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{ | |
"metadata": { | |
"name": "gene_correlations_2" | |
}, | |
"nbformat": 3, | |
"nbformat_minor": 0, | |
"worksheets": [ | |
{ | |
"cells": [ | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"## Ways of looking at gene correlations.\n", | |
"\n", | |
"2013-02-14: Sergey Karayev for Jessica Backus\n", | |
"updated 2013-02-14" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"from genome_analysis import *\n", | |
"\n", | |
"genes = ['Highly-Detailed', 'Decorative']\n", | |
"df_full = get_dataframe()\n", | |
"df = df_full[genes].copy()\n", | |
"print(df)" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"Loading DataFrame took 3.761 s\n", | |
"<class 'pandas.core.frame.DataFrame'>\n", | |
"Index: 19239 entries, 4d8b92c34eb68a1b2c000496 to 508b16a91dd4b40002000100\n", | |
"Data columns:\n", | |
"Highly-Detailed 1120 non-null values\n", | |
"Decorative 539 non-null values\n", | |
"dtypes: float64(2)" | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"\n" | |
] | |
} | |
], | |
"prompt_number": 1 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"from matplotlib_venn import venn2\n", | |
"from pprint import pprint\n", | |
"\n", | |
"def report(df, gene1, gene2):\n", | |
" df = df[[gene1, gene2]]\n", | |
" print('# works total: {}'.format(df.shape[0]))\n", | |
" print('# works with a nonzero value for at least one of the genes: {}'.format(\n", | |
" sum((df[gene1]>0) | (df[gene2]>0))))\n", | |
" print('# works with a nonzero value for {}: {}'.format(gene1,\n", | |
" sum(df[gene1]>0)))\n", | |
" print('# works with a nonzero value for {}: {}'.format(gene2,\n", | |
" sum(df[gene2]>0)))\n", | |
" print('# works with a nonzero value for both genes: {}'.format(\n", | |
" sum((df[gene1]>0) & (df[gene2]>0))))\n", | |
" print('% works with a nonzero value for {} given a value for {}: {}'.format(gene2, gene1,\n", | |
" 100.* sum((df[gene1]>0) & (df[gene2]>0)) / sum(df[gene1]>0)))\n", | |
" print('% works with a nonzero value for {} given a value for {}: {}'.format(gene1, gene2,\n", | |
" 100.* sum((df[gene1]>0) & (df[gene2]>0)) / sum(df[gene2]>0)))\n", | |
"\n", | |
" venn2(subsets = (sum(df[gene1]>0), sum(df[gene2]>0), sum((df[gene1]>0) & (df[gene2]>0))), set_labels = (gene1, gene2))\n", | |
" \n", | |
"report(df, 'Highly-Detailed', 'Decorative')" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"# works total: 19239\n", | |
"# works with a nonzero value for at least one of the genes: 1470\n", | |
"# works with a nonzero value for Highly-Detailed: 1120\n", | |
"# works with a nonzero value for Decorative: 539\n", | |
"# works with a nonzero value for both genes: 189\n", | |
"% works with a nonzero value for Decorative given a value for Highly-Detailed: 16.875\n", | |
"% works with a nonzero value for Highly-Detailed given a value for Decorative: 35.0649350649\n" | |
] | |
}, | |
{ | |
"output_type": "display_data", | |
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PH7svyhqcxZGGI+zavUvuUq4ooNKnvr4ey4kTorcoXFFTRwerzx7jjjmTLnqbX0+ZM2kk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\nWVlZPPLIIzz88MOXfW2CIFxZUC1U+8Q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| |
"text": [ | |
"<matplotlib.figure.Figure at 0x10e482e50>" | |
] | |
} | |
], | |
"prompt_number": 2 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Note the assymetry!\n", | |
"\n", | |
"If a work has a value for Highly-detailed, it also has the Decorative gene only 16% of the time; but if a work has a value for Decorative, it also has the Highly-detailed gene 35% of the time.\n", | |
"\n", | |
"And note that the number of times that both works have value 0 dwarfs the number of times either one has a nonzero value.\n", | |
"\n", | |
"So how should we measure the relationship between the two genes?" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"# 1a: Correlation of works where both genes have non-zero values.\n", | |
"fig = plot_correlation(\n", | |
" df[df>0].dropna(), 'Highly-Detailed', 'Decorative',\n", | |
" 'Works where both genes have non-zero values')" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "display_data", | |
"png": 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19/dHkyZNuCE9DQwMZPrcp0yZgrZt20JTUxNhYWHV7p+amopRo0bBysoKzZs3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\nQmCC\n", | |
"text": [ | |
"<matplotlib.figure.Figure at 0x10d575b10>" | |
] | |
} | |
], | |
"prompt_number": 5 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"# 1b. Correlation of works where both genes have non-zero values,\n", | |
"# and treating any value above 30 as a 100 and below 30 as 0.\n", | |
"df2 = df[df>0].dropna().copy()\n", | |
"df2[df2<30] = 0\n", | |
"df2[df2>=30] = 100\n", | |
"fig = plot_correlation(\n", | |
" df2, 'Highly-Detailed', 'Decorative',\n", | |
" 'Works where both genes have non-zero values, treated as 0/1')" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "display_data", | |
"png": 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HB5Ovr6/YPnv2LKmpqZGhoaH4T19fXzxOvN61tXjxYmrcuDEZGBiQoaEhqamp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\nTdP45JNPmDp1Ko2NjeTl5dGvXz+9w+tyBQUFeof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| |
"text": [ | |
"<matplotlib.figure.Figure at 0x10eca6410>" | |
] | |
} | |
], | |
"prompt_number": 6 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"# 2a. Correlation of all works, treating missing gene values as 0.\n", | |
"fig = plot_correlation(\n", | |
" df.fillna(0), 'Highly-Detailed', 'Decorative',\n", | |
" 'Treating missing gene values as 0')" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "display_data", | |
"png": 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1TP3/sAhpCnU/j+sXEIwxNnv27AYtmRrTsWNHJhQKZYbZrDvE8LvvvsvWrFmj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\njY1FamoqXn31VRw+fBjz58+HnZ0dYmJicOrUKQD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| |
"text": [ | |
"<matplotlib.figure.Figure at 0x10c808b90>" | |
] | |
} | |
], | |
"prompt_number": 6 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"# 2b. Correlation of all works, but treating any value above 30 as a 100 and below 30 as 0.\n", | |
"df2 = df.fillna(0).copy()\n", | |
"df2[df2<30] = 0\n", | |
"df2[df2>=30] = 100\n", | |
"fig = plot_correlation(\n", | |
" df2, 'Highly-Detailed', 'Decorative',\n", | |
" 'Treating missing gene values as 0 and 0/1')" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "display_data", | |
"png": 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RNV7T2lBo/ZWj9OzY7i6rj7mozTNaIQS8vb0BPHlxzZQpU6TXhQJP8v3vf/+7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\nHn7nnXcwfvz4as1f1e/Km37FihWIjo5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| |
"text": [ | |
"<matplotlib.figure.Figure at 0x107344d50>" | |
] | |
} | |
], | |
"prompt_number": 8 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"# 3a. Correlation of works where at least one of the genes has a non-zero value.\n", | |
"df3 = df[((df['Highly-Detailed']>0) | (df['Decorative']>0))].copy().fillna(0)\n", | |
"fig = plot_correlation(\n", | |
" df3, 'Highly-Detailed', 'Decorative',\n", | |
" 'Works where at least one of the genes has a non-zero value')" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "display_data", | |
"png": 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ggGvXrmk80AYHB2Pnzp1IS0tDYWEhvvnmGwQFBdXb5FZfBVSfcePG4fr164iN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\nUB4pFAo8//zzGDlyJIDWm4uioiKEhobi+eefR48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| |
"text": [ | |
"<matplotlib.figure.Figure at 0x10e646310>" | |
] | |
} | |
], | |
"prompt_number": 9 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"# 3b. Correlation of works where at least one of the genes has a non-zero value, and treating any value above 30 as a 100 and below 30 as 0.\n", | |
"\n", | |
"df3 = df[((df['Highly-Detailed']>0) | (df['Decorative']>0))].copy().fillna(0)\n", | |
"df3[df3<30] = 0\n", | |
"df3[df3>=30] = 100\n", | |
"fig = plot_correlation(\n", | |
" df3, 'Highly-Detailed', 'Decorative',\n", | |
" 'Works where at least one of the genes has a non-zero value and 0/1')" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "display_data", | |
"png": 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Ozs7Izs7mhk1MTFqcyB89ejT3/8ePH6O+vh4ODg7cuPr6elhbW0vc3q5duxAa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\nGyOATxKBNQAAAABJRU5ErkJggg==\n", | |
"text": [ | |
"<matplotlib.figure.Figure at 0x10da41d50>" | |
] | |
} | |
], | |
"prompt_number": 10 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"So you see that we can go from a strong positive correlation to a negative correlation, based on what we assume it means when works are missing gene values.\n", | |
"\n", | |
"In my opinion, it makes the most sense to treat missing gene values as a 0, and so plots #2 make the most sense.\n", | |
"\n", | |
"Another solution to the original gene graph problem may be to use not correlations but averaged co-occurrence (average of \"if A is present, chance that B is present\" and \"if B is present, chance that A is present.\")\n", | |
"\n", | |
"I updated the previous writeup on correlations with this version." | |
] | |
} | |
], | |
"metadata": {} | |
} | |
] | |
} |
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