{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# testing"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"import os,sys,time,glob,math\n",
"from importlib import reload\n",
"import subprocess\n",
"from collections import OrderedDict\n",
"import numpy as np\n",
"import pandas as pd\n",
"pd.set_option('display.width', 120)\n",
"pd.set_option('max_colwidth', 80)\n",
"%matplotlib inline\n",
"import matplotlib as mpl\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import epitopepredict as ep\n",
"from epitopepredict import base, sequtils, peptutils, utilities, tepitope, web, plotting, mhclearn"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['basicmhc1', 'tepitope', 'netmhciipan', 'netmhcpan', 'mhcflurry', 'mhcnuggets', 'iedbmhc1', 'iedbmhc2']\n"
]
}
],
"source": [
"print (base.predictors)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"seqs = ['MRRVILPTAPPEYMEAIYPVRSNSTIARGGNSNTGFLTPESVNGDTPSNPLRPIADDTIDHASHTPGSVSSA',\n",
" 'FILEAMVNVISGPKVLMKQIPIWLPLGVADQKTYSFDSTTAAIMLASYTITHFGKATNPLVRVNRLGPGIPDHP',\n",
" 'LRLLRIGNQAFLQEFVLPPVQLPQYFTFDLTALKLITQPLPAATWTDDTPTGSNGALRPGISFHPKLRPILLPN',\n",
" 'KSGKKGNSADLTSPEKIQAIMTSLQDFKIVPIDPTKNIMGIEVPETLVHKLTGKKVTSKNGQPIIPVLLPKYIGL',\n",
" 'DPVAPGDLTMVITQDCDTCHSPASLPAVIEK']\n",
"#prots=sequtils.fasta_to_dataframe('../tempseq.fa')\n",
"prots = sequtils.genbank_to_dataframe('../MTB-H37Rv.gb',cds=True)\n",
"seqs = peptutils.create_random_sequences(5000)\n",
"m2alleles = base.get_preset_alleles('mhc2_supertypes')\n",
"m1alleles = base.get_preset_alleles('mhc1_supertypes')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"basicmhc1 predictor\n",
"basicmhc1 ['HLA-A*02:01', 'HLA-A*02:03', 'HLA-A*02:06', 'HLA-A*03:01', 'HLA-A*68:02', 'HLA-A*11:01']\n",
"tepitope predictor\n",
"tepitope ['HLA-DRB1*1601', 'HLA-DRB1*0101', 'HLA-DRB1*0102', 'HLA-DRB1*0103', 'HLA-DRB1*0104', 'HLA-DRB1*0105']\n",
"netmhciipan predictor\n",
"netmhciipan ['DRB1_0101', 'DRB1_0102', 'DRB1_0103', 'DRB1_0104', 'DRB1_0105', 'DRB1_0106']\n",
"netmhcpan predictor\n",
"netmhcpan ['BoLA-100901', 'BoLA-100902', 'BoLA-101901', 'BoLA-102001', 'BoLA-102101', 'BoLA-102301']\n",
"mhcflurry predictor\n",
"mhcflurry ['BoLA-6*13:01', 'Eqca-1*01:01', 'H-2-Db', 'H-2-Dd', 'H-2-Kb', 'H-2-Kd']\n",
"mhcnuggets predictor\n",
"mhcnuggets ['BoLA-208:01', 'BoLA-212:01', 'BoLA-613:01', 'BoLA-641:01', 'BoLA-A11:01', 'BoLA-DRB302:01']\n",
"iedbmhc1 predictor\n",
"iedbmhc1 ['BoLA-AW10', 'BoLA-D18.4', 'BoLA-HD6', 'BoLA-JSP.1', 'BoLA-T2C', 'BoLA-T2a']\n",
"iedbmhc2 predictor\n",
"iedbmhc2 ['H2-IAb', 'H2-IAd', 'HLA-DPA1*01/DPB1*04:01', 'HLA-DPA1*01:03/DPB1*02:01', 'HLA-DPA1*02:01/DPB1*01:01', 'HLA-DPA1*02:01/DPB1*05:01']\n"
]
}
],
"source": [
"reload(base)\n",
"for p in base.predictors:\n",
" P=base.get_predictor(p)\n",
" print (P)\n",
" x=P.get_alleles()[:6]\n",
" print (p,x)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"reload(base)\n",
"reload(sequtils)\n",
"\n",
"def benchmark_cpus(P, seqs, alleles, c=5):\n",
" procs=range(1,c)\n",
" tt=[]\n",
" for c in procs:\n",
" st=time.time()\n",
" P.predict_peptides(seqs, alleles=alleles, cpus=c)\n",
" #P.predictProteins(prots[:20], alleles=m2alleles, cpus=c)\n",
" t=time.time()-st\n",
" tt.append(t)\n",
" plt.plot(procs,tt)\n",
" plt.xlabel('cpus')\n",
" \n",
"def benchmark_seqs(P, seqs, alleles, n=4):\n",
" tt=[]\n",
" n=len(seqs)/n\n",
" x=range(n,len(seqs)+1,n)\n",
" print n,x\n",
" for i in x:\n",
" s=seqs[:i] \n",
" st=time.time()\n",
" P.predict_peptides(s, alleles=alleles)\n",
" #P.predictProteins(s, alleles=m2alleles, cpus=c)\n",
" t=time.time()-st\n",
" tt.append(t)\n",
" print ([i[1]/i[0] for i in zip(x,tt)])\n",
" plt.plot(x,tt)\n",
" plt.xlabel('peptides')\n",
" plt.ylabel('time')\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### basic tests MHC-I"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"trained model: HLA-A*24:02 1668 9\n",
"trained model: HLA-A*24:02 456 10\n",
"trained model: HLA-B*44:03 570 9\n",
"trained model: HLA-B*44:03 439 10\n",
"10.746431350708008\n"
]
}
],
"source": [
"reload(base)\n",
"reload(mhclearn)\n",
"seqs = peptutils.create_random_sequences(10)\n",
"#df = pd.DataFrame(seqs,columns=['peptide'])\n",
"#P = ep.get_predictor('iedbmhc1')\n",
"#P = ep.get_predictor('netmhcpan')\n",
"#P = ep.get_predictor('mhcflurry')\n",
"#P = ep.get_predictor('mhcnuggets')\n",
"P=base.get_predictor('basicmhc1')\n",
"st=t=time.time()\n",
"res=P.predict_peptides(seqs, alleles=m1alleles, cpus=1)\n",
"print (time.time()-st)\n",
"#print (res)\n",
"#print (P.promiscuous_binders(n=1))#cutoff_method='score',cutoff=1000))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### basic tests MHC-II"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"a 2.5\n",
"b 55.0\n",
"Name: 0.5, dtype: float64"
]
},
"execution_count": 91,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.DataFrame(np.array([[1, 1], [2, 10], [3, 100], [4, 100]]),\n",
" columns=['a', 'b'])\n",
"df.quantile(.5,interpolation='linear')"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'DRB1_0101'"
]
},
"execution_count": 87,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def allele_mapping(allele):\n",
" if allele.startswith('HLA-DQA'):\n",
" return allele.replace('*','').replace(':','')\n",
" else:\n",
" return allele.replace('*','_').replace(':','').replace('HLA-DRB','DRB')\n",
"allele_mapping('HLA-DRB1*01:01')\n",
"#allele_mapping('HLA-DQA1*03:02-DQB1*03:02')"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"netmhciipan predictor\n",
"netMHCIIpan -f /tmp/tmpi2vf_oo7.pep -inptype 1 -a HLA-DQA10302-DQB10302\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>peptide</th>\n",
" <th>pos</th>\n",
" <th>1-log50k(aff)</th>\n",
" <th>Affinity</th>\n",
" <th>core</th>\n",
" <th>allele</th>\n",
" <th>name</th>\n",
" <th>score</th>\n",
" <th>rank</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>IDWIHAYEHAAIINC</td>\n",
" <td>4</td>\n",
" <td>0.336</td>\n",
" <td>1322.92</td>\n",
" <td>HAYEHAAII</td>\n",
" <td>HLA-DQA1*0302-DQB1*0302</td>\n",
" <td>temp</td>\n",
" <td>1322.92</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>RDFSCTTAKSQQLPL</td>\n",
" <td>8</td>\n",
" <td>0.244</td>\n",
" <td>3576.98</td>\n",
" <td>CTTAKSQQL</td>\n",
" <td>HLA-DQA1*0302-DQB1*0302</td>\n",
" <td>temp</td>\n",
" <td>3576.98</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>IDEPSCNTHVWADIS</td>\n",
" <td>2</td>\n",
" <td>0.233</td>\n",
" <td>4026.28</td>\n",
" <td>NTHVWADIS</td>\n",
" <td>HLA-DQA1*0302-DQB1*0302</td>\n",
" <td>temp</td>\n",
" <td>4026.28</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>DNSLVMPILHRYARE</td>\n",
" <td>3</td>\n",
" <td>0.221</td>\n",
" <td>4587.52</td>\n",
" <td>DNSLVMPIL</td>\n",
" <td>HLA-DQA1*0302-DQB1*0302</td>\n",
" <td>temp</td>\n",
" <td>4587.52</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>VSIVRKVSEWEHVGI</td>\n",
" <td>9</td>\n",
" <td>0.209</td>\n",
" <td>5201.73</td>\n",
" <td>IVRKVSEWE</td>\n",
" <td>HLA-DQA1*0302-DQB1*0302</td>\n",
" <td>temp</td>\n",
" <td>5201.73</td>\n",
" <td>5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>RNMMCETWNPIMHCP</td>\n",
" <td>0</td>\n",
" <td>0.190</td>\n",
" <td>6376.22</td>\n",
" <td>ETWNPIMHC</td>\n",
" <td>HLA-DQA1*0302-DQB1*0302</td>\n",
" <td>temp</td>\n",
" <td>6376.22</td>\n",
" <td>6.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>GHHQQMANGDGMYKH</td>\n",
" <td>7</td>\n",
" <td>0.152</td>\n",
" <td>9700.81</td>\n",
" <td>QMANGDGMY</td>\n",
" <td>HLA-DQA1*0302-DQB1*0302</td>\n",
" <td>temp</td>\n",
" <td>9700.81</td>\n",
" <td>7.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>KGQLKCEDAHCLHYR</td>\n",
" <td>5</td>\n",
" <td>0.141</td>\n",
" <td>10889.96</td>\n",
" <td>LKCEDAHCL</td>\n",
" <td>HLA-DQA1*0302-DQB1*0302</td>\n",
" <td>temp</td>\n",
" <td>10889.96</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>GWDDAWPGSSGRRVS</td>\n",
" <td>6</td>\n",
" <td>0.127</td>\n",
" <td>12602.80</td>\n",
" <td>WDDAWPGSS</td>\n",
" <td>HLA-DQA1*0302-DQB1*0302</td>\n",
" <td>temp</td>\n",
" <td>12602.80</td>\n",
" <td>9.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>HLIHMFKHFHHGDER</td>\n",
" <td>1</td>\n",
" <td>0.114</td>\n",
" <td>14525.20</td>\n",
" <td>FKHFHHGDE</td>\n",
" <td>HLA-DQA1*0302-DQB1*0302</td>\n",
" <td>temp</td>\n",
" <td>14525.20</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" peptide pos 1-log50k(aff) Affinity core allele name score rank\n",
"0 IDWIHAYEHAAIINC 4 0.336 1322.92 HAYEHAAII HLA-DQA1*0302-DQB1*0302 temp 1322.92 1.0\n",
"1 RDFSCTTAKSQQLPL 8 0.244 3576.98 CTTAKSQQL HLA-DQA1*0302-DQB1*0302 temp 3576.98 2.0\n",
"2 IDEPSCNTHVWADIS 2 0.233 4026.28 NTHVWADIS HLA-DQA1*0302-DQB1*0302 temp 4026.28 3.0\n",
"3 DNSLVMPILHRYARE 3 0.221 4587.52 DNSLVMPIL HLA-DQA1*0302-DQB1*0302 temp 4587.52 4.0\n",
"4 VSIVRKVSEWEHVGI 9 0.209 5201.73 IVRKVSEWE HLA-DQA1*0302-DQB1*0302 temp 5201.73 5.0\n",
"5 RNMMCETWNPIMHCP 0 0.190 6376.22 ETWNPIMHC HLA-DQA1*0302-DQB1*0302 temp 6376.22 6.0\n",
"6 GHHQQMANGDGMYKH 7 0.152 9700.81 QMANGDGMY HLA-DQA1*0302-DQB1*0302 temp 9700.81 7.0\n",
"7 KGQLKCEDAHCLHYR 5 0.141 10889.96 LKCEDAHCL HLA-DQA1*0302-DQB1*0302 temp 10889.96 8.0\n",
"8 GWDDAWPGSSGRRVS 6 0.127 12602.80 WDDAWPGSS HLA-DQA1*0302-DQB1*0302 temp 12602.80 9.0\n",
"9 HLIHMFKHFHHGDER 1 0.114 14525.20 FKHFHHGDE HLA-DQA1*0302-DQB1*0302 temp 14525.20 10.0"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"reload(base)\n",
"P = base.get_predictor('netmhciipan')\n",
"#P = base.get_predictor('iedbmhc2')\n",
"print (P)\n",
"allele='HLA-DQA1*0302-DQB1*0302'\n",
"seqs = peptutils.create_random_sequences(10,length=15)\n",
"r=P.predict_peptides(seqs, alleles=allele, show_cmd=True, cpus=1)\n",
"r"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"a=pd.read_csv('../epitopepredict/mhcdata/iedb_mhc2_alleles.csv').allele\n",
"for i in a.sort_values().unique():\n",
" print i"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### create quantiles/cutoffs for each predictor "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#%reset out\n",
"\n",
"reload(base)\n",
"#p='tepitope'\n",
"#p='mhcflurry'\n",
"p='netmhciipan'\n",
"#p='basicmhc1'\n",
"savepath='../standard/%s/' %p\n",
"df = sequtils.fasta_to_dataframe('../epitopepredict/mhcdata/immunogens.faa')\n",
"P = base.get_predictor(p)\n",
"print (P)\n",
"#print P.get_alleles()\n",
"all_alleles=P.get_alleles()[1500:1700]\n",
"test_alleles=['HLA-DQA10303-DQB10303','HLA-DQA10501-DQB10501','HLA-DPA10201-DPB10201','HLA-DPA10103-DPB10101']\n",
"print (len(all_alleles))\n",
"res = pd.read_csv('../epitopepredict/mhcdata/quantiles_%s.csv' %p,index_col=0)\n",
"print (len(res))\n",
"\n",
"P.predict_sequences(df,alleles=all_alleles, path=savepath, cpus=8, length=11)#, verbose=True)\n",
"#P.predict_sequences(df,alleles=['HLA-C*02:02'], path=savepath, cpus=4)\n",
"P.load(savepath)\n",
"#print (P.data)\n",
"new=base.get_quantiles(P)\n",
"print (new)\n",
"\n",
"new.to_csv('quantiles_%s.csv' %p,float_format='%.3f')\n",
"\n",
"#f,ax=plt.subplots(1,1,figsize=(18,5))\n",
"#ax.pcolor(res,cmap='coolwarm')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import mhcnuggets\n",
"spath='/usr/local/lib/python2.7/dist-packages/mhcnuggets/saves/production/'\n",
"tmp=subprocess.check_output('ls %s' %spath, shell=True)\n",
"x=[i[:-3] for i in tmp.split('\\n')]\n",
"for i in x:\n",
" print i"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"netMHCpan appears to be installed\n",
"BoLA-1:00901 1166.9\n",
"BoLA-1:00902 298.3\n",
"BoLA-1:01901 66.9\n",
"BoLA-1:02001 714.7\n",
"BoLA-1:02101 919.5\n",
"BoLA-1:02301 534.3\n",
"BoLA-1:02801 481.7\n",
"BoLA-1:02901 1673.3\n",
"BoLA-1:03101 377.3\n",
"BoLA-1:03102 449.0\n",
"BoLA-1:04201 1696.4\n",
"BoLA-1:04901 647.5\n",
"BoLA-1:06101 947.3\n",
"Name: 0.99, dtype: float64\n"
]
}
],
"source": [
"reload(base)\n",
"p='netmhcpan'\n",
"P=base.get_predictor(p)\n",
"P.check_install()\n",
"print P.get_allele_cutoffs(.99)[:13]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## iedb mhc2"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"iedbmhc2 predictor\n",
"predictions done for 2 sequences in 4 alleles\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
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"\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>pos</th>\n",
" <th>allele</th>\n",
" <th>seq_num</th>\n",
" <th>start</th>\n",
" <th>end</th>\n",
" <th>core_peptide</th>\n",
" <th>peptide</th>\n",
" <th>ic50</th>\n",
" <th>percentile_rank</th>\n",
" <th>core</th>\n",
" <th>name</th>\n",
" <th>method</th>\n",
" <th>score</th>\n",
" <th>rank</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>HLA-DRB1*01:01</td>\n",
" <td>1</td>\n",
" <td>177</td>\n",
" <td>191</td>\n",
" <td>LNFLQTNGL</td>\n",
" <td>KNFLNFLQTNGLNAI</td>\n",
" <td>5.2</td>\n",
" <td>0.97</td>\n",
" <td>LNFLQTNGL</td>\n",
" <td>Bla-g-1</td>\n",
" <td>nn_align</td>\n",
" <td>5.2</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>HLA-DRB1*01:01</td>\n",
" <td>1</td>\n",
" <td>365</td>\n",
" <td>379</td>\n",
" <td>LNFLQTNGL</td>\n",
" <td>KNFLNFLQTNGLNAI</td>\n",
" <td>5.2</td>\n",
" <td>0.97</td>\n",
" <td>LNFLQTNGL</td>\n",
" <td>Bla-g-1</td>\n",
" <td>nn_align</td>\n",
" <td>5.2</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>HLA-DRB1*01:01</td>\n",
" <td>1</td>\n",
" <td>179</td>\n",
" <td>193</td>\n",
" <td>FLQTNGLNA</td>\n",
" <td>FLNFLQTNGLNAIEF</td>\n",
" <td>6.1</td>\n",
" <td>1.81</td>\n",
" <td>FLQTNGLNA</td>\n",
" <td>Bla-g-1</td>\n",
" <td>nn_align</td>\n",
" <td>6.1</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>HLA-DRB1*01:01</td>\n",
" <td>1</td>\n",
" <td>367</td>\n",
" <td>381</td>\n",
" <td>FLQTNGLNA</td>\n",
" <td>FLNFLQTNGLNAIEF</td>\n",
" <td>6.1</td>\n",
" <td>1.81</td>\n",
" <td>FLQTNGLNA</td>\n",
" <td>Bla-g-1</td>\n",
" <td>nn_align</td>\n",
" <td>6.1</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>HLA-DRB1*01:01</td>\n",
" <td>1</td>\n",
" <td>178</td>\n",
" <td>192</td>\n",
" <td>LNFLQTNGL</td>\n",
" <td>NFLNFLQTNGLNAIE</td>\n",
" <td>6.3</td>\n",
" <td>1.99</td>\n",
" <td>LNFLQTNGL</td>\n",
" <td>Bla-g-1</td>\n",
" <td>nn_align</td>\n",
" <td>6.3</td>\n",
" <td>5.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" pos allele seq_num start end core_peptide peptide ic50 percentile_rank core name \\\n",
"0 0 HLA-DRB1*01:01 1 177 191 LNFLQTNGL KNFLNFLQTNGLNAI 5.2 0.97 LNFLQTNGL Bla-g-1 \n",
"1 1 HLA-DRB1*01:01 1 365 379 LNFLQTNGL KNFLNFLQTNGLNAI 5.2 0.97 LNFLQTNGL Bla-g-1 \n",
"2 2 HLA-DRB1*01:01 1 179 193 FLQTNGLNA FLNFLQTNGLNAIEF 6.1 1.81 FLQTNGLNA Bla-g-1 \n",
"3 3 HLA-DRB1*01:01 1 367 381 FLQTNGLNA FLNFLQTNGLNAIEF 6.1 1.81 FLQTNGLNA Bla-g-1 \n",
"4 4 HLA-DRB1*01:01 1 178 192 LNFLQTNGL NFLNFLQTNGLNAIE 6.3 1.99 LNFLQTNGL Bla-g-1 \n",
"\n",
" method score rank \n",
"0 nn_align 5.2 1.0 \n",
"1 nn_align 5.2 1.0 \n",
"2 nn_align 6.1 3.0 \n",
"3 nn_align 6.1 3.0 \n",
"4 nn_align 6.3 5.0 "
]
},
"execution_count": 75,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"reload(base)\n",
"prots = ep.fasta_to_dataframe('cockroach_allergens.fa')\n",
"P = base.get_predictor('iedbmhc2')\n",
"#P = base.get_predictor('netmhciipan')\n",
"print (P)\n",
"b = P.predict_sequences(prots[:2], alleles=m2alleles[:4], cpus=1, method='nn_align') # show_cmd=True,\n",
"b.head()"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
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" var render_items = [{\"docid\":\"9de9c6b9-bd0f-4a1b-9182-e33093286a6e\",\"elementid\":\"e30f2dee-07a8-4d4f-bfc7-706fd290e390\",\"modelid\":\"48c13719-3330-406a-b75b-f0892fceed20\"}];\n",
" root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
"\n",
" }\n",
" if (root.Bokeh !== undefined) {\n",
" embed_document(root);\n",
" } else {\n",
" var attempts = 0;\n",
" var timer = setInterval(function(root) {\n",
" if (root.Bokeh !== undefined) {\n",
" embed_document(root);\n",
" clearInterval(timer);\n",
" }\n",
" attempts++;\n",
" if (attempts > 100) {\n",
" console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n",
" clearInterval(timer);\n",
" }\n",
" }, 10, root)\n",
" }\n",
"})(window);"
],
"application/vnd.bokehjs_exec.v0+json": ""
},
"metadata": {
"application/vnd.bokehjs_exec.v0+json": {
"id": "48c13719-3330-406a-b75b-f0892fceed20"
}
},
"output_type": "display_data"
}
],
"source": [
"reload(plotting)\n",
"from bokeh.io import show, output_notebook\n",
"output_notebook()\n",
"p = plotting.bokeh_plot_tracks([P],name='Bla-g-1',cutoff=.95,n=2,width=800)\n",
"show(p)"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" peptide pos name alleles score mean median_rank core\n",
"3 ATVVQQQTIAS 93 ZEBOVgp2 7 4.7800 3.821451 4.0 VVQQQTIAS\n",
"2 ALIQITKRVPI 275 ZEBOVgp2 6 7.0000 4.093070 5.5 IQITKRVPI\n",
"29 LVQVICKLGKD 241 ZEBOVgp2 5 4.1000 3.585800 4.0 VQVICKLGK\n",
"17 IHIRSRGDIPR 294 ZEBOVgp2 4 4.5487 4.255150 1.0 IHIRSRGDI\n",
"6 DLLVMTTGRAT 142 ZEBOVgp2 4 4.0000 2.504830 8.5 LVMTTGRAT\n",
"1 AFNNLNSTTSL 198 ZEBOVgp2 3 3.7000 2.830000 4.0 FNNLNSTTS\n",
"56 YDLLVMTTGRA 141 ZEBOVgp2 3 4.0000 2.976440 10.0 LLVMTTGRA\n",
"14 GWVCVFQLQDG 322 ZEBOVgp2 2 2.8000 2.400000 7.0 WVCVFQLQD\n",
"34 PVYDMAKTISS 119 ZEBOVgp2 2 2.7800 2.590000 7.0 YDMAKTISS\n",
"10 EVVQTLASLAT 84 ZEBOVgp2 2 4.0000 2.300000 10.0 VQTLASLAT\n",
"ZEBOVgp2 tepitope 329 11 340\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f83db00e1d0>"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"<matplotlib.figure.Figure at 0x7f83df83df50>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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10dTU1N9mOA5Q3b1cjnq9l8utr9x3U5pa1pfn3OaxoV7PbX9Q19knHMdxHMdx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\nAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f83d88b9a10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"reload(base)\n",
"P=base.get_predictor('tepitope')\n",
"path='../zaire_test/tepitope/'\n",
"fname = '../zaire_test/tepitope/ZEBOVgp2.csv'\n",
"P.load(path=fname)\n",
"\n",
"#b = P.get_binders(cutoff=.95, name='ZEBOVgp2')\n",
"b = P.get_binders(cutoff=.95, cutoff_method='default')\n",
"x=P.promiscuous_binders(b, n=2)\n",
"print x\n",
"\n",
"plotting.plot_tracks([P],name='ZEBOVgp2',cutoff=.95,n=2)\n"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"498\n",
" allele pos start end length peptide method percentile_rank ann_ic50 ann_rank \\\n",
"1 HLA-A*01:01 1 65 75 11 NWMAQLGPWNY Consensus (ann/smm) 2.00 2283.88 0.7 \n",
"0 HLA-A*01:01 1 66 76 11 WMAQLGPWNYA Consensus (ann/smm) 1.90 7181.33 0.7 \n",
"2 HLA-A*01:01 1 45 55 11 LIWLMVFQLAA Consensus (ann/smm) 2.40 13708.56 1.9 \n",
"3 HLA-A*01:01 1 47 57 11 WLMVFQLAAIG Consensus (ann/smm) 7.30 21147.59 6.6 \n",
"4 HLA-A*01:01 1 44 54 11 GLIWLMVFQLA Consensus (ann/smm) 8.35 24855.69 14.0 \n",
"5 HLA-A*01:01 1 41 51 11 MLIGLIWLMVF Consensus (ann/smm) 9.15 20987.35 6.3 \n",
"6 HLA-A*01:01 1 42 52 11 LIGLIWLMVFQ Consensus (ann/smm) 11.10 26368.89 17.0 \n",
"7 HLA-A*01:01 1 40 50 11 LMLIGLIWLMV Consensus (ann/smm) 13.35 20288.49 5.7 \n",
"8 HLA-A*01:01 1 32 42 11 WFVSLFIGLML Consensus (ann/smm) 15.10 18366.00 4.2 \n",
"18 HLA-A*01:01 1 70 80 11 LGPWNYAIAFA Consensus (ann/smm) 25.90 33383.30 45.0 \n",
"13 HLA-A*01:01 1 68 78 11 AQLGPWNYAIA Consensus (ann/smm) 24.00 31220.70 34.0 \n",
"10 HLA-A*01:01 1 76 86 11 AIAFAFMITGL Consensus (ann/smm) 18.50 25431.88 15.0 \n",
"9 HLA-A*01:01 1 78 88 11 AFAFMITGLLL Consensus (ann/smm) 16.95 19558.44 4.9 \n",
"11 HLA-A*01:01 1 39 49 11 GLMLIGLIWLM Consensus (ann/smm) 19.00 25539.96 15.0 \n",
"24 HLA-A*01:01 1 10 20 11 NDFTVSAVSRT Consensus (ann/smm) 30.00 32713.58 41.0 \n",
"\n",
" smm_ic50 smm_rank core name ic50 score rank \n",
"1 1918.757101 3.3 NWMAQLGPWNY Rv0011c 2101.318550 2101.318550 1.0 \n",
"0 1774.271187 3.1 WMAQLGPWNYA Rv0011c 4477.800594 4477.800594 2.0 \n",
"2 1610.719810 2.9 LIWLMVFQLAA Rv0011c 7659.639905 7659.639905 3.0 \n",
"3 4853.108490 8.0 WLMVFQLAAIG Rv0011c 13000.349245 13000.349245 4.0 \n",
"4 1548.887946 2.7 GLIWLMVFQLA Rv0011c 13202.288973 13202.288973 5.0 \n",
"5 6486.643050 12.0 MLIGLIWLMVF Rv0011c 13736.996525 13736.996525 6.0 \n",
"6 3221.217130 5.2 LIGLIWLMVFQ Rv0011c 14795.053565 14795.053565 7.0 \n",
"7 12942.554427 21.0 LMLIGLIWLMV Rv0011c 16615.522214 16615.522214 8.0 \n",
"8 17539.612729 26.0 WFVSLFIGLML Rv0011c 17952.806364 17952.806364 9.0 \n",
"18 4168.885815 6.8 LGPWNYAIAFA Rv0011c 18776.092907 18776.092907 10.0 \n",
"13 7852.717969 14.0 AQLGPWNYAIA Rv0011c 19536.708984 19536.708984 11.0 \n",
"10 14093.537001 22.0 AIAFAFMITGL Rv0011c 19762.708500 19762.708500 12.0 \n",
"9 20750.090729 29.0 AFAFMITGLLL Rv0011c 20154.265365 20154.265365 13.0 \n",
"11 15206.175552 23.0 GLMLIGLIWLM Rv0011c 20373.067776 20373.067776 14.0 \n",
"24 11376.796767 19.0 NDFTVSAVSRT Rv0011c 22045.188384 22045.188384 15.0 \n",
" peptide pos name alleles ic50 mean median_rank core\n",
"6 GPWNYAIAFAF 1 Rv0011c 2 1464.115691 1517.820942 1.0 GPWNYAIAFAF\n",
"11 MLIGLIWLMVF 1 Rv0011c 2 465.893130 3007.641921 4.0 MLIGLIWLMVF\n",
"2 FMITGLLLTMR 1 Rv0011c 2 855.544546 1952.471828 4.0 FMITGLLLTMR\n",
"18 TVSAVSRTPMK 1 Rv0011c 2 3472.691529 5601.765903 4.0 TVSAVSRTPMK\n",
"Rv0011c iedbmhc1 1 11 12\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f83d7d2f250>"
]
},
"execution_count": 77,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"<matplotlib.figure.Figure at 0x7f83d7ddf750>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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iUWAJcFLlfESsiIgxwBqymdQXI+LkiFghqRdwMLmiMSLeSMeHtnPZnYGXIuL1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\notXMzMzMSs9Fq5mZmZmV3v8HsEdqULizkWEAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f83d7d257d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"reload(base)\n",
"#check we can still get binders using path only\n",
"#p='tepitope'\n",
"p='iedbmhc1'\n",
"path='../mtb_results/%s/' %p\n",
"P=base.get_predictor(p)\n",
"fname = '../results/%s/Rv0011c.csv' %p\n",
"P.load(path=fname)\n",
"print len(P.data)\n",
"print P.data[:15]\n",
"b = P.get_binders(cutoff=.95)\n",
"x=P.promiscuous_binders(b, n=2)\n",
"print x\n",
"\n",
"plotting.plot_tracks([P],name='Rv0011c',cutoff=.95,n=2)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"reload(base)\n",
"path='../zaire_test/tepitope/'\n",
"files = glob.glob(os.path.join(path, '*.csv'))\n",
"P=base.get_predictor('tepitope')\n",
"tempdata = base.results_from_csv(path, file_limit=100)\n",
"cuts = P.get_allele_cutoffs(tempdata, 5.0)\n",
"\n",
"P=base.get_predictor('tepitope')\n",
"P.load(path=path)\n",
"res1 = P.get_binders(cutoff_method='score', cutoff=6 )\n",
"print res1[40:50]\n",
"P=base.get_predictor('tepitope')\n",
"res2 = P.get_binders(path=path, cutoff_method='score', cutoff=6 )\n",
"print res2[40:50]\n"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2607\n"
]
}
],
"source": [
"def results_to_hdf(files):\n",
" for f in files:\n",
" df=pd.read_csv(f,index_col=0).reset_index()\n",
" n=os.path.splitext(os.path.basename(f))[0]\n",
" #print n\n",
" df.to_hdf('test.hdf5', key='results', mode='a') \n",
"\n",
"#results_to_hdf(files[:1000])\n",
"\n",
"df=pd.read_hdf('test.hdf5','results')\n",
"print len(df)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"cached file found\n",
"cached file found\n",
"tepitope\n",
" name score binders\n",
"188 Rv0194 4.75 78\n",
"2007 Rv2048c 4.85 76\n",
"977 Rv0987 4.80 69\n",
"3727 Rv3823c 5.20 67\n",
"196 Rv0202c 5.12 67\n",
"3807 Rv3910 4.76 66\n",
"500 Rv0507 5.10 63\n",
"3700 Rv3795 4.40 62\n",
"669 Rv0676c 5.05 62\n",
"95 Rv0101 4.60 60\n",
"mhcflurry\n",
" name score binders\n",
"90 Rv0194 1125.729577 8\n",
"245 Rv1258c 570.995621 7\n",
"282 Rv2048c 1077.257869 6\n",
"113 Rv0236c 630.997108 5\n",
"457 Rv3158 1311.639817 5\n",
"456 Rv3157 540.972151 5\n",
"35 Rv0083 490.419022 5\n",
"47 Rv0102 1759.127012 5\n",
"21 Rv0051 1876.516231 5\n",
"98 Rv0206c 774.351574 5\n"
]
}
],
"source": [
"reload(base)\n",
"\n",
"path='../mtb_results'\n",
"name = 'Rv0019c'\n",
"\n",
"def get_summary_tables(path, limit=None, **kwargs):\n",
" \"\"\"Get binder results summary for all proteins in path.\n",
" Args:\n",
" path: path to results\n",
" \"\"\"\n",
"\n",
" data = web.get_results_tables(path, view='promiscuous', **kwargs)\n",
" for i in data:\n",
" #seqs = web.get_sequences(P)\n",
" print i\n",
" x = data[i].groupby('name').agg({'peptide':np.size,'score':np.median}).reset_index()\n",
" x = x.rename(columns={'peptide':'binders'})\n",
" #x['binder_density'] = x.length/x.\n",
" x = x.sort_values(by='binders',ascending=False)\n",
" print x[:10]\n",
" return data\n",
"\n",
"d = get_summary_tables(path, n=3, cutoff=.95)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"predictions done for 5 sequences in 8 alleles\n",
" peptide core pos score name allele rank\n",
"0 MRRVILPTAPP MRRVILPTA 0 -0.08 0 HLA-DRB1*0101 1.0\n",
"17 YPVRSNSTIAR VRSNSTIAR 17 -0.16 0 HLA-DRB1*0101 2.0\n",
"18 PVRSNSTIARG VRSNSTIAR 18 -0.16 0 HLA-DRB1*0101 2.0\n"
]
}
],
"source": [
"reload(tepitope)\n",
"reload(base)\n",
"seqs = peptutils.create_random_sequences(500, length=11)\n",
"#for i in seqs[:100]:\n",
"# print i\n",
"\n",
"m2alleles = base.get_preset_alleles('mhc2_supertypes')\n",
"P = ep.get_predictor('tepitope')\n",
"#x1 = P.predict_peptides(seqs[:20], alleles=m2alleles)\n",
"x=P.predict_proteins(prots, alleles=m2alleles)\n",
"print x[:3]\n",
"#x2 = P._predict_peptides(seqs[:20], alleles=m2alleles)\n",
"#print len(x1), len(x2)\n",
"#print x1.equals(x2)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"reload(base)\n",
"halleles=['HLA-A*23:01']\n",
"seqs = peptutils.create_random_sequences(10)\n",
"P = base.get_predictor('iedbmhc1')\n",
"print P.method\n",
"st=time.time()\n",
"df=sequtils.fasta_to_dataframe('Rv0011c.fa')\n",
"r=P.predict_sequences(df, alleles=halleles, show_cmd=True)\n",
"#P.predict_peptides(seqs, alleles=halleles, show_cmd=True, method='smm',cpus=4)\n",
"print time.time()-st\n",
"#print P.data"
]
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"netMHCpan -BA -f /tmp/tmp8Eakrg.pep -inptype 1 -a HLA-A02:07\n",
"netMHCpan -BA -f /tmp/tmpWjyIOT.pep -inptype 1 -a HLA-A01:01\n",
"netMHCpan -BA -f /tmp/tmpdiMav7.pep -inptype 1 -a HLA-B02:01\n",
"netMHCpan -BA -f /tmp/tmpTqd_5a.pep -inptype 1 -a HLA-C02:02\n",
"predictions done for 1 sequences in 4 alleles\n",
" peptide pos name alleles score mean median_rank core\n",
"10 IAFAFMITGLL 76 NC_000962.3:c13995-13714 2 7105.3 13083.45 9.0 IAFAFMITL\n",
"6 FTVSAVSRTPM 11 NC_000962.3:c13995-13714 1 1275.0 1275.00 1.0 FTVSAVSPM\n",
"18 SLFIGLMLIGL 34 NC_000962.3:c13995-13714 1 7135.8 7135.80 1.0 SLFIGLMGL\n",
"17 NWMAQLGPWNY 64 NC_000962.3:c13995-13714 1 8165.8 8165.80 1.0 WMAQGPWNY\n",
"7 FVSLFIGLMLI 32 NC_000962.3:c13995-13714 1 7562.5 7562.50 2.0 FLFIGLMLI\n",
"9 GLMLIGLIWLM 38 NC_000962.3:c13995-13714 1 8355.4 8355.40 3.0 GLMGLIWLM\n",
"0 AAIGSQAPTAL 53 NC_000962.3:c13995-13714 1 9127.2 9127.20 3.0 AAIGAPTAL\n",
"15 MAQLGPWNYAI 66 NC_000962.3:c13995-13714 1 8754.3 8754.30 4.0 MLGPWNYAI\n",
"1 AFAFMITGLLL 77 NC_000962.3:c13995-13714 1 10382.0 10382.00 4.0 FAFMTGLLL\n",
"14 LMLIGLIWLMV 39 NC_000962.3:c13995-13714 1 9955.3 9955.30 5.0 MLIGLIWLV\n",
"4 FAFMITGLLLT 78 NC_000962.3:c13995-13714 1 11339.8 11339.80 6.0 FMITGLLLT\n",
"19 SVWFVSLFIGL 29 NC_000962.3:c13995-13714 1 12472.0 12472.00 7.0 SVWSLFIGL\n",
"2 AFMITGLLLTM 79 NC_000962.3:c13995-13714 1 13400.5 13400.50 8.0 FMITGLLLM\n",
"13 IWLMVFQLAAI 45 NC_000962.3:c13995-13714 1 13481.2 13481.20 9.0 IMVFQLAAI\n",
"20 WMAQLGPWNYA 65 NC_000962.3:c13995-13714 1 16403.9 16403.90 11.0 WMLGPWNYA\n",
"11 IGLIWLMVFQL 42 NC_000962.3:c13995-13714 1 17213.4 17213.40 13.0 IIWLMVFQL\n",
"12 IGLMLIGLIWL 37 NC_000962.3:c13995-13714 1 18541.5 18541.50 14.0 IMLIGLIWL\n",
"3 AIAFAFMITGL 75 NC_000962.3:c13995-13714 1 18721.9 18721.90 15.0 AIAFAFMGL\n",
"8 GLIWLMVFQLA 43 NC_000962.3:c13995-13714 1 20355.6 20355.60 17.0 GLIWVFQLA\n"
]
}
],
"source": [
"reload(base)\n",
"halleles=['HLA-A*02:07','HLA-A*01:01','HLA-B*02:01','HLA-C*02:02']\n",
"#seqs = peptutils.create_random_sequences(20)\n",
"P = base.get_predictor('netmhcpan')\n",
"#P.predict_peptides(seqs, alleles=halleles)\n",
"df=sequtils.fasta_to_dataframe('Rv0011c.fa')\n",
"r=P.predict_sequences(df, alleles=halleles, show_cmd=True)\n",
"#print P.data\n",
"print P.promiscuous_binders(cutoff=.95)\n",
"#benchmark_seqs(P, seqs, halleles)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"reload(base)\n",
"halleles=['HLA-A*02:07']\n",
"#seqs = peptutils.create_random_sequences(500)\n",
"P = base.get_predictor('mhcflurry')\n",
"res1=P.predict_peptides(seqs, alleles=halleles)\n",
"#P.predictProteins(prots, alleles=halleles)\n",
"\n",
"#benchmark_seqs(P, seqs, halleles)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"reload(base)\n",
"#seqs = peptutils.create_random_sequences(500)\n",
"P=base.get_predictor('mhcnuggets')\n",
"P.path = '/local/mhcnuggets'\n",
"res2=P.predict_peptides(seqs, alleles=halleles, show_cmd=True)\n",
"print len(res2)\n",
"#benchmark_seqs(P, seqs, halleles)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"reload(base)\n",
"def compare_predictors(p1, p2, allele, n=20, cpus=1, **kwargs):\n",
" seqs = peptutils.create_random_sequences(n)\n",
" P1 = ep.get_predictor(p1)\n",
" P2 = ep.get_predictor(p2)\n",
" res1=P1.predict_peptides(seqs, alleles=allele, cpus=cpus) \n",
" res2=P2.predict_peptides(seqs, alleles=allele, cpus=cpus, **kwargs) \n",
" x=res1.merge(res2,on='peptide')\n",
" f,ax=plt.subplots(1,2,figsize=(12,5))\n",
" ax=ax.flat\n",
" x.plot(P1.scorekey,P2.scorekey,kind='scatter',c='black',alpha=.7,ax=ax[0])\n",
" ax[0].set_xlabel(P1.name)\n",
" ax[0].set_ylabel(P2.name)\n",
" b1=P1.get_binders()#cutoff_method='score', cutoff=500)\n",
" b2=P2.get_binders()#cutoff_method='score', cutoff=500)\n",
" f = utilities.venndiagram([b1.peptide, b2.peptide],[P1.name,P2.name],colors=('y','b'),ax=ax[1])\n",
" return \n",
"\n",
"compare_predictors('mhcflurry', 'mhcnuggets', 'HLA-A*30:02', 100, cpus=1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#df = pd.pivot_table(P.data, index='allele',values='score',columns=['pos','peptide'])\n",
"#sns.heatmap(df)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"from bokeh.plotting import figure, show\n",
"from bokeh.io import output_notebook"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
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"}(window));"
],
"application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"<div style='background-color: #fdd'>\\n\"+\n \"<p>\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"</p>\\n\"+\n \"<ul>\\n\"+\n \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n \"<li>use INLINE resources instead, as so:</li>\\n\"+\n \"</ul>\\n\"+\n \"<code>\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"</code>\\n\"+\n \"</div>\"}};\n\n function display_loaded() {\n var el = document.getElementById(\"60d656a7-2304-46ad-a2f6-6a4fd6018614\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n }\n finally {\n delete root._bokeh_onload_callbacks\n }\n console.info(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(js_urls, callback) {\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = js_urls.length;\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var s = document.createElement('script');\n s.src = url;\n s.async = false;\n s.onreadystatechange = s.onload = function() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: all BokehJS libraries loaded\");\n run_callbacks()\n }\n };\n s.onerror = function() {\n console.warn(\"failed to load library \" + url);\n };\n console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.getElementsByTagName(\"head\")[0].appendChild(s);\n }\n };var element = document.getElementById(\"60d656a7-2304-46ad-a2f6-6a4fd6018614\");\n if (element == null) {\n console.log(\"Bokeh: ERROR: autoload.js configured with elementid '60d656a7-2304-46ad-a2f6-6a4fd6018614' but no matching script tag was found. \")\n return false;\n }\n\n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.14.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.14.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.14.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.14.min.js\"];\n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n \n function(Bokeh) {\n \n },\n function(Bokeh) {\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.14.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.14.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.14.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.14.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.14.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.14.min.css\");\n }\n ];\n\n function run_inline_js() {\n \n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }if (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(\"60d656a7-2304-46ad-a2f6-6a4fd6018614\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(js_urls, function() {\n console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"output_notebook()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"preds=['tepitope','netmhciipan','mhcflurry']\n",
"for p in preds[:1]:\n",
" P=base.get_predictor(p) \n",
" fname = '../mtb_results/%s/Rv0011c.csv' %p\n",
" P.load(path=fname) \n",
" d=P.data\n",
" b=P.get_binders(cutoff=.96)\n",
" \n",
" #pl=bokeh_plot_grid(P,name='Rv0019c',palette='Blues',width=800, cutoff=.95) \n",
" #show(pl)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def get_bokeh_colors(palette='Set1'):\n",
"\n",
" from bokeh.palettes import brewer\n",
" n = len(base.predictors)\n",
" pal = brewer[palette][n]\n",
" i=0\n",
" clrs={}\n",
" for m in base.predictors:\n",
" clrs[m] = pal[i]\n",
" i+=1\n",
" return clrs\n",
"\n",
"\n",
"def bokeh_summary_plot(df, savepath=None):\n",
" \"\"\"Summary plot\"\"\"\n",
"\n",
" from bokeh.plotting import figure\n",
" from bokeh.layouts import column\n",
" from bokeh.models import ColumnDataSource,Range1d,HoverTool,TapTool,CustomJS,OpenURL\n",
"\n",
" TOOLS = \"pan,wheel_zoom,hover,tap,reset,save\"\n",
"\n",
" colors = get_bokeh_colors()\n",
" df=df.rename(columns={'level_0':'predictor'})\n",
" df['color'] = [colors[x] for x in df['predictor']]\n",
" p = figure(title = \"Summary\", tools=TOOLS, width=500, height=500)\n",
" p.xaxis.axis_label = 'binder_density'\n",
" p.yaxis.axis_label = 'binders'\n",
"\n",
" #make metric for point sizes\n",
" #df['point_size'] = df.binder_density\n",
" source = ColumnDataSource(data=df)\n",
" \n",
" p.circle(x='binder_density', y='binders', line_color='black', fill_color='color',\n",
" fill_alpha=0.4, size=10, source=source, legend='predictor')\n",
" hover = p.select(dict(type=HoverTool))\n",
" hover.tooltips = OrderedDict([\n",
" (\"name\", \"@name\"), \n",
" (\"length\", \"@length\"), \n",
" (\"binders\", \"@binders\"),\n",
" (\"binder_density\", \"@binder_density\"),\n",
" (\"top_peptide\", \"@top_peptide\"), \n",
" (\"max_score\", \"@max_score\"), \n",
" ])\n",
" p.toolbar.logo = None\n",
" if savepath != None:\n",
" url = \"http://localhost:8000/sequence?savepath=%s&name=@name\" %savepath\n",
" taptool = p.select(type=TapTool)\n",
" taptool.callback = OpenURL(url=url)\n",
" \n",
" callback = CustomJS(args=dict(source=source), code=\"\"\"\n",
" var data = source.data;\n",
" var f = cb_obj.value\n",
"\n",
" data['x'] = f\n",
" source.trigger('change');\n",
" source.change.emit();\n",
" \"\"\")\n",
"\n",
" from bokeh.layouts import widgetbox\n",
" from bokeh.models.widgets import Select\n",
" menu = [(i,i) for i in df.columns]\n",
" select = Select(title='X', value='A', options=list(df.columns), width=8)\n",
" select.js_on_change('value', callback)\n",
" \n",
" #layout = column(p, select, sizing_mode='scale_width')\n",
" return p\n",
"\n",
"reload(web)\n",
"data = web.get_summary_tables('../listeria')\n",
"df = pd.concat(data).reset_index()\n",
"\n",
"p=bokeh_summary_plot(df, 'listeria')\n",
"show(p)\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Index([u'level_0', u'level_1', u'name', u'max_score', u'top_peptide', u'binders', u'clusters', u'locus_tag',\n",
" u'translation', u'description', u'type', u'length', u'binder_density'],\n",
" dtype='object')\n",
"[4.37186576 0. 0. 0. 0. 0.\n",
" 0. 1.60730359 0. 0. 0. 0.\n",
" 0. 0. 0.73293044 0. 0. 0.\n",
" 0. 0. 0. 0.20573486 0. 0.\n",
" 0. 0. 0. 0. 0.14144272 0.\n",
" 0. 0. 0. 0. 0. 0.05143371\n",
" 0. 0. 0. 0. 0. 0.\n",
" 0.02571686 0. 0. 0. 0. 0.\n",
" 0. 0.00642921]\n"
]
},
{
"data": {
"text/html": [
"\n",
"<div class=\"bk-root\">\n",
" <div class=\"bk-plotdiv\" id=\"0fa12371-a2f5-495a-a78a-c083df4ad110\"></div>\n",
"</div>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/javascript": [
"(function(root) {\n",
" function embed_document(root) {\n",
" \n",
" var docs_json = {\"88392572-1749-4b9f-bd43-e969565a3a51\":{\"roots\":{\"references\":[{\"attributes\":{\"bottom\":{\"value\":0},\"fill_color\":{\"value\":\"#036564\"},\"left\":{\"field\":\"left\"},\"line_color\":{\"value\":\"#033649\"},\"right\":{\"field\":\"right\"},\"top\":{\"field\":\"top\"}},\"id\":\"2c25c402-49a9-44f2-b973-0ad29fa719df\",\"type\":\"Quad\"},{\"attributes\":{},\"id\":\"c30e7921-b789-4993-a61a-45282d5350b3\",\"type\":\"SaveTool\"},{\"attributes\":{\"formatter\":{\"id\":\"a056482e-1794-4f81-b21f-f1e26320383e\",\"type\":\"BasicTickFormatter\"},\"plot\":{\"id\":\"e6e29b5b-486d-40f9-a49a-517722ecb304\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"caa1646e-0909-4241-a3f7-007de59ddbe4\",\"type\":\"BasicTicker\"}},\"id\":\"85a3fe6b-2a6b-4d25-8cf6-6541962c49e9\",\"type\":\"LinearAxis\"},{\"attributes\":{},\"id\":\"a056482e-1794-4f81-b21f-f1e26320383e\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"plot\":null,\"text\":\"Summary\"},\"id\":\"6acf074b-f243-4a30-b876-f5bc93169394\",\"type\":\"Title\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"right\",\"top\",\"left\"],\"data\":{\"left\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[50]},\"right\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[50]},\"top\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[50]}}},\"id\":\"3067b73c-85d1-4d7d-80ef-2596c701c1ea\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"callback\":null},\"id\":\"72c28451-c4c3-469c-b2d0-3a3df205c0c5\",\"type\":\"TapTool\"},{\"attributes\":{\"formatter\":{\"id\":\"2e9c6b47-1213-4105-9622-814766fc1f39\",\"type\":\"BasicTickFormatter\"},\"plot\":{\"id\":\"e6e29b5b-486d-40f9-a49a-517722ecb304\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"5c52cff0-cc57-4b8f-b46b-7f3404b738b0\",\"type\":\"BasicTicker\"}},\"id\":\"300b3eda-6db8-41a9-9571-6d3580e6348c\",\"type\":\"LinearAxis\"},{\"attributes\":{},\"id\":\"5c52cff0-cc57-4b8f-b46b-7f3404b738b0\",\"type\":\"BasicTicker\"},{\"attributes\":{\"below\":[{\"id\":\"300b3eda-6db8-41a9-9571-6d3580e6348c\",\"type\":\"LinearAxis\"}],\"left\":[{\"id\":\"85a3fe6b-2a6b-4d25-8cf6-6541962c49e9\",\"type\":\"LinearAxis\"}],\"plot_height\":300,\"plot_width\":400,\"renderers\":[{\"id\":\"300b3eda-6db8-41a9-9571-6d3580e6348c\",\"type\":\"LinearAxis\"},{\"id\":\"6a2fb9d2-d68c-4bef-814f-30fbd250771a\",\"type\":\"Grid\"},{\"id\":\"85a3fe6b-2a6b-4d25-8cf6-6541962c49e9\",\"type\":\"LinearAxis\"},{\"id\":\"7ea8c42f-edc5-4d75-a6ed-a9ab89195c7f\",\"type\":\"Grid\"},{\"id\":\"b31d785a-5705-450e-a3ed-cb03e8fc5a2f\",\"type\":\"GlyphRenderer\"}],\"title\":{\"id\":\"6acf074b-f243-4a30-b876-f5bc93169394\",\"type\":\"Title\"},\"toolbar\":{\"id\":\"e6bdd75f-2ba9-421f-ac05-6e8e9caac575\",\"type\":\"Toolbar\"},\"x_range\":{\"id\":\"6b603dc7-4a65-44d0-91ef-3226dff10d27\",\"type\":\"DataRange1d\"},\"x_scale\":{\"id\":\"301fd490-ccf5-4b24-b4d8-7bbafc916744\",\"type\":\"LinearScale\"},\"y_range\":{\"id\":\"c1bd4287-76ce-448e-98b1-94b09759cbc7\",\"type\":\"DataRange1d\"},\"y_scale\":{\"id\":\"aceda83b-745b-4a73-a835-e514fbe12607\",\"type\":\"LinearScale\"}},\"id\":\"e6e29b5b-486d-40f9-a49a-517722ecb304\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{\"callback\":null},\"id\":\"c1bd4287-76ce-448e-98b1-94b09759cbc7\",\"type\":\"DataRange1d\"},{\"attributes\":{\"plot\":{\"id\":\"e6e29b5b-486d-40f9-a49a-517722ecb304\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"5c52cff0-cc57-4b8f-b46b-7f3404b738b0\",\"type\":\"BasicTicker\"}},\"id\":\"6a2fb9d2-d68c-4bef-814f-30fbd250771a\",\"type\":\"Grid\"},{\"attributes\":{\"callback\":null},\"id\":\"6b603dc7-4a65-44d0-91ef-3226dff10d27\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"2e9c6b47-1213-4105-9622-814766fc1f39\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{},\"id\":\"acdef115-5b62-4286-9e95-f8bd50ce4f25\",\"type\":\"PanTool\"},{\"attributes\":{\"data_source\":{\"id\":\"3067b73c-85d1-4d7d-80ef-2596c701c1ea\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"2c25c402-49a9-44f2-b973-0ad29fa719df\",\"type\":\"Quad\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"df96cab2-c612-41e6-9a17-c0843732a84f\",\"type\":\"Quad\"},\"selection_glyph\":null,\"view\":{\"id\":\"3a363625-f15c-4758-b487-a48684faaa4e\",\"type\":\"CDSView\"}},\"id\":\"b31d785a-5705-450e-a3ed-cb03e8fc5a2f\",\"type\":\"GlyphRenderer\"},{\"attributes\":{},\"id\":\"aceda83b-745b-4a73-a835-e514fbe12607\",\"type\":\"LinearScale\"},{\"attributes\":{},\"id\":\"e514aa1a-5179-44c7-afb6-008c16658b47\",\"type\":\"ResetTool\"},{\"attributes\":{},\"id\":\"caa1646e-0909-4241-a3f7-007de59ddbe4\",\"type\":\"BasicTicker\"},{\"attributes\":{\"source\":{\"id\":\"3067b73c-85d1-4d7d-80ef-2596c701c1ea\",\"type\":\"ColumnDataSource\"}},\"id\":\"3a363625-f15c-4758-b487-a48684faaa4e\",\"type\":\"CDSView\"},{\"attributes\":{\"bottom\":{\"value\":0},\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"left\":{\"field\":\"left\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"right\":{\"field\":\"right\"},\"top\":{\"field\":\"top\"}},\"id\":\"df96cab2-c612-41e6-9a17-c0843732a84f\",\"type\":\"Quad\"},{\"attributes\":{},\"id\":\"301fd490-ccf5-4b24-b4d8-7bbafc916744\",\"type\":\"LinearScale\"},{\"attributes\":{\"dimension\":1,\"plot\":{\"id\":\"e6e29b5b-486d-40f9-a49a-517722ecb304\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"caa1646e-0909-4241-a3f7-007de59ddbe4\",\"type\":\"BasicTicker\"}},\"id\":\"7ea8c42f-edc5-4d75-a6ed-a9ab89195c7f\",\"type\":\"Grid\"},{\"attributes\":{},\"id\":\"f170dbc8-07fd-4ba6-a594-c1f9a7168d6e\",\"type\":\"WheelZoomTool\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"acdef115-5b62-4286-9e95-f8bd50ce4f25\",\"type\":\"PanTool\"},{\"id\":\"f170dbc8-07fd-4ba6-a594-c1f9a7168d6e\",\"type\":\"WheelZoomTool\"},{\"id\":\"8c7d575e-c231-47fb-b790-23898110b98c\",\"type\":\"HoverTool\"},{\"id\":\"72c28451-c4c3-469c-b2d0-3a3df205c0c5\",\"type\":\"TapTool\"},{\"id\":\"e514aa1a-5179-44c7-afb6-008c16658b47\",\"type\":\"ResetTool\"},{\"id\":\"c30e7921-b789-4993-a61a-45282d5350b3\",\"type\":\"SaveTool\"}]},\"id\":\"e6bdd75f-2ba9-421f-ac05-6e8e9caac575\",\"type\":\"Toolbar\"},{\"attributes\":{\"callback\":null},\"id\":\"8c7d575e-c231-47fb-b790-23898110b98c\",\"type\":\"HoverTool\"}],\"root_ids\":[\"e6e29b5b-486d-40f9-a49a-517722ecb304\"]},\"title\":\"Bokeh Application\",\"version\":\"0.12.14\"}};\n",
" var render_items = [{\"docid\":\"88392572-1749-4b9f-bd43-e969565a3a51\",\"elementid\":\"0fa12371-a2f5-495a-a78a-c083df4ad110\",\"modelid\":\"e6e29b5b-486d-40f9-a49a-517722ecb304\"}];\n",
" root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
"\n",
" }\n",
" if (root.Bokeh !== undefined) {\n",
" embed_document(root);\n",
" } else {\n",
" var attempts = 0;\n",
" var timer = setInterval(function(root) {\n",
" if (root.Bokeh !== undefined) {\n",
" embed_document(root);\n",
" clearInterval(timer);\n",
" }\n",
" attempts++;\n",
" if (attempts > 100) {\n",
" console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n",
" clearInterval(timer);\n",
" }\n",
" }, 10, root)\n",
" }\n",
"})(window);"
],
"application/vnd.bokehjs_exec.v0+json": ""
},
"metadata": {
"application/vnd.bokehjs_exec.v0+json": {
"id": "e6e29b5b-486d-40f9-a49a-517722ecb304"
}
},
"output_type": "display_data"
}
],
"source": [
"def bokeh_summary2(df, savepath=None):\n",
" \"\"\"Summary plot\"\"\"\n",
"\n",
" from bokeh.plotting import figure\n",
" from bokeh.layouts import column\n",
" from bokeh.models import ColumnDataSource,Range1d,HoverTool,TapTool,CustomJS,OpenURL\n",
"\n",
" TOOLS = \"pan,wheel_zoom,hover,tap,reset,save\"\n",
" p = figure(title = \"Summary\", tools=TOOLS, width=400, height=300)\n",
" source = ColumnDataSource(data=df) \n",
" hist, edges = np.histogram(df.binders, density=True, bins=50)\n",
" print hist \n",
" p.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:],\n",
" fill_color=\"#036564\", line_color=\"#033649\")\n",
" \n",
" return p\n",
"\n",
"data = web.get_summary_tables('../listeria')\n",
"df = pd.concat(data).reset_index()\n",
"print df.columns\n",
"p=bokeh_summary2(df)\n",
"show(p)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### getting peptides from clusters - standardise and add to workflow"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" peptide pos name alleles score mean median_rank core locus_tag gene start end \\\n",
"0 CRFNTTLISKI 245 ZEBOVgp7 9 7.20 5.041358 1.0 CRFNTTLIS ZEBOVgp7 L 245 256 \n",
"1 FRLMRTNFLIK 200 ZEBOVgp1 9 7.52 4.584448 7.0 FRLMRTNFL ZEBOVgp1 NP 200 211 \n",
"\n",
" n-mer hydro net_charge \n",
"0 KDLITCRFNTTLISKIAEIE 0.40 0 \n",
"1 HMMVIFRLMRTNFLIKFLLI 0.55 3 \n"
]
}
],
"source": [
"reload(analysis)\n",
"P=base.get_predictor(p) \n",
"path = '../listeria/tepitope/'\n",
"path = '../zaire_test/tepitope/'\n",
"P.load(path) \n",
"pb = P.promiscuous_binders(n=3,cutoff=.98)\n",
"\n",
"#print cl\n",
"genome = sequtils.genbank_to_dataframe('../epitopepredict/testing/zaire-ebolavirus.gb',cds=True)\n",
"\n",
"#cl = analysis.find_clusters(pb)\n",
"x = analysis.get_nmer(pb, genome, how='split', length=20)\n",
"x=analysis.peptide_properties(x, 'n-mer')\n",
"print x[:2]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"xcl=[]\n",
"for c in np.arange(.93,.99,.01):\n",
" pb = P.promiscuous_binders(n=2,cutoff=c)\n",
" cl = analysis.find_clusters(pb)\n",
" xcl.append((c,len(cl)))\n",
" \n",
"plt.plot(*zip(*xcl))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.2"
}
},
"nbformat": 4,
"nbformat_minor": 4
}