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Updated Quant. Analysis IPython Notebook
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"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is a (*slightly modified*) reproduction of Simon's quantification comparison analysis, but in an iPython Notebook"
]
},
{
"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"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 122
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Some constants and useful variables"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"MEAN_FL = 250\n",
"small = 1e-2 # for log things (use same value as Harold)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 123
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Some basic functions"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Truncate values in colname < val to 0\n",
"def filterExpression(df, colname=\"TPM\", val=0.01):\n",
" df[colname][df[colname] < val] = 0"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 124
},
{
"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": 125
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load all of the simulation files for Salmon & Kallisto"
]
},
{
"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": 126
},
{
"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": 127
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For all the simulations for each method, merge the results into a single data frame for easier analysis"
]
},
{
"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": 128
},
{
"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": 129
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load the ground truth data, the results of simulation 1 for Salmon, and the results on the full data for Kallisto"
]
},
{
"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')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 130
},
{
"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')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 131
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*Apparently* there are some transcripts that *are* in the simulation, but they *are not* in the ground truth file ...\n",
"\n",
"This code drops expressions for ground truth transcripts that were not included in the simulation, and \n",
"sets \"ground truth\" values of 0 for those transcripts (mentioned above) that are in the simulation but not the \n",
"expression ground truth file.\n",
"\n",
"The original TPMs were on a larger dataset, so re-normalize them (but do it according to rounded counts --- *thanks for pointing this out Harold!*)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Load the ground truth files\n",
"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",
"\n",
"# There were transcripts that *were* selected for the simulation, but they *were not* in the\n",
"truth = truth.loc[kallisto.index]\n",
"truth.fillna(0, inplace=True)\n",
"# Since we filled all NaN's with 0, replace the length here\n",
"truth[\"Length\"] = kallisto[\"Length\"]\n",
"# Round the counts\n",
"roundedCounts = truth[\"NumReads\"].round()\n",
"\n",
"# The transcript fraction \n",
"truth[\"EffectiveLen\"] = truth[\"Length\"] - MEAN_FL\n",
"# Don't allow a negative effective length\n",
"truth[\"EffectiveLen\"][truth[\"EffectiveLen\"] < 0] = 0\n",
"\n",
"# Any reads from transcripts with no effective length\n",
"strangeReads = roundedCounts[truth[\"EffectiveLen\"] <= 0]\n",
"print(\"Number of 'Strange' reads = {}\".format(len(strangeReads[strangeReads > 0])))\n",
"\n",
"# From the true transcript fractions, compute the true TPM with effective lengths\n",
"transcriptFracs = (roundedCounts / truth[\"EffectiveLen\"])\n",
"transcriptFracs[transcriptFracs == np.inf] = 0\n",
"trueTPMEffectiveLen = 1000000.0 * (transcriptFracs / sum(transcriptFracs.values))\n",
"truth[\"TPM_EL_truth\"] = trueTPMEffectiveLen\n",
"\n",
"# From the true transcript fractions, compute the true TPM with actual lengths\n",
"transcriptFracs = (roundedCounts / truth[\"Length\"])\n",
"transcriptFracs[transcriptFracs == np.inf] = 0\n",
"trueTPMRealLen = 1000000.0 * (transcriptFracs / sum(transcriptFracs.values))\n",
"truth[\"TPM_RL_truth\"] = trueTPMRealLen\n",
"\n",
"# Now, let the true counts be our rounded counts\n",
"truth[\"NumReads\"] = roundedCounts"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Number of 'Strange' reads = 0\n"
]
}
],
"prompt_number": 132
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create some data frames for Salmon and Kallisto that contain the ground truth values as columns"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"truthSalmon = truth.join(mergedSalmon, lsuffix='_truth', rsuffix='_salmon')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 133
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"truthKallisto = truth.join(kallisto, lsuffix='_truth', rsuffix='_kallisto')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 134
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Some simple correlations to start off\n",
"-------------------------------------\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# correlation with the average salmon TPM \n",
"print(\"TPM correlation Salmon (Avg) vs. Truth (RealLen) = {}\".format(truthSalmon.corr(method='spearman')[\"TPM_RL_truth\"][\"TPMAvg\"]))\n",
"print(\"TPM correlation Salmon (Avg) vs. Truth (EffLen) = {}\".format(truthSalmon.corr(method='spearman')[\"TPM_EL_truth\"][\"TPMAvg\"]))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"TPM correlation Salmon (Avg) vs. Truth (RealLen) = 0.83495978804\n",
"TPM correlation Salmon (Avg) vs. Truth (EffLen) = 0.834077786529"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 135
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# and with the first replicate\n",
"print(\"TPM correlation Salmon (Sim1) vs. Truth (RealLen) = {}\".format(truthSalmon.corr(method='spearman')[\"TPM_RL_truth\"][\"TPM_salmon\"]))\n",
"print(\"TPM correlation Salmon (Sim1) vs. Truth (EffLen) = {}\".format(truthSalmon.corr(method='spearman')[\"TPM_EL_truth\"][\"TPM_salmon\"]))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"TPM correlation Salmon (Sim1) vs. Truth (RealLen) = 0.835845618235\n",
"TPM correlation Salmon (Sim1) vs. Truth (EffLen) = 0.834965112553"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 136
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# correlation with Kallisto\n",
"print(\"TPM correlation Kallisto vs. Truth (RealLen) = {}\".format(truthKallisto.corr(method='spearman')[\"TPM_RL_truth\"][\"TPM_kallisto\"]))\n",
"print(\"TPM correlation Kallisto vs. Truth (EffLen) = {}\".format(truthKallisto.corr(method='spearman')[\"TPM_EL_truth\"][\"TPM_kallisto\"]))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"TPM correlation Kallisto vs. Truth (RealLen) = 0.83485831708\n",
"TPM correlation Kallisto vs. Truth (EffLen) = 0.835781563454\n"
]
}
],
"prompt_number": 137
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Coefficient of Variation Plots \n",
"------------------------------\n",
"\n",
"As noted by Harold, these don't really have the same meaning between Salmon and Kallisto, but I'm reproducing them here since they were in the original analysis.\n"
]
},
{
"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": "display_data",
"png": 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9+exubv7FE2RzBerSSS56/aEs7phR6XCrVpSD/JPtu0ns5/OkjAK8ILkA5PJ5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\nIiIiUu3+P3yYryb4wci2AAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x111a57ed0>"
]
}
],
"prompt_number": 138
},
{
"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": "display_data",
"png": 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8cRO11RneumA2jRPrS7q/fBDw2JImXmnaxbRJ9bxtwRxqazIl3WeS+dodPLVs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\nAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x112f0b2d0>"
]
}
],
"prompt_number": 139
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"TPM correlation plots\n",
"---------------------"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"p = sns.regplot(np.log(truthSalmon[\"TPM_RL_truth\"].values+1), np.log(truthSalmon[\"TPM_salmon\"]+1), truthSalmon)\n",
"p.set_xlabel(\"true TPM (RealLen)\")\n",
"p.set_ylabel(\"Salmon (Sim1) TPM\")\n",
"p.set_title(\"Salmon TPM (Sim1) correlation\")\n",
"sr = truthSalmon.corr(method='spearman')[\"TPM_RL_truth\"][\"TPM_salmon\"]\n",
"print(\"Spearman r = {}\".format(sr))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Spearman r = 0.835845618235\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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zbSF8Hid15UEOnenm7TNdBH0udm9fREHATSRu0NQRpjDoobo0gMupk+d3EY4Z\nuJw6/qzzOJlKY1p2jaN0xsTl0HA5db740FsjVhRFeR52balh+9pKivKVMlhoTOZTWAx8BViFbUb6\nQyll92wJthAxTWtE8S6FYjZo64ny8HNnCMcNFlfk8eCulXhcDnweJ7u21fHNx08Qihl84xfHiSWM\nXEbxQDTF+29dzjd/eZJI3EAD7rl+MdtWl/Oh21fyxJ5zxJNpioIezrWHMNI6OqBrFi6XE9O02H+q\nKydHns8ugX1ztsmNanCzMJnMfPQ14CjwDexEti9jO54V4/C27OLJN5twOHRuXF/Bjo1TcwYqFJfL\nk2825XognO8I8+aJDm7ZZH//Tp3vI5ktLJdIZQhFU6QzFg7dDvusvzCAZVn4vS4sYM+xdratLqem\nNMBv3b8OsDux/euPjxCOhdB1DSMDCWMoCNHrdrBjUzU7r6mirNCnGtwscCZTClVSyt0AQoinsFcL\n04oQ4k+BjwAmtgL6hJQyOflV849owuCJN87bqwQNXnjrAqvqCikv8s+1aIqrgGQqg5E2SRkZnA6d\npDGUX5o3WGLCsojEbIUQyuY8uBwamYxJKGaQMkwcDo3S/LF+B5cTblpfRWdvjEgiPWy/zg3rKrhj\nay2VxX7V4OYKYTKlkPv0pZSmEGJa7SJCiCXAp4A1UsqkEOIR4FeB70znfWaDZCozxmyUSKnEb8Xl\nk0iliSczFATcE5pjVtYVcKShB8uy0HWN4S1xNq0spaUrwoHTXZimaYcOZo+lM7a50zQtYgkDh0PH\nyFhYlkV7b4yfv3aW9t4YsXia7lAiN6ZD17h2dRl3XltHTVlQ9TS4wphMKawSQuwfti2GbVtSyu2X\nee8QdiKcXwiRAfzAhcscc04oyvMgaguRLXbERm1ZgOpsWWCF4p0im/v50csNGGmT6hI/d1+3iPJC\n/5jy0em0RUmBl0zGxOVycObCAHdsqwPsdpfXr6tg7/F2MqY1Iu/Nwp68eD1OioNudF1nIJpiIJLk\na784TltPjOSwaCIN2LSihLu217G0ukApgyuUyZTCPZMcu+xVg5SyVwjxZaAJiANPSymfu9xx5wJN\n0/jQ7Ss43dRHMM9HZYHnoi0MFVcHLV0R3jrdhdft4OaN1ZdUzO6Xb5zHSJsYaZO3z3RT3zJARbGf\nj969itKCoSS0gNeJy6njctrfucCwqqQHTnXy7adOEUuMzTcA2x8Q8LrQHbpd3wj45i9Pca49MuK8\n2rIAH7wwHc4RAAAgAElEQVRtBaK2UPU0uMKZ7Bu6S0r5FzN1YyHEcuAPgCXAAPCoEOLXpJTfm+ia\nd5KmP5tUVCys1Pz5/n4OslDl7OqL8/Dz9Rhp25TYGUry2Qe3THk8p9OBy6nTH0liWaA7dBJGhrcb\nevnwXUPpQ+/euYILvTHqmweoKPFzzapy+hNpYgmDx/eeIzFOApqm2X0NSgt91FbkEQonaOmK0tUf\nH5GT4HHpVBQF+LPf2EZN2ex9vxfqZ34lMJlSuBeYMaUAbAP2SCl7AIQQPwFuBCZUCgsh/n+h5Cko\nOaeX8eQ80tBDLDEUpXOmqY9zTb0jZvKTceO6Cp7Yew7TtHDoGi6HjpE2iUaTI+7101caeetUB5GY\nQXNHiMOyk4wJfo+DSCI9ZlnvdIDT4SDf7yKaMDh3YYCWrsiILOTqUj+r6oooL/SydXU5brRZ+xwW\n8md+JTCXnddOAX8phPABCWAXsG8O5VEopo2+cJLnDzbT3hPNReVkTIt//8lRPnjbCpZVX3zWvUWU\nsagiyIlzffz45QY6+2J4PU7KC3y8dqSN6tIAiVSaZ/Y3j4g4MjL270kjg9NhO6e17D+aplFbGkDT\nLLpCKWJxY0R9orJCL3ddu4jr1pYT8KnS71cjkykFMcrRPJzLdjRLKQ8LIR4CDmCHpL4F/NfljKlQ\nzBeefPM8oZhBQdDDQCSFpkFJgZeEkeGXb5znMw9suOgYpmVHAh1psHNGfR4nLpfGoy83UJjnIZMx\nSRp2P+SJsCxwOXTy/W6S6QzRuGH3M9A0zGHaoDDoZtfWWnZsqs4ltymuTiZTCq3AHzFBO87puLmU\n8h+Bf5yOsRSK+UQ869j1eZxksn2LB4MPjLQ52aWAXUbi4efOcLSxh76w3ctA1zSShl1aIhxN0Rue\nWkpPfsBNab6bM62hoVVB1nEQ8Dq5bXMNt22upWicHAXF1cdkSiEipXx51iRRKK4gtqwq40J3FAu7\nd7HL5SBpZNCAWzZVXfT6E+d6aWwLEUukMbOhpE4HaNmH+sUUglPH7ooWcFFR7OdsW5jMsAWFBmxY\nVsJHdq+ktFAlWSqGmEufgkJxxbJ5ZRkl+V66+uMsqsgj6HPR0hUhP+CmYlimezKVwe3Sx5STHlxd\nJIwhR7FlwZKqfM5kK5iOhw5sXFHMTRuqOFTfw8nzfSNKaIPtgF5RV8Bv379uRAE9hQIuEpI6a1Jc\nIcSTaaJx1ZhOAd39cd6SdrG4xRV5+DxOVtYW5o4nUxkefl5yviNCQcDNh3etHFEWZd2SYl44eIGW\nrihgh5CalkX3QHxS263bpbN9TSVPvtFMY1sot1/XNG7aVM3mFcUUBjzUlAVzeQ0KxXAmK53dM5uC\nLHReeLuFx149C5rGzmuqee+OZXMtkmKOSKTSfOfp00SyE4TG1hCfft+GEUlfe0+0c77DThAbiKZ4\n8s0mPnb3UO6B2+XgzmtrONXUi2mBrkHGhJ6Byc1GJvBfvziR29aAjctLuOf6xVx/TS09PZEJr1Uo\nQJmPpoW+SIIfPHeGTMYuiPfE3vNsWF7M8urCi1+suOLoCSVzCgEgHDfoDSeoKhkqfZIcVRtr9HY6\nY/KNJ06RyfqkM1MM7Rje5GZVXSH33biENYuL7JpIqpS1YgoopTANnGsLDSX+WJCxLE6e61dKYQFj\npE1+secc59pCVJb4ee/Ny6ZcoqIo6MHndhDPPuj9HieFwZGRPZtXlnLoTDcJI4OuwXVrK2i8MMC+\nU50U5rlp6YwSjr0zU+TiyjzefcMSNq0swaErE5Hi0rjot1wI4QauAwbtIWeBNxdiieuZorJkbPG7\nGlUQb0Hz2pHWXM/tcMsAz+xvmrJJ0O918mu7V/Hq4VYAbtlUjc8z8k+tvMjPb79nHc2dEYrzvZxt\nG+A/fn6MdMZE12AKUatjKAi4+eDO5Vy7pgyXU833FO+MyTqv1QF/BrwPaABasPMTaoGVQoifAl+U\nUjbPhqDzGZdDpyjPw0DE1pNBv0vFfC9w+iOpSbcvRk1pgF+9YyVg+wye2dcEmsb1ayvIz/ZOLgh6\nOHq2h5+/2sjZ9nAuh2C8VLQVNflkMiZn28f6BBy6Rm2Zn9//wCYK81R/cMXlMdl04nvAv2O34YwP\nPyCE8AL3Z8+5ZebEWxgUBj0sr86nayCB06njcztGhB0qFgZ94SQXuiOUFfpYu6SIo409uUiftUuK\n3tGYSSPDt395kv5sY5tjjT2UFfkIxwxM0+Rwfc+ELVwHXQABr5MNy0p49sDI+ZfX7eCu7YvYIkqp\nKgmoyryKaWHBeJ4sy7Lmc/GpSNzg1SOteL0urllWQlHe/F4pLJRiXrMl54WuCA89fZpU2sSha7z/\nlmV4PU7Ot4epLPazevFIpdDSGWHP8XacDo2d19SwannZuHJe6Irw9SdO5rY7+2K5ctVtPbEp9fR2\nO3VSw+xJXreD69ZW8P5blxOcYnE9UJ/5dLNQ5Cwvz7+k5/w7MjwKIYJSShXbNoyjDT3sP9mJ7tBJ\npzLceW3dXIukuAQOyq7cgzdjWrx5spOPv2s1S6vGFq4LRVN895nTud7HzR0R/vp/lIw7bkHQk3uo\n9wwkiCczJFIZBiKpESWqJ2O4QvC5HXz07lVct7byEl+hQjE13ul688TFT7l6iMQNfvZaI609US50\nRnh6fxNtPdG5FktxCXhdIxvHWJbFufbQuMXmOvvjOYUA0J/tVjYeQZ+LjStKaOuO5sJULQvbf3AJ\n8zcNcGh26YoDp7umfqFCcYlM5mi+l/EL32mAb5z9Vy39kSQDkRSmZQEW4ZhBR29sRFy6Yn5z88Zq\nzndEaO2Jomsa59pDfOepCKUFXj7xrjUjwlHLC314XA4SqTSaplEYcFMQ9HC6MUxze5gT53vpj6RY\nXJnH9WsreGLP+RGz/UEK/C6iiQwuJ8SS44cb5ftdYFkksvkHDoeGQ+UbKGaQycxHjwGvjLNfA4Iz\nI87CxK6EaeYSjXTNuiRb79VCXzjJ6eY+8v1u1i4pnmtxRuD3OvnUu9eSTGX4vz89Sjhuz4e6BxIc\nbujmhnVD5pqgz0V5oZcjDT24nA4e2LGMhpZ+vvbYcXpCCWLJNMV5XnrDScKx1IRVUWOpDE4HjFf5\nuqbUzyfuWc2iinwee/0srx+1eyyX5XvZfe2iy369b5xoRzb1U5zv5c5tdarFpiLHZErhDPBJKWXj\n6ANCiKs+DHU4kbiRUwhgmwZ6BhKsqJ07meYbvaEEX3/8RC6h64a1EXZvv/yH23TjcTtymb9W1ug/\nemZ+uKGb5q4oRfl2+Ofzb7UQ9LuJJAxbAVh2GKov7SCRNHG7HMSHtcTUALdLw7Qs4sbIxbgGFARd\nOJ0OugeSLKvWeeCW5bzn5qX0hZMEvK4xOQ+XyrHGHp7eZ/8Jn20PkzQyvP/W5Zc1puLKYTKfwteB\niaZz/zodNxdCFAohfiSEOCmEOCGEuH46xp1twrHUCPOwBoRilxbXfqVzuqk/pxAADtV3z6E0k3P3\n9jpC0RTtPTEGIikKAiObziSGvQ7TsjjXFuLQmS46++JEE2lSaZN4Mk1vKMmB0x3cuK6c4UVQNQ2S\nhoWRHmuddTg0koZdIXWwGB6AQ9cpLfBdtkIAaO2JjdhuG7WtuLqZUClIKf+PlPLABMe+NE33/xfg\nl1LKNcBG4ORFzp+XLKkc2bzbAta8w7j2K5Wg3zVqe/5298qYkOdzUV7kozDo4Ym950ccLwi47XIm\npkXKyKBpGkbaHDeaKGmYvHSoFccwpTA8CnW0d2CwZHYoZlBXPjNW2sUVwUm3FVc3U5p2CCH82JnM\nufOllJcVgSSEKAB2SCk/lh0vDQxczphzRUtnZIxH/nTTALVleeOefzWyfmkxzZ0RjtR3kxdw874d\nS+dapAlJpNJouoYj+8gevjJ47Ugbz7/VgmlZOHWNW7bW8PSbzZN2U8uMc8iha+T73ei6RTiWJm1a\nWKaFhm3CqisLsm1V2XS/NABWLSri/bcs43Sz7VPYsfHiTX8UVw9TqX30e8AXgT5GZuBf7l/1UqBL\nCPEtYBNwEPh9KeWCW8tq40SDqDpkI9E0jXuuX8w91y+eMxkudEXImBa15UF0bexn1tEb4/E95wjF\nUpimlfMtbF9TnjvnjRPtAOi6hmVZHG/sI5pIjxlrMjSgutSPruvUlgU5cKoThwZpzcLncVJe5OO9\nO5aOabwznaxfVsL6ZePnViiubqayUvgssEpK2ToD994CfEZKuV8I8c/AnwCfn+b7zDgVRX50bcgs\noAHLqscmPSlmn9NNfTyx9zztvfZcI+BzsXpRIR+8bcUYxfDIC/X0Dcs3uHFdJctq8lleXZDbZ6RN\nYgmDlGESS6Zp7opgXULxuoBXx+VwUlMWRNQWcsumKqpK/Jw810txvpfNK0upK8+jpEDVMFLMDVNR\nCi0zoBDALrDXIqXcn93+EbZSmJCyeWqO6Y8bI+zEFtAXTbNtnso7yHx9P0fzTuVMpNI8tucQ8WQ6\nV4ba73XS0BoilrZYOkxxZ0yLaDI9ohvZskVFXL++Knf8H76zj5auKCkjg2lZeNwOjEtYJGhAxtQo\nLvDwwG2CNUvtOI77d3rY1hWlujSQi2iaKa70z3y2WShyXgpTUQp/I4T4BvAEkMD+bltSyl9ezo2l\nlO1CiGYhhJBSSuz2n8cnu2a+1hlpON83Zl9Taz9dXfMrFn84C6Vuy6CcoWgKt0u/pJ7CA9EUsYRh\nN77PeoFThommaYQG4nS5Rtr4llXlcarJ7n/sdTko9rty79FT+5rYd6Ijd66maXhcDgzDHLd+kQY4\nnRqWaZE27YgjXdMoCHrQNdhzuIXSoN23+bvPSJJGBpdT59d2CRZXzsyDZqF95vOdhSLnpTKVv7B7\ngfuAlYz0KVyWUsjyu8D3sj0bGoBPTMOYs46oK0TXNczsw0EDrllROrdCXSGYpsWjL9Zz4nwfDl3j\n/puWsHH51N7bfL+LFdUF1LcOkOd3EU/aD95tq8qozva7iCfTvHmiAyNjcvuWGhZX5BFPplk/rKjh\nvpMd/Pil+pzDePABn81mGHPfgNfJx94l6A2leP1oOykjQzyZJpGy//e6HbmmO28c7yCZzV4z0iZ7\nj7fPmFJQKKbCVJTCe4Elo8tnTwdSysPAtdM97mxjWhYOHczsQ8Oha1gz6CS8mjje2MOJ7EosY1o8\nvvc865eVjOsoHo1lwXXryikr8lIY9LC4Mg+vy0FB9oFsWhbffUbSmq1TdaShh99+zzoCXjt81jQt\nnnzzPD99pXGkedACt1sf02PB43Lg9+rEkxm+8cRp3E4dTdNIpjKUFHiwAL/HwcZlJdy43s6QHm6u\nGm9boZhtpqIUGoBLC6+4yujsi5MZtobKWBbNHWEWV6gZ3+VijIrnzGSypqCLKAXTtPj+c5KG1hAA\nW0QZ29dUjDgnEjNyCgHszPTmjjAXumPI5n5aOiN0DcQZr7p1PDn0gS+pzKM3HCcSy9Afsfc7dZ2k\nmSHgdeUe9NtWl/OBW5ePSEC79ZpqmjrC9IaTFAbc7LymZgrvikIxc0xFKZwBnhdC/AwYDM2wpJRf\nnTmxFhY+rzNnswZ7JjkdmacLgZSRobUnStDnorRg+uskrltWQlWJP5d1u2Nj1ZT6Djd1hnMKIRo3\neHZ/M0Y6w303LMGdrYjq9zrxe5zEsiUoMhmT//j5ccJxw7YKaUxa3tqha3jdOpG4QTJlomlgZf0H\ngzrLwqK8yMdv3b+O8sKx709h0MOn37eBcNwg6HOqnsqKOWcqTy4v0AhsmGFZFiyWZeFwQDo7eXTo\nXBVdsOLJNN968hRd/XF0De65fjFbV5Vf/MJLwONy8Il3raG5M4LX7cj5AsbDSJu8drSN3lCC0mwU\nTyyRJpTtenaovgenrnP/zXaKjdOh8+CulTy+5xzn2kL0hhIjeyNPoBACXidJI40GGGmLeDKBy6nj\n0DVbMVh20byMabGkKp+Ny0o43dRHKJpiRU3BmPF0XRtTSuNK5Xx7mPbeGIsqgqqK8DzlokpBSvnx\nWZBjQVNa4EPTdGDwiaJN+vCabxhpk2cPNNPWHWVRZR53bKnNJW5NxtHGHrr6bVeTacELb12YdqUA\ntp19Knkf33nyFIcbutE0jbyAi1V1hRw6Y9dYygu4cehazlwUiqXwOB1UlwSIJgy6+hMT6YAcbqfO\n/TctoarUz38+dgJrWFRT0OciHDPQdY3FFXnctrmG5TUF9IWTPPLCmZwJ6t03LmGLmN5M5XTGpKkj\ngselU1M2f0tWHK7v5uevncXCXmV9eJdQ+TzzkMn6KdwkpXx9or4KlxuSeiXR0RvLRR6BPVM81xam\nbBxzwXzk+YMt7D/VCUBLdxSvy8GOTdUXvW60s9fhmDvnend/nEMN3aSzU/2+kEllkZ8P7xL8Ys85\nLOxKremMyVceOUQ4bhBPGOQF3DS2hi6qEHQNFlUEiSXTnGuLEPA6GYikAA2P28G9Ny2lrStCXXke\n162tyPkRXj3SOsIncbSxZ1qVgpE2+e+nT9PcZTdCvGl9Jbu2DXX9O3mulz3H2nE5dT5012o8cxj/\ncKi+O/c+Z0yLIw3dSinMQyZbKXwceB34Y8ZfSCulkMW0Rsaqm5ZFYrwi+fOUzr6RlUU6+qYWaLZp\nRSnHzvZwviOCy6HzruvmroRFTyiB06HnlIJpWhTle9i2uhy3S+eRFxpIpjJ0JmO098QI+p3EExn6\nwskJ/Qaalu14pttOgqaOCP2RFAGfi3TGwutxogH5ATfBgIcHx1Gk+aMK/023majhwkBOIQC8fqyd\nWzZV43Y56O6P8+NXGnPfza/97Bj/8z1r58xvMbrHSED1HJmXTKgUpJSfyv6/c9akWaDo2tg/MucC\n6lmytCqfs+1DSThTnb25nDofvWs1/ZEkPo9zTp3rNWVBqkv8tPfGSGcsqkr8OVPWxuWlfPvJ04A9\nuzFNi3giTTpjjRtZNEh+wE3KyJDJWLbCyZi5ekQZ06Ik35szs000Ab9lUzW94STn2kJUlQSmvXf3\naN+VQ9dyMvWEEiMmK6FokngyQ9A3N0rhzmvr6I8kae+NsaQyn1s2Xnw1qph9JjMflQExKWU0u70D\n+AC20/nfpZQLZyo8wxgZM5e8pmHPMM1LqIcz19y8sQqvx5nzKVxK4p2uaxTPcGmGqRD0udh9bR37\nTnZSWuBlWXU+e4+1U1MaoK4iiNetE0vYJi9T46IKobTAg8/j4ro15ew90YFlWUTjaXweW9tXlfjR\n0DAyJqUFXm7eVE08OrZPs9vl4FduWzFTL5vlNflsWFbC0cYeHLpddHBQUVSXBkZEV9WW541oKzrb\n5PvdfPLetXN2f8XUmOwb8jPgo0CDEGIV8CTwENlkNuxCeQpgcUUexXke+iNJNE3D53Gwftn8LXEx\nGk3TuHb19DuIZ5O3ZBc/frmBcCxFMlvqOp2xcDg01i0tJuhz0R9JoWuABqlRHc/qygKsrCvgcEMP\nHpcTl1NnaWUe99ywhN3bF5EyTFq6Iuw93o7H5WDzylJeO9JGJG5w3Zpygn73uEphptE0jQduWcau\nbbW4HPqI1Vqe383H37WaA6c7cTl07t+5klgkMesyKhYWkymFQillQ/b3B4FHpZS/I4TwYpe5VmRx\nuxx87sNb+OmrjThdDu7cUkNBwDPXYl0RjFdXaDwOnu6iL5wkk7EwMkPXpDMWR+p7cDo10mlrQody\nwOfiI7tXs7MrwqEz3fg8Tm7eYBfDczp0nA4dUVeIqCsE4F9/dCRXUfWXbzSxdmU5/jl0tI/2XQxS\nVujL+XoCPpdSCoqLMplSGJ7DfwP2KgEpZUIIYcyoVAuQkgIvv3nf2gVdJCuZyqDr4Johh0hTR5j+\nSIolVXkTPsQGGYim+MHzZ+gJJ6kq8vGh21dOavpwu3Q7Amyc57IF47a+HM6FbjtUtbYsSO1FwjqN\ntDmixLYFdPbGWFK2cMKQFYqJmEwpDAgh7gFagRuxo5EQQujYCW2KYbx9povvPiPRNDsW/dYFVq7g\nuQPNvH6sHV3TuPu6RdNuTnrjRHuuWbzf4+ST966Z1Bfx3IFm2ntjuJw6TZ0RXjncyt3XLRr33EQy\nTXtP1C6Jka2AMVkm8h1bathzrH1Ez2j3JShCl1O3nfNtdsa0x+VgWU0BZkpVg1EsfCYLQ/h94EvA\nS8DfSCnbsvvfDeyf6KKrkXAsxf/9yVH6wkl6Q0keeuo0zR0LZ7XQ2h3l9WN2RzHTsnjqzSZil9hN\n7GK8eXyo7HQsmeZIY8+k5w+/v2VZyOZ+nj/YQnf/2HDZ/3zsGGfbwuiaPWufTCE4HRq7ty/isx/a\nRH7Ahd/roKTAw+aVl1bV9kO3r2DnNdVcv7aCT7xrNSUzUOJDoZgLJgtJPQysG2f/z4Gfz6RQC42D\npzrGNNl57kAzn1ggkRap9MhAMtOyxhSiu1x8Hif90SGLpO8ifRG2rCrjXLs9E++PJHMmm4OnO/kf\n96+jIOihqSPMj15q4FRTH6Y5eTSRQ7edssX5Xhy6xoqaQj513zqONHRTVuhjxyWGR3pcjgW3GlQo\npsJkIam1UsqWyS4WQtRIKS9Mv1gLi/zgWPt4nm/hFMSrKw+yuCKP89nVzYZlJRQE3Bw41cm59jAV\nxT5uWl81pdIXE3HvDYt55IV6wnGDlbUFbL1IU/p1S4opDHoIJzN878mTOLMZwr2hJD99pZGbN1Xz\ny73naemKkM5M7i/wuh1UFPsBSKdNXnz7Am6XzqEz3ZiWHSjgdl35taoUiqkw2ZPrYSFEPfAw8IaU\nMgQghMjDdjw/CCwHbplxKec540UaVZYunLLZDl3nI7sFja0hHA6NZVX5HDzdxRNvnAfg+DnbuXr7\nltopjdfVH+dsW4iSAm+uv3FNWZDP/som0hlzjCM7ZWSIpzLk+V0jSmfUlAYoK8vjqT1nGYimiMQM\nQtEkb57s5JUjbUwFp0NjSWUe/ZEUfeEkRjpDLGkQTaQpCLjxe10cO9vLphWl4xarmwqxRBojrdJ2\nFFcGkymFW7D9B78LPJp1MINtHXkF+LqU8meXK4AQwgEcwO7X/O7LHW8ucDkcI5ybGguvWcpgyOUg\nTaN8Iufbp+Yjae2O8u2nTmFky03cdW0d16+zG8pomjYmA7exNcQPX6wnaWRYVB7k1+4UudLWg/zq\nHSt5Yu85TpzrI2NZGBOUEHHodnHCWCJN0sig6xo3rK/kru11fOn7b2fP0YnEDNA0Yok0/mxDnUzG\nJBI38LgcU/7sTMvip680cuxsL36vi3uuW8S6pQsnP0WhGI/JfAoW8BjwWFYhDHriuqWU02lw/n3g\nBLBwptaj8PucBLwuogk7Utc2Vyxsx2NViT/nDDbSJkkjw8nzfaxZXDTpdccae3IKAeDtM91cv64S\n07L4xevnONrYQ9Dn4gM7l1NbFuTJN87n2lE2dUZ4S3ZRWx7k+Nlegj4X990aoLLYzyfvXcuXH3mb\nvvDECWJBnxuvx0ldeZBbNlVTmOehtizIQDRF0O8GNMKxoSJ2gywqD7LvZCeNL9TjcTn44M7lLJ/C\nqkE29XPsbG/2Pcrwi9fPsnZJUa4UhkKxEJmS4TurBDqn++ZCiFrgHuCLwB9O9/izRWHQw7KqPBrb\nQuiaRkWJn4oi/1yLdVlsX1uBkTY5fq6XxtYQHb0xfvhiPbdeUz1pd7DRRc4Gt4819nCo3i5jPRBN\n8fPXzvLp920Y49C+0B3hxy83YloWAa+TUCLNxqXFPPpiPcfP9k0qs9/rIGVkcLl0ltcU5LJ78/0u\nllTmcbYthJHOkDEt1iwqYs2SQgaiBi6Hzr5sldikkeGJvef5vQ9svOh7lEqPlN3IWEyhKZxCMa+Z\na2/oP2FXYV3Q9XMtCy70RInE09ntKOZkcZELAF3T2LGpmlTaHFE19WhDz6RK4bq1FbR2R5HN/ZQU\neLnvBjubNjosxDRjWjRcGODvv3sQr8eZ63KW73dx6Ex3djYPsaTB03vP8aMXzkwaZqprtvJJGna1\n2pbOKF9//AS/ed9afB4nmmbX7n/7TBdG2mTTilIaWwf46atn7fsk7D4I3mxEVHKKFW5X1RVSUeTL\nvT83rq+8LGe8QjEfmDOlIIS4D+iUUr4thNg5V3JMB/UX+ukZGDJrhONpXj/axu5rx0+2Wkjk+V2T\nbo/G6dD54DgF4NYsLuK1I22EoilCsRQuh04ybZJMp9iwrJiNK0rp7o/z30+fxkibuXIUydTQbHxx\nRZBQLEV/JDVCSVjYPhyHppEXdBNPGMjmfr7xxAl+4541+LN9kof3aD7cMJQn4XU7RyiCHRurcr8P\nRO16SXnjZGB73A4+cc8azreHqa7MJ6gimBRXAHO5UrgRuD+bNe0F8oUQD0kpPzrRBWVl89PtcLyp\nf8y+uGHOW3kHmYp8d5cECSUyHKnvorTQx0fftZayokv3l5SWBllRV8j+kx12NVmXRjpjYlrQ3BXh\n7fpuOnpipMdJNsgPuPmt927A49L5/35yFI2RDT4sy/4pK/YRiqXoDiWxLIujjb381+Mn+eL/vGnM\nmBUlQZo7h/oQ3LatjjVLiikIeqirsN+Xn7x4hlcP2RHXd12/hLtvWDLua6utLhx3/3xkvn8nB1Fy\nzh1TUgpCiF3Y4aeD51tSyq9ezo2llH8G/Fl2/FuBP5pMIQDztqZQMpEas89MZ+atvMAl1Wjatbma\nXZuzyV3p9KTXReIGHX0xygp85A9rKNPSGeFIfTcelwPT56JnIIGWLWHdlq07NBHJVJrn3zwHmoZT\n12xHbnapoAEFQTf5ATd3bKnlu8+exjItdF1DA06f76OjI5Qz6wxEUxw604Vbh6piPx19cRZVBLlh\ndXnO+dzVFaatJ8oLB5pzMjz+WiMrqvImbJKzEGpeLQQZQck511xUKQghvgNsBd4CZjIYe8Ea4cdL\nfJrLhjNzRVtPlIeeOk3CyGCaJpXFfsqL/Ny2uWZEoTqv24FpTlyxdDSmaVHfGmJlTQED0dQIf01J\nvhCrAb8AACAASURBVIeA302+382G5SXcF1nCIy/U5457XHru3rFEmm8+cZJQ1mexuCLI5z68edxo\nIXOcFct4+8Y755dvnM/5VN67Y9m0d1tTKGaSqTy5bgDWSSlnrDKqlPJl4OWZGn+mGR17D6CPs+9K\nZ8+xdhJGhngiTddAnAvdMfweJ/UtA1yzspSiPA/d/XH6wolLmgFYll1Eb+3SYo409ozwJwzEUiyr\nKeCBW5fjdOjs2FTNofpuZPMAaHDzhqpcQlxzZzinEADOd0SIJtJj2kSC3aBm9aJCTv3/7d15eFT3\neejx7zkzo9GKFrSBAAkBP1bjgPGCbeyAN2zHW1xn8c3iuk/a2yftTfO0aeKmve1zn96muWl7fds0\nzU1ip8lN4jjBS+w42MF2MGAMxpjNZvkBWpAQAi1oGUmzn/vHORrNoIURMJoZeD/P40Sz6Mx7hHTe\nc37n93tfZ2hwhRP/+ew6fIbdugOA/qEQr2xv4tE71CT2Voj0SiYptDB+t0EB9A+Mzpe+MZ67XASC\nEd47coZo1GKFqogdVN0uk1A4SmfvkDPOb+EPhjne1otvKERPvx9/KMJEZZVMg4QaRqZpkJ/n5tE7\nFOXFueTneuzZSc6MJa/HRV31NCpL8mIxlBTmUFKYg8dtcujEWU6c7mdOVRElRd6E7efluMjNGbs6\nqmEYPLJ2Pq1nfLhMg5rzlNMe1utLXEfR45v6xjtCXIxkkoIGXldKvQgM/4Zf9D2Fy8lYPY0X115Y\nyYRMF41a/Pi1w7R1DQKw91gnf3j/UrweF7dcPZO3D5xKOKhHnO5n7d2D522Y43EZRC1wG5Cb66a2\nspCVi6v5SH1Z7Cx93coafr29iWjUwmUa5Hk9TC8eKcH9u/dbefdQB5ZlkZ/rprjQS8sZH3Oqiqgq\nzefe1XVs23+KHI/J3dfXjnmVN8w0DOZUTe5G4qLaUt49fCa2r0tkhbPIMskkhTzsvsxXpTiWrNXR\nM7qbVUePn8VpiCXVunr9tHQM4HKaUXf3B2jvGqS2uojSIi9LakvZceg0wZB9OWDBhAXrTAPm1kyj\n1xekbyCI6by1rMhLXq6H9avrCAza5yLt3YOEwlFWLaykq8+PaRisVBWx4nq9A0G27D+Fx20SDEVi\nZSxmlo80v1mpKlipJi7GdzHmVBXx2N2LOHayl/LiXJbNnZ6yzxIiFc6bFLTWj01BHFktJ2f02WbO\nOMMS2azHF+Cnm47QcXYI04Tp03LJ8biYVpDDBw1ddPcHWFRbytYDExers2tDuZg7o5DbV82hfyjI\nW3vb8HpcnO0PYOcbg/auAb73wn5WqQrmzijiqV8fpLPPj2EYzJyez5d+b3lCcT3LudlQWuSlbzBI\nJGqxbmUNc2dM7drIZLq3CZGpkpl9ZAJ/CNyOfeK3Cfi+UxtJAB5zdFKwUryiORyJcrJjgFyva8pK\nary1t43ewRAlRV76B4IEw1EeWTuf93UHG3c04w9GCIYiY64+9npM+z4DUFJozxb6g48tobw4j0Aw\nQtOpfppP9+P1uMCyx+YHA2EGA2GaTvVRX11Ea+cAljMs09TeT1dfgOqykX0vKfRy7aJKdh0+Q0mh\nfdWydoX0PBBiMpIZPvomsAL4IfZJ3ueBBdjlKQSw73jnqOd2HTrD6qUzxnj3xQuFo/z4tcO0dtjz\n+9etqGHN1ZNrEnMhws4dYtOAiGUx6A9x4HgX7x4+Td8EN9YrS3PxuFzUVhWR4zExDIMbllZRXpxH\njy/AwFCIz9ypiEYtvvvShxxq6iYctbCiVqyK6dGTvQmTlqNRC7dr9PyHe26oZcWCcqJRi5nlBVKc\nTohJSiYprAdWDk9JVUo9i71mQZKCI2+M4SOvJ3XDR0dazsYSAsDmvW3ceFU1rjGuWC6l65dUsXVf\nG32DdgKIuAx26zMTJgQA32CYsmkujrT0cPPyam67ZjaFeR72Huvk5bebiFoWM6bn88l18znbH6C8\nJI9BfwjfYCh2Q3l6cS4G9n0DDIMZZfnjThGdMb1gzOeFEOeX7Aora5yvBVBSOLoBfXkKe/aee/A3\nTTCmYNbw1n2nEgrbhSMWZ/tHr+Ye5jbBNE3CkSidvX6iUYudB89w4rSPP7p/Ka+/1xJbiHaqa5DD\nzT0U5nnwDYXsWkOWXeiuuCCHh2+pp28wyPYP2vG4TG5fNSvlSVCIK1EySeE1YKNSKn746LWURpVl\ncr2jrwryc1N3wFo4u4SFs0s40tKDyzS454baKanO+UFjFxOWKz1H1LKnmYYjFpGohcdtkuMx7RlL\n3YMJacyyLEzDbqjz6o5m/KEI9904l/U318dKCVSV5bNgVvbUGRIiGyWTFL6KfaP5487j54HvpSyi\nLDQYd/Y88tylbXwfzzQNPrluPj2+IF6PGRt3T6WhQJhoNMp52iED4HGZWNiJIN/rZtWiKvYf78Tt\nsu8nuE2D4kIvd103hxe3NtLV5yccifLm+yf5xLr5/MHHlqR8f4QQY0tmSmoE+A/nPzGG4sLRY9sl\nRamtd2MYRlJlFy5Wjy/At58/QHN735grkU0DXC6TuuoiasrzeV934g9GMDBwuU0+fbuy1xKoCn77\nXguWM020uCCH4vrpDPjDvLy9CbfLwB+K8KttjfzZI1enfL+EEGNLZkpqFfAnwHwSq6R+IpWBZZOq\n0nxME6LOQdMwuOAm8JlEt/Tw7ecP4BsafSPZANxuE8uyKCnMoTDPwyfXKRbMKuHFbY1EIharl1XH\nForNn1XM/FmjfyaGkdjP2h8YfdUlhJg6yQwf/QrYjb0+YfhcUW42x+nu82PFnUVbFpzsHKC2Ojsb\nyp043c8vfneMg01jt790FjODZVFRkkeOx0V3f4BT3QOsXjaD1cuSn4q7pK6M7R+027OKsGc4CSHS\nJ5mkkK+1/mLKI8lig87ZrTH8P5bd4jHbnD47yIbNx9l9pGPiNxpQNi2X/sGRfXSZBtPG6E52PoV5\nHr5w3xIa2voozPNM+epjIUSiZJLCLqXUcq31/pRHk6XU7BIK89z0D4XBgtwck+sXV6c7rPM6cbqf\njp4higty2LKvje0ftCcUs/N6TDxukwF/ODbpyGUaeNwmBXke8rxupk/zYhgGa1fUUDZt9NTcZBTk\neriqXmoECZEJkkkK/wFsUUq1AMOV3yyt9XWpCyu7WFgJB9NoNPPH13Z+cIofvXKIHl8A32AoId45\nVYV8/JZ69hztpKm9H9PwEwpHKZ3mxbLs0tlzKgv56Irz1xUa9IfZ9F4Lvb4AS+eWcc3CytTumBDi\noiSTFP4f8PfAHkY6r12SY55SajbwY6DS2eb3tNb/eim2PZUONZ9lMBCODR+FwlG2f3CK9dfXpju0\nMfmDYX7y6iFaO3wJyw6qSvN4+NZ5XLOwAsMwWDq3jOZ2H5ZlsXnPSVo7B/C4TD5750IW1ZYm9Vkv\nbG3g2MleABrb+ynKz0HNlrUGQmSqZJLCkNb6n1L0+SHgy1rrvUqpQmC3UmqT1vpQij4vJUoKzpka\natjF2TJNKBzljd0tvLKjmYGhkVk+LtPgusWV/MG9SxIWwblMM9YrYu6MaXT1+SnIdU9qXUR7V2L/\n5VNdA5IUhMhgySy7fVUpdXcqPlxr3a613ut87QMOAamv7HaJ1VZPY1ZFIRb2zKOyablctzhzZtFE\nolHe2nuSr353O7/43fGEhJDjNrh5+Qw+t37RhKuiTdOgoiRv0gvl5lSPNKkxgNrqyTWtuRCHm8/y\nxu5WjrX2pvyzhLjcJHOl8IfA15RSPhI7r13SwWGlVB12Ndadl3K7U8E3FGLQH8LtMjAwCEeidPQM\nUVU2NSWtx/JBYxf7j3XRPxik4VRfQiMgw8BZW5CDYcBtK2elrIDfgzfPpawol96BAEvryqhL8TTd\n3UfO8Ot3mgHYduAUD99aL41uhJiEZJLCqlQH4QwdbQC+5FwxjKmiIvVnmRdi4FQvvqFwrECbPxDB\nF4qyLE3xHj1xlmfeOEpXj59geGQBhWkazKkqxB+MJDSrr6wsoiKFTWE+NePihosm8+/euKUhYTFc\n85kB1l5Xd1Gfn6xM/f2Mlw0xgsSZTsmUuWhKZQBKKQ/wHPATrfWLE713uDBapgkOhfDmuPAHwhiG\nPWXTbVlpifdYaw9PvXKI02eHEp6vKS8Awx7eCoWiBFwRTNNg9ZIqckhPrMmoqCiaVGx5HpNQXCL0\nuowp2bfJxpkO2RAjSJzpNm5SUErtmuD7LsmUVKWUATwFHNRaP3mx20uX0iIva1fMZOfBM7hcBovm\nlDC7amrbMbac6WfD5uMcaOhOeD7P62LFggrO9gdii+wK8z3cdUMty2pLE64Y4umWHn6zo5lwJMrq\npdUUF+aQ53Uzb2Zml++47ZpZDPrDtHUOMLuqiFs/knW3qIRIq4muFKaiic5NwGeA/UqpPc5zT2it\nX52Cz76kHri5nhULKiguzqcox8Scoo5fZ3qG2PC7Y+w+0pEwT7iuuogZ0/OZVWGvJ3hxayO6tSf2\nupozfkIIBCNseOs4oXAUK2rxzOtHKSny4nGbXL+4ivXXz0nxXl243Bw3j6ydn+4whMha4yYFrfXm\nVH+41nobyc2Aygpzqoqm7JKyxxfghS0NbP+gnUjcyrl5M6fxyNp5qNmJ6wgeXDOXN99vpdcXZFl9\nGQtry8aNcygYjg3B+EMRwpEoUeczdh0+zR3XSoMbIS5XyVRJLcHuqXA1MNxOzNJar0tlYGJsA/4Q\nL21rZPOeNkJxtaxrygt4ZO08rqqfPmZf4jyvm3tX1yX1GdMKcqirLnJWM4PbZcZu3ua4XWNeBQ0F\nwuTmuKQnshBZLpnZR08DB4GFwN8Aj2NXTRVTKBCMsPHdZn77bgv+YCT2fEVJLh+/ZR7XLq68ZENW\npmHw6O2Kfcc6CUeitHUOcKCxG6/b5ME1cxMO/AP+ED/dpDnVNUhpoZdH71iQ0lakQojUSiYpzNda\nf1wpdb/W+mdKqeeAzSmOSzjCkShv7G7llXeaE/oaFBfkcP/Nddxy9cyUDOV43CarFo0sRbnvprm4\nTGPUAret+05xqmsQgLO+AK+/18qnbltwyeMRQkyNZJLC8IK1oFJqOtANlKcuJAEQjVpsO3CKl7Y1\n0t0fiD1fkOvm7htquWPVLDzuyS046/EFeHl7E32+IDcsn8k185Nf1BU/9z9eMBRJeBw457EQIrsk\nkxSOOMngZ8A7QC8yfDTK8bZefvtuC26Pi9VLKi94Fa1lWew6fIYXtjQkrDXwelzcvqqGe26oI8+b\nzD/baM9vaaDljL028LUdTeS6DZbWlV3QtoZds7CCg03dBMJRXKbBDdIkR4islszitc84X/6Ls3ah\nGMi6KaOp5A+G+eWbxwiEo3jcJi9ubaSmvHDSPZQPNHTy3FsNnDg9sqjbNCDHY1KY62F6cd4FJwSw\nO8QlPO71j/PO5NVUFPJHDyyjrXOAipJcKkvTV9pDCHHxkj7CKKVKgelAo9ZaGunG8Q2FCMStoo1E\nLfoGgkknhWOtZ/nl5gaOxhVwMw27VaVu7SEahb7BIC9uaWDezGJmV17YwriFs0t4/2gnAC6XybxL\n1Ee6tMg76QQohMhME61o/inwLaesdRmwH3voqEIp9XWt9fenKshMV1aUy4zp+bEbrqWFXqqTKIbX\n2uHjl787NmoV8kfml/PI2nkcbDqLbhlZcBYMR+kfDF5wnPeurqOqLJ9eX5AbV8yiwC3TR4UQiSa6\nUlg5XNYa+Cx2KYo7lVKzgFcASQoO0zT47J0Lee/IGfLzvSysmYY3Z/ybwB09Q2zYfJz3jpxJaHKz\naE4Jj6ydH+tmNhSI4M1xEXCmoBbmeS6qyqhpGrGS3pdr3RYhxMWZKCnEDzjfDLwIoLVuVUpFx/6W\nK1ee182a5TMnPNiOtwq5rrqIT6ydP6qbWf3MaTx+z2I272nDm2Py8K3zyM+98HsKQghxPhMdYSyl\nVA32FNSPAn8X95qsTpqEAX+Il95u4q09JxNKWc+Yns/Hb6lnpaoYdyXw1fPLuXq+zAAWQkyNiZLC\nN7D7MoeAbVrrDwGUUquB5imILesFQxF+s6OZTe+1MhQYuTc/fZqXB9bM5cZlM6ascN6VZsAfom8g\nSHlx7qTXcwhxJZuoIN4vlVLbgGpgb9xLzcAXUh1YNhtehbxxRzN9gyOrkIvyPdy7upZ1K2fhdklB\nuVRpaOvj2TePEgxHKSvy8tjdiyjKz0l3WEJkhQkHqLXWp4BT5zzXltKIsljUsnhtRxPPvHaErrg1\nAXleF3deO5v119emrO3l+byxu5XdR86Q53Xz0Jr6y7Jj1LA332+NDdN19wfYefA0t6+aneaohMgO\nctfyErBXIZ/mxa1NtHcPxp7PcZt8dEUN999UN+mG95fS0dYeth2wc/uQ0ythxdIZaYsn1eJndAFY\nY79NCDEGSQoX6YOGTp7b0khz+8iMI5dpcONV1Ty0pp6SwvQv6uobCCU89g2FYv0RLkfrrqnh2TeP\nEQpHKS30cv1iKb0hRLLSmhSUUuuBJwEX8AOt9TfTGc9kHGvtYcNbx9EtI6uQDQNuWj6T+1bXUlGS\nORO0FswqJt/rZmAohGEaLJtbNqra6eVk3sxi/tvDy+kdCFJRnEtOmobshMhGaUsKSikX8G3gduAk\nsEsp9ZLW+lC6YkpGy5l+nn+rgX3HuxKeX1JXimlF2bavjYfX1E9JLFHLSpi9dO7jYQN++8pgMBCm\nsjSPu66z22mGo1HcE5TdjsaNwwxvN2pZYFlELAvDMHCbJt19fl579wRDgTArFkxn+fyKWDlvy7Ji\n2xmedntujMNxjxd/Ms793sI8T6zdqOXEOhUmuw8Xs89CpEI6rxSuA45prZsAlFI/Bx4AMjIpnDk7\nyAtbGth1+AzxIy8LZhXz0Jq5/K9nRiZoffnb2/j3P1tDXoruI3T3+fn5m8fo6h2ifkYxj6ydxxvv\nt7L7SAd5OS4+fuu82KpogFd3nsAfjBAKRzl2spe/fXonGCb9AwFKCr388YPLmFM1cuP5ZIePH248\nzMmOAaJRi6ICD9curMQ0DbbtP0V/XF8H0zQShqK2HWi3fy4107hhaRUb3mpgKGCvyHaZBjkek7kz\npvHY3YsIhqL88nfH6B0I4s1xEQxFyM/18PCt9ZNaub1xZ3Ns3x+6ZR71M0e+d+u+Nrbsb8Ntmtx7\nY+0FV689n76BIE/9+iAnOweYVVnIJ9fNp2CCf//WDh8bNh+nfzDEVfVl3H/zXEkOIiOkc15kDdAS\n97jVeS6jnO33858bD/HXP9jJzkMjCWF2ZQFf+r2reOIz1yQkhGFffHJrymLauPMEHT1DRC041tbL\nC1sbePfQGSJRC58/zHNvHU94fyAUYSgQttdKWHC2P0h3rx/LgrP9AX7yW53w/he2NXKyY4BQ2O7P\n3DcQZOeh07x9IDEhAOPemzh6so/n4hIC2IUCg6EIjaf62bjjBC+93UjPQJDBQJjGtj58Q2F8QyGe\n39KQ9M9Ct/SMu++nugZ4c89JwhELfyjCr7Y1xkqGXGqvbGugtXMAC2g542PznpMTvv9XWxvpHQgS\ntSz2He/iwDlXnkKkSzqvFCZ9p3Mqp1H6BkM8+/oRXn2nKaH95czyAh69axG3rKg575BEquKNkNj0\nJhSxEh5HolA2vRCXc99g/Y1z+b8vHMAwDEzTHrKIWhaGYX9PIBxJiDUUifuncXbRInE4Kak4I2O9\n38DCImJBOGrvRyBkYBgGhmE/DkUsyssLYz/fiX6ODad9iftuWbF97x4MjWoOVDgtj5IUVHT1DSV+\nlmWYE8Ydiib+m7ly3FPy+50tU5ElzvRJZ1I4CcRPHp+NfbUwrqko4BYIhdm44wSv725l0D+yCrm0\n0Mu9q2v56IoaTNOgs9M3wVZsqYp3WW0pjSftG9wet8n1iyvp7Bmid8CuoHr1wul0d43EV1dRwGPr\nF/LM60eJWhb9gyEGA2Esy7KL5C2qTIh1xbzpNJ/qIxyNYkUtPC6T0kIvlhVlKBBJqNs0Hq/HpK66\nCN3amzBF1MAiP8fNsrpSBvwhNu9tw+0ycbsMctwmoXCUlQsqYj/f8xXuq5zmJd/rju37qoUVsX0v\nyjEpK/LGmhUtnF1CyB+kw3/hlWbHc8NVMzhwrJOoZeEyDRbNKp4w7uX1ZWzdb08TLsh1M6ssL+W/\n39lSBFHiTK+0DWIqpdzAEeA2oA14F/j0eDeaLcuyUvkPEIpEeGP3SV7beSJ2gAH7huWd187mrutm\nT1gu4fF/fDPh8dNfW5eyWAFOnO6no2eI2uoiyovz8A2FONrSQ36um4VzSsf8nt6BIMdaeykuzMF0\nu3j/UDsLZpWMWVvpaGsPHzaeBSyqSvNYVFuGYcCHDd3sb+iko8dPjsfgqvrpBIJRjrf10tXjJxiJ\nML+mhAfX1DO9OJd3D7az48PT5Oe5mTtzGtGIxVX15dRW22dYx0/20uMLMqMsj9Nnh0bFn8wf3kT7\nHghGONR8FrfbYElt6mZdVVQUse9QO6e6BphZXsCM6QXn/Z5jrb30DQaZV1NMcUHqV1xny0FM4ry0\nKiunTeqXPq13tpRSdzMyJfUprfU3xntvqpJCJBpl2/52Xnmnic64TmS5OS7WrZzFvatrJ9XtLFt+\nUSTOSysb4syGGEHivNQmmxTSuk5Ba70R2JiOz45Go7x3uIOXtjfS1jmyCtnjMlmzvJr718xlWn76\nF54JIcRUuiJXNO8/3smLWxtpOmcV8vVLKnnw5rmUl0ifYSHElemKSgr6RA8vbmvg8ImRFpcG8JEF\n5Ty0pp5ZF9j7WAghLhdXRFJoau/jV9sa2X+sK2Ee7OLaUh5cM5cFs0rSFpsQQmSSyzoptHX6ePnt\nJnYd7kiYYz93RhEPralnWX1qVrcKIUS2uiyTQlfvIC9vb+adD04Tioy0v5xZXsB9N9Vx7cIKzAlq\n/gghxJXqskoKvb4Av9nZzJa9pwiERlYhVxTncs/qWm5ePiNWqE0IIcRol0VS8A2GeGN3C6/vbmUg\nbhVycWEOd107m7Ura/B6LotdFUKIlMrqI6U/GOatPW28tusEPb6RVcgFuW5uu2YWd1w7e8JKlUII\nIRJlZVIIhMK888FpNu5spqNnZBWy1+Pilo/M4J7raynOgI5nQgiRbbIqKYTCEXbrDl55p5mTHQOx\n590ugxuXVnPP6loqS2XhmRBCXKisSQq7Drbzk42HaGjriz1nGnDNwkruv6mOmgpZeCaEEBcra5LC\n/3hqZ8Lj5fOmc9+NddTPnDZlrRaFEOJylzVJYZiaXcx9N9axuK5M2hcKIcQlljVJ4fpl1axaUM6K\nBRUpq4kvhBBXuqxJCk98/rqEbmJCCCEuvaxZ3uuSqwMhhEi5tF0pKKW+BXwMCALHgd/XWvemKx4h\nhBDpvVL4LbBUa301oIEn0hiLEEII0niloLXeFPdwJ/BwumIRQghhy5R7Co8Dv0l3EEIIcaVL6d1b\npdQmoHqMl/5Ka/2y856vAyu11hNeKViWZU30uhBCiNGMSa7uTenwkdb6joleV0o9BtwD3JbM9jo6\n+i9BVKlVUVEkcV5CEuelkw0xgsSZbumcfbQe+Apwq9baf773CyGESL103lP4N6AQ2KSU2qOU+k4a\nYxFCCEF6Zx8tSNdnCyGEGFumzD4SQgiRASQpCCGEiJGkIIQQIkaSghBCiBhJCkIIIWIkKQghhIiR\npCCEECJGkoIQQogYSQpCCCFiJCkIIYSIkaQghBAiRpKCEEKIGEkKQgghYiQpCCGEiJGkIIQQIiat\nSUEp9edKqahSqiydcQghhLClLSkopWYDdwDN6YpBCCFEonReKfwL8Jdp/HwhhBDnSEtSUEo9ALRq\nrfen4/OFEEKMLWU9mpVSm4DqMV76OvAEcGfcc0aq4hBCCJG8KT8YK6WWAW8Ag85Ts4CTwHVa6zNT\nHY8QQogMopRqlNlHQgiRGTJhnYKV7gCEEEIIIYQQQgghhBBCCCGEEEIkL6vWByilvgV8DAgCx4Hf\n11r3pjeqEUqp9cCTgAv4gdb6m2kOaRSnvMiPgUrsm/zf01r/a3qjGptSygW8h73Q8b50xzMWpVQJ\n8ANgKfbP83Gt9Y70RjWaUuoJ4DNAFDiA/bcTSG9UoJR6GrgXOKO1vsp5rgx4FqgFmoBPaK170hYk\n48aZccejseKMe+3PgW8B5Vrr7vG2kQmzjybjt8BSrfXVgMZeBJcRnAPYt4H1wBLg00qpxemNakwh\n4Mta66XADcAXMzROgC8BB8nsGWr/B/iN1noxsBw4lOZ4RlFK1QFfAFY6BwoX8Km0BjXih9h/M/G+\nBmzSWivsNU1fm/KoRhsrzkw8Ho0V56RqzWVVUtBab9JaR52HO7EXvmWK64BjWusmrXUI+DnwQJpj\nGkVr3a613ut87cM+iM1Mb1SjKaVmAfdgn4Vn5BWtUqoYWKO1fhpAax1O95niOPqwTwbylVJuIB97\nwWjaaa23AmfPefp+4EfO1z8CHpzSoMYwVpyZeDwa5+cJk6g1l1VJ4RyPA79JdxBxaoCWuMetznMZ\nyzmDXIH9C51p/jfwFezhjkw1F+hQSv1QKfW+Uur7Sqn8dAd1Lmeo4J+BE0Ab0KO1fj29UU2oSmt9\n2vn6NFCVzmCSlGnHo5jJ1prLuKSglNqklDowxn/3xb3n60BQa/2zNIZ6rkwe4hhFKVUIbAC+5Fwx\nZAyl1Mewx0T3kKFXCQ43sBL4jtZ6JTBAZgx1JFBKzQP+DKjDviosVEr9l7QGlSSttUWG/21l6PEI\nAOck5a+Av417esK/qZQVxLtQWus7JnpdKfUY9rDCbVMSUPJOArPjHs/GvlrIOEopD/Ac8BOt9Yvp\njmcMNwL3K6XuAXKBaUqpH2utP5fmuM7Vin0Gtst5vIEMTArAKmC71roLQCn1PPbP+KdpjWp8p5VS\n1VrrdqXUDCBja6Jl8PFo2Dzsk4F9Simwh7h2K6XGrTWXcUlhIs7snq8At2qt/emO5xzvAQucKpeh\ngwAABSxJREFUIZk24JPAp9Ma0RiUUgbwFHBQa/1kuuMZi9b6r7DPblBK3Qr8RQYmBJyDVotSSmmt\nNXA78GG64xrDYeBvlFJ5gB87znfTG9KEXgI+D3zT+f9MPHHJ9OMRAFrrA8QNvymlGoFrLqfZR/8G\nFAKblFJ7lFLfSXdAw7TWYeBPgNewZ8w8q7XOuJkowE3YUxPXOj/DPc4vdybL5OGDPwV+qpTahz37\n6B/SHM8oWut92NOQ3wOGx5W/l76IRiilngG2AwudBPv7wD8CdyilNLDOeZxWY8T5OBl4PIqLU8X9\nPONl8t+SEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhLg8ZHIJAXGZU0r9HfA/nQKCqfqMb2OvzQC7\nvPVx7AVcFvZK3wZgCAhgVw/9e631s0qpjwJvAv+ktf7LuO1tBm4BCrXWg2N8XhXwotZ6tfO4ydm+\nH3ux6D9orZ+5iP35KPAtrfW1zuPoeLFcwLbvBe7RWn/xYrclsle2LV4Tl5f/DuSM9YJTzfOiaa3/\nRGu9Qmu9ArsUycPO45VOhUvLee4jwGeBHyqlpjvffgR4QCllOjHVY1cYnWgB0FeB78Y9Ht7+Cuxy\n1T9w+gVkHK31K8DNToVacYXKqjIX4vKhlPp358vtSqkIsBa7N0EYUNhF2x4Cdmuty53vqQN2aa0r\nnMf3YJfDyMVudPJlrfUFV3zVWu9VSvVj14qxAB/wAXAXsBG75MKPsa8wxtonN/Ao8DfjbP9DZ/vz\ngG6l1ELsarDl2MnxSa31fzrb+gmwEPACx7Cb9yTdaEYp9Xngj7H/xnuBP9Zaa6dWz6NAN7AM6MFO\nWsNVSZ8HPkcGrswWU0OuFERaxA1RrHbO2of7ECwH7nKqjhqMc1buVP78a+BurfUq7CYyv7jAcAxn\nm2uxD8JHGRla/RF2MgC7ntVElTBXAG1a64Fxtn8z9pXGUSeB/Aw7kV0HrAG+5iQKsKvXXqu1Xo5d\nNuWrye6MUmoN8Ahwi/Oz+Sfg6bi3rAL+XGu9zNn2n8a9tp3MLe4mpoBcKYhMYgEbtNZDSbz3Luwz\n7i1O9UcAl1KqQmvdMYnPNIANSik/djOah7XWfcPb1FpvVkp9x7lqOaC17o77vHPNZXTzmuHtG8B8\n4FNa6x6l1BJgEfDzuO3lOM8dAT6vlHrUea7AeS5Z9wFXAzudbRtASdzrb2uth+Pcgd2Ra9hJoH4S\nnyUuM5IURKaJP8sOk3g1m3vOe1/VWn+eizM85n9wgvf8AruA3GNJbGvc7Sulfg/4hlLqFewDdadz\nryGBc6b/X7Gvorqc5PCF8+9Kgqe11n87zmvxFT2jJB4HLGQCyhVNho9EOvWTeAZ7rnbA4wwVgT0W\nPmwTsN454wZAKXXtpQ8RsBPCN7HvK0ykiQm67WmtNwB7gL/ALmc9qJT6zPDrSqlFSqkioBj7PkC3\nUsqL3dVrIucexF8GPqeUqnG261JKrTzPNobNAhqTfK+4DMmVgkinfwbeVEoNYt9ohrizba11WCn1\nJezSxB3AK8Ova62POgfUp5w+ATnANmAXl0as45fWug17XD7+tbHsBWqUUgVj3FcY9gR2+9P/wB7m\neVIp9RXs6bDtwCeAV7HLm2ugE9gCxCe8cz//iFJq+Dmf1nqx0w3sJaWUC/tn8wvg/fj9Onc/HTcC\nmdyqUwghsodS6kln5k9WUkrtlSmpVzYZPhLi0voG9v2ArOMsXntba52RbWSFEEIIIYQQQgghhBBC\nCCGEEEIIIYQQQgghhBBZ4f8Dk8LVHtHSpVkAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x11323d810>"
]
}
],
"prompt_number": 140
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"p = sns.regplot(np.log(truthSalmon[\"TPM_EL_truth\"].values+1), np.log(truthSalmon[\"TPM_salmon\"]+1), truthSalmon)\n",
"p.set_xlabel(\"true TPM (EffLen)\")\n",
"p.set_ylabel(\"Salmon (Sim1) TPM\")\n",
"p.set_title(\"Salmon TPM (Sim1) correlation\")\n",
"sr = truthSalmon.corr(method='spearman')[\"TPM_EL_truth\"][\"TPM_salmon\"]\n",
"print(\"Spearman r = {}\".format(sr))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Spearman r = 0.834965112553\n"
]
},
{
"metadata": {},
"output_type": "display_data",
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hMdVC88NzaMdlwcrqAvWUo5gXkikTt1snP+jhvTcv4/l9zaTSJiuq8nn97TZa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\nOOVUCCGmynhJ4R+w+zIHgR1a63cBlFIbgYYZiG3WsyyLXe+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JcY10AfsMbF/k8VOkYFIA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\nRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x11486e3d0>"
]
}
],
"prompt_number": 141
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"p = sns.regplot(np.log(truthKallisto[\"TPM_RL_truth\"].values+1), np.log(truthKallisto[\"TPM_kallisto\"].values+1), truthKallisto)\n",
"p.set_xlabel(\"true TPM (RealLen)\")\n",
"p.set_ylabel(\"Kallisto (FullData) TPM\")\n",
"p.set_title(\"Kallisto TPM correlation\")\n",
"sr = truthKallisto.corr(method='spearman')[\"TPM_RL_truth\"][\"TPM_kallisto\"]\n",
"print(\"Spearman r = {}\".format(sr))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Spearman r = 0.83485831708\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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Ae7fVs25pqeppcJ0ymz6FAayGPV4hRAbwAi2zKM9VURbw8Pady1T0keKqOdcS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\nbnXZG7LfMKyrJogwRdesVcX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g6j6FNPCfwP9MlwBCCBuwH6tf88PTdd+ZJDVGtEtmnpdS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\n",
"text": [
"<matplotlib.figure.Figure at 0x11617eb50>"
]
}
],
"prompt_number": 142
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"p = sns.regplot(np.log(truthKallisto[\"TPM_EL_truth\"].values+1), np.log(truthKallisto[\"TPM_kallisto\"].values+1), truthKallisto)\n",
"p.set_xlabel(\"true TPM (EffLen)\")\n",
"p.set_ylabel(\"Kallisto (FullData) TPM\")\n",
"p.set_title(\"Kallisto TPM correlation\")\n",
"sr = truthKallisto.corr(method='spearman')[\"TPM_EL_truth\"][\"TPM_kallisto\"]\n",
"print(\"Spearman r = {}\".format(sr))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Spearman r = 0.835781563454\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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jTCBTlMPp0HA5dRprgvyPd2/g17vO8Ny+tlHaRs2LQtxz4zI2ixrKQx6cqsFN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\nMcYYY4wxxpgp5f8DdXQoRZ69zWEAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x112f58950>"
]
}
],
"prompt_number": 143
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Correlations between numbers of reads\n",
"-------------------------------------\n",
"\n",
"This is useful because it \"sidesteps\" the effective length issue"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"p = sns.regplot(np.log(truthSalmon[\"NumReads_truth\"].values+1), np.log(truthSalmon[\"NumReads_salmon\"]+1), truthSalmon)\n",
"p.set_xlabel(\"true NumReads\")\n",
"p.set_ylabel(\"Salmon (Sim1) NumReads\")\n",
"p.set_title(\"Salmon NumReads correlation\")\n",
"sr = truthSalmon.corr(method='spearman')[\"NumReads_truth\"][\"NumReads_salmon\"]\n",
"print(\"Spearman r = {}\".format(sr))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Spearman r = 0.834552169711\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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+xmyiJkCez8nWtZXcv20pmplWpatnCbkohQ4p5U+nXJI5SNDnwu00iGdmeE5D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\nqy5e+G2AzCnmveurWDi3RJOBUkoluOTmV1qWzY43mti6u47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"text": [
"<matplotlib.figure.Figure at 0x112f71350>"
]
}
],
"prompt_number": 144
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"p = sns.regplot(np.log(truthSalmon[\"NumReads_truth\"].values+1), np.log(truthKallisto[\"NumReads_kallisto\"]+1), truthKallisto)\n",
"p.set_xlabel(\"true NumReads\")\n",
"p.set_ylabel(\"Kallisto (Sim1) NumReads\")\n",
"p.set_title(\"Kallisto NumReads correlation\")\n",
"sr = truthKallisto.corr(method=\"spearman\")[\"NumReads_truth\"][\"NumReads_kallisto\"]\n",
"print(\"Spearman r = {}\".format(sr))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Spearman r = 0.836134057877\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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GUMBDJmNhWRYbV5SQH/Ly0tH2UXWJfB4X99yxljU1heQFPapa6QIgF6VQI6Vc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\njDHGGGOMMcYYY4wxxhhjzOD4/0gOOkY1pMXNAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x113325090>"
]
}
],
"prompt_number": 145
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"No big changes, but let's see how those correlations looks if we apply some \"reasonable\" cutoffs\n",
"------------------------------------------------------------------------------------------------"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Maintain an un-filtered copy\n",
"truthSalmonUnfiltered = truthSalmon.copy()\n",
"truthKallistoUnfiltered = truthKallisto.copy()\n",
"\n",
"# No big changes, but let's see how everything looks if we apply some \"reasonable\" cutoffs\n",
"filterExpression(truthSalmon, colname=\"NumReads_salmon\", val=1)\n",
"filterExpression(truthSalmon, colname=\"TPM_salmon\", val=0.01)\n",
"filterExpression(truthSalmon, colname=\"TPMAvg\", val=0.01)\n",
"filterExpression(truthSalmon, colname=\"NumReads_truth\", val=1)\n",
"filterExpression(truthSalmon, colname=\"TPM_RL_truth\", val=0.01)\n",
"filterExpression(truthSalmon, colname=\"TPM_EL_truth\", val=0.01)\n",
"\n",
"filterExpression(truthKallisto, colname=\"NumReads_kallisto\", val=1)\n",
"filterExpression(truthKallisto, colname=\"TPM_kallisto\", val=0.01)\n",
"filterExpression(truthKallisto, colname=\"NumReads_truth\", val=1)\n",
"filterExpression(truthKallisto, colname=\"TPM_RL_truth\", val=0.01)\n",
"filterExpression(truthKallisto, colname=\"TPM_EL_truth\", val=0.01)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 146
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"(Filtered) TPM correlation plots\n",
"--------------------------------"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"p = sns.regplot(np.log(truthSalmon[\"TPM_RL_truth\"].values+1), np.log(truthSalmon[\"TPM_salmon\"]+1), truthSalmon)\n",
"p.set_xlabel(\"true TPM (RealLen)\")\n",
"p.set_ylabel(\"Salmon (Sim1) TPM\")\n",
"p.set_title(\"Salmon TPM (Sim1) correlation\")\n",
"sr = truthSalmon.corr(method='spearman')[\"TPM_RL_truth\"][\"TPM_salmon\"]\n",
"print(\"Spearman r = {}\".format(sr))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Spearman r = 0.84651452142\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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jVIZzRZGP27fUsWllMQVBj+p2tkiYytH84BzKcUmwsqZQzXIU84JpWaSMDN4R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\n",
"text": [
"<matplotlib.figure.Figure at 0x1131a98d0>"
]
}
],
"prompt_number": 147
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"p = sns.regplot(np.log(truthSalmon[\"TPM_EL_truth\"].values+1), np.log(truthSalmon[\"TPM_salmon\"]+1), truthSalmon)\n",
"p.set_xlabel(\"true TPM (EffLen)\")\n",
"p.set_ylabel(\"Salmon (Sim1) TPM\")\n",
"p.set_title(\"Salmon TPM (Sim1) correlation\")\n",
"sr = truthSalmon.corr(method='spearman')[\"TPM_EL_truth\"][\"TPM_salmon\"]\n",
"print(\"Spearman r = {}\".format(sr))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Spearman r = 0.845740329822\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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AB0PXcmUnXLqTOzAd3C6dT9y9mif2NJCxLHr6E6PCDTOWTSKexrZs+sNJikM+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\n",
"text": [
"<matplotlib.figure.Figure at 0x1130d86d0>"
]
}
],
"prompt_number": 148
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"p = sns.regplot(np.log(truthKallisto[\"TPM_RL_truth\"].values+1), np.log(truthKallisto[\"TPM_kallisto\"].values+1), truthKallisto)\n",
"p.set_xlabel(\"true TPM (RealLen)\")\n",
"p.set_ylabel(\"Kallisto (FullData) TPM\")\n",
"p.set_title(\"Kallisto TPM correlation\")\n",
"sr = truthKallisto.corr(method='spearman')[\"TPM_RL_truth\"][\"TPM_kallisto\"]\n",
"print(\"Spearman r = {}\".format(sr))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Spearman r = 0.833023060229\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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1bKwvUjLW1yjzuaYwgNWwxyuEyAJeoGUe7bkiSoMe3rFnlco+Ulwx51tC7D/d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\n8FHCZHKllAuYudneLkBFqR+3yyAUtk8qpmlQXXn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vM97Khgq2HzgNQJHfTU1FQcb/vs/1\nWeYKiTO7sjaIqZRyA4eB9wFtwFvA7051o9myLCtT/wMsy+JMzzAv7DzJrkPt9tWmY+HcYh7aWM81\nS6pS7mPyENKTX8psEtnm9kFOtg/SsKCU6spihkZCHDnVR6HfHWu44jWe6GW37iAwEqJmTjG180rR\nJ3tYUlPOqsV2o3CmZ5iaOcXMnVXIkZY+3j3eC1iUFXtxmyYFPjdjwTAHjnXT2TeK12Nw1cIK+gJB\n3j3ew/BYGLdpsKSmnPdvamB2mZ+3Dp3htb2tuE2DZXUVeN0mVzdUsnBe4hXW0EgIfaqPoknxp/PF\nm/zeG0/08NqeVqrK/dx3Qx1HWvpxu+1YTTMzf/JVVSXsbzzD6e4AhX43w6NhZpf5U94bOdrSz8Bw\nkEXVZZRNkVV3umPMh5OYxDm95swpPa8/+qze2VJK3cPElNQnUk11zVSj0N03wstvn2L7gTbGQhPV\nyubOKuCBDfWsXz4HM26Gzrnkyx+KxDm98iHOfIgRJM7pdr6NQjaHj9Bavwi8mI1j9w2N8druVrbt\na4ndmAQoL/Zy73q7yI3blX5jIIQQM0FWG4VsGBoOsv3AaX75Tgu9QxNFbor8bu5YU8Od62rxe1NX\nNhNCiJnqsmkUhkeD7Grs5OW3munoHYlt93lc3LJ6AfesX0hpYebHdYUQIpfN+EZhLBhm35EuXtjV\nzKmOiRk5bpfBDcvn8eCNdcwuO3txkRBCXI5mbKMwFozQ2NzDizubOdIyMS3SNOC6pXN48MY6qquK\nsxihEELknhnXKATDEY63DfDCzpMcPNaT8NyK+goe2ljHojQWngkhxOVoxjQKoXCEls4AL+9q5p3D\nnQnJ3BZXl/LAhjpWNMyWugZCCJFC3jcK4UiEM93DbN3dws7fthOKTKw1qK4q4v4bFrL2yrkZW7Qk\nhBAzSd42CtGoRUffMNv2tLL9wGlGgxOJ16rK/dxz/RXcsGIePk/evkUhhLjk8u6MGbUsevpHefNA\nG9v2tiXUQy4r8nL7mhpuXV1DoT/v3poQQmRd3pw5Lcuid3CMnYfO8OruFnoGJhaeFfrc3LK6mjvW\n1FBWPP0ZMIUQ4nKRN43CyztP8ty2I5zuHo5t83pMNl49n7vW1lI1qzCL0QkhxMyQN43Cv23eH/vZ\nZRqsWzaHe65fSHVVkcwoEkKIaZI3jQI4uf8XV3Lv+iuon18mM4qEEGKa5U2j8L41taxaVMHS2nI8\n7ukvpyiEECKPGoU/fngV/X3D536hEEKIC5Y3BQO8HukdCCFEpmWtp6CU+jpwPxAEmoBPa637U/+W\nEEKITMpmT+EVYLnWehWggS9nMRYhhBBksaegtd4a93AX8MFsxSKEEMKWK/cUPgO8kO0ghBDicpfR\nif5Kqa3AvCRPfUVr/QvnNX8NXKu1TtlTsKy4XNhCCCHSYpzn6t6MDh9pre9I9bxS6lPAvcD70tlf\nZ+fgNESVWVVVJRLnNJI4p08+xAgSZ7Zlc/bR3cAXgZu11qPZikMIIcSEbN5T+BegGNiqlNqrlPpW\nFmMRQghBdmcfLcnWsYUQQiSXK7OPhBBC5ABpFIQQQsRIoyCEECJGGgUhhBAx0igIIYSIkUZBCCFE\njDQKQgghYqRREEIIESONghBCiBhpFIQQQsRIoyCEECJGGgUhhBAx0igIIYSIkUZBCCFEjDQKQggh\nYrLaKCilPq+UiiqlKrIZhxBCCFvWGgWlVC1wB3AyWzEIIYRIlM2ewj8Bf5nF4wshhJgkK42CUuoh\noEVrfSAbxxdCCJFcxmo0K6W2AvOSPPXXwJeBO+O2GZmKQwghRPou+clYKbUCeBUYdjbVAK3AOq11\nx6WORwghRA5RSh2X2UdCCJEbcmGdgpXtAIQQQgghhBBCCCGEEEIIIYQQQqQvr9YHKKW+DtwPBIEm\n4NNa6/7sRjVBKXU38BjgAh7XWn8tyyGdxUkv8hQwB/sm/79rrf85u1Elp5RyAe9gL3R8INvxJKOU\nKgceB5Zjf56f0VrvzG5UZ1NKfRl4FIgCB7G/O2PZjQqUUk8C9wEdWuurnW0VwNPAQuAE8CGtdV/W\ngmTKOHPufJQszrjnPg98HajUWvdMtY9cmH10Pl4BlmutVwEaexFcTnBOYP8K3A1cBfyuUmpZdqNK\nKgT8udZ6ObAe+JMcjRPgc8AhcnuG2jeBF7TWy4CVQGOW4zmLUqoO+H3gWudE4QI+ktWgJnwH+zsT\n70vAVq21wl7T9KVLHtXZksWZi+ejZHGeV665vGoUtNZbtdZR5+Eu7IVvuWIdcFRrfUJrHQJ+BDyU\n5ZjOorU+o7Xe5/w8hH0SW5DdqM6mlKoB7sW+Cs/JHq1SqgzYpLV+EkBrHc72leIUBrAvBgqVUm6g\nEHvBaNZprbcDvZM2Pwh81/n5u8D7L2lQSSSLMxfPR1N8nnAeuebyqlGY5DPAC9kOIk41cCrucYuz\nLWc5V5Crsf+gc83/Br6IPdyRq+qBTqXUd5RSe5RS/6GUKsx2UJM5QwXfAJqBNqBPa/3L7EaV0lyt\ndbvzczswN5vBpCnXzkcx55trLucaBaXUVqXUwST/PBD3mr8GglrrH2Qx1MlyeYjjLEqpYmAz8Dmn\nx5AzlFL3Y4+J7iVHewkON3At8C2t9bVAgNwY6kiglFoE/FegDrtXWKyU+lhWg0qT1toix79bOXo+\nAsC5SPkK8Ldxm1N+pzKWEO9Caa3vSPW8UupT2MMK77skAaWvFaiNe1yL3VvIOUopD/Bj4Hta659m\nO54kNgAPKqXuBfxAqVLqKa31J7Ic12Qt2FdgbzuPN5ODjQKwBtihte4GUEo9h/0Zfz+rUU2tXSk1\nT2t9Rik1H8jZnGg5fD4atwj7YmC/UgrsIa7dSqkpc83lXKOQijO754vAzVrr0WzHM8k7wBJnSKYN\n+DDwu1mNKAmllAE8ARzSWj+W7XiS0Vp/BfvqBqXUzcAXcrBBwDlpnVJKKa21Bm4H3s12XEm8B/yN\nUqoAGMWO863shpTSz4FPAl9z/puLFy65fj4CQGt9kLjhN6XUceC6mTT76F+AYmCrUmqvUupb2Q5o\nnNY6DPwp8DL2jJmntdY5NxMFuBF7auKtzme41/njzmW5PHzwZ8D3lVL7sWcffTXL8ZxFa70fexry\nO8D4uPK/Zy+iCUqpHwI7gKVOA/tp4B+AO5RSGrjNeZxVSeL8DDl4PoqLU8V9nvFy+bskhBBCCCGE\nEEIIIYQQQgghhBBCCCGEEEIIIWaGXE4hIGY4pdTfAf/LSSCYqWP8K/baDLDTWzdhL+CysFf6HgNG\ngDHs7KH/U2v9tFLqFuA14B+11n8Zt7/XgZuAYq31cJLjzQV+qrW+wXl8wtn/KPZi0a9qrX94Ee/n\nFuDrWuu1zuPoVLFcwL7vA+7VWv/Jxe5L5K98W7wmZpb/DniTPeFk87xoWus/1Vqv1lqvxk5F8kHn\n8bVOhkvL2XYN8HHgO0qp2c6vHwYeUkqZTkwN2BlGUy0A+ivg/8Q9Ht//aux01Y879QJyjtZ6C7DR\nyVArLlN5leZCzBxKqX9zftyhlIoAt2LXJggDCjtp2+8Au7XWlc7v1AFva62rnMf3YqfD8GMXOvlz\nrfUFZ3zVWu9TSg1i54qxgCHgt8BdwIvYKReewu5hJHtPbuCjwN9Msf93nf0vAnqUUkuxs8FWRL1w\n9AAAAvBJREFUYjeOj2mt/9PZ1/eApYAPOIpdvCftQjNKqU8Cf4T9He8H/khrrZ1cPR8FeoAVQB92\nozWelfQ54BPk4MpscWlIT0FkRdwQxQ3OVft4HYKVwF1O1lGDKa7Kncyf/w24R2u9BruIzDMXGI7h\n7PNW7JPwESaGVr+L3RiAnc8qVSbM1UCb1jowxf43Yvc0jjgNyA+wG7J1wCbgS05DAXb22rVa65XY\naVP+Kt03o5TaBDwC3OR8Nv8IPBn3kjXA57XWK5x9/1ncczvI3eRu4hKQnoLIJRawWWs9ksZr78K+\n4n7Dyf4I4FJKVWmtO8/jmAawWSk1il2M5oNa64HxfWqtX1dKfcvptRzUWvfEHW+yes4uXjO+fwNY\nDHxEa92nlLoKuBL4Udz+vM62w8AnlVIfdbYVOdvS9QCwCtjl7NsAyuOe/7XWejzOndgVuca1Ag3n\ncSwxw0ijIHJN/FV2mMTerH/Sa1/SWn+SizM+5n8oxWuewU4g96k09jXl/pVSDwN/r5Tagn2i7nLu\nNSRwrvQ/i92L6nYah98/91tJ8KTW+m+neC4+o2eUxPOAhUxAuazJ8JHIpkESr2AnOwN4nKEisMfC\nx20F7nauuAFQSq2d/hABu0H4GvZ9hVROkKLantZ6M7AX+AJ2OuthpdSj488rpa5USpUAZdj3AXqU\nUj7sql6pTD6J/wL4hFKq2tmvSyl17Tn2Ma4GOJ7ma8UMJD0FkU3fAF5TSg1j32iGuKttrXVYKfU5\n7NTEncCW8ee11kecE+oTTp0AL/Am8DbTI1bxS2vdhj0uH/9cMvuAaqVUUZL7CuO+jF3+9NvYwzyP\nKaW+iD0d9gzwIeAl7PTmGugC3gDiG7zJxz+slBrfNqS1XuZUA/u5UsqF/dk8A+yJf1+T36djA5DL\npTqFECJ/KKUec2b+5CWl1D6Zknp5k+EjIabX32PfD8g7zuK1X2utc7KMrBBCCCGEEEIIIYQQQggh\nhBBCCCGEEEIIIYQQIi/8f80N/xD1Oy/RAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x116346090>"
]
}
],
"prompt_number": 149
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"p = sns.regplot(np.log(truthKallisto[\"TPM_EL_truth\"].values+1), np.log(truthKallisto[\"TPM_kallisto\"].values+1), truthKallisto)\n",
"p.set_xlabel(\"true TPM (EffLen)\")\n",
"p.set_ylabel(\"Kallisto (FullData) TPM\")\n",
"p.set_title(\"Kallisto TPM correlation\")\n",
"sr = truthKallisto.corr(method='spearman')[\"TPM_EL_truth\"][\"TPM_kallisto\"]\n",
"print(\"Spearman r = {}\".format(sr))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Spearman r = 0.834136306562\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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/1+3auIz771zPyQt9hKNJigrcfPWHr9LeE2IcmaIsDkPD6dCpryrg/7nvKn71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\n3Q18Hlivqt2pisMYY8yAVN5T+GcgH9giIntF5LspjMUYYwy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"text": [
"<matplotlib.figure.Figure at 0x116038990>"
]
}
],
"prompt_number": 150
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"(Filtered) Read Count correlation plots\n",
"---------------------------------------"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"p = sns.regplot(np.log(truthSalmon[\"NumReads_truth\"].values+1), np.log(truthSalmon[\"NumReads_salmon\"]+1), truthSalmon)\n",
"p.set_xlabel(\"true NumReads\")\n",
"p.set_ylabel(\"Salmon (Sim1) NumReads\")\n",
"p.set_title(\"Salmon NumReads correlation\")\n",
"sr = truthSalmon.corr(method='spearman')[\"NumReads_truth\"][\"NumReads_salmon\"]\n",
"print(\"Spearman r = {}\".format(sr))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Spearman r = 0.839419059657\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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B4Pc62ba2irfvWI5umSrGYI6Qj1LoklL+ZNolmYcEfS7cTiM3a3IaOkXZSE6F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\nOkfV850trFlSTGXx0NbZjdnC9WPRMxD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3DUwEeLi2lWtOdzA3QduOUiqxZJLCM8BTIhL7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\nfdHlXg9cs3AKm26cw4xRitQppZSKl3FJoac3zDO76nh+X0O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"text": [
"<matplotlib.figure.Figure at 0x113325090>"
]
}
],
"prompt_number": 151
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"p = sns.regplot(np.log(truthSalmon[\"NumReads_truth\"].values+1), np.log(truthKallisto[\"NumReads_kallisto\"]+1), truthKallisto)\n",
"p.set_xlabel(\"true NumReads\")\n",
"p.set_ylabel(\"Kallisto (Sim1) NumReads\")\n",
"p.set_title(\"Kallisto NumReads correlation\")\n",
"sr = truthKallisto.corr(method=\"spearman\")[\"NumReads_truth\"][\"NumReads_kallisto\"]\n",
"print(\"Spearman r = {}\".format(sr))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Spearman r = 0.837290633981\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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bkeKy+OB9a/jWI0dJpNJcs6qSlfXFl32unZtq2bmplraeCN947ASGaaKnDfpC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\nMcYYY4wxxhhjjDHGGGOMMcYYMzz+P6LRKpqR+wr9AAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x111a8e7d0>"
]
}
],
"prompt_number": 152
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def relDiff(c1, c2, DF, verbose=False):\n",
" rd = pd.DataFrame(data = {\"Name\" : DF.index, \"relDiff\" : np.zeros(len(DF.index))*np.nan})\n",
" rd.set_index(\"Name\", inplace=True)\n",
" bothZero = DF[(DF[c1] == 0) & (DF[c2] == 0)].index\n",
" nonZero = DF.index - bothZero\n",
" if (verbose):\n",
" print(\"Zero occurs in both columns {} times\".format(len(rd.loc[bothZero])))\n",
" print(\"Nonzero occurs in at least 1 column {} times\".format(len(rd.loc[nonZero])))\n",
" allRD = 2.0 * ((DF[c1] - DF[c2]) / (DF[c1] + DF[c2]).abs())\n",
" assert(len(rd.loc[nonZero][\"relDiff\"]) == len(allRD[nonZero]))\n",
" rd[\"relDiff\"][nonZero] = allRD[nonZero]\n",
" rd[\"relDiff\"][bothZero] = 0.0\n",
" return rd, nonZero"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 153
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We use the same definition here as Harold. The *relative difference* between the predicted expression $x_i$ and the \n",
"true expression $y_i$ is given as:\n",
"\n",
"$$d_i = \\frac{x_i - y_i}{\\frac{1}{2}\\left|x_i + y_i\\right|}$$\n",
"\n",
"*Further note:* Here, we are dealing with the truncated data. Specifically, this still considers **all** transcripts, but it treats any TPM < 0.01 as 0 and any read count < 1 as 0. I believe that these are truly the more representative statistics (because, e.g. it normalizes between methods that truncate expression values internally and those that do not --- see below). With the un-truncated estimates, methods that prefer to report a vanishingly small (but non-zero) estimated abundance are **heavily** penalized under the relative difference metric. For example, imagine a set of transcripts with true expression (TPM) 0 and estimated expression (TPM) of $1 \\times 10^{-30}$ --- each of these transcripts would have a relative difference of $2 = \\frac{1 \\times 10^{-30} - 0}{\\frac{1}{2}\\left|1 \\times 10^{-30} + 0\\right|}$. This is (in terms of absolute value), the largest possible relative difference, but, because of the estimated expression value (which is miniscule) it is not particularly meaningful. Let's take a look at how significant of a differerence this can make by looking at some (non-zero) median relative errors:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"rdS, nzS = relDiff(\"TPM_salmon\", \"TPM_RL_truth\", truthSalmonUnfiltered)\n",
"medRelDiffSalmonUnfilt = rdS[\"relDiff\"][nzS].median()\n",
"\n",
"X = truthSalmonUnfiltered.copy()\n",
"filterExpression(X, colname='TPM_salmon', val=1e-30)\n",
"\n",
"rdS, nzS = relDiff(\"TPM_salmon\", \"TPM_RL_truth\", X)\n",
"medRelDiffSalmonFilt = rdS[\"relDiff\"][nzS].median()\n",
"\n",
"filterExpression(X, colname='TPM_RL_truth', val=1e-30)\n",
"\n",
"rdS, nzS = relDiff(\"TPM_salmon\", \"TPM_RL_truth\", X)\n",
"medRelDiffSalmonFiltBoth = rdS[\"relDiff\"][nzS].median()\n",
"\n",
"print(\"Median (non-zero) relative differences\\n========================================\")\n",
"print(\"Unfiltered = {}\".format(medRelDiffSalmonUnfilt))\n",
"print(\"Filtered (salmon TPM < 1e-30 => 0.0) = {}\".format(medRelDiffSalmonFilt))\n",
"print(\"Filtered (salmon TPM < 1e-30 => 0.0 and true TPM < 1e-30 => 0.0) = {}\".format(medRelDiffSalmonFiltBoth))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Median (non-zero) relative differences\n",
"========================================\n",
"Unfiltered = 0.456184462872\n",
"Filtered (salmon TPM < 1e-30 => 0.0) = 0.0453498332992\n",
"Filtered (salmon TPM < 1e-30 => 0.0 and true TPM < 1e-30 => 0.0) = 0.0453498332992\n"
]
}
],
"prompt_number": 154
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"As we can see above, the median relative difference of $\\sim 0.46$ (relative difference always falls in $\\left[-2.0, 2.0\\right]$, so this value is seemingly large) is caused by **tiny** estimated TPMs ($< 1 \\times 10^{-30}$) for transcripts with a true TPM that is even smaller (likely $0$). By truncating these tiny TPM values to 0, we obtain a median relative difference of $0.046$ (an order of magnitude smaller) --- this is *without any knowledge of the ground truth expression* and also without setting those expressions to $0$. If we also truncate to $0$ any tiny true TPM values, the median relative difference remains essentially the same (as expected). Thus, applying such a filter to estimated expression values yields a much more meaningful comparison between methods, **especially since some methods already apply a gentle filtering to eliminate such small estimated expression values *internally* **(see e.g. [here](https://github.com/pachterlab/kallisto/blob/9e2cbaf558adf074b573ef346cc17a212b82197c/src/EMAlgorithm.h#L167)). Thus, we proceed withe these results below.\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First, we'll look at TPMs again\n",
"==============================="
]
},
{
"cell_type": "code",
"collapsed": true,
"input": [
"rdK, nzK = relDiff(\"TPM_kallisto\", \"TPM_RL_truth\", truthKallisto)\n",
"medRelDiffKallisto = rdK[\"relDiff\"].median()\n",
"\n",
"rdS, nzS = relDiff(\"TPM_salmon\", \"TPM_RL_truth\", truthSalmon)\n",
"medRelDiffSalmon = rdS[\"relDiff\"].median()\n",
"relDiffTPM = pd.DataFrame(rdK).join(pd.DataFrame(rdS), lsuffix='_kallisto', rsuffix='_salmon')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 168
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print(\"Kallisto\")\n",
"print(\"===============================\")\n",
"print(\"Median relative difference = {}\".format(medRelDiffKallisto))\n",
"print(\"Median relative difference (non-zero) = {}\".format(rdK[\"relDiff\"][nzK].median()))\n",
"print(\"Mean relative difference = {}\".format(rdK[\"relDiff\"].mean()))\n",
"\n",
"print(\"\\n\\nSalmon\")\n",
"print(\"===============================\")\n",
"print(\"Median relative difference = {}\".format(medRelDiffSalmon))\n",
"print(\"Median relative difference (non-zero) = {}\".format(rdS[\"relDiff\"][nzS].median()))\n",
"print(\"Mean relative difference = {}\".format(rdS[\"relDiff\"].mean()))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Kallisto\n",
"===============================\n",
"Median relative difference = 0.0\n",
"Median relative difference (non-zero) = 0.0356676963046\n",
"Mean relative difference = 0.0744219582712\n",
"\n",
"\n",
"Salmon\n",
"===============================\n",
"Median relative difference = 0.0\n",
"Median relative difference (non-zero) = -0.0182341267813\n",
"Mean relative difference = 0.042149132981\n"
]
}
],
"prompt_number": 169
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"p = plt.hist(relDiffTPM[\"relDiff_salmon\"].values, bins=100)\n",
"_ = plt.axes().set_ylim((0,700))\n",
"_ = plt.axes().set_title(\"Salmon TPM Relative Difference\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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Ksg9WW6XaGGSr+3MDsCJNryA7azpEC/uySN9sAF6bajsL2JUbrpoudeuMiN6I\n6EjTS4GONgt8aI++rKtd+jLV8Algs6SbqjRrqE9bccnmL4A5wFgHfl/SlRFxEvAxSa9K7c7nycvT\nPiHp/dNc56uBm4ETgN3AfZLOz9cZEc8Evpw2mQ18rh3rTO1a1p/pks11wCnkLtlsl76s1DcR8V8B\nJP1zajN25cwjZJcZV7w0tpV1RsRbgDcDT5BduvcOSXdOc41rgJeRvR4HgL8EDh+rMbVph76sWWc7\n9GWq8yVkV+b9mCeHbN5J9rvUVn1qZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZva09v8BRVARUNKv\nDu4AAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x11317b990>"
]
}
],
"prompt_number": 170
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"p = plt.hist(relDiffTPM[\"relDiff_kallisto\"].values, bins=100)\n",
"_ = plt.axes().set_ylim((0,700))\n",
"_ = plt.axes().set_title(\"Kallisto TPM Relative Difference\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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jL2vqlL5MNVwLbIuIKys0a6hP2zFl83fALGC8A38WEWskzQOuiYhzUruzeHR6\n2rUR8dEW1/lq4FPAscAe4PaIOCtfp6SnAd9Kh8wEvtKJdaZ2bevPNGVzI3ACuSmbndKX5fpG0tsA\nIuJzqc34zJmHyKYZl50a2846Jb0D+EvgEbKpe38dET9vcY03AC8l+30cBP4XcMR4jalNJ/Rl1To7\noS9TnS8im5n3bzw6ZHMZ2d9SR/WpmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmdnj2v8Hhls4aEZG\ncd8AAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x11668b210>"
]
}
],
"prompt_number": 171
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"f, ax = plt.subplots(2, 1, sharex=True)\n",
"p = sns.regplot(truthSalmon[\"TPM_RL_truth\"].values, rdS[\"relDiff\"].values, scatter_kws={'alpha' : 0.3}, fit_reg=False, ax=ax[0])\n",
"p2 = sns.regplot(truthKallisto[\"TPM_RL_truth\"].values, rdK[\"relDiff\"].values, scatter_kws={'alpha' : 0.3}, fit_reg=False, ax=ax[1])\n",
"_ = ax[0].semilogx()\n",
"_ = ax[1].semilogx()\n",
"_ = ax[0].set_xlim(1, 2e5)\n",
"_ = ax[0].set_ylabel(\"Rel. diff Salmon\")\n",
"_ = ax[1].set_ylabel(\"Rel. diff Kallisto\")\n",
"_ = ax[1].set_xlabel(\"True TPM\")\n",
"_ = ax[0].set_title(\"Rel. diff vs. TPM\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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Z1FG6W2WZ6H97436XoStm9RVLGzdjgXpZ9KX1whg3iNHkMu1+wI17Xb58s02Q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J0U5tlvwZadPjvKYi7jTLIc1BLhPLPIgwdbZ6irYi6b2Usxl28IId4ICJLCKy8CmytK74+fBWzg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\nFAqFQqFQKBQKhULxmPD/AWiHyrJS1MH/AAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x11333da90>"
]
}
],
"prompt_number": 172
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, with Counts\n",
"==============================="
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"rdK, nzK = relDiff(\"NumReads_kallisto\", \"NumReads_truth\", truthKallisto)\n",
"medRelDiffKallisto = rdK[\"relDiff\"].median()\n",
"\n",
"rdS, nzS = relDiff(\"NumReads_salmon\", \"NumReads_truth\", truthSalmon)\n",
"medRelDiffSalmon = rdS[\"relDiff\"].median()\n",
"relDiffReads = pd.DataFrame(rdK).join(pd.DataFrame(rdS), lsuffix='_kallisto', rsuffix='_salmon')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 173
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print(\"Kallisto\")\n",
"print(\"===============================\")\n",
"print(\"Median relative difference = {}\".format(medRelDiffKallisto))\n",
"print(\"Median relative difference (non-zero) = {}\".format(rdK[\"relDiff\"][nzK].median()))\n",
"print(\"Mean relative difference = {}\\n\".format(rdK[\"relDiff\"].mean()))\n",
"\n",
"print(\"\\n\\nSalmon\")\n",
"print(\"===============================\")\n",
"print(\"Median relative difference = {}\".format(medRelDiffSalmon))\n",
"print(\"Median relative difference (non-zero) = {}\".format(rdS[\"relDiff\"][nzS].median()))\n",
"print(\"Mean relative difference = {}\\n\".format(rdS[\"relDiff\"].mean()))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Kallisto\n",
"===============================\n",
"Median relative difference = 0.0\n",
"Median relative difference (non-zero) = 0.00831900446451\n",
"Mean relative difference = 0.0288764285793\n",
"\n",
"\n",
"\n",
"Salmon\n",
"===============================\n",
"Median relative difference = 0.0\n",
"Median relative difference (non-zero) = 0.00222828383726\n",
"Mean relative difference = 0.0333436111112\n",
"\n"
]
}
],
"prompt_number": 174
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"p = plt.hist(np.clip(relDiffReads[\"relDiff_salmon\"], -2.0, 2.0), bins=100)\n",
"_ = plt.axes().set_ylim((0,800))\n",
"_ = plt.axes().set_title(\"Salmon Reads Relative Difference\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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PiYjpE1tmw7/Dlj0XAST9GBis0aQd+rKROqHFfQkgabekX6bpA2SfYTarrNmI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\nzMzMzMzMzMzMzMzM7BHt/wMHg5v2eCLgBQAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x112f620d0>"
]
}
],
"prompt_number": 175
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"p = plt.hist(np.clip(relDiffReads[\"relDiff_kallisto\"], -2.0, 2.0), bins=100)\n",
"_ = plt.axes().set_ylim((0,800))\n",
"_ = plt.axes().set_title(\"Kallisto Reads Relative Difference\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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9j+wzzKaUNKurT5v9xehLge8Nsnywz+9p1a8sGgB+EBF3RcSFzS6mjGb350RJ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\n",
"text": [
"<matplotlib.figure.Figure at 0x1133bcb90>"
]
}
],
"prompt_number": 176
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"f, ax = plt.subplots(2, 1, sharex=True)\n",
"p = sns.regplot(truthSalmon[\"NumReads_truth\"].values, relDiffReads[\"relDiff_salmon\"].values, scatter_kws={'alpha' : 0.3}, fit_reg=False, ax=ax[0])\n",
"p2 = sns.regplot(truthKallisto[\"NumReads_truth\"].values, relDiffReads[\"relDiff_kallisto\"].values, scatter_kws={'alpha' : 0.3}, fit_reg=False, ax=ax[1])\n",
"_ = ax[0].semilogx()\n",
"_ = ax[1].semilogx()\n",
"_ = ax[0].set_xlim(1, 2e5)\n",
"_ = ax[0].set_ylabel(\"Rel. diff Salmon\")\n",
"_ = ax[1].set_ylabel(\"Rel. diff Kallisto\")\n",
"_ = ax[1].set_xlabel(\"True Counts\")\n",
"_ = ax[0].set_title(\"Rel. diff vs. Read Count\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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h/FwVx9TphymjYcBcs7RD80vSjJW2Rz9MUbNsR7RK2TaolkxG4xILQz+mO4wI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"text": [
"<matplotlib.figure.Figure at 0x11667b450>"
]
}
],
"prompt_number": 177
}
],
"metadata": {}
}
]
}
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