ID index \\\n",
"0 A1-B000127-3_38_F-1-1 A1-B000127-3_38_F-1-1 NEXT-i7-IDT-185 GCCACTTA \n",
"1 A2-B000127-3_38_F-1-1 A2-B000127-3_38_F-1-1 NEXT-i7-IDT-1 CTGATCGT \n",
"2 A3-B000127-3_38_F-1-1 A3-B000127-3_38_F-1-1 NEXT-i7-IDT-97 GCCTATCA \n",
"3 A4-B000127-3_38_F-1-1 A4-B000127-3_38_F-1-1 NEXT-i7-IDT-9 CGGCTAAT \n",
"4 A5-B000127-3_38_F-1-1 A5-B000127-3_38_F-1-1 NEXT-i7-IDT-105 CTACTTGG \n",
"\n",
" I5_Index_ID index2 well_id iteration \n",
"0 NEXT-i5-IDT-1 ACGATCAG A1 1 \n",
"1 NEXT-i5-IDT-97 TGATAGGC A2 1 \n",
"2 NEXT-i5-IDT-9 ATTAGCCG A3 1 \n",
"3 NEXT-i5-IDT-105 CCAAGTAG A4 1 \n",
"4 NEXT-i5-IDT-17 AGATCGTC A5 1 \n",
"\n",
"\n",
"--- Before, using old indices: ---\n",
" Sample_ID Sample_Name I7_Index_ID index \\\n",
"0 L8-B002775-3_39_F-1-1 L8-B002775-3_39_F-1-1 NEXT-i7-IDT-1 CTGATCGT \n",
"1 L10-B002775-3_39_F-1-1 L10-B002775-3_39_F-1-1 NEXT-i7-IDT-193 CTTCGTTC \n",
"2 L13-B002775-3_39_F-1-1 L13-B002775-3_39_F-1-1 NEXT-i7-IDT-2 ACTCTCGA \n",
"3 L17-B002775-3_39_F-1-1 L17-B002775-3_39_F-1-1 NEXT-i7-IDT-194 GTCTAGGT \n",
"4 L20-B002775-3_39_F-1-1 L20-B002775-3_39_F-1-1 NEXT-i7-IDT-3 TGAGCTAG \n",
"\n",
" I5_Index_ID index2 \n",
"0 NEXT-i5-IDT-185 TAAGTGGC \n",
"1 NEXT-i5-IDT-377 GAGGCATT \n",
"2 NEXT-i5-IDT-186 AGTCAGGT \n",
"3 NEXT-i5-IDT-378 ACACCTCA \n",
"4 NEXT-i5-IDT-187 GCCTTAAC \n",
"--- After changing indices: ---\n",
" Sample_ID Sample_Name I7_Index_ID index \\\n",
"0 L8-B002775-3_39_F-1-1 L8-B002775-3_39_F-1-1 NEXT-i7-IDT-185 GCCACTTA \n",
"1 L10-B002775-3_39_F-1-1 L10-B002775-3_39_F-1-1 NEXT-i7-IDT-1 CTGATCGT \n",
"2 L13-B002775-3_39_F-1-1 L13-B002775-3_39_F-1-1 NEXT-i7-IDT-97 GCCTATCA \n",
"3 L17-B002775-3_39_F-1-1 L17-B002775-3_39_F-1-1 NEXT-i7-IDT-9 CGGCTAAT \n",
"4 L20-B002775-3_39_F-1-1 L20-B002775-3_39_F-1-1 NEXT-i7-IDT-105 CTACTTGG \n",
"\n",
" I5_Index_ID index2 well_id iteration \n",
"0 NEXT-i5-IDT-1 ACGATCAG A1 1 \n",
"1 NEXT-i5-IDT-97 TGATAGGC A2 1 \n",
"2 NEXT-i5-IDT-9 ATTAGCCG A3 1 \n",
"3 NEXT-i5-IDT-105 CCAAGTAG A4 1 \n",
"4 NEXT-i5-IDT-17 AGATCGTC A5 1 \n",
"\n",
"\n",
"--- Before, using old indices: ---\n",
" Sample_ID Sample_Name I7_Index_ID \\\n",
"0 J14-MAA001867-3_39_F-1-1 J14-MAA001867-3_39_F-1-1 NEXT-i7-IDT-1 \n",
"1 J16-MAA001867-3_39_F-1-1 J16-MAA001867-3_39_F-1-1 NEXT-i7-IDT-193 \n",
"2 J17-MAA001867-3_39_F-1-1 J17-MAA001867-3_39_F-1-1 NEXT-i7-IDT-2 \n",
"3 J18-MAA001867-3_39_F-1-1 J18-MAA001867-3_39_F-1-1 NEXT-i7-IDT-194 \n",
"4 J19-MAA001867-3_39_F-1-1 J19-MAA001867-3_39_F-1-1 NEXT-i7-IDT-3 \n",
"\n",
" index I5_Index_ID index2 \n",
"0 CTGATCGT NEXT-i5-IDT-17 AGATCGTC \n",
"1 CTTCGTTC NEXT-i5-IDT-209 AACACTGG \n",
"2 ACTCTCGA NEXT-i5-IDT-18 GCTCAGTT \n",
"3 GTCTAGGT NEXT-i5-IDT-210 TTGGTGCA \n",
"4 TGAGCTAG NEXT-i5-IDT-19 GTCCTAAG \n",
"--- After changing indices: ---\n",
" Sample_ID Sample_Name I7_Index_ID \\\n",
"0 J14-MAA001867-3_39_F-1-1 J14-MAA001867-3_39_F-1-1 NEXT-i7-IDT-185 \n",
"1 J16-MAA001867-3_39_F-1-1 J16-MAA001867-3_39_F-1-1 NEXT-i7-IDT-1 \n",
"2 J17-MAA001867-3_39_F-1-1 J17-MAA001867-3_39_F-1-1 NEXT-i7-IDT-97 \n",
"3 J18-MAA001867-3_39_F-1-1 J18-MAA001867-3_39_F-1-1 NEXT-i7-IDT-9 \n",
"4 J19-MAA001867-3_39_F-1-1 J19-MAA001867-3_39_F-1-1 NEXT-i7-IDT-105 \n",
"\n",
" index I5_Index_ID index2 well_id iteration \n",
"0 GCCACTTA NEXT-i5-IDT-1 ACGATCAG A1 1 \n",
"1 CTGATCGT NEXT-i5-IDT-97 TGATAGGC A2 1 \n",
"2 GCCTATCA NEXT-i5-IDT-9 ATTAGCCG A3 1 \n",
"3 CGGCTAAT NEXT-i5-IDT-105 CCAAGTAG A4 1 \n",
"4 CTACTTGG NEXT-i5-IDT-17 AGATCGTC A5 1 \n",
"\n",
"\n",
"--- Before, using old indices: ---\n",
" Sample_ID Sample_Name I7_Index_ID \\\n",
"0 K14-MAA000924-3_8_M-1-1 K14-MAA000924-3_8_M-1-1 NEXT-i7-IDT-1 \n",
"1 K15-MAA000924-3_8_M-1-1 K15-MAA000924-3_8_M-1-1 NEXT-i7-IDT-193 \n",
"2 K16-MAA000924-3_8_M-1-1 K16-MAA000924-3_8_M-1-1 NEXT-i7-IDT-2 \n",
"3 K17-MAA000924-3_8_M-1-1 K17-MAA000924-3_8_M-1-1 NEXT-i7-IDT-194 \n",
"4 K18-MAA000924-3_8_M-1-1 K18-MAA000924-3_8_M-1-1 NEXT-i7-IDT-3 \n",
"\n",
" index I5_Index_ID index2 \n",
"0 CTGATCGT NEXT-i5-IDT-17 AGATCGTC \n",
"1 CTTCGTTC NEXT-i5-IDT-209 AACACTGG \n",
"2 ACTCTCGA NEXT-i5-IDT-18 GCTCAGTT \n",
"3 GTCTAGGT NEXT-i5-IDT-210 TTGGTGCA \n",
"4 TGAGCTAG NEXT-i5-IDT-19 GTCCTAAG \n",
"--- After changing indices: ---\n",
" Sample_ID Sample_Name I7_Index_ID \\\n",
"0 K14-MAA000924-3_8_M-1-1 K14-MAA000924-3_8_M-1-1 NEXT-i7-IDT-185 \n",
"1 K15-MAA000924-3_8_M-1-1 K15-MAA000924-3_8_M-1-1 NEXT-i7-IDT-1 \n",
"2 K16-MAA000924-3_8_M-1-1 K16-MAA000924-3_8_M-1-1 NEXT-i7-IDT-97 \n",
"3 K17-MAA000924-3_8_M-1-1 K17-MAA000924-3_8_M-1-1 NEXT-i7-IDT-9 \n",
"4 K18-MAA000924-3_8_M-1-1 K18-MAA000924-3_8_M-1-1 NEXT-i7-IDT-105 \n",
"\n",
" index I5_Index_ID index2 well_id iteration \n",
"0 GCCACTTA NEXT-i5-IDT-1 ACGATCAG A1 1 \n",
"1 CTGATCGT NEXT-i5-IDT-97 TGATAGGC A2 1 \n",
"2 GCCTATCA NEXT-i5-IDT-9 ATTAGCCG A3 1 \n",
"3 CGGCTAAT NEXT-i5-IDT-105 CCAAGTAG A4 1 \n",
"4 CTACTTGG NEXT-i5-IDT-17 AGATCGTC A5 1 \n",
"\n",
"\n",
"--- Before, using old indices: ---\n",
" Sample_ID Sample_Name I7_Index_ID \\\n",
"0 K12-MAA000917-3_11_M-1-1 K12-MAA000917-3_11_M-1-1 NEXT-i7-IDT-1 \n",
"1 K13-MAA000917-3_11_M-1-1 K13-MAA000917-3_11_M-1-1 NEXT-i7-IDT-193 \n",
"2 K14-MAA000917-3_11_M-1-1 K14-MAA000917-3_11_M-1-1 NEXT-i7-IDT-2 \n",
"3 K15-MAA000917-3_11_M-1-1 K15-MAA000917-3_11_M-1-1 NEXT-i7-IDT-194 \n",
"4 K16-MAA000917-3_11_M-1-1 K16-MAA000917-3_11_M-1-1 NEXT-i7-IDT-3 \n",
"\n",
" index I5_Index_ID index2 \n",
"0 CTGATCGT NEXT-i5-IDT-17 AGATCGTC \n",
"1 CTTCGTTC NEXT-i5-IDT-209 AACACTGG \n",
"2 ACTCTCGA NEXT-i5-IDT-18 GCTCAGTT \n",
"3 GTCTAGGT NEXT-i5-IDT-210 TTGGTGCA \n",
"4 TGAGCTAG NEXT-i5-IDT-19 GTCCTAAG \n",
"--- After changing indices: ---\n",
" Sample_ID Sample_Name I7_Index_ID \\\n",
"0 K12-MAA000917-3_11_M-1-1 K12-MAA000917-3_11_M-1-1 NEXT-i7-IDT-185 \n",
"1 K13-MAA000917-3_11_M-1-1 K13-MAA000917-3_11_M-1-1 NEXT-i7-IDT-1 \n",
"2 K14-MAA000917-3_11_M-1-1 K14-MAA000917-3_11_M-1-1 NEXT-i7-IDT-97 \n",
"3 K15-MAA000917-3_11_M-1-1 K15-MAA000917-3_11_M-1-1 NEXT-i7-IDT-9 \n",
"4 K16-MAA000917-3_11_M-1-1 K16-MAA000917-3_11_M-1-1 NEXT-i7-IDT-105 \n",
"\n",
" index I5_Index_ID index2 well_id iteration \n",
"0 GCCACTTA NEXT-i5-IDT-1 ACGATCAG A1 1 \n",
"1 CTGATCGT NEXT-i5-IDT-97 TGATAGGC A2 1 \n",
"2 GCCTATCA NEXT-i5-IDT-9 ATTAGCCG A3 1 \n",
"3 CGGCTAAT NEXT-i5-IDT-105 CCAAGTAG A4 1 \n",
"4 CTACTTGG NEXT-i5-IDT-17 AGATCGTC A5 1 \n"
]
}
],
"source": [
"sample_cols = ['Sample_ID', 'Sample_Name']\n",
"\n",
"for basename, novaseq in novaseqs.items():\n",
" print('\\n\\n--- Before, using old indices: ---')\n",
" print(novaseq.head())\n",
" new_novaseq = pd.concat([novaseq[sample_cols], samplesheets[index_cols]], axis=1)\n",
" \n",
" csv = os.path.join(data_folder, basename)\n",
" new_novaseq.to_csv(csv, index=False)\n",
" print('--- After changing indices: ---')\n",
" print(new_novaseq.head())"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Sample_ID</th>\n",
" <th>Sample_Name</th>\n",
" <th>I7_Index_ID</th>\n",
" <th>index</th>\n",
" <th>I5_Index_ID</th>\n",
" <th>index2</th>\n",
" <th>well_id</th>\n",
" <th>iteration</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>K12-MAA000917-3_11_M-1-1</td>\n",
" <td>K12-MAA000917-3_11_M-1-1</td>\n",
" <td>NEXT-i7-IDT-185</td>\n",
" <td>GCCACTTA</td>\n",
" <td>NEXT-i5-IDT-1</td>\n",
" <td>ACGATCAG</td>\n",
" <td>A1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>K13-MAA000917-3_11_M-1-1</td>\n",
" <td>K13-MAA000917-3_11_M-1-1</td>\n",
" <td>NEXT-i7-IDT-1</td>\n",
" <td>CTGATCGT</td>\n",
" <td>NEXT-i5-IDT-97</td>\n",
" <td>TGATAGGC</td>\n",
" <td>A2</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>K14-MAA000917-3_11_M-1-1</td>\n",
" <td>K14-MAA000917-3_11_M-1-1</td>\n",
" <td>NEXT-i7-IDT-97</td>\n",
" <td>GCCTATCA</td>\n",
" <td>NEXT-i5-IDT-9</td>\n",
" <td>ATTAGCCG</td>\n",
" <td>A3</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>K15-MAA000917-3_11_M-1-1</td>\n",
" <td>K15-MAA000917-3_11_M-1-1</td>\n",
" <td>NEXT-i7-IDT-9</td>\n",
" <td>CGGCTAAT</td>\n",
" <td>NEXT-i5-IDT-105</td>\n",
" <td>CCAAGTAG</td>\n",
" <td>A4</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>K16-MAA000917-3_11_M-1-1</td>\n",
" <td>K16-MAA000917-3_11_M-1-1</td>\n",
" <td>NEXT-i7-IDT-105</td>\n",
" <td>CTACTTGG</td>\n",
" <td>NEXT-i5-IDT-17</td>\n",
" <td>AGATCGTC</td>\n",
" <td>A5</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sample_ID Sample_Name I7_Index_ID \\\n",
"0 K12-MAA000917-3_11_M-1-1 K12-MAA000917-3_11_M-1-1 NEXT-i7-IDT-185 \n",
"1 K13-MAA000917-3_11_M-1-1 K13-MAA000917-3_11_M-1-1 NEXT-i7-IDT-1 \n",
"2 K14-MAA000917-3_11_M-1-1 K14-MAA000917-3_11_M-1-1 NEXT-i7-IDT-97 \n",
"3 K15-MAA000917-3_11_M-1-1 K15-MAA000917-3_11_M-1-1 NEXT-i7-IDT-9 \n",
"4 K16-MAA000917-3_11_M-1-1 K16-MAA000917-3_11_M-1-1 NEXT-i7-IDT-105 \n",
"\n",
" index I5_Index_ID index2 well_id iteration \n",
"0 GCCACTTA NEXT-i5-IDT-1 ACGATCAG A1 1 \n",
"1 CTGATCGT NEXT-i5-IDT-97 TGATAGGC A2 1 \n",
"2 GCCTATCA NEXT-i5-IDT-9 ATTAGCCG A3 1 \n",
"3 CGGCTAAT NEXT-i5-IDT-105 CCAAGTAG A4 1 \n",
"4 CTACTTGG NEXT-i5-IDT-17 AGATCGTC A5 1 "
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_novaseq.head()"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Sample_ID</th>\n",
" <th>Sample_Name</th>\n",
" <th>I7_Index_ID</th>\n",
" <th>index</th>\n",
" <th>I5_Index_ID</th>\n",
" <th>index2</th>\n",
" <th>well_id</th>\n",
" <th>iteration</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>K12-MAA000917-3_11_M-1-1</td>\n",
" <td>K12-MAA000917-3_11_M-1-1</td>\n",
" <td>NEXT-i7-IDT-185</td>\n",
" <td>GCCACTTA</td>\n",
" <td>NEXT-i5-IDT-1</td>\n",
" <td>ACGATCAG</td>\n",
" <td>A1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>K13-MAA000917-3_11_M-1-1</td>\n",
" <td>K13-MAA000917-3_11_M-1-1</td>\n",
" <td>NEXT-i7-IDT-1</td>\n",
" <td>CTGATCGT</td>\n",
" <td>NEXT-i5-IDT-97</td>\n",
" <td>TGATAGGC</td>\n",
" <td>A2</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>K14-MAA000917-3_11_M-1-1</td>\n",
" <td>K14-MAA000917-3_11_M-1-1</td>\n",
" <td>NEXT-i7-IDT-97</td>\n",
" <td>GCCTATCA</td>\n",
" <td>NEXT-i5-IDT-9</td>\n",
" <td>ATTAGCCG</td>\n",
" <td>A3</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>K15-MAA000917-3_11_M-1-1</td>\n",
" <td>K15-MAA000917-3_11_M-1-1</td>\n",
" <td>NEXT-i7-IDT-9</td>\n",
" <td>CGGCTAAT</td>\n",
" <td>NEXT-i5-IDT-105</td>\n",
" <td>CCAAGTAG</td>\n",
" <td>A4</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>K16-MAA000917-3_11_M-1-1</td>\n",
" <td>K16-MAA000917-3_11_M-1-1</td>\n",
" <td>NEXT-i7-IDT-105</td>\n",
" <td>CTACTTGG</td>\n",
" <td>NEXT-i5-IDT-17</td>\n",
" <td>AGATCGTC</td>\n",
" <td>A5</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sample_ID Sample_Name I7_Index_ID \\\n",
"0 K12-MAA000917-3_11_M-1-1 K12-MAA000917-3_11_M-1-1 NEXT-i7-IDT-185 \n",
"1 K13-MAA000917-3_11_M-1-1 K13-MAA000917-3_11_M-1-1 NEXT-i7-IDT-1 \n",
"2 K14-MAA000917-3_11_M-1-1 K14-MAA000917-3_11_M-1-1 NEXT-i7-IDT-97 \n",
"3 K15-MAA000917-3_11_M-1-1 K15-MAA000917-3_11_M-1-1 NEXT-i7-IDT-9 \n",
"4 K16-MAA000917-3_11_M-1-1 K16-MAA000917-3_11_M-1-1 NEXT-i7-IDT-105 \n",
"\n",
" index I5_Index_ID index2 well_id iteration \n",
"0 GCCACTTA NEXT-i5-IDT-1 ACGATCAG A1 1 \n",
"1 CTGATCGT NEXT-i5-IDT-97 TGATAGGC A2 1 \n",
"2 GCCTATCA NEXT-i5-IDT-9 ATTAGCCG A3 1 \n",
"3 CGGCTAAT NEXT-i5-IDT-105 CCAAGTAG A4 1 \n",
"4 CTACTTGG NEXT-i5-IDT-17 AGATCGTC A5 1 "
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"correct_nova7.head()"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Sample_ID False\n",
"Sample_Name False\n",
"I7_Index_ID False\n",
"index False\n",
"I5_Index_ID False\n",
"index2 False\n",
"well_id False\n",
"iteration False\n",
"dtype: bool"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(new_novaseq != correct_nova7).any()"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(8832, 8)"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_novaseq.shape"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(8832, 8)"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"samplesheets.shape"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\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>I7_Index_ID</th>\n",
" <th>I5_Index_ID</th>\n",
" <th>index</th>\n",
" <th>index2</th>\n",
" <th>Sample_ID</th>\n",
" <th>Sample_Name</th>\n",
" <th>well_id</th>\n",
" <th>iteration</th>\n",
" </tr>\n",
" <tr>\n",
" <th>letter_row</th>\n",
" <th>number_col</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=\"5\" valign=\"top\">A</th>\n",
" <th>1</th>\n",
" <td>NEXT-i7-IDT-97</td>\n",
" <td>NEXT-i5-IDT-1</td>\n",
" <td>GCCTATCA</td>\n",
" <td>ACGATCAG</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>A1</td>\n",
" <td>23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>NEXT-i7-IDT-9</td>\n",
" <td>NEXT-i5-IDT-97</td>\n",
" <td>CGGCTAAT</td>\n",
" <td>TGATAGGC</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>A2</td>\n",
" <td>23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>NEXT-i7-IDT-105</td>\n",
" <td>NEXT-i5-IDT-9</td>\n",
" <td>CTACTTGG</td>\n",
" <td>ATTAGCCG</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>A3</td>\n",
" <td>23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>NEXT-i7-IDT-17</td>\n",
" <td>NEXT-i5-IDT-105</td>\n",
" <td>GACGATCT</td>\n",
" <td>CCAAGTAG</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>A4</td>\n",
" <td>23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>NEXT-i7-IDT-113</td>\n",
" <td>NEXT-i5-IDT-17</td>\n",
" <td>TGACTGAC</td>\n",
" <td>AGATCGTC</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>A5</td>\n",
" <td>23</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" I7_Index_ID I5_Index_ID index index2 \\\n",
"letter_row number_col \n",
"A 1 NEXT-i7-IDT-97 NEXT-i5-IDT-1 GCCTATCA ACGATCAG \n",
" 2 NEXT-i7-IDT-9 NEXT-i5-IDT-97 CGGCTAAT TGATAGGC \n",
" 3 NEXT-i7-IDT-105 NEXT-i5-IDT-9 CTACTTGG ATTAGCCG \n",
" 4 NEXT-i7-IDT-17 NEXT-i5-IDT-105 GACGATCT CCAAGTAG \n",
" 5 NEXT-i7-IDT-113 NEXT-i5-IDT-17 TGACTGAC AGATCGTC \n",
"\n",
" Sample_ID Sample_Name well_id iteration \n",
"letter_row number_col \n",
"A 1 None None A1 23 \n",
" 2 None None A2 23 \n",
" 3 None None A3 23 \n",
" 4 None None A4 23 \n",
" 5 None None A5 23 "
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# samplesheets.to_csv(os.path.join(data_folder, 'backwards_rotated_samplesheets.csv'))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"i5_to_seq"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (maca-dash)",
"language": "python",
"name": "myenv"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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