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load dense contact matrix
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{
"cells": [
{
"metadata": {
"ExecuteTime": {
"start_time": "2017-07-16T19:08:27.242929Z",
"end_time": "2017-07-16T19:08:27.929535Z"
},
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "import numpy as np\nimport pandas as pd\nimport cooler",
"execution_count": 1,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### Get the chrom sizes for your dataset in the correct order as a pandas `Series`\n\nThe utility function `fetch_chromsizes` will fetch chromInfo.txt files from UCSC and by default will sort them in the order chr#, X, Y, M, and will ignore unplaced, random and alt contigs. You can also use `cooler.util.read_chromsizes` to parse a chromsizes text file into a Series."
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2017-07-16T19:08:27.931329Z",
"end_time": "2017-07-16T19:08:28.353867Z"
},
"trusted": true
},
"cell_type": "code",
"source": "chromsizes = cooler.util.fetch_chromsizes('hg19')\nchromsizes",
"execution_count": 2,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 2,
"data": {
"text/plain": "chr1 249250621\nchr2 243199373\nchr3 198022430\nchr4 191154276\nchr5 180915260\nchr6 171115067\nchr7 159138663\nchr8 146364022\nchr9 141213431\nchr10 135534747\nchr11 135006516\nchr12 133851895\nchr13 115169878\nchr14 107349540\nchr15 102531392\nchr16 90354753\nchr17 81195210\nchr18 78077248\nchr19 59128983\nchr20 63025520\nchr21 48129895\nchr22 51304566\nchrX 155270560\nchrY 59373566\nchrM 16571\nName: length, dtype: int64"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### Create a genomic bin dataframe at the known resolution\n\nIf the resolution is fixed, use `cooler.binnify`. If the data is restriction fragment-delimited, use `cooler.util.digest`."
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2017-07-16T19:08:28.356368Z",
"end_time": "2017-07-16T19:08:28.407786Z"
},
"trusted": true
},
"cell_type": "code",
"source": "# assume our data doesn't contain bins for chrY or chrM\nchromsizes_truncated = chromsizes.loc[:'chrX']\n\nbinsize = 1000000\nbins = cooler.binnify(chromsizes_truncated, binsize)\nbins.tail()",
"execution_count": 3,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 3,
"data": {
"text/plain": " chrom start end\n3048 chrX 151000000 152000000\n3049 chrX 152000000 153000000\n3050 chrX 153000000 154000000\n3051 chrX 154000000 155000000\n3052 chrX 155000000 155270560",
"text/html": "<div>\n<style>\n .dataframe thead tr:only-child th {\n text-align: right;\n }\n\n .dataframe thead th {\n text-align: left;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>chrom</th>\n <th>start</th>\n <th>end</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>3048</th>\n <td>chrX</td>\n <td>151000000</td>\n <td>152000000</td>\n </tr>\n <tr>\n <th>3049</th>\n <td>chrX</td>\n <td>152000000</td>\n <td>153000000</td>\n </tr>\n <tr>\n <th>3050</th>\n <td>chrX</td>\n <td>153000000</td>\n <td>154000000</td>\n </tr>\n <tr>\n <th>3051</th>\n <td>chrX</td>\n <td>154000000</td>\n <td>155000000</td>\n </tr>\n <tr>\n <th>3052</th>\n <td>chrX</td>\n <td>155000000</td>\n <td>155270560</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### Load the dense contact matrix as a numpy array"
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2017-07-16T19:08:28.409914Z",
"end_time": "2017-07-16T19:08:34.984273Z"
},
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "# Assuming these are unbalanced counts, so using dtype=int\nA = np.loadtxt('example.1000kb.txt', delimiter='\\t', dtype=int)",
"execution_count": 4,
"outputs": []
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2017-07-16T19:08:34.986289Z",
"end_time": "2017-07-16T19:08:34.989548Z"
},
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "# Make sure the number of bins matches the dimension of the matrix\nassert A.shape[0] == len(bins)",
"execution_count": 5,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### Use the `ArrayLoader` to store the data into a .cool file"
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2017-07-16T19:08:34.990825Z",
"end_time": "2017-07-16T19:08:39.286744Z"
},
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "binner = cooler.io.ArrayLoader(bins, A, chunksize=10000000)\ncooler.io.create('example.1000kb.cool', bins, binner, assembly='hg19')",
"execution_count": 6,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### Balance the cooler\nThis can be done using `cooler.ice.iterative_correction` or via the command line. The default parameters in the command line are better, so we'll use that here:"
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2017-07-16T19:08:39.288556Z",
"end_time": "2017-07-16T19:08:55.093006Z"
},
"trusted": true
},
"cell_type": "code",
"source": "!cooler balance example.1000kb.cool",
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"text": "INFO:cooler:Balancing \"example.1000kb.cool\"\nINFO:cooler:variance is 151938252.36210483\nINFO:cooler:variance is 53573611.61508307\nINFO:cooler:variance is 9766008.98355753\nINFO:cooler:variance is 3319617.3592718868\nINFO:cooler:variance is 846728.7856142406\nINFO:cooler:variance is 265967.26635314483\nINFO:cooler:variance is 75927.5810234732\nINFO:cooler:variance is 23247.679238198718\nINFO:cooler:variance is 6906.461358587581\nINFO:cooler:variance is 2100.779247208095\nINFO:cooler:variance is 633.4006090314527\nINFO:cooler:variance is 192.54597961227174\nINFO:cooler:variance is 58.402281212446574\nINFO:cooler:variance is 17.768178467024004\nINFO:cooler:variance is 5.404018112096133\nINFO:cooler:variance is 1.6456285315737742\nINFO:cooler:variance is 0.501197209023473\nINFO:cooler:variance is 0.1527349919271526\nINFO:cooler:variance is 0.04655397589828711\nINFO:cooler:variance is 0.014194159764527786\nINFO:cooler:variance is 0.00432848665911563\nINFO:cooler:variance is 0.0013202069516614174\nINFO:cooler:variance is 0.00040271886260597665\nINFO:cooler:variance is 0.0001228605474862479\nINFO:cooler:variance is 3.748532057589517e-05\nINFO:cooler:variance is 1.1437831404705412e-05\nINFO:cooler:variance is 3.4902229701285726e-06\n",
"name": "stdout"
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### We're done!"
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2017-07-16T19:08:55.097903Z",
"end_time": "2017-07-16T19:08:55.126927Z"
},
"trusted": true
},
"cell_type": "code",
"source": "c = cooler.Cooler('example.1000kb.cool')\nc.info",
"execution_count": 8,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 8,
"data": {
"text/plain": "{'bin-size': 1000000,\n 'bin-type': 'fixed',\n 'creation-date': '2017-07-16T15:08:39.282837',\n 'format': 'HDF5::Cooler',\n 'format-url': 'https://github.com/mirnylab/cooler',\n 'format-version': 2,\n 'generated-by': 'cooler-0.7.5',\n 'genome-assembly': 'hg19',\n 'metadata': {},\n 'nbins': 3053,\n 'nchroms': 23,\n 'nnz': 4011620,\n 'sum': 143683243}"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Raw counts and balancing weights are stored separately. Balancing weights are applied by default (balance=True)."
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2017-07-16T19:08:55.129444Z",
"end_time": "2017-07-16T19:08:55.978502Z"
},
"trusted": true
},
"cell_type": "code",
"source": "c.matrix(balance=False)[:, :]",
"execution_count": 9,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 9,
"data": {
"text/plain": "array([[ 106, 195, 59, ..., 1, 3, 0],\n [ 195, 1972, 1054, ..., 6, 8, 0],\n [ 59, 1054, 3306, ..., 3, 6, 0],\n ..., \n [ 1, 6, 3, ..., 2182, 1266, 0],\n [ 3, 8, 6, ..., 1266, 5469, 0],\n [ 0, 0, 0, ..., 0, 0, 0]], dtype=int32)"
},
"metadata": {}
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2017-07-16T19:08:55.980207Z",
"end_time": "2017-07-16T19:08:56.033748Z"
},
"trusted": true
},
"cell_type": "code",
"source": "# bins filtered during balancing are filled with NaN\nmat = c.matrix().fetch('chr1')\nmat",
"execution_count": 10,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 10,
"data": {
"text/plain": "array([[ nan, nan, nan, ..., nan, nan,\n nan],\n [ nan, nan, nan, ..., nan, nan,\n nan],\n [ nan, nan, nan, ..., nan, nan,\n nan],\n ..., \n [ nan, nan, nan, ..., 0.3268817, nan,\n nan],\n [ nan, nan, nan, ..., nan, nan,\n nan],\n [ nan, nan, nan, ..., nan, nan,\n nan]])"
},
"metadata": {}
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2017-07-16T19:08:56.035204Z",
"end_time": "2017-07-16T19:08:56.549820Z"
},
"trusted": true
},
"cell_type": "code",
"source": "%matplotlib inline\nimport matplotlib.pyplot as plt\n\nplt.matshow(np.log10(mat), cmap='jet')",
"execution_count": 11,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 11,
"data": {
"text/plain": "<matplotlib.image.AxesImage at 0x7f7e49626b00>"
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": "<matplotlib.figure.Figure at 0x7f7e49be1c50>",
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4ql5LlCxlycrOlqVPNDBZQqIzEcKMXY059awQC4pIVAsNqi4dEpbnGHK9JUIG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\nT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iK16JsUyNM2GNkms4RY5GlzQJYMHXpINUSXwIs0uUkFm4hJtknTpUva/NsmzzaTNCmq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\nCEybsH3EzkIh7ZK9VJlm1eq5LjPLoIaJQtxfHj3DD/zONzj3d69z5Tyce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},
"metadata": {}
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2017-07-16T19:08:56.552529Z",
"end_time": "2017-07-16T19:08:56.707876Z"
},
"trusted": true
},
"cell_type": "code",
"source": "c.matrix(as_pixels=True, join=True, balance=True).fetch('chr2:10,000,000-20,000,000', 'chr5').head()",
"execution_count": 12,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 12,
"data": {
"text/plain": " chrom1 start1 end1 chrom2 start2 end2 count balanced\n0 chr2 10000000 11000000 chr5 0 1000000 8 NaN\n1 chr2 10000000 11000000 chr5 1000000 2000000 7 NaN\n2 chr2 10000000 11000000 chr5 2000000 3000000 11 0.000297\n3 chr2 10000000 11000000 chr5 3000000 4000000 5 0.000139\n4 chr2 10000000 11000000 chr5 4000000 5000000 8 0.000195",
"text/html": "<div>\n<style>\n .dataframe thead tr:only-child th {\n text-align: right;\n }\n\n .dataframe thead th {\n text-align: left;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>chrom1</th>\n <th>start1</th>\n <th>end1</th>\n <th>chrom2</th>\n <th>start2</th>\n <th>end2</th>\n <th>count</th>\n <th>balanced</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>chr2</td>\n <td>10000000</td>\n <td>11000000</td>\n <td>chr5</td>\n <td>0</td>\n <td>1000000</td>\n <td>8</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>1</th>\n <td>chr2</td>\n <td>10000000</td>\n <td>11000000</td>\n <td>chr5</td>\n <td>1000000</td>\n <td>2000000</td>\n <td>7</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>2</th>\n <td>chr2</td>\n <td>10000000</td>\n <td>11000000</td>\n <td>chr5</td>\n <td>2000000</td>\n <td>3000000</td>\n <td>11</td>\n <td>0.000297</td>\n </tr>\n <tr>\n <th>3</th>\n <td>chr2</td>\n <td>10000000</td>\n <td>11000000</td>\n <td>chr5</td>\n <td>3000000</td>\n <td>4000000</td>\n <td>5</td>\n <td>0.000139</td>\n </tr>\n <tr>\n <th>4</th>\n <td>chr2</td>\n <td>10000000</td>\n <td>11000000</td>\n <td>chr5</td>\n <td>4000000</td>\n <td>5000000</td>\n <td>8</td>\n <td>0.000195</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "",
"execution_count": null,
"outputs": []
}
],
"metadata": {
"kernelspec": {
"name": "python3",
"display_name": "Python 3",
"language": "python"
},
"language_info": {
"name": "python",
"version": "3.6.1",
"mimetype": "text/x-python",
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