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olgabot/92_dropout_plot.ipynb

Created August 29, 2017 18:23
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92_dropout_plot.ipynb
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"source": [
"# Interactive jupyter widgets - use IntSlider directly for more control\n",
"from ipywidgets import IntSlider, interact\n",
"\n",
"# Convert RGB colors to hex cor portability\n",
"from matplotlib.colors import rgb2hex\n",
"\n",
"# Visualize networks\n",
"import networkx\n",
"\n",
"# Numerical python\n",
"import numpy as np\n",
"\n",
"# Pandas for dataframes\n",
"import pandas as pd\n",
"\n",
"# K-nearest neighbors cell clustering from Dana Pe'er's lab\n",
"import phenograph\n",
"\n",
"# Make color palettes\n",
"import seaborn as sns\n",
"\n",
"# Bokeh - interactive plotting in the browser\n",
"from bokeh.plotting import figure, show, output_file\n",
"from bokeh.models import HoverTool, ColumnDataSource\n",
"from bokeh.models.widgets import Panel, Tabs\n",
"from bokeh.layouts import widgetbox\n",
"from bokeh.io import output_notebook\n",
"\n",
"# This line is required for the plots to appear in the notebooks\n",
"output_notebook()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import xarray as xr"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import os\n",
"\n",
"data_folder = os.path.abspath(os.path.join('~', 'projects', 'cshl-singlecell-2017'))\n",
"! mkdir -p $data_folder"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (cell: 13300, cell_feature: 1, ensembl_id: 47003, gene: 47003, gene_feature: 1)\n",
"Coordinates:\n",
" gene (ensembl_id) object 'ENSG00000000003.14' ...\n",
" * cell (cell) object 'group1_0000' 'group1_0001' 'group1_0002' ...\n",
" * gene_feature (gene_feature) int64 0\n",
" * cell_feature (cell_feature) object 'group'\n",
"Dimensions without coordinates: ensembl_id\n",
"Data variables:\n",
" counts (cell, gene) float64 0.0 0.0 0.0 0.0 1.0 0.0 1.0 1.0 0.0 ...\n",
" gene_metadata (gene, gene_feature) object 'TSPAN6' 'TNMD' 'DPM1' ...\n",
" cell_metadata (cell, cell_feature) object 'group1' 'group1' 'group1' ..."
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"netcdf = os.path.join(data_folder, 'trisomy21.netcdf')\n",
"\n",
"ds = xr.open_dataset(netcdf)\n",
"ds"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Try making the \"dropout\" plot from Peter Sims"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.DataArray 'counts' (gene: 47003)>\n",
"array([ 984, 9, 2578, ..., 1, 3, 4])\n",
"Dimensions without coordinates: gene"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"num_cells_greater0 = (ds.counts > 0).sum(dim='cell')\n",
"num_cells_greater0"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.DataArray 'counts' (gene: 47003)>\n",
"array([ 7.398496e-02, 6.766917e-04, 1.938346e-01, ..., 7.518797e-05,\n",
" 2.255639e-04, 3.007519e-04])\n",
"Dimensions without coordinates: gene"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fraction_genes_greater0 = num_cells_greater0/num_cells_greater0.max()\n",
"fraction_genes_greater0"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"num_cells_greater0.name = 'Number of cells with count > 0'"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# genes_to_use = (per_gene_means > 0) & (per_gene_means )"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(47003,)"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"per_gene_means.shape"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.IndexVariable 'gene' (gene: 47003)>\n",
"array([Interval(-1.1240000000000001, -1.0649999999999999, closed='right'),\n",
" Interval(-3.1829999999999998, -3.1240000000000001, closed='right'),\n",
" Interval(-0.65300000000000002, -0.59399999999999997, closed='right'),\n",
" ..., Interval(-4.1299999999999999, -4.0650000000000004, closed='right'),\n",
" Interval(-3.653, -3.5939999999999999, closed='right'),\n",
" Interval(-3.536, -3.4769999999999999, closed='right')], dtype=object)"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bins = xr.IndexVariable(dims='gene', data=pd.cut(per_gene_means, bins=100))\n",
"bins"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.DataArray 'Log10 Mean Count' (gene: 91)>\n",
"array([-4.123852, -3.822822, -3.64673 , -3.521792, -3.424882, -3.3457 ,\n",
" -3.278754, -3.220762, -3.148352, -3.082459, -3.028506, -2.963631,\n",
" -2.906381, -2.857176, -2.802426, -2.743978, -2.68563 , -2.626557,\n",
" -2.569306, -2.506051, -2.449651, -2.388102, -2.329108, -2.270351,\n",
" -2.211073, -2.152228, -2.093355, -2.036041, -1.97442 , -1.918959,\n",
" -1.860151, -1.80081 , -1.741338, -1.681927, -1.623249, -1.565079,\n",
" -1.505821, -1.446089, -1.387755, -1.330584, -1.27178 , -1.212565,\n",
" -1.152533, -1.094503, -1.034701, -0.976166, -0.917681, -0.858787,\n",
" -0.799986, -0.742167, -0.68211 , -0.624757, -0.565417, -0.506586,\n",
" -0.447687, -0.387608, -0.328292, -0.272613, -0.212814, -0.157089,\n",
" -0.093793, -0.038794, 0.023694, 0.083778, 0.140282, 0.194546,\n",
" 0.256533, 0.314832, 0.372526, 0.431174, 0.497467, 0.548207,\n",
" 0.610109, 0.6723 , 0.726071, 0.790208, 0.849095, 0.909344,\n",
" 0.957706, 1.02225 , 1.08794 , 1.141016, 1.198741, 1.264565,\n",
" 1.319888, 1.363606, 1.438874, 1.507395, 1.558466, 1.603718,\n",
" 1.758805])\n",
"Coordinates:\n",
" * gene (gene) object (-4.13, -4.065] (-3.83, -3.771] (-3.653, -3.594] ..."
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"per_gene_mean_bins = per_gene_means.groupby(bins).mean()\n",
"per_gene_mean_bins"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.DataArray 'counts' (gene: 91)>\n",
"array([ 7.518797e-05, 1.499286e-04, 2.242303e-04, 2.981254e-04,\n",
" 3.717869e-04, 4.452612e-04, 5.201899e-04, 5.935607e-04,\n",
" 7.020252e-04, 8.145742e-04, 9.216411e-04, 1.068474e-03,\n",
" 1.218512e-03, 1.360297e-03, 1.540110e-03, 1.758959e-03,\n",
" 2.012360e-03, 2.303704e-03, 2.630462e-03, 3.029463e-03,\n",
" 3.434370e-03, 3.950113e-03, 4.518386e-03, 5.150030e-03,\n",
" 5.901951e-03, 6.759824e-03, 7.712088e-03, 8.784127e-03,\n",
" 1.009199e-02, 1.144870e-02, 1.299170e-02, 1.487582e-02,\n",
" 1.698197e-02, 1.927202e-02, 2.206303e-02, 2.516693e-02,\n",
" 2.873684e-02, 3.273083e-02, 3.721050e-02, 4.212348e-02,\n",
" 4.789030e-02, 5.470503e-02, 6.207011e-02, 7.054700e-02,\n",
" 7.952624e-02, 8.972944e-02, 1.019448e-01, 1.147519e-01,\n",
" 1.296997e-01, 1.449549e-01, 1.628695e-01, 1.824358e-01,\n",
" 2.032234e-01, 2.276905e-01, 2.528870e-01, 2.818363e-01,\n",
" 3.108001e-01, 3.428329e-01, 3.748977e-01, 4.078146e-01,\n",
" 4.500563e-01, 4.826613e-01, 5.277106e-01, 5.649548e-01,\n",
" 6.013665e-01, 6.444659e-01, 6.808543e-01, 7.128524e-01,\n",
" 7.543374e-01, 7.756827e-01, 8.314189e-01, 8.478091e-01,\n",
" 8.534471e-01, 9.030707e-01, 9.196992e-01, 9.465789e-01,\n",
" 9.350607e-01, 9.702358e-01, 9.584891e-01, 9.902381e-01,\n",
" 9.830694e-01, 9.958797e-01, 9.966692e-01, 9.970526e-01,\n",
" 9.965664e-01, 9.950627e-01, 9.921805e-01, 9.756140e-01,\n",
" 9.997744e-01, 9.998496e-01, 1.000000e+00])\n",
"Coordinates:\n",
" * gene (gene) object (-4.13, -4.065] (-3.83, -3.771] (-3.653, -3.594] ..."
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mean_cells_per_bin = (ds.counts> 0).groupby(bins).mean()\n",
"mean_cells_per_bin"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"np.percentile?"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.DataArray 'Number of cells with count > 0' (gene: 91)>\n",
"array([ 1.000000e+00, 2.000000e+00, 3.000000e+00, 4.000000e+00,\n",
" 4.800000e+00, 5.000000e+00, 6.000000e+00, 7.000000e+00,\n",
" 8.000000e+00, 1.000000e+01, 1.100000e+01, 1.300000e+01,\n",
" 1.500000e+01, 1.600000e+01, 1.900000e+01, 2.100000e+01,\n",
" 2.400000e+01, 2.800000e+01, 3.200000e+01, 3.600000e+01,\n",
" 4.100000e+01, 4.700000e+01, 5.400000e+01, 6.200000e+01,\n",
" 7.100000e+01, 8.100000e+01, 9.300000e+01, 1.060000e+02,\n",
" 1.225500e+02, 1.380000e+02, 1.570000e+02, 1.800000e+02,\n",
" 2.050000e+02, 2.320000e+02, 2.670000e+02, 2.990000e+02,\n",
" 3.457000e+02, 3.970000e+02, 4.493500e+02, 5.111000e+02,\n",
" 5.826500e+02, 6.679500e+02, 7.529000e+02, 8.496000e+02,\n",
" 9.541000e+02, 1.080350e+03, 1.218700e+03, 1.383200e+03,\n",
" 1.583600e+03, 1.716100e+03, 1.929000e+03, 2.176650e+03,\n",
" 2.386200e+03, 2.673000e+03, 2.996550e+03, 3.234750e+03,\n",
" 3.778100e+03, 4.103800e+03, 4.387700e+03, 4.654000e+03,\n",
" 5.135300e+03, 5.349200e+03, 6.354850e+03, 6.698400e+03,\n",
" 6.819200e+03, 7.511000e+03, 7.688000e+03, 8.585450e+03,\n",
" 8.796950e+03, 9.203950e+03, 9.754200e+03, 1.032350e+04,\n",
" 1.015825e+04, 1.058100e+04, 1.163460e+04, 1.205205e+04,\n",
" 1.127940e+04, 1.249100e+04, 1.216300e+04, 1.315150e+04,\n",
" 1.254960e+04, 1.323200e+04, 1.319575e+04, 1.320220e+04,\n",
" 1.316675e+04, 1.315830e+04, 1.297835e+04, 1.185975e+04,\n",
" 1.329350e+04, 1.329800e+04, 1.330000e+04])\n",
"Coordinates:\n",
" * gene (gene) object (-4.13, -4.065] (-3.83, -3.771] (-3.653, -3.594] ..."
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"quantile95_cells_per_bin = num_cells_greater0.groupby(\n",
" bins, squeeze=False).reduce(\n",
" np.percentile, q=5)\n",
"quantile95_cells_per_bin"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.DataArray 'Number of cells with count > 0' (gene: 91)>\n",
"array([ 7.518797e-05, 1.503759e-04, 2.255639e-04, 3.007519e-04,\n",
" 3.609023e-04, 3.759398e-04, 4.511278e-04, 5.263158e-04,\n",
" 6.015038e-04, 7.518797e-04, 8.270677e-04, 9.774436e-04,\n",
" 1.127820e-03, 1.203008e-03, 1.428571e-03, 1.578947e-03,\n",
" 1.804511e-03, 2.105263e-03, 2.406015e-03, 2.706767e-03,\n",
" 3.082707e-03, 3.533835e-03, 4.060150e-03, 4.661654e-03,\n",
" 5.338346e-03, 6.090226e-03, 6.992481e-03, 7.969925e-03,\n",
" 9.214286e-03, 1.037594e-02, 1.180451e-02, 1.353383e-02,\n",
" 1.541353e-02, 1.744361e-02, 2.007519e-02, 2.248120e-02,\n",
" 2.599248e-02, 2.984962e-02, 3.378571e-02, 3.842857e-02,\n",
" 4.380827e-02, 5.022180e-02, 5.660902e-02, 6.387970e-02,\n",
" 7.173684e-02, 8.122932e-02, 9.163158e-02, 1.040000e-01,\n",
" 1.190677e-01, 1.290301e-01, 1.450376e-01, 1.636579e-01,\n",
" 1.794135e-01, 2.009774e-01, 2.253045e-01, 2.432143e-01,\n",
" 2.840677e-01, 3.085564e-01, 3.299023e-01, 3.499248e-01,\n",
" 3.861128e-01, 4.021955e-01, 4.778083e-01, 5.036391e-01,\n",
" 5.127218e-01, 5.647368e-01, 5.780451e-01, 6.455226e-01,\n",
" 6.614248e-01, 6.920263e-01, 7.333985e-01, 7.762030e-01,\n",
" 7.637782e-01, 7.955639e-01, 8.747820e-01, 9.061692e-01,\n",
" 8.480752e-01, 9.391729e-01, 9.145113e-01, 9.888346e-01,\n",
" 9.435789e-01, 9.948872e-01, 9.921617e-01, 9.926466e-01,\n",
" 9.899812e-01, 9.893459e-01, 9.758158e-01, 8.917105e-01,\n",
" 9.995113e-01, 9.998496e-01, 1.000000e+00])\n",
"Coordinates:\n",
" * gene (gene) object (-4.13, -4.065] (-3.83, -3.771] (-3.653, -3.594] ..."
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"percent_quantile95 = quantile95_cells_per_bin/quantile95_cells_per_bin.max()\n",
"percent_quantile95"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(47003,)"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"per_gene_means.shape"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(47003,)"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fraction_genes_greater0.shape"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.DataArray 'Log10 Mean Count' (gene: 91)>\n",
"array([-4.123852, -3.822822, -3.64673 , -3.521792, -3.424882, -3.3457 ,\n",
" -3.278754, -3.220762, -3.148352, -3.082459, -3.028506, -2.963631,\n",
" -2.906381, -2.857176, -2.802426, -2.743978, -2.68563 , -2.626557,\n",
" -2.569306, -2.506051, -2.449651, -2.388102, -2.329108, -2.270351,\n",
" -2.211073, -2.152228, -2.093355, -2.036041, -1.97442 , -1.918959,\n",
" -1.860151, -1.80081 , -1.741338, -1.681927, -1.623249, -1.565079,\n",
" -1.505821, -1.446089, -1.387755, -1.330584, -1.27178 , -1.212565,\n",
" -1.152533, -1.094503, -1.034701, -0.976166, -0.917681, -0.858787,\n",
" -0.799986, -0.742167, -0.68211 , -0.624757, -0.565417, -0.506586,\n",
" -0.447687, -0.387608, -0.328292, -0.272613, -0.212814, -0.157089,\n",
" -0.093793, -0.038794, 0.023694, 0.083778, 0.140282, 0.194546,\n",
" 0.256533, 0.314832, 0.372526, 0.431174, 0.497467, 0.548207,\n",
" 0.610109, 0.6723 , 0.726071, 0.790208, 0.849095, 0.909344,\n",
" 0.957706, 1.02225 , 1.08794 , 1.141016, 1.198741, 1.264565,\n",
" 1.319888, 1.363606, 1.438874, 1.507395, 1.558466, 1.603718,\n",
" 1.758805])\n",
"Coordinates:\n",
" * gene (gene) object (-4.13, -4.065] (-3.83, -3.771] (-3.653, -3.594] ..."
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"per_gene_mean_bins"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.DataArray 'Log10 Mean Count' (gene: 47003)>\n",
"array([-1.067328, -3.169609, -0.610235, ..., -4.123852, -3.64673 , -3.521792])\n",
"Coordinates:\n",
" * gene (gene) object (-4.13, -4.065] (-3.83, -3.771] (-3.653, -3.594] ..."
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"per_gene_means"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.IndexVariable 'gene' (gene: 47003)>\n",
"array([Interval(-1.1240000000000001, -1.0649999999999999, closed='right'),\n",
" Interval(-3.1829999999999998, -3.1240000000000001, closed='right'),\n",
" Interval(-0.65300000000000002, -0.59399999999999997, closed='right'),\n",
" ..., Interval(-4.1299999999999999, -4.0650000000000004, closed='right'),\n",
" Interval(-3.653, -3.5939999999999999, closed='right'),\n",
" Interval(-3.536, -3.4769999999999999, closed='right')], dtype=object)"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bins"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--- Bin: (-4.13, -4.065] ---\n",
"\tBefore filtering (13300, 5488)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-3.83, -3.771] ---\n",
"\tBefore filtering (13300, 3530)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-3.653, -3.594] ---\n",
"\tBefore filtering (13300, 2537)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-3.536, -3.477] ---\n",
"\tBefore filtering (13300, 1918)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-3.477, -3.418] ---\n",
"\tBefore filtering (13300, 1557)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-3.359, -3.3] ---\n",
"\tBefore filtering (13300, 1256)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-3.3, -3.241] ---\n",
"\tBefore filtering (13300, 1031)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-3.241, -3.183] ---\n",
"\tBefore filtering (13300, 833)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-3.183, -3.124] ---\n",
"\tBefore filtering (13300, 1481)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-3.124, -3.065] ---\n",
"\tBefore filtering (13300, 662)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-3.065, -3.006] ---\n",
"\tBefore filtering (13300, 1086)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-3.006, -2.947] ---\n",
"\tBefore filtering (13300, 859)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-2.947, -2.888] ---\n",
"\tBefore filtering (13300, 708)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-2.888, -2.83] ---\n",
"\tBefore filtering (13300, 696)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-2.83, -2.771] ---\n",
"\tBefore filtering (13300, 786)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-2.771, -2.712] ---\n",
"\tBefore filtering (13300, 685)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-2.712, -2.653] ---\n",
"\tBefore filtering (13300, 747)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-2.653, -2.594] ---\n",
"\tBefore filtering (13300, 657)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-2.594, -2.536] ---\n",
"\tBefore filtering (13300, 673)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-2.536, -2.477] ---\n",
"\tBefore filtering (13300, 675)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-2.477, -2.418] ---\n",
"\tBefore filtering (13300, 542)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-2.418, -2.359] ---\n",
"\tBefore filtering (13300, 630)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-2.359, -2.3] ---\n",
"\tBefore filtering (13300, 476)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-2.3, -2.241] ---\n",
"\tBefore filtering (13300, 543)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-2.241, -2.183] ---\n",
"\tBefore filtering (13300, 494)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-2.183, -2.124] ---\n",
"\tBefore filtering (13300, 477)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-2.124, -2.065] ---\n",
"\tBefore filtering (13300, 438)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-2.065, -2.006] ---\n",
"\tBefore filtering (13300, 450)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-2.006, -1.947] ---\n",
"\tBefore filtering (13300, 452)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-1.947, -1.888] ---\n",
"\tBefore filtering (13300, 396)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-1.888, -1.83] ---\n",
"\tBefore filtering (13300, 480)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-1.83, -1.771] ---\n",
"\tBefore filtering (13300, 455)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-1.771, -1.712] ---\n",
"\tBefore filtering (13300, 465)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-1.712, -1.653] ---\n",
"\tBefore filtering (13300, 431)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-1.653, -1.594] ---\n",
"\tBefore filtering (13300, 454)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-1.594, -1.535] ---\n",
"\tBefore filtering (13300, 461)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-1.535, -1.477] ---\n",
"\tBefore filtering (13300, 475)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-1.477, -1.418] ---\n",
"\tBefore filtering (13300, 478)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-1.418, -1.359] ---\n",
"\tBefore filtering (13300, 508)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-1.359, -1.3] ---\n",
"\tBefore filtering (13300, 483)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-1.3, -1.241] ---\n",
"\tBefore filtering (13300, 474)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-1.241, -1.183] ---\n",
"\tBefore filtering (13300, 520)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-1.183, -1.124] ---\n",
"\tBefore filtering (13300, 539)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-1.124, -1.065] ---\n",
"\tBefore filtering (13300, 527)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-1.065, -1.006] ---\n",
"\tBefore filtering (13300, 515)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-1.006, -0.947] ---\n",
"\tBefore filtering (13300, 508)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-0.947, -0.888] ---\n",
"\tBefore filtering (13300, 499)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-0.888, -0.83] ---\n",
"\tBefore filtering (13300, 509)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-0.83, -0.771] ---\n",
"\tBefore filtering (13300, 485)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-0.771, -0.712] ---\n",
"\tBefore filtering (13300, 432)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-0.712, -0.653] ---\n",
"\tBefore filtering (13300, 456)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-0.653, -0.594] ---\n",
"\tBefore filtering (13300, 432)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-0.594, -0.535] ---\n",
"\tBefore filtering (13300, 404)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-0.535, -0.477] ---\n",
"\tBefore filtering (13300, 378)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-0.477, -0.418] ---\n",
"\tBefore filtering (13300, 342)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-0.418, -0.359] ---\n",
"\tBefore filtering (13300, 296)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-0.359, -0.3] ---\n",
"\tBefore filtering (13300, 262)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-0.3, -0.241] ---\n",
"\tBefore filtering (13300, 245)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-0.241, -0.182] ---\n",
"\tBefore filtering (13300, 223)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-0.182, -0.124] ---\n",
"\tBefore filtering (13300, 181)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-0.124, -0.0648] ---\n",
"\tBefore filtering (13300, 163)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-0.0648, -0.00599] ---\n",
"\tBefore filtering (13300, 152)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (-0.00599, 0.0528] ---\n",
"\tBefore filtering (13300, 138)\n",
"\tAfter filtering: (13300, 2)\n",
"--- Bin: (0.0528, 0.112] ---\n",
"\tBefore filtering (13300, 129)\n",
"\tAfter filtering: (13300, 1)\n",
"--- Bin: (0.112, 0.17] ---\n",
"\tBefore filtering (13300, 103)\n",
"\tAfter filtering: (13300, 1)\n",
"--- Bin: (0.17, 0.229] ---\n",
"\tBefore filtering (13300, 101)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (0.229, 0.288] ---\n",
"\tBefore filtering (13300, 80)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (0.288, 0.347] ---\n",
"\tBefore filtering (13300, 64)\n",
"\tAfter filtering: (13300, 2)\n",
"--- Bin: (0.347, 0.406] ---\n",
"\tBefore filtering (13300, 48)\n",
"\tAfter filtering: (13300, 1)\n",
"--- Bin: (0.406, 0.465] ---\n",
"\tBefore filtering (13300, 50)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (0.465, 0.523] ---\n",
"\tBefore filtering (13300, 39)\n",
"\tAfter filtering: (13300, 1)\n",
"--- Bin: (0.523, 0.582] ---\n",
"\tBefore filtering (13300, 36)\n",
"\tAfter filtering: (13300, 1)\n",
"--- Bin: (0.582, 0.641] ---\n",
"\tBefore filtering (13300, 26)\n",
"\tAfter filtering: (13300, 1)\n",
"--- Bin: (0.641, 0.7] ---\n",
"\tBefore filtering (13300, 25)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (0.7, 0.759] ---\n",
"\tBefore filtering (13300, 17)\n",
"\tAfter filtering: (13300, 1)\n",
"--- Bin: (0.759, 0.818] ---\n",
"\tBefore filtering (13300, 24)\n",
"\tAfter filtering: (13300, 1)\n",
"--- Bin: (0.818, 0.876] ---\n",
"\tBefore filtering (13300, 13)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (0.876, 0.935] ---\n",
"\tBefore filtering (13300, 22)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (0.935, 0.994] ---\n",
"\tBefore filtering (13300, 21)\n",
"\tAfter filtering: (13300, 1)\n",
"--- Bin: (0.994, 1.053] ---\n",
"\tBefore filtering (13300, 6)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (1.053, 1.112] ---\n",
"\tBefore filtering (13300, 17)\n",
"\tAfter filtering: (13300, 1)\n",
"--- Bin: (1.112, 1.171] ---\n",
"\tBefore filtering (13300, 5)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (1.171, 1.229] ---\n",
"\tBefore filtering (13300, 10)\n",
"\tAfter filtering: (13300, 1)\n",
"--- Bin: (1.229, 1.288] ---\n",
"\tBefore filtering (13300, 10)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (1.288, 1.347] ---\n",
"\tBefore filtering (13300, 6)\n",
"\tAfter filtering: (13300, 1)\n",
"--- Bin: (1.347, 1.406] ---\n",
"\tBefore filtering (13300, 3)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (1.406, 1.465] ---\n",
"\tBefore filtering (13300, 4)\n",
"\tAfter filtering: (13300, 1)\n",
"--- Bin: (1.465, 1.523] ---\n",
"\tBefore filtering (13300, 6)\n",
"\tAfter filtering: (13300, 1)\n",
"--- Bin: (1.523, 1.582] ---\n",
"\tBefore filtering (13300, 3)\n",
"\tAfter filtering: (13300, 1)\n",
"--- Bin: (1.582, 1.641] ---\n",
"\tBefore filtering (13300, 1)\n",
"\tAfter filtering: (13300, 0)\n",
"--- Bin: (1.7, 1.759] ---\n",
"\tBefore filtering (13300, 1)\n",
"\tAfter filtering: (13300, 1)\n"
]
}
],
"source": [
"grouped = ds.counts.groupby(bins)\n",
"\n",
"filtereds = []\n",
"\n",
"for i, (vbin, subgroup) in zip(range(1000), grouped):\n",
" print(f'--- Bin: {vbin} ---')\n",
" n_cells_thresh = percent_quantile95.sel(gene=vbin)\n",
"# subgroup = grouped.groups[vbin.values.item()]\n",
" print('\\tBefore filtering', subgroup.shape)\n",
" subgroup_cells_greater0 = (subgroup > 0).sum(dim='cell')\n",
" # Make this a percentage\n",
" subgroup_cells_greater0 = subgroup_cells_greater0/subgroup_cells_greater0.max()\n",
" gene_filter = subgroup_cells_greater0 <= n_cells_thresh\n",
" filtered = subgroup.sel(gene=gene_filter)\n",
" print('\\tAfter filtering:', filtered.shape)\n",
" filtereds.append(filtered)"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1.0"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"n_cells_thresh.item()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"bin_boundaries = np.linspace(per_gene_means.min(), \n",
" per_gene_means.max(), num=100)"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{Interval(-0.023800000000000002, 0.0356, closed='right'): [0],\n",
" Interval(0.98599999999999999, 1.046, closed='right'): [1]}"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds.counts.groupby_bins('gene', bin_boundaries).groups"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(13300, 2537)"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"subgroup.shape"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/olgabot/anaconda3/envs/cshl-sca-2017/lib/python3.6/site-packages/ipykernel_launcher.py:5: RuntimeWarning: Tried to set a label via parameter 'y' in func 'scatter' but couldn't find such an argument. \n",
"(This is a programming error, please report to the matplotlib list!)\n",
" \"\"\"\n"
]
},
{
"ename": "TypeError",
"evalue": "scatter() missing 2 required positional arguments: 'x' and 'y'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-31-c995e5c09469>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m color='Crimson')\n\u001b[1;32m 4\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0max_joint\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mper_gene_mean_bins\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpercent_quantile95\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Yellow'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0max_joint\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m~/anaconda3/envs/cshl-sca-2017/lib/python3.6/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1896\u001b[0m warnings.warn(msg % (label_namer, func.__name__),\n\u001b[1;32m 1897\u001b[0m RuntimeWarning, stacklevel=2)\n\u001b[0;32m-> 1898\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1899\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1900\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: scatter() missing 2 required positional arguments: 'x' and 'y'"
]
},
{
"data": {
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lE//EH9/4NEmWLH5j+mPKH/8BG3/1JJZgkOS1OUADBdmLyMscO+fLZrVQlJs2ofeWy6NV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\nQ2rrGmowA8cXrhxp27L6IABvnBx7eO9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ephl5g4WlbZq+gmwXY0cVMvP8oRTkuOTMSaQE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\nCCHi0v8D4b/TDiExTQEAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10b9a96d8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"g = sns.jointplot(per_gene_means.values, fraction_genes_greater0.values)\n",
"g.ax_joint.plot(per_gene_mean_bins, mean_cells_per_bin, \n",
" color='Crimson')\n",
"g.ax_joint.plot(per_gene_mean_bins, percent_quantile95, color='Yellow')\n",
"# g.ax_joint.scatter()"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.linear_model import LogisticRegression"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sns.regplot(per_gene_means.values, fraction_genes_greater0.values, \n",
" logistic=True)"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 7.39849624e-02, 6.76691729e-04, 1.93834586e-01, ...,\n",
" 7.51879699e-05, 2.25563910e-04, 3.00751880e-04])"
]
},
"execution_count": 87,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fraction_genes_greater0.values"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
" intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
" penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
" verbose=0, warm_start=False)"
]
},
"execution_count": 98,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"regressor = LogisticRegression()\n",
"X = per_gene_means.values[:, np.newaxis]\n",
"y = fraction_genes_greater0 > 0.5\n",
"regressor.fit(X, y)"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x134f8c7f0>]"
]
},
"execution_count": 100,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x13acb4cc0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"# and plot the result\n",
"plt.figure(1, figsize=(4, 3))\n",
"plt.clf()\n",
"plt.scatter(X.ravel(), y, color='black', zorder=20)\n",
"X_test = np.linspace(per_gene_means.min(), per_gene_means.max(), 300)\n",
"\n",
"\n",
"def model(x):\n",
" return 1 / (1 + np.exp(-x))\n",
"loss = model(X_test * regressor.coef_ + regressor.intercept_).ravel()\n",
"plt.plot(X_test, loss, color='red', linewidth=3)"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x13c65b4e0>]"
]
},
"execution_count": 109,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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BtYYBRWRkCQGlOuuxr3sow5sLt6B9CoJyJGvtJxu71YwF7Z6052DzXm1hEx+R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\nRK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x13c65b128>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"g = sns.jointplot(per_gene_means.values, fraction_genes_greater0.values)\n",
"g.ax_joint.plot(X_test, loss, color='red', linewidth=3)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# counts = counts.fillna(0)\n",
"# print(counts.shape)\n",
"# counts.head()"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# csv = os.path.join(data_folder, 'counts.csv.gz')\n",
"# counts.to_csv(csv, compression='gzip')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# gene_id_to_symbol = pd.Series(dict(counts.index.values))\n",
"# gene_id_to_symbol.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# csv = os.path.join(data_folder, 'gene_id_to_symbol.csv')\n",
"# gene_id_to_symbol.to_csv(csv)\n",
"# ! head $csv"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import phenograph"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import gseapy"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"gmt = '~/Downloads/hg_chr.gmt'\n",
"! head $gmt"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"gseapy.ssgsea"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%%time\n",
"\n",
"correls = counts.rank().corr()\n",
"print(correls.shape)\n",
"correls.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"csv = os.path.join(data_folder, 'spearman_correlation.csv.gz')\n",
"correls.to_csv(csv, compression='gzip')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (cshl-sca-2017)",
"language": "python",
"name": "cshl-sca-2017"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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