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Commit 7d735fba authored by Holger Dinkel's avatar Holger Dinkel
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add Bernd's ipython notebooks about ChipSeqEll/HTSeq

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users/klaus/ChipSeqElli/ChiPSeqElli.ipynb 0 → 100644
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{
"metadata": {
"name": "",
"signature": "sha256:b92512874c91aad166c4ddaa7e5ab025f3b507daa94800a828d27019a8d7c6e8"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import HTSeq\n",
"import itertools\n",
"import numpy as np\n",
"from numpy import random\n",
"import matplotlib \n",
"import os\n",
"import pandas as pd\n",
"from pandas import Series, DataFrame\n",
"\n",
"import re\n",
"\n",
"\n",
"data_path = \"/g/scb/patil/klaus/AlignedDataElli/\"\n",
"gtf=\"/g/huber/users/klaus/Data/Genomes/Mus_musculus/GRCm38_ENSEMBL78/Mus_musculus.GRCm38.78.gtf\"\n",
"\n",
"halfwinwidth = 3000\n",
"fragmentsize = 150"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%load_ext rpy2.ipython\n",
"import rpy2\n",
"import pandas.rpy.common as com"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"The rpy2.ipython extension is already loaded. To reload it, use:\n",
" %reload_ext rpy2.ipython\n"
]
}
],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We follow the instructions here:\n",
"\n",
"http://www-huber.embl.de/users/anders/HTSeq/doc/tss.html#streaming-through-all-reads\n",
"\n",
"closely and use HTSeq to genereate coverage vectors for TSS\n",
"\n",
"We first load the bam files and the gtf file\n",
"\n",
"> itertools.islice(alignment_file, 5,6).next()\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"bams = os.listdir(data_path)\n",
"#print re.search('s.bam$',bams[2])\n",
"#print \n",
"\n",
"\n",
"# filter to select only the SORTED bams\n",
"bams = filter(lambda x: re.search('s.bam$',x), bams)\n",
"bams = [data_path + x for x in bams] \n",
"print bams"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"['/g/scb/patil/klaus/AlignedDataElli/p63ChIP_1h_s.bam', '/g/scb/patil/klaus/AlignedDataElli/p63ChIP_wt_s.bam', '/g/scb/patil/klaus/AlignedDataElli/p63ChIP_4h_s.bam']\n"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"os.listdir(data_path)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 47,
"text": [
"['p63ChIP_4h.bam',\n",
" 'p63ChIP_1h_s.bam',\n",
" 'p63ChIP_wt_s.bam',\n",
" 'p63ChIP_wt.sam',\n",
" 'p63ChIP_wt_s.bam.bai',\n",
" 'p63ChIP_1h.bam',\n",
" 'p63ChIP_4h_s.bam',\n",
" 'p63ChIP_1h_s.bam.bai',\n",
" 'p63ChIP_1h.sam',\n",
" 'p63ChIP_wt.bam',\n",
" 'p63ChIP_4h.sam',\n",
" 'p63ChIP_4h_s.bam.bai']"
]
}
],
"prompt_number": 47
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We now can read in the bam files and the gtf file"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"gtffile = HTSeq.GFF_Reader(gtf)\n",
"bamsHT = [HTSeq.BAM_Reader(x) for x in bams]\n",
"bamsHT "
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 5,
"text": [
"[<HTSeq.BAM_Reader at 0x7fca6e183c50>,\n",
" <HTSeq.BAM_Reader at 0x7fca6cc20c50>,\n",
" <HTSeq.BAM_Reader at 0x7fca6cc20c90>]"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# load ENSEMBL IDs and get their genomic coordinates\n",
"\n",
"#os.listdir(\".\")\n",
"genes = pd.read_csv(\"ElliGenes.csv\")\n",
"del genes[\"Unnamed: 0\"]\n",
"genes = genes.rename(columns={'x':'geneID'})\n",
"genes.ix[1:10]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>geneID</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1 </th>\n",
" <td> ENSMUSG00000023067</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2 </th>\n",
" <td> ENSMUSG00000024521</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3 </th>\n",
" <td> ENSMUSG00000044551</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4 </th>\n",
" <td> ENSMUSG00000046070</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5 </th>\n",
" <td> ENSMUSG00000034457</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6 </th>\n",
" <td> ENSMUSG00000027356</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7 </th>\n",
" <td> ENSMUSG00000020184</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8 </th>\n",
" <td> ENSMUSG00000048458</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9 </th>\n",
" <td> ENSMUSG00000020326</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td> ENSMUSG00000024965</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 46,
"text": [
" geneID\n",
"1 ENSMUSG00000023067\n",
"2 ENSMUSG00000024521\n",
"3 ENSMUSG00000044551\n",
"4 ENSMUSG00000046070\n",
"5 ENSMUSG00000034457\n",
"6 ENSMUSG00000027356\n",
"7 ENSMUSG00000020184\n",
"8 ENSMUSG00000048458\n",
"9 ENSMUSG00000020326\n",
"10 ENSMUSG00000024965"
]
}
],
"prompt_number": 46
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"?genes # genes is a DataFrame now\n",
"\n",
"genes[\"geneID\"].at[5]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 12,
"text": [
"'ENSMUSG00000034457'"
]
}
],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# create gene set to retain unique IDs\n",
"geneSet = set(genes[\"geneID\"])\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# test membership\n",
"assert 'ENSMUSG0000034457' in geneSet"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "AssertionError",
"evalue": "",
"output_type": "pyerr",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mAssertionError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-49-17f4e481f9f6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# test membership\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;32massert\u001b[0m \u001b[1;34m'ENSMUSG0000034457'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mgeneSet\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mAssertionError\u001b[0m: "
]
}
],
"prompt_number": 49
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"windows = Series(index = geneSet, dtype=\"object\")\n",
"poss = Series(index = geneSet, dtype=\"object\")"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# get TSS positions for candidate genes and create windows\n",
"tsspos = HTSeq.GenomicArrayOfSets( \"auto\", stranded=False )\n",
"for feature in gtffile:\n",
" if feature.type == \"exon\" and feature.attr[\"exon_number\"] == \"1\" and feature.attr[\"gene_id\"] in geneSet:\n",
" # print feature.attr[\"gene_id\"]\n",
" p = feature.iv.start_d_as_pos\n",
" window = HTSeq.GenomicInterval( p.chrom, max(p.pos - halfwinwidth,0), p.pos + halfwinwidth, \".\" )\n",
" tsspos[ window ] += p\n",
" # save TSS window in Series\n",
" windows[feature.attr[\"gene_id\"]] = window\n",
" # save TSS position in Series\n",
" poss[feature.attr[\"gene_id\"]] = p"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# join windwos and poss in a data frame\n",
"#anno = DataFrame(dict(windows=windows, positions=poss), columns=[\"windows\",\"positions\"])\n",
"anno = DataFrame(dict(windows=windows, positions=poss))\n",
"\n",
"print anno.columns.values\n",
"print anno\n",
"#print anno"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"['positions' 'windows']\n",
" positions windows\n",
"ENSMUSG00000074063 8:119437188/+ 8:[119434188,119440188)/.\n",
"ENSMUSG00000003873 7:45466897/- 7:[45463897,45469897)/.\n",
"ENSMUSG00000072845 5:86468989/- 5:[86465989,86471989)/.\n",
"ENSMUSG00000070583 4:147868978/+ 4:[147865978,147871978)/.\n",
"ENSMUSG00000021668 13:96519159/- 13:[96516159,96522159)/.\n",
"ENSMUSG00000021185 12:100885879/+ 12:[100882879,100888879)/.\n",
"ENSMUSG00000029055 4:155019427/- 4:[155016427,155022427)/.\n",
"ENSMUSG00000045730 18:62179958/- 18:[62176958,62182958)/.\n",
"ENSMUSG00000019577 6:5496274/- 6:[5493274,5499274)/.\n",
"ENSMUSG00000027663 3:32366013/- 3:[32363013,32369013)/.\n",
"ENSMUSG00000031298 X:160427291/+ X:[160424291,160430291)/.\n",
"ENSMUSG00000025507 7:141443993/- 7:[141440993,141446993)/.\n",
"ENSMUSG00000051910 7:115846104/- 7:[115843104,115849104)/.\n",
"ENSMUSG00000070644 1:133367286/+ 1:[133364286,133370286)/.\n",
"ENSMUSG00000036960 3:145099103/- 3:[145096103,145102103)/.\n",
"ENSMUSG00000015647 2:180178402/- 2:[180175402,180181402)/.\n",
"ENSMUSG00000032135 9:44136860/+ 9:[44133860,44139860)/.\n",
"ENSMUSG00000022074 14:69767471/+ 14:[69764471,69770471)/.\n",
"ENSMUSG00000021798 14:34588680/- 14:[34585680,34591680)/.\n",
"ENSMUSG00000020423 1:134079119/- 1:[134076119,134082119)/.\n",
"ENSMUSG00000020184 10:117710713/- 10:[117707713,117713713)/.\n",
"ENSMUSG00000037316 8:25785208/- 8:[25782208,25788208)/.\n",
"ENSMUSG00000047854 16:62814675/+ 16:[62811675,62817675)/.\n",
"ENSMUSG00000032092 9:45042424/+ 9:[45039424,45045424)/.\n",
"ENSMUSG00000074968 2:110761563/- 2:[110758563,110764563)/.\n",
"ENSMUSG00000007379 3:103149163/+ 3:[103146163,103152163)/.\n",
"ENSMUSG00000024965 19:7019468/- 19:[7016468,7022468)/.\n",
"ENSMUSG00000023067 17:29098206/+ 17:[29095206,29101206)/.\n",
"ENSMUSG00000056076 5:140419304/+ 5:[140416304,140422304)/.\n",
"ENSMUSG00000037725 8:22185818/- 8:[22182818,22188818)/.\n",
"... ... ...\n",
"ENSMUSG00000017756 13:73763696/+ 13:[73760696,73766696)/.\n",
"ENSMUSG00000030641 7:92874212/- 7:[92871212,92877212)/.\n",
"ENSMUSG00000062939 1:52105448/+ 1:[52102448,52108448)/.\n",
"ENSMUSG00000037509 1:34801740/+ 1:[34798740,34804740)/.\n",
"ENSMUSG00000037759 14:44988194/+ 14:[44985194,44991194)/.\n",
"ENSMUSG00000052087 13:55379168/+ 13:[55376168,55382168)/.\n",
"ENSMUSG00000023341 16:97558411/+ 16:[97555411,97561411)/.\n",
"ENSMUSG00000042106 9:107985631/- 9:[107982631,107988631)/.\n",
"ENSMUSG00000044816 1:65123113/- 1:[65120113,65126113)/.\n",
"ENSMUSG00000070645 1:133351908/+ 1:[133348908,133354908)/.\n",
"ENSMUSG00000027356 2:132941962/- 2:[132938962,132944962)/.\n",
"ENSMUSG00000019558 X:73678892/+ X:[73675892,73681892)/.\n",
"ENSMUSG00000026104 1:52152252/+ 1:[52149252,52155252)/.\n",
"ENSMUSG00000028992 4:149485162/- 4:[149482162,149488162)/.\n",
"ENSMUSG00000032358 9:76545803/- 9:[76542803,76548803)/.\n",
"ENSMUSG00000020326 11:40753937/- 11:[40750937,40756937)/.\n",
"ENSMUSG00000028211 4:11164458/+ 4:[11161458,11167458)/.\n",
"ENSMUSG00000017386 11:78165515/- 11:[78162515,78168515)/.\n",
"ENSMUSG00000084128 8:106136684/- 8:[106133684,106139684)/.\n",
"ENSMUSG00000021277 12:111248482/+ 12:[111245482,111251482)/.\n",
"ENSMUSG00000068744 3:108386064/+ 3:[108383064,108389064)/.\n",
"ENSMUSG00000030107 6:121245905/+ 6:[121242905,121248905)/.\n",
"ENSMUSG00000040918 1:164262352/+ 1:[164259352,164265352)/.\n",
"ENSMUSG00000048458 3:105705457/+ 3:[105702457,105708457)/.\n",
"ENSMUSG00000022372 15:66803748/- 15:[66800748,66806748)/.\n",
"ENSMUSG00000036596 5:35525570/- 5:[35522570,35528570)/.\n",
"ENSMUSG00000086321 4:83390667/- 4:[83387667,83393667)/.\n",
"ENSMUSG00000042179 19:58728886/+ 19:[58725886,58731886)/.\n",
"ENSMUSG00000027208 2:126034972/+ 2:[126031972,126037972)/.\n",
"ENSMUSG00000044813 4:45532469/- 4:[45529469,45535469)/.\n",
"\n",
"[148 rows x 2 columns]\n"
]
}
],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dates = pd.date_range('1/1/2000', periods=8)\n",
"\n",
"df = DataFrame(np.random.randn(8, 4), index=dates, columns=['A', 'B', 'C', 'D'])\n",
"\n",
"df[\"A\"]\n",
"df.columns.values"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 264,
"text": [
"array(['A', 'B', 'C', 'D'], dtype=object)"
]
}
],
"prompt_number": 264
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# remove NAs by usage of dropna\n",
"test = windows.copy()\n",
"test.dropna()\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 19,
"text": [
"ENSMUSG00000074063 8:[119434188,119440188)/.\n",
"ENSMUSG00000003873 7:[45463897,45469897)/.\n",
"ENSMUSG00000072845 5:[86465989,86471989)/.\n",
"ENSMUSG00000070583 4:[147865978,147871978)/.\n",
"ENSMUSG00000021668 13:[96516159,96522159)/.\n",
"ENSMUSG00000021185 12:[100882879,100888879)/.\n",
"ENSMUSG00000029055 4:[155016427,155022427)/.\n",
"ENSMUSG00000045730 18:[62176958,62182958)/.\n",
"ENSMUSG00000019577 6:[5493274,5499274)/.\n",
"ENSMUSG00000027663 3:[32363013,32369013)/.\n",
"ENSMUSG00000031298 X:[160424291,160430291)/.\n",
"ENSMUSG00000025507 7:[141440993,141446993)/.\n",
"ENSMUSG00000051910 7:[115843104,115849104)/.\n",
"ENSMUSG00000070644 1:[133364286,133370286)/.\n",
"ENSMUSG00000036960 3:[145096103,145102103)/.\n",
"...\n",
"ENSMUSG00000020326 11:[40750937,40756937)/.\n",
"ENSMUSG00000028211 4:[11161458,11167458)/.\n",
"ENSMUSG00000017386 11:[78162515,78168515)/.\n",
"ENSMUSG00000084128 8:[106133684,106139684)/.\n",
"ENSMUSG00000021277 12:[111245482,111251482)/.\n",
"ENSMUSG00000068744 3:[108383064,108389064)/.\n",
"ENSMUSG00000030107 6:[121242905,121248905)/.\n",
"ENSMUSG00000040918 1:[164259352,164265352)/.\n",
"ENSMUSG00000048458 3:[105702457,105708457)/.\n",
"ENSMUSG00000022372 15:[66800748,66806748)/.\n",
"ENSMUSG00000036596 5:[35522570,35528570)/.\n",
"ENSMUSG00000086321 4:[83387667,83393667)/.\n",
"ENSMUSG00000042179 19:[58725886,58731886)/.\n",
"ENSMUSG00000027208 2:[126031972,126037972)/.\n",
"ENSMUSG00000044813 4:[45529469,45535469)/.\n",
"Length: 147, dtype: object"
]
}
],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# get the windows only\n",
"anno = anno.dropna()\n",
"test = anno.columns.values\n",
"print test\n",
"anno.loc[:,\"windows\"] "
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"['positions' 'windows']\n"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 18,
"text": [
"ENSMUSG00000074063 8:[119434188,119440188)/.\n",
"ENSMUSG00000003873 7:[45463897,45469897)/.\n",
"ENSMUSG00000072845 5:[86465989,86471989)/.\n",
"ENSMUSG00000070583 4:[147865978,147871978)/.\n",
"ENSMUSG00000021668 13:[96516159,96522159)/.\n",
"ENSMUSG00000021185 12:[100882879,100888879)/.\n",
"ENSMUSG00000029055 4:[155016427,155022427)/.\n",
"ENSMUSG00000045730 18:[62176958,62182958)/.\n",
"ENSMUSG00000019577 6:[5493274,5499274)/.\n",
"ENSMUSG00000027663 3:[32363013,32369013)/.\n",
"ENSMUSG00000031298 X:[160424291,160430291)/.\n",
"ENSMUSG00000025507 7:[141440993,141446993)/.\n",
"ENSMUSG00000051910 7:[115843104,115849104)/.\n",
"ENSMUSG00000070644 1:[133364286,133370286)/.\n",
"ENSMUSG00000036960 3:[145096103,145102103)/.\n",
"...\n",
"ENSMUSG00000020326 11:[40750937,40756937)/.\n",
"ENSMUSG00000028211 4:[11161458,11167458)/.\n",
"ENSMUSG00000017386 11:[78162515,78168515)/.\n",
"ENSMUSG00000084128 8:[106133684,106139684)/.\n",
"ENSMUSG00000021277 12:[111245482,111251482)/.\n",
"ENSMUSG00000068744 3:[108383064,108389064)/.\n",
"ENSMUSG00000030107 6:[121242905,121248905)/.\n",
"ENSMUSG00000040918 1:[164259352,164265352)/.\n",
"ENSMUSG00000048458 3:[105702457,105708457)/.\n",
"ENSMUSG00000022372 15:[66800748,66806748)/.\n",
"ENSMUSG00000036596 5:[35522570,35528570)/.\n",
"ENSMUSG00000086321 4:[83387667,83393667)/.\n",
"ENSMUSG00000042179 19:[58725886,58731886)/.\n",
"ENSMUSG00000027208 2:[126031972,126037972)/.\n",
"ENSMUSG00000044813 4:[45529469,45535469)/.\n",
"Name: windows, Length: 147, dtype: object"
]
}
],
"prompt_number": 18
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# get test reads from unsorted BAM file\n",
"atest= list(itertools.islice(HTSeq.BAM_Reader('/g/scb/patil/klaus/AlignedDataElli/p63ChIP_1h.bam'),1,1e4))\n",
"\n",
"for r in atest:\n",
" #print r\n",
" if r.iv is not None: \n",
" r.iv.length = fragmentsize\n",
"\n",
"atest[1:5]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 20,
"text": [
"[<SAM_Alignment object: Read 'SOLEXAWS1_0001:5:1:1592:1019#0/1' aligned to 13:[107227837,107227987)/+>,\n",
" <SAM_Alignment object: Read 'SOLEXAWS1_0001:5:1:2803:1020#0/1' aligned to 19:[31615405,31615555)/->,\n",
" <SAM_Alignment object: Read 'SOLEXAWS1_0001:5:1:3715:1021#0/1' aligned to 5:[124572706,124572856)/->,\n",
" <SAM_Alignment object: Read 'SOLEXAWS1_0001:5:1:2263:1020#0/1' aligned to 15:[80638244,80638394)/+>]"
]
}
],
"prompt_number": 20
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# test directionality of the positions, downstream and upstream are flipped for - strand FEATURES!\n",
"start = 31615300\n",
"end = start + 6000\n",
"print \"window start end\", start, end\n",
"print \"read start end\", atest[2].iv.start, atest[2].iv.end\n",
"print \"position in profile for Window\", end-atest[2].iv.end, end-atest[2].iv.start\n",
"print \"position in profile for Window method2\", end-atest[2].iv.start_d, end-atest[2].iv.end_d"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"window start end 31615300 31621300\n",
"read start end 31615405 31615555\n",
"position in profile for Window 5745 5895\n",
"position in profile for Window method2 5746 5896\n"
]
}
],
"prompt_number": 184
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# test iterating through the windows\n",
"for g, ann in anno.iterrows():\n",
" #print g, ann['windows']\n",
" for r in atest:\n",
" if r.iv is not None:\n",
" if r.iv.overlaps(ann[\"windows\"]): \n",
" print r ,\"is in\", ann[\"windows\"]\n",
" if ann[\"positions\"].strand == \"+\":\n",
" startInWindow = max(r.iv.start - ann[\"positions\"].pos + halfwinwidth,0)\n",
" endInWindow = min(r.iv.end - ann[\"positions\"].pos + halfwinwidth,5999)\n",
" else:\n",
" startInWindow = max(ann[\"positions\"].pos + halfwinwidth - r.iv.end,0)\n",
" endInWindow = min(ann[\"positions\"].pos + halfwinwidth - r.iv.start,5999)\n",
" print \"start in Window\", startInWindow, \"end in Window\", endInWindow\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"<SAM_Alignment object: Read 'SOLEXAWS1_0001:5:1:12120:1540#0/1' aligned to 15:[100720083,100720233)/+> is in 15:[100717476,100723476)/.\n",
"start in Window 3243 end in Window 3393\n",
"<SAM_Alignment object: Read 'SOLEXAWS1_0001:5:1:1991:1333#0/1' aligned to X:[97376439,97376589)/->"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" is in X:[97374171,97380171)/.\n",
"start in Window 3582 end in Window 3732\n",
"<SAM_Alignment object: Read 'SOLEXAWS1_0001:5:1:11153:1052#0/1' aligned to 13:[73762336,73762486)/+>"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" is in 13:[73760696,73766696)/.\n",
"start in Window 1640 end in Window 1790\n",
"<SAM_Alignment object: Read 'SOLEXAWS1_0001:5:1:13112:1524#0/1' aligned to 2:[126033348,126033498)/+>"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" is in 2:[126031972,126037972)/.\n",
"start in Window 1376 end in Window 1526\n"
]
}
],
"prompt_number": 22
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# get a coverage vector for all windows using all aligned reads\n",
"#\n",
"coverages4h = DataFrame(data=np.zeros(shape = (len(genes), 2*halfwinwidth)), index=genes[\"geneID\"].values)\n",
"coverages1h = DataFrame(data=np.zeros(shape = (len(genes), 2*halfwinwidth)), index=genes[\"geneID\"].values)\n",
"coveragesWT = DataFrame(data=np.zeros(shape = (len(genes), 2*halfwinwidth)), index=genes[\"geneID\"].values)\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 26
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# test adding numbers\n",
"coveragesWT.ix[\"ENSMUSG00000020326\",] += 1\n",
"coveragesWT.ix[ [\"ENSMUSG00000020326\",\"ENSMUSG00000026989\"],]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
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" <th></th>\n",
" <th>0</th>\n",
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" <th>2</th>\n",
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" <th>8</th>\n",
" <th>9</th>\n",
" <th>...</th>\n",
" <th>5990</th>\n",
" <th>5991</th>\n",
" <th>5992</th>\n",
" <th>5993</th>\n",
" <th>5994</th>\n",
" <th>5995</th>\n",
" <th>5996</th>\n",
" <th>5997</th>\n",
" <th>5998</th>\n",
" <th>5999</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>ENSMUSG00000020326</th>\n",
" <td> 4</td>\n",
" <td> 4</td>\n",
" <td> 4</td>\n",
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" <td> 4</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>ENSMUSG00000026989</th>\n",
" <td> 0</td>\n",
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"</table>\n",
"<p>2 rows \u00d7 6000 columns</p>\n",
"</div>"
],
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" 0 1 2 3 4 5 6 7 8 \\\n",
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"ENSMUSG00000026989 0 0 0 0 0 0 0 0 0 \n",
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" 9 ... 5990 5991 5992 5993 5994 5995 5996 \\\n",
"ENSMUSG00000020326 4 ... 4 4 4 4 4 4 4 \n",
"ENSMUSG00000026989 0 ... 0 0 0 0 0 0 0 \n",
"\n",
" 5997 5998 5999 \n",
"ENSMUSG00000020326 4 4 4 \n",
"ENSMUSG00000026989 0 0 0 \n",
"\n",
"[2 rows x 6000 columns]"
]
}
],
"prompt_number": 132
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"bams"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 24,
"text": [
"['/g/scb/patil/klaus/AlignedDataElli/p63ChIP_1h_s.bam',\n",
" '/g/scb/patil/klaus/AlignedDataElli/p63ChIP_wt_s.bam',\n",
" '/g/scb/patil/klaus/AlignedDataElli/p63ChIP_4h_s.bam']"
]
}
],
"prompt_number": 24
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# get coverage for 1hr\n",
"for g, ann in anno.iterrows():\n",
" for r in bamsHT[0][ann[\"windows\"]]:\n",
" if r.iv is not None and r.aligned and r.flag <> 256:\n",
" r.iv.length = fragmentsize # extend to fragment size\n",
" if ann[\"positions\"].strand == \"+\":\n",
" startInWindow = max(r.iv.start - ann[\"positions\"].pos + halfwinwidth,0)\n",
" endInWindow = min(r.iv.end - ann[\"positions\"].pos + halfwinwidth,5999)\n",
" else:\n",
" startInWindow = max(ann[\"positions\"].pos + halfwinwidth - r.iv.end,0)\n",
" endInWindow = min(ann[\"positions\"].pos + halfwinwidth - r.iv.start,5999)\n",
" coverages1h.ix[g,startInWindow : endInWindow ] += 1"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 27
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# get coverage for WT\n",
"for g, ann in anno.iterrows():\n",
" for r in bamsHT[1][ann[\"windows\"]]:\n",
" if r.iv is not None and r.aligned and r.flag <> 256:\n",
" r.iv.length = fragmentsize # extend to fragment size\n",
" if ann[\"positions\"].strand == \"+\":\n",
" startInWindow = max(r.iv.start - ann[\"positions\"].pos + halfwinwidth,0)\n",
" endInWindow = min(r.iv.end - ann[\"positions\"].pos + halfwinwidth,5999)\n",
" else:\n",
" startInWindow = max(ann[\"positions\"].pos + halfwinwidth - r.iv.end,0)\n",
" endInWindow = min(ann[\"positions\"].pos + halfwinwidth - r.iv.start,5999)\n",
" coveragesWT.ix[g,startInWindow : endInWindow ] += 1"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 28
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# get coverage for 4hr\n",
"for g, ann in anno.iterrows():\n",
" for r in bamsHT[2][ann[\"windows\"]]:\n",
" if r.iv is not None and r.aligned and r.flag <> 256:\n",
" r.iv.length = fragmentsize # extend to fragment size\n",
" if ann[\"positions\"].strand == \"+\":\n",
" startInWindow = max(r.iv.start - ann[\"positions\"].pos + halfwinwidth,0)\n",
" endInWindow = min(r.iv.end - ann[\"positions\"].pos + halfwinwidth,5999)\n",
" else:\n",
" startInWindow = max(ann[\"positions\"].pos + halfwinwidth - r.iv.end,0)\n",
" endInWindow = min(ann[\"positions\"].pos + halfwinwidth - r.iv.start,5999)\n",
" coverages4h.ix[g,startInWindow : endInWindow ] += 1"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 29
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# check mean max coverage\n",
"coverages4h.apply(max, axis = 0).mean()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 33,
"text": [
"17.569333333333333"
]
}
],
"prompt_number": 33
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# turn coverage frames into R data.frames\n",
"covWT = com.convert_to_r_dataframe(coveragesWT)\n",
"covWT"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 32,
"text": [
"<DataFrame - Python:0x7fca67e10710 / R:0x8e00380>\n",
"[Float..., Float..., Float..., ..., Float..., Float..., Float...]\n",
" X0: <class 'rpy2.robjects.vectors.FloatVector'>\n",
" <FloatVector - Python:0x7fca679585f0 / R:0x50cf1e0>\n",
"[0.000000, 1.000000, 0.000000, ..., 0.000000, 0.000000, 0.000000]\n",
" X1: <class 'rpy2.robjects.vectors.FloatVector'>\n",
" <FloatVector - Python:0x7fca68d9ecf8 / R:0x5ea7a50>\n",
"[0.000000, 1.000000, 0.000000, ..., 0.000000, 0.000000, 0.000000]\n",
" X2: <class 'rpy2.robjects.vectors.FloatVector'>\n",
" <FloatVector - Python:0x7fca68d9efc8 / R:0x503a750>\n",
"[0.000000, 1.000000, 0.000000, ..., 0.000000, 0.000000, 0.000000]\n",
" ...\n",
" X0: <class 'rpy2.robjects.vectors.FloatVector'>\n",
" <FloatVector - Python:0x7fca6bd024d0 / R:0x87bf810>\n",
"[1.000000, 0.000000, 0.000000, ..., 0.000000, 0.000000, 8.000000]\n",
" X1: <class 'rpy2.robjects.vectors.FloatVector'>\n",
" <FloatVector - Python:0x7fca6bd02248 / R:0x87013f0>\n",
"[1.000000, 0.000000, 0.000000, ..., 0.000000, 0.000000, 8.000000]\n",
" X2: <class 'rpy2.robjects.vectors.FloatVector'>\n",
" <FloatVector - Python:0x7fca6bd02998 / R:0x8b7bf70>\n",
"[1.000000, 0.000000, 0.000000, ..., 0.000000, 0.000000, 8.000000]"
]
}
],
"prompt_number": 32
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# write coverages to csv files\n",
"coveragesWT.to_csv(\"covWT.csv\")\n",
"coverages1h.to_csv(\"cov1h.csv\")\n",
"coverages4h.to_csv(\"cov4h.csv\")"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 30
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# check whether this has worked\n",
"os.listdir(\".\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 31,
"text": [
"['ChipSeqElli_cache',\n",
" 'ChipSeqElli.Rmd',\n",
" 'ChipSeqElli.R',\n",
" 'ChIPSeqTrimming.sh',\n",
" 'ChipSeqElli.html',\n",
" 'createBT2Index.sh',\n",
" 'ChIPSeqTrimming.sh~',\n",
" 'cluster-calls-Elli-ChIP-Seq.sh',\n",
" 'cluster-calls-Elli-ChIP-Seq.sh~',\n",
" 'bowtie2A.sh',\n",
" 'ChIPQC-vignette.pdf',\n",
" 'ChIPQC.pdf',\n",
" 'ChIPQCSampleReport.pdf',\n",
" 'sam2bam.sh',\n",
" 'scriptsAleks',\n",
" 'CoverageChIPSeq.py',\n",
" 'ChiPSeqElli.ipynb',\n",
" 'ElliGenes.csv',\n",
" '.ipynb_checkpoints',\n",
" 'bamSort.sh',\n",
" 'bamSortIndex.sh',\n",
" 'covWT.csv',\n",
" 'cov1h.csv',\n",
" 'cov4h.csv',\n",
" 'ChipSeqElli_files',\n",
" 'backGElli.csv',\n",
" 'ChiPSeqElliControlSet.ipynb',\n",
" 'covBackWT.csv',\n",
" 'covBack1h.csv',\n",
" 'covBack4h.csv',\n",
" 'ElliGenes.txt',\n",
" 'backgroundGenesTSS.pdf']"
]
}
],
"prompt_number": 31
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%R\n",
"rnorm(10)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"text": [
" [1] 0.4145233 1.3614705 0.3975463 0.2241936 -1.4913814 -0.2301498\n",
" [7] 0.1769197 0.4039700 -0.5776179 -0.5291591\n"
]
}
],
"prompt_number": 21
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%R\n",
"\n",
"library(plyr)\n",
"library(dplyr)\n",
"library(magrittr)\n",
"library(ggplot2)\n",
"library(tidyr)\n",
"\n",
"covWT <- read.csv(\"covWT.csv\")\n",
"covWT$time <- rep(\"WT\", dim(covWT)[1] )\n",
"cov1h <- read.csv(\"cov1h.csv\")\n",
"cov1h$time <- rep(\"1h\", dim(cov1h)[1] )\n",
"cov4h <- read.csv(\"cov4h.csv\")\n",
"cov4h$time <- rep(\"4h\", dim(cov4h)[1] )\n",
"covs <- rbind.fill(covWT, cov1h, cov4h)\n",
"names(covs) <- c(\"geneID\", setdiff(seq(-3000,3000),0), \"time\")\n",
"print(sample_n(covs, 10)[, sample(5900,10)])\n",
"\n",
"dataGG <- covs %>%\n",
" gather(key = \"posRelToTSS\", value = \"coverage\", -geneID, -time)\n",
" \n",
"dataGG$posRelToTSS %<>% as.integer()\n",
"\n",
"covs_collapsed <- dataGG %>%\n",
" group_by(time, posRelToTSS) %>%\n",
" summarize(coverageC = mean(coverage))\n",
"print(covs_collapsed)\n",
"\n",
"#qplot(posRelToTSS, coverageC, color = time, \n",
" # data=covs_collapsed, geom=\"smooth\") "
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"text": [
" 2744 -2958 1004 1422 281 784 -1505 1017 -2615 -2580\n",
"3 3 0 0 1 2 1 3 0 3 2\n",
"58 0 3 0 3 1 3 1 4 0 2\n",
"352 4 0 5 7 5 1 1 3 0 0\n",
"374 8 5 0 0 1 0 2 0 1 1\n",
"300 2 0 4 0 7 1 0 4 0 0\n",
"57 2 0 4 3 2 0 1 1 0 0\n",
"138 4 0 0 7 2 0 3 1 5 10\n",
"343 0 0 9 4 1 0 0 10 2 0\n",
"70 0 1 0 2 2 3 0 0 0 0\n",
"363 2 0 5 1 1 0 1 5 0 0\n",
" coverageC\n",
"1 1.713629\n"
]
}
],
"prompt_number": 45
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"covs"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'covs' is not defined",
"output_type": "pyerr",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-39-669ca51bd5a9>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mcovs\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mNameError\u001b[0m: name 'covs' is not defined"
]
}
],
"prompt_number": 39
}
],
"metadata": {}
}
]
}
\ No newline at end of file
users/klaus/ChipSeqElli/ChiPSeqElliLocal.ipynb 0 → 100644
+ 256
− 0
View file @ 7d735fba
{
"metadata": {
"name": "",
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"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"# pylab.rcParams['figure.figsize'] = (10.0, 8.0)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "ERROR",
"evalue": "Error in parse(text = x, srcfile = src): <text>:1:41: unexpected ','\n1: pylab.rcParams['figure.figsize'] = (10.0,\n ^\n",
"output_type": "pyerr",
"traceback": [
"Error in parse(text = x, srcfile = src): <text>:1:41: unexpected ','\n1: pylab.rcParams['figure.figsize'] = (10.0,\n ^\n"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"library(plyr)\n",
"library(dplyr)\n",
"library(magrittr)\n",
"library(ggplot2)\n",
"library(tidyr)\n",
"\n",
"\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stderr",
"text": [
"\n",
"Attaching package: \u2018dplyr\u2019\n",
"\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"The following objects are masked from \u2018package:plyr\u2019:\n",
"\n",
" arrange, count, desc, failwith, id, mutate, rename, summarise,\n",
" summarize\n",
"\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"The following object is masked from \u2018package:stats\u2019:\n",
"\n",
" filter\n",
"\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"The following objects are masked from \u2018package:base\u2019:\n",
"\n",
" intersect, setdiff, setequal, union\n",
"\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"\n",
"Attaching package: \u2018tidyr\u2019\n",
"\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"The following object is masked from \u2018package:magrittr\u2019:\n",
"\n",
" extract\n",
"\n"
]
}
],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"covWT <- read.csv(\"covWT.csv\")\n",
"covWT$time <- rep(\"WT\", dim(covWT)[1] )\n",
"cov1h <- read.csv(\"cov1h.csv\")\n",
"cov1h$time <- rep(\"1h\", dim(cov1h)[1] )\n",
"cov4h <- read.csv(\"cov4h.csv\")\n",
"cov4h$time <- rep(\"4h\", dim(cov4h)[1] )\n",
"covs <- rbind.fill(covWT, cov1h, cov4h)\n",
"names(covs) <- c(\"geneID\", setdiff(seq(-3000,3000),0), \"time\")\n",
"print(sample_n(covs, 10)[, sample(5900,10)])\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" -1017 1739 -200 619 760 2205 2697 -1716 2142 -1122\n",
"298 0 0 1 0 1 0 0 4 1 1\n",
"367 0 0 2 3 0 2 3 3 2 0\n",
"21 0 2 2 3 7 0 0 1 4 0\n",
"286 3 3 4 3 0 1 3 1 1 0\n",
"317 5 0 9 2 1 3 3 4 0 2\n",
"336 2 0 0 0 0 2 0 0 2 11\n",
"410 5 10 4 4 0 1 0 0 0 0\n",
"411 2 0 1 3 5 0 0 0 0 2\n",
"277 0 0 4 3 1 0 0 0 0 3\n",
"320 3 0 0 2 2 0 0 1 0 0\n"
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dataGG <- covs %>%\n",
" gather(key = \"posRelToTSS\", value = \"coverage\", -geneID, -time)\n",
" \n",
"dataGG$posRelToTSS %<>% as.integer()\n",
"\n",
"covs_collapsed <- dataGG %>%\n",
" group_by(time, posRelToTSS) %>%\n",
" summarize(coverageC = mean(coverage))\n",
"print(covs_collapsed)\n",
"\n",
"qplot(posRelToTSS, coverageC, color = time, \n",
" data=covs_collapsed, geom=\"smooth\") "
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Source: local data frame [18,000 x 3]\n",
"Groups: time\n",
"\n",
" time posRelToTSS coverageC\n",
"1 1h 1 1.087838\n",
"2 1h 2 1.094595\n",
"3 1h 3 1.094595\n",
"4 1h 4 1.087838\n",
"5 1h 5 1.101351\n",
"6 1h 6 1.101351\n",
"7 1h 7 1.094595\n",
"8 1h 8 1.087838\n",
"9 1h 9 1.087838\n",
"10 1h 10 1.094595\n",
".. ... ... ...\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"geom_smooth: method=\"auto\" and size of largest group is >=1000, so using gam with formula: y ~ s(x, bs = \"cs\"). Use 'method = x' to change the smoothing method.\n"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 3,
"text": []
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dev.cur(width = 25)\n",
"\n",
"\n",
"qplot(posRelToTSS, coverageC, color = time, \n",
" data=covs_collapsed, geom=\"smooth\") \n",
"#dev.off()\n",
"#dev.off()\n",
"#dev.off()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "ERROR",
"evalue": "Error in dev.cur(width = 25): unused argument (width = 25)\n",
"output_type": "pyerr",
"traceback": [
"Error in dev.cur(width = 25): unused argument (width = 25)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"geom_smooth: method=\"auto\" and size of largest group is >=1000, so using gam with formula: y ~ s(x, bs = \"cs\"). Use 'method = x' to change the smoothing method.\n"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 10,
"text": []
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"
}
],
"prompt_number": 10
}
],
"metadata": {}
}
]
}
\ No newline at end of file
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