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Last active July 17, 2017 10:36
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Multinomial simulation
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{
"cells": [
{
"cell_type": "code",
"execution_count": 196,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"source": [
"%pylab inline"
]
},
{
"cell_type": "code",
"execution_count": 206,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"cells = 250\n",
"genes = 2000\n",
"min_counts = 100\n",
"max_counts = 10000\n",
"\n",
"depths = np.round(np.random.uniform(min_counts, max_counts, size=(cells)))\n",
"\n",
"prob_vec = np.random.uniform(size=(genes))\n",
"prob_vec /= prob_vec.sum()\n",
"\n",
"counts = np.zeros((cells, genes))\n",
"tpm = np.zeros((cells, genes))\n",
"for c in range(cells):\n",
" counts[c] = np.random.multinomial(depths[c], prob_vec)\n",
" tpm[c] = counts[c] / counts[c].sum()\n",
"\n",
"tpm *= 1e6"
]
},
{
"cell_type": "code",
"execution_count": 207,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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N7EygHzi61IlmdjFwMUBvb2+Nq9FcirfSfHJ36VXLSlshIkko233k7ouABcBO4Etm9lMz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\nFBRERCTv/wPALdekPWbsVAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x128a46710>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(depths, np.log10(tpm + 1).mean(1));\n",
"plt.xlabel('Count depth')\n",
"plt.ylabel('Mean(log(TPM + 1))');"
]
},
{
"cell_type": "code",
"execution_count": 221,
"metadata": {},
"outputs": [],
"source": [
"masked_geom_mean = np.zeros((cells))\n",
"for c in range(cells):\n",
" masked_geom_mean[c] = np.exp(np.log(counts[c, counts[c] > 0]).mean())"
]
},
{
"cell_type": "code",
"execution_count": 229,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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1/grYJr3OhZWruEnaBvg+8CrgIeDoag3bZpZd+TQVO3QVePypZxiqMcvp8XNncdWtD7gn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\nAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x119741828>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(depths, masked_geom_mean)\n",
"plt.xlabel('Count depth')\n",
"plt.ylabel('Geometric mean of (nz) counts');"
]
},
{
"cell_type": "code",
"execution_count": 230,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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qkm5277BUM6PWlYqqxAxtHqICGLbIcHf3WyLef0T1Vslcm9m6UXfsPBB5nTWuBYBhYlfI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\nRLgDQA4R7gCQQ4Q7AOQQ4Q4AOfT/Aer9BmhcIDl3AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1196fa3c8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(masked_geom_mean, np.log10(tpm + 1).mean(1));"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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