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Created May 19, 2015 21:52
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SalmonKallistoAnalysis
{
"metadata": {
"name": "",
"signature": "sha256:e84d99a6fb9022485598ea1302542338af57e9f5fdd2504968289e17c332be98"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import scipy as sp\n",
"import scipy.stats as stats"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Truncate values in colname < val to 0\n",
"def filterExpression(df, colname=\"TPM\", val=0.01):\n",
" df[df[colname] < val] = 0"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 23
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Join a list of DataFrames together, merging on the index and renaming conflicting columns\n",
"def joinFrames(frames):\n",
" current = frames[0]\n",
" for i, frame in enumerate(frames[1:], 2):\n",
" current = current.join(frame, rsuffix='{}'.format(i))\n",
" return current"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"salmon_files = [\"sim_test_salmon_{}/quant.sf\".format(i) for i in xrange(1,11)]\n",
"salmonDataFrames = []\n",
"for fn in salmon_files:\n",
" # Rename #Name => Name\n",
" df = pd.read_csv(fn, sep='\\t', skiprows=11)\n",
" df = df.rename(columns={'# Name': 'Name'})\n",
" df = df.set_index('Name')\n",
" salmonDataFrames.append(df)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"kallisto_files = [\"sim_test_kallisto_bs/bs_abundance_{}.txt\".format(i) for i in xrange(10)]\n",
"kallistoDataFrames = []\n",
"for fn in kallisto_files:\n",
" # Rename #Name => Name\n",
" df = pd.read_csv(fn, sep='\\t')\n",
" df = df.rename(columns={'target_id' : 'Name', 'length' : 'Length', 'est_counts' : 'NumReads', 'tpm' : 'TPM'})\n",
" df = df.set_index('Name')\n",
" kallistoDataFrames.append(df)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"mergedSalmon = joinFrames(salmonDataFrames)\n",
"mergedSalmon[\"NumReadsAvg\"] = mergedSalmon[[\"NumReads\"] + [\"NumReads{}\".format(i) for i in xrange(2,11)]].mean(axis=1)\n",
"mergedSalmon[\"TPMAvg\"] = mergedSalmon[[\"TPM\"] + [\"TPM{}\".format(i) for i in xrange(2,11)]].mean(axis=1)\n",
"mergedSalmon[\"NumReadsStdDev\"] = mergedSalmon[[\"NumReads\"] + [\"NumReads{}\".format(i) for i in xrange(2,11)]].std(axis=1)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"mergedKallisto = joinFrames(kallistoDataFrames)\n",
"mergedKallisto[\"NumReadsAvg\"] = mergedKallisto[[\"NumReads\"] + [\"NumReads{}\".format(i) for i in xrange(2,11)]].mean(axis=1)\n",
"mergedKallisto[\"TPMAvg\"] = mergedKallisto[[\"TPM\"] + [\"TPM{}\".format(i) for i in xrange(2,11)]].mean(axis=1)\n",
"mergedKallisto[\"NumReadsStdDev\"] = mergedKallisto[[\"NumReads\"] + [\"NumReads{}\".format(i) for i in xrange(2,11)]].std(axis=1)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"truth = pd.read_csv('data/quant_bias_corrected.sf', sep='\\t', skiprows=12)\n",
"# Rename #Name => Name\n",
"truth = truth.rename(columns={'# Name':'Name'})\n",
"truth = truth.set_index('Name')\n",
"filterExpression(truth)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"salmon = pd.read_csv('sim_test_salmon_1/quant.sf', sep='\\t', skiprows=11)\n",
"salmon = salmon.rename(columns={'# Name': 'Name'})\n",
"salmon = salmon.set_index('Name')\n",
"filterExpression(salmon)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"kallisto = pd.read_csv('sim_test_kallisto/abundance.txt', sep='\\t')\n",
"kallisto = kallisto.rename(columns={'target_id' : 'Name', 'length' : 'Length', 'est_counts' : 'NumReads', 'tpm' : 'TPM'})\n",
"kallisto = kallisto.set_index('Name')\n",
"filterExpression(kallisto)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# The original TPMs were on a larger dataset, so re-normalize them\n",
"truth = truth.loc[kallisto.index]\n",
"truth = truth.fillna(0)\n",
"x = 1000000.0 * (truth[\"TPM\"] / truth[\"TPM\"].sum())\n",
"truth[\"TPM\"] = x"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 55
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Apply some reasonable filters (Salmon does better with no filtering i.e. on the very low end)\n",
"filterExpression(mergedSalmon, colname=\"NumReads\", val=1)\n",
"filterExpression(mergedSalmon, colname=\"TPM\", val=0.01)\n",
"truthSalmon = truth.join(mergedSalmon, lsuffix='_truth', rsuffix='_salmon')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 56
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Apply some reasonable filters \n",
"filterExpression(mergedKallisto, colname=\"NumReads\", val=1)\n",
"filterExpression(mergedKallisto, colname=\"TPM\", val=0.01)\n",
"\n",
"truthKallisto = truth.join(kallisto, lsuffix='_truth', rsuffix='_kallisto')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 57
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# correlation with the average salmon TPM \n",
"truthSalmon.corr(method='spearman')[\"TPM_truth\"][\"TPMAvg\"]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 58,
"text": [
"0.84378228965579938"
]
}
],
"prompt_number": 58
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# and with the first replicate\n",
"truthSalmon.corr(method='spearman')[\"TPM_truth\"][\"TPM_salmon\"]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 78,
"text": [
"0.84375208053402606"
]
}
],
"prompt_number": 78
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# correlation with Kallisto\n",
"truthKallisto.corr(method='spearman')[\"TPM_truth\"][\"TPM_kallisto\"]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 79,
"text": [
"0.83658299923652046"
]
}
],
"prompt_number": 79
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"p = sns.regplot(mergedSalmon[\"NumReadsAvg\"], mergedSalmon[\"NumReadsStdDev\"] / mergedSalmon[\"NumReadsAvg\"], fit_reg=False)\n",
"p.set_ylabel(\"CV\")\n",
"p.set_title(\"Variability Salmon\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 80,
"text": [
"<matplotlib.text.Text at 0x1012bce90>"
]
},
{
"metadata": {},
"output_type": "display_data",
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c+orjDvn7aGR09/Rx64+Wsbsn+0PynXuf4yUxg4O5kPqO+57j8ee2ANk++K7X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\nRilDP02wOxcAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x10a8acc90>"
]
}
],
"prompt_number": 80
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"p = sns.regplot(mergedKallisto[\"NumReadsAvg\"], mergedKallisto[\"NumReadsStdDev\"] / mergedKallisto[\"NumReadsAvg\"], fit_reg=False)\n",
"p.set_ylabel(\"CV\")\n",
"p.set_title(\"Variability Kallisto\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 81,
"text": [
"<matplotlib.text.Text at 0x107dbad90>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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LF4rcePezrNt0yPGnREQkQZQgJMjcKpM1jRaKbN7Vz/fu28DOfdEWBhERSSYl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\nRERERERERETk6PD/A0pI8oCr8meBAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x1023345d0>"
]
}
],
"prompt_number": 81
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"p = sns.regplot(np.log(truthSalmon[\"TPM_truth\"].values+1), np.log(truthSalmon[\"TPMAvg\"]+1), truthSalmon)\n",
"p.set_xlabel(\"true TPM\")\n",
"p.set_ylabel(\"Salmon TPM\")\n",
"p.set_title(\"Salmon TPM correlation\")\n",
"stats.spearmanr(truthSalmon[\"TPM_truth\"], truthSalmon[\"TPMAvg\"])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 82,
"text": [
"(0.84378228965579938, 6.9804526878296429e-307)"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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8snShEBNYts13917i1JV+3C6V5x9aypolpYVu1pQe2ljLQDTFlc4RakqDPLm1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\n",
"text": [
"<matplotlib.figure.Figure at 0x107858150>"
]
}
],
"prompt_number": 82
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"p = sns.regplot(np.log(truthKallisto[\"TPM_truth\"].values+1), np.log(truthKallisto[\"TPM_kallisto\"].values+1), truthKallisto)\n",
"p.set_xlabel(\"true TPM\")\n",
"p.set_ylabel(\"Kallisto TPM\")\n",
"p.set_title(\"Kallisto TPM correlation\")\n",
"stats.spearmanr(truthKallisto[\"TPM_truth\"], truthKallisto[\"TPM_kallisto\"])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 83,
"text": [
"(0.83658299923652069, 8.2442397318752635e-297)"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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zrdLy1rowT9y5gIUNUVRNoSLsJeDzynzzW4xEkDJasaCKRY0xzvQmUBRYUB9l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\n6gnMgk111M8D65vZfawfs2B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ZhvFZAMMwCsDo9exzrvH7NNYvreWHb55GVRU+fPdC/L65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"text": [
"<matplotlib.figure.Figure at 0x10902cf50>"
]
}
],
"prompt_number": 83
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
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