{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "Collapsed": "false" }, "outputs": [], "source": [ "%%capture --no-stdout\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import scanpy as sc\n", "import cna\n", "np.random.seed(0)" ] }, { "cell_type": "markdown", "metadata": { "Collapsed": "false" }, "source": [ "# covarying neighborhood analysis tutorial" ] }, { "cell_type": "markdown", "metadata": { "Collapsed": "false" }, "source": [ "This notebook demonstrates the basic use of the `cna` python package. Before you run it, make sure you either install `cna` by running `pip install cna` or by cloning the `cna` github repo and adding it to your python path. (In the latter case you'll likely need to manually install the `multianndata` package, which is a dependency of `cna`.)" ] }, { "cell_type": "markdown", "metadata": { "Collapsed": "false" }, "source": [ "## table of contents:\n", "1. [minimal working example](#first-section)\n", "1. [processing data into the correct format](#second-section)\n", "1. Additional CNA features: [additional results produced by cna's association function](#third-section)\n", "1. Additional CNA features: [working directly with the NAM and NAM PCs](#fourth-section)" ] }, { "cell_type": "markdown", "metadata": { "Collapsed": "false" }, "source": [ "## 1. minimal working example " ] }, { "cell_type": "markdown", "metadata": { "Collapsed": "false" }, "source": [ "Let's run `cna` and visualize results for a toy dataset in the commonly used `AnnData` format that already has a UMAP nearest-neighbor graph. You can learn more about data pre-processing and formatting in Section 2." ] }, { "cell_type": "markdown", "metadata": { "Collapsed": "false" }, "source": [ "### 1.1 read in data" ] }, { "cell_type": "markdown", "metadata": { "Collapsed": "false" }, "source": [ "First, we'll read in the dataset and convert it to the format required for `cna`. This involves placing sample-level metadata in a dedicated pandas dataframe. (See Section 2 for more info)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "Collapsed": "false" }, "outputs": [], "source": [ "# read in data\n", "# NB: if data are already in MultiAnnData format (see section 2), they can be read via `cna.read`\n", "#. for example, try out: d = cna.read('data_multianndata.h5ad') instead of runnign this code block\n", "\n", "import anndata as ad\n", "d = ad.read_h5ad('data_anndata.h5ad')\n", "\n", "from multianndata import MultiAnnData\n", "d = MultiAnnData(d)\n", "d.obs_to_sample(['case','male','batch'])" ] }, { "cell_type": "markdown", "metadata": { "Collapsed": "false" }, "source": [ "### 1.2 perform case/control analysis" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "Collapsed": "false" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "qcd NAM not found; computing and saving\n", "\ttaking step 1\n", "\tmedian kurtosis: 16.40326205754668\n", "\ttaking step 2\n", "\tmedian kurtosis: 12.75799245750447\n", "\ttaking step 3\n", "\tmedian kurtosis: 6.1154849181504485\n", "\ttaking step 4\n", "\tmedian kurtosis: 3.177490071480447\n", "stopping after 4 steps\n", "throwing out neighborhoods with batch kurtosis >= 6\n", "keeping 10000 neighborhoods\n", "covariate-adjusted NAM not found; computing and saving\n", "\twith ridge 100000.0 median batch kurtosis = 1.981293621817167\n", "computing SVD\n", "performing association test\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/data/srlab1/yakir/py/cna/cna/tools/_association.py:74: UserWarning: global association p-value attained minimal possible value. Consider increasing Nnull\n", " warnings.warn('global association p-value attained minimal possible value. '+\\\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "computing neighborhood-level FDRs\n", "\n", "global association p-value: 0.000999000999000999\n" ] } ], "source": [ "# perform association test for case/ctrl status, controlling for sex as a covariate and accounting for potential batch effect\n", "res = cna.tl.association(d, #dataset\n", " d.samplem.case, #sample-level attribute of intest (case/control status)\n", " covs=d.samplem[['male']], #covariates to control for (in this case just one)\n", " batches=d.samplem.batch) #batch assignments for each sample so that cna can account for batch effects\n", "\n", "print('\\nglobal association p-value:', res.p)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "Collapsed": "false" }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# visualize which populations are expanded or depleted among case samples relative to cntrls\n", "cna.pl.umap_ncorr(d, #dataset\n", " res, #cna results object\n", " scatter0={'alpha':0.5, 's':20}, #plotting parameters for neighborhoods that pass FDR\n", " scatter1={'alpha':0.05, 's':20}) #plotting parameters for neighborhoods that don't pass FDR\n", "plt.title('p = {:.2e}'.format(res.p))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "Collapsed": "false" }, "source": [ "In the plot above, each cell is colored according to its neighborhood coefficient, *i.e.*, color corresponds to the correlation of abundance in each cell's corresponding neighborhood to case/control status. Neighborhoods that don't pass FDR 5% are shown in gray. As this plot shows, cases have a greater abundance of cells from the left-hand cluster relative to controls." ] }, { "cell_type": "markdown", "metadata": { "Collapsed": "false" }, "source": [ "### 1.3 analyze a more sophisticated phenotype" ] }, { "cell_type": "markdown", "metadata": { "Collapsed": "false" }, "source": [ "Let's next perform an association test for male/female sex rather than case/control status..." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "Collapsed": "false" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "covariate-adjusted NAM not found; computing and saving\n", "\twith ridge 100000.0 median batch kurtosis = 1.9949274142866247\n", "computing SVD\n", "performing association test\n", "computing neighborhood-level FDRs\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# perform association test for male/female sex\n", "# we control for case/control status as a covariate and account for potential batch effects\n", "res = cna.tl.association(d, d.samplem.male, covs=d.samplem[['case']], batches=d.samplem.batch)\n", "\n", "# visualize the results\n", "cna.pl.umap_ncorr(d, res,\n", " scatter0={'alpha':0.5, 's':20},\n", " scatter1={'alpha':0.05, 's':20})\n", "plt.title('p = {:.2e}'.format(res.p))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "Collapsed": "false" }, "source": [ "It's now clear that a sub-population of the left-hand cluster is more abundant in males. CNA automatically picked up on this without needing to be given a resolution parameter.\n", "\n", "Note that it looks like some cells in the right-hand cluster pass our FDR threshold of 5%. However, these cells comprise only ~6% of the cells that pass the threshold and so are likely false discoveries." ] }, { "cell_type": "markdown", "metadata": { "Collapsed": "false" }, "source": [ "### 1.4 relate results to clusters" ] }, { "cell_type": "markdown", "metadata": { "Collapsed": "false" }, "source": [ "If this dataset had been clustered, two clusters would likely have been defined." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "Collapsed": "false" }, "outputs": [ { "data": { "image/png": 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D1/t4bnMlQJQ+jgG2Pzm4WwHNcLMSQd24CDuCHsA4BLJmBPhgi7x2qzZ0/3PlTX62hKECI7zHgL0Z7nbZ0UM3n25JmzPVF0DkJJ2kDf9G25vcpnlBDyJXOJcX7GpaXgiBi0hrcptezQTay0GPZQSh+6QZztC1r2l5PnuCHgDgkw8UziPC3WFYThgufKHrzXJtnuFmTQ16AIBPtit8+0O98rExeTHCFbpe4O4LehgAihK2ssh2BVyhMJKw/XTaLq+hMAB7dCg8n05Dt3E2XLhmuuH7iTkeIS0LAcou3ym1XknrVLiet13l/zdiRYlm2EKXgwSAZYY91sfV0I/3hQJ5i0b+954vkPsl9eX5uuH3DLVw1ekOrulGZYmBWl3EXk49b51yDilkXs93wKKlyW36Zb7vy/z3K67lw1+jbMIVupLU6PxFUmPQwyijlKQHCV7gSoUCOdhRmRXG0E1JmhD0MMqMh1QCkBS+NV0peoErSdX01QUghWWmO9hnoU52dxQbSa+kDSwzAPEWfOgO9lmw/djvWLSrxZ0X9CAABCcMywtR6LMwVlGqQwZQgjCEbpyCKARrOQCCFIbQ7Ql6AD4Kw/sNIEDBhoC3nhuvblxUMQCxFlzoeuGzP7D7B4enAwMxFkzoDlYsRLEmdzRxWsMGMExQM90dik/FwnDpoAcAIDj+h643y632/b7hMYF1XSC+gpjpsqYp7SV4gXgKInRZ0/SWVvYTvED8+Be6jc79anTaFN3eCsWaIGa8QOz403shXv0VikU/BiBG/Jrpxqm/QrFYbgFixK/QJVgK47lwQIz4FboES35WPL0UQPmYDd3BzTNmuvnR1ByImYSxK7N5BgBXMFe94M1w681cPDIuqsW9JuhBAPCPyeUFlhRGN5U6XSBeTIYum2djw7FoIEZMhu4WebvzGBlLMECMmAtdb1d+g6QBY/eICpYYgNgwfwy40UmLfguj4SgwEBN+HI5gbXd0bDoCMeFH6HLianQ8TQKICfMn0rxH82BkPJodiAnTJ9L2Saoydo/oYAkGiAmTM6ztInDHqk6NThtVDED0cSItHBx59br7CF4g2jiRFi5VYg0ciDSTodts8NpRFufH0wORZzJ01xi8NgBYiTXd8LkY9AAAmMOabvj0BT0AAOaY7jKG4s0IegAAzDETuo3OS5L+x8i1o49PCECElf9Emhe4j5X9uvHQLz4hAJFmYqa70cA142IvTwcGos1E6E4wcM24eJgTaUC0mQhdnhRRusnimWlApJkI3T0Grhkn1DcDEVb+0G1xH5fUUvbrxgfVC0CEmarTXWTounFA9QIQYWYeTMnDKEvVrRa3JuhBADDH1Ez3nKHrRt3moAcAwKzyh65X8jS17NeNvu+p0QWiz8RMd7ukiQauG3VXUaMLRJ+J0KXkqXR/JHiBaDMRupQ8lW6iOBwBRJqJ0OUxPePDJwUgwkyELo/pGR8+KQARxppuuLjicAQQaSZClxrd8aBsDIg0k4/rQfH4gQVEnInQ5RlfAFAAJWPhwg8sIOJMhO4WSb0GrhsH/MACIs5EP91XJW2Q1F32a0cflQtAxJnZSGtxX820KCR4ASCH6eoF1iiLs5/eC0C0mQ5d1iiLM0HSXoIXiC7TobtFUp/he0QNTwQGIszM43pyNToXRFPzYrlqcTm4AkSQ2X/YPEViNAMFXmdZBogo07MpPiaP7F+S+oe91itKx4DIMh26dBwbWbW8zmLdmd/bJW2g6Q0QXQnD1++QVG/4HrabKKmLR68D8eBH9QJHgkfHJwIgJsyGLkeCx4qNMyAmzJclecH7b+P3sRcbZ0CM+FULysfn/Ng4A2LG9EZaFhtqV1pH2ALx49dMlw21oS4SuEA8+RO6gxtq7fLqUQ2fPQ61fkkbgx4EgGCY772Qj3c8+KD/Nw5cu6QtzHKB+AomdCWp0YnbbNeV9ACBC8RbkJ2s0gHeOwiO6EUBxF6Qobs7wHsHhdI5IOaCC90W93FJLwd2/2Bw8gyIuWAbZXvBu07xKCfj5BmAADfScnnVDPvlPSMsigYkrWcTDUA4QleKehkZj98BICno5YVc3iwwqt3IWMsFIClMoevZrOit77KWC+CycIVu9PrvdosuYgByhGdNN1ej0ya7u5J1S9pM2AIYLlwz3UE2HyK4JAIXQAFhDV2bN54qxXFfAAWENXRt779r80wdgEHhDN0r++92S7oY6JjyGyjwus0zdQAGhXMjbTSNzkuSHgt6GPKOMO+VNDnntV5RsQCggHDOdEcz2LOhPcBRdOeZkfOgSQAjsnOmm6vROSup2ue79kl6hHAFUCw7Z7pDzTB47XVqcR0Nzqqzs1kCF0BJojDTbdPYD1K48p7gMBYX1eJeU9KYAKCAKMx085WX9cprkJ47O11XxDVT4om9AAywf6YrZdtCbpdXH9uhQk/cLTwrdiWdk7dUUfj7AWCcohG6Y+WF8/ASL1fSrkxFBAAYFYXlhbHLX+L1AIELwC/xmukCQMDiNdMFgIARugDgI0IXAHxE6AKAjwhdAPARoQsAPiJ0AcBHhC4A+IjQBQAfEboA4CNCFwB8ROgCgI/+H+374hxDjiwMAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sc.tl.leiden(d, resolution = 0.1)\n", "plt.scatter(*d.obsm['X_umap'][d.obs.leiden=='0'].T, c='orangered', label='Cluster 1')\n", "plt.scatter(*d.obsm['X_umap'][d.obs.leiden=='1'].T, c='purple', label='Cluster 0')\n", "plt.legend(); plt.axis('off')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "Collapsed": "false" }, "source": [ "We can examine within-cluster heterogeneity characterized by CNA by visualizing the distribution of neighborhood coefficients within each cluster." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "Collapsed": "false" }, "outputs": [ { "data": { "image/png": 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RH0YiHtO94fPmht3/MeLIQ2MfuDEmqoJZD+QJ4HVgKeCL1IFFxAPcBhwHlALLRORJVV0dUOwCoEZVp4rIYuD3wKKAx/8EPBepmEz3hh02D08Yz88aXkBm8biIxWOMSQzBJJBsVf1JFI59ALBeVTcCiMiDwAIgMIEsAK5yf38EuFVERFVVRBYCG4GmKMRmAmROLCYjLwdvQ2hv9ZCpEyMbkDEmIQTThPW0iHw5CscuAkoC7pe627oto6odQB0wXERygJ8Av+7rICJykYgsF5HlVVVVEQk82YgIecVFITdfDZk+JR5hG2OiLJgEcilOEmkVkXoRaRCR+ggcW7rZpkGW+TXwJ1Vt7OsgqnqHqs5V1bkjR44MIUwDkDtlAqSnhXTLnTE13uEbY6KgzyYsVc2L0rFLgfEB98cB23ooUyoiqUA+UA0cCJwhItfjDDP2i0irqt4apViTXvbEYvAE0+K5uyyrgRgzKPX5jSAiAiwBJqnqNSIyHhijqu+FeexlwDR3WpQyYDFwTpcyTwJfA94GzgBeVlUFDguI7yqg0ZJHdGVOGAee0KZOy7TpS4wZlII5pfwL4AeOBq4BGnFGT+0fzoFVtUNELgZewJmo9S5VXSUiVwPLVfVJ4E7gPhFZj1PzWBzOMU3oMorGgqf/Y7E86emkDRsahYiMMfEWTAI5UFX3FZEPAVS1RkQickmxqj4LPNtl2xUBv7cCZ/axj6siEYvpXfqYQkjv/589Y7glD2MGq2ASiNe9ZkMBRGQkTo3EJJH0YUMhpf9NWGlDcqMQjTFf8Le1429tJTV/SLxDSTrBfCPcDDwGjBKR3wBvAL+NalQm4aRkZeLJzHQ60vtxS82zBGKiq6O+nqa16+MdRlIKZhTWv0TkfeAYnGG1C1V1TdQjMwknJScbX3NLv57jsQRioszv7UB9EZskw/RDb9O5D1HVehEZBlQCDwQ8NkxVq2MRoEkcnswMvK1t/XpOis3Aa6JM29rxtXvjHUZS6q0Gcj/OdO7vs+sFfuLenxzFuEwCSklP7/e1ICnpGVGKxhhHR1MTvpbWeIeRlHqbzv0k96ctX2scGenO1eX9IP0sb0x/eesbaa9viHcYSSmYNdFPFZH8gPsF7kSGJtl4PKHdjImilvJKWqusRT0eghmFdaWq1nXeUdVa4MrohWQSlqT0O3l0ndzMmEhr2FpKfWl5vMNISsE0aHeXZEKbFMkMaOrxQFr/OsU1hGtHjAmaKtUbttLW4PSDeLIy4x1RUgnmv3u5iNwoIlNEZLKI/AmnY90kGT8482H146ZWBTFRVPvRKprrGvH5lYr/vRnvcJJOMAnkEqAdeAj4N9AKfDeaQZnE5Pdrv5uw/D6btMBEiSobH3/h88/a5hffwG/DeWMqmAsJm4Dd1is3ycev9LtT3GcXeJko2b70dapLtn/+mWyub2Tzv59i8pLT4hxZ8ujtQsI/q+plIvIUuy/0hKqeEtXITGJRxZeS0u8+EL81YZkoaNpcwpqnXtrt87jpjfcpmDWdYV+aHafIkktvNZB/uD//EItATGLztbaFNJlih9VATIS17qhmxe334/P5d1ujRoGP73uMuQVDyJ1k69BEW2/fCDe4P7+sqq92vcUiOJM4vM0tIV0D4uuwBGIip21nDR/eei8tTT1/Hjs6fHxw+/00bSmNd7iDXm81kDEicgRwiog8SJf1yVX1g6hGZhKKrzOB9JO3wzrRTWS0lFfw4e3301zX2Odnsb3Ny/K/3s8+Xz+D/Ok261K09JZArsDpPB8H/JFdE4jirFBokkRHS2u/+z8AfAqogkifZY3pSc2qz/j4oWfwtrYH/Tn0+pQP7nmUWScfQ+FB+0Y5wuTUWwIpV9X5InKFql4ds4hMQmpvag5pTXQ/TvJJzc6KfFBm0FOfj83Pv8rGNz9AVfv9GfQpfPLkS9RuKWPqqSfgsdmhI6q3BHIzsB+wELAEkuS8za0hz2vlbWyyBGL6ralsO2v+8wK1FTtDGsARqGTlOnaWbmfWKcdQYE1aEdNbAvGKyN1AkYjc3PVBVf1e9MIyicbb0gppoc2s297QRNaoERGOyAxWHc0tbF76Jls/Wovf7w/5c9dVc1Mryx94hrEzJjHlxMPJKLAlcMPVWwI5CTgWp6/Dpi5Jcm1h1EDaGpsjHI0ZjPztXkrfep/Ny1fR3trm9JtFYTbnbeu3UvHXByjeZybFhx9AmtWOQ9bbeiA7gAdFZI2qfhTDmEwCam9pswRioqKjpZVt733Elg8/pa1zYagoLwPgU9j04aeUrNzA+L2mMv7gfUm35Zf7LZhZdVtE5CWgUFVni8jewCmqem2UYzMJpNVqICbCWnbWUPrex5R9uokOb4ezMcbrx3T4/Wz66DO2rlxP4ZTxjN9/L/KKRsc0hoEsmATyN+BHwO0AqvqxiNwPWAJJIq3tHSEN4wVobenfOupm8PJ7O9ixdgNlqzawc9sOd2v/p8iJNB+wbVM52zaVUzAin7GzplA4e7qN2upDMAkkW1Xfk13H8XdEKR6TgPzeDlrbvCEN4wW39mKSlyp1JeWUr9lAxaZteDtnzA3x8xRttTUN1L65grXvfMLI4tGM2WMywyaNQ2x1zd0Ek0B2iMgU3AkVReQMwJb/SiItNXVhNS00NVsNJOmo0rC9ioq1m6jYtI2WwJOIAfJF7AO2b93O9q3bSc9Ip3DCaAqnTyR//BjEFkoDgksg3wXuAGaKSBmwCVgS1ahMQmmqbQiricHrd4YBp9lqcYNeY+VOKtZtpWJLOc2NLV88EOcmqnC1+6Fk03ZKNm0nIyudwuLRFE4tJn/sqKSeZSGY9UA2AseKSA6QoqoN0Q/LJJKm2oawzxqbdtZRMM4SyGDUvLOW7eu2ULG1gqaGgAETA6Sm0V9t7T62ri9j6/oysrIzKCwezagp4xlSODzpkkmfCURE8oErgcPd+68CV6tqXZRjMwmiqb4p7Pbqxpo6CsYVRigiE2+tdQ1UbChh+5btNNQ1fvFAgvZrREtLm5fN60rYvK6E7JxMCotHM3pqMTnDC+IdWkwE04R1F7ASOMu9fx5wN2DLfiWJhvomSAnvbLKxxiquA11rQyOVG8uo2LKdutqApBHmZ2OwaG7xsmltCZvWlpCbl0Xh+EIKJ48je1h+vEOLmmASyBRVPT3g/q9FZEW0AjKJxeftoNnrh/Tw2rAbrCN9QGqpb6Ry8zYqt+2grrbpiwfC/DwMdo1tPhrXb2PD+m3k5GYyauwIRk0YQ96w/EHVzBXshYSHquobACJyCNDSx3PMINGwsxaNwBlmQ2Mr/g4fKal2tprQVKnfWUdVaQVV26tpbAj4Vx+kfRrR1tTiZdOGcjZtKCcrK51RY4Yzsmgk+aOGDfjRXMEkkG8B/3D7QgBqgPOjFpFJKHXV9RFp1/YDDTX15I8cGn5QJqLU76euqobK0kqqKmpoaW3/4sEk69OItpb2DrZsqWDLlgrS01MZOWooo4pGMrRw+IA8uQpmFNZHwBwRGeLer496VCZh1NY3R6y5oq6mwRJIAmmobWD71goqKmqcC0U7WfNUTLQDZZV1lFXWkZa6iVGjChgzfhQFIwoGTDNXjwlERC4H6lT1TvgicYjIJYBHVf8cmxBNvKjfT21Da8Q6SWtqmyiOyJ5MqPw+PxWllZSWVFJXH9A8ZR3hceX1Q9n2Wsq215KTnc64cSMZO3EMngSvlfRWA/kG0N06kHcAywBLIINcY30zXj8Ra/uuqW9B/f4B3+47EPl9fsq2bmfLlkpa2+IzcaEJTlObj7UbtrNxayXF40ZSPHlswiaS3hKIqmp7NxvbRAZI/cqEpXpnZPo/OnUA9XWN5A+1hXxiRpWqihrWrd9Gc2ffhvVrDAheP2zYWkVpeQ1TJo9mTNGIhDv56rUPREQKVbWi67bohmQSxY76loi3h++sabYEEiNNjS18tqGcnXXu1eHWtzEgtQGrN1VSWlnHjCljyC9InHVLeksgNwDPiMgPgA/cbfsB1wN/iHZgJr683g5qm9sj3jZeVdeErUgdXa2t7WzaWsW2qnpUsf6NQaK+pYNlK0sYNTSHKRNGkpMb/5UUe1uR8B8iUgVcDczGmY13FXClqj4Xo/hMnFTtbIjI9R9dNbT6aGlpJyvLzoYjrbW1nS1l1ZRV1eO3xDFoVda3UrWyhMKhOUweP5zs7PjNMddrE5abKCxZJKHtdS2Qnhadfdc0MckSSMS0tXvZXF5H2c5G/KqQFp2/m0kcCmxvbKdiTTmFQ3OYMnYoWZmx/7sHcyGhSTKtbV6qmzuidgZbXtvMxNH5CdchONCo38/WqgY2bq/H51eQFLDhLUlFge11rVTWlzN+RC6TxxTgieEgCUsgZjel1U1RHeLZ3AHVjW0MHxL/NtyBqt3r4+MtO6lt9jqJw1qrkpof2FLdQlVjO/tMGk52RmxqI5ZAzC58Pj/b6tuiPtSzpKbFEkiIfD4/H2ypprHNZ0NyzS6afcryzdUcMGk4menR/3rv60r0HqnqjZEPx8RbaU0rzsS50f1i2l7npa7ZS362tdf317qKJmqb/UT7b2QGpg4frN7WyL4To78mSW8pKs/9OQPYH3jSvX8y8FokDi4iJwI34VTA/66q13V5PAP4B87w4Z3AIlXd7D72M+ACnKWLv6eqL0QipmTm8ynrK5rx+WJzvHXbm5g7OTkW3omU5jYfmyqbnVFWxvSgvKaN6hFehuVG9wStt2G8vwYQkf8C+3YuZSsiVwH/DvfAIuIBbgOOA0qBZSLypKquDih2AVCjqlNFZDHwe2CRiMwCFgN7AmOBpSIyXVVj9NU3OG2sbKapNXZv4baaNnY2eBmeZ7WQYKjCypIGvPYpN0H4eEs9h80chscTvZEVwTSSFeNMHNmpHZgYgWMfAKx311xHRB4EFgCBCWQBcJX7+yPAre40KguAB1W1DdgkIuvd/b0dgbiSUnOrj1Vbm+iI8ZfThxsbOHqvoaSk2PChvmysaGFrpS3MZYKzo62DlVsbmTMpr+/CIQomgdwHvCcij7n3FwL3RuDYRUBJwP1S4MCeyqhqh4jUAcPd7e90eW5RdwcRkYuAiwCKi20u2J6s2NRIuzf27SLVDV7Wl7cwvSg75sceSGoaO/hgg3tluTFBWlvWzMgh6YwdnhGV/QezHshvROQ54DCcYcdfV9UPI3Ds7k45u/579FQmmOc6G1XvwJlBmLlz59q/Xze2VLSypao1bsf/aFMjY4ZmkJdtY1F78sH6hpjXDs3gsHx9AycNTY9KLT/YcV4+nKHG6v6MhFJgfMD9ccC2HsqUikgqkA9UB/lcE4SmFh9vrWmgvSN+MbSjvLayjvlzrSmrO6pQumO3ibGNCUp7u4/6Zh8FuZEf1tvnHkXkUuBC4FGcM/9/isgdqnpLmMdeBkwTkUlAGU6n+DldyjwJfA2nb+MM4GVVVRF5ErhfRG7E6USfBrwXZjxJx+dTXlpRR2tbpM4JQldZ4+W9TxuZNyt67bUDWXa6h8YWq4KY/kv1CNkZ0RnyHUxKugA4UFWbAETk9zhf6GElELdP42LgBZxhvHep6ioRuRpYrqpPAncC97md5NU4SQa33MM4He4dwHdtBFb/vb26kcoab98FY2Tl5mZG5KcxtSh+k8MlIhGYN3MI/32/xvpATL8dOCOP9LToJBDRPj6RIvIJsL+qtrr3M4FlqrpXVCKKorlz5+ry5cvjHUZC+HhDM2+vaYh3GLvxiPCVeUMZM9yG9na1taKd/62oo9Ub/xqjSXypKcIhew5h5oTwTshE5H1VndvtMYJ4/t3Au+4orM4htHeGFZGJq3Ulbbz2UUP3ow7iTnnm7VpOPWwow/Ntpp1AxYXpnHnEcN5Z1cSarXYxoenZ5NGZHLp3Lvm50R2Y0mcNBEBE9gUOde++HqFRWDFnNRDYWNbOM2/XJPyXT3amh0VHDWVIlP8BBqrqeh/vrmpifVlLwv8tTexMKMxg3p45jI5gDT7cGgg4o7CUyI7CMjG2obSdp9+ujdlUJeFoaPLx0Ms1nHnUUAryLIl0NWyIh/kHDaG+MYflnzazektLXK7jMfGXkgLTx2VxwKxsRhTEttYeTB9I11FYpwKRGIUVc8lcA/l0cxtPvVmLb4Cl/z8q8JMAABhLSURBVNwsD4uOKWDkUGvO6k1rm5/3P23lw3XNNlorSWSmpzBnShZz98gmLyd6E2v2VgMJJoF8DBwUMAorB3hbVfeOeKRRlqwJZNmqFpYurx+wTR1Z6SmccXQBxaOtY70vPp/yyfo23l3dxI66OF7cY6JmSLaHuTOz2W+PLNLTon/dVLhNWILThNXJh617NiCowtJ3G3lnVVO8QwlLU4uffz5fw4JD89lzanSmZBgsPB5hnxmZzJmeyWdb2nj7k2ZKKu0ixMFg1NA05s3OZq+pGQlzwW1/R2GBMxeWjcJKcC2tfh55qZ71ZYNj8r0On/Lwy7UcuiOXYw/MQRLj/ydhicCMiRnMmJjBtsoO3vq4mTWbW+kYqNXQJOVJgcljMzl472wmFaUl3Oc+2FFY+wGH4NQ8XrNRWImtvKqD+5+vY2f94GzCmDYug7OOH0JOli2o1B/NLX4+WNPGB2tb2F6dOBeQmt0NH5LKnGmZ7L9nJvlxHkQSVh+IuwMPUEhAjUVVt0YswhgZ7AlEFd78sIXn327A6xvcZ5pDsj0sOj6fqcXWLxKKip0+PljTyqqNrVTVDs4TjYGmIMfDrEmZfGmPDIrHJM7nOtxO9EuAK4EKvuj/UOtETyz1jX4eebGBNZvjN6turInA4V/K4cRDckhNTbC6/QBSsdPHR2vbWLOplbIqr02XEkOjhqayx6RM5kzPYNzo1IRrooLwE8h6nLmwdkYjuFgarAlk+co2Hn25nubWATZGN0IKh6Vy7lfyKR5jQ33DVdfo5+O1baza0M760ja8HZZNIsmTApPGZLDnlHTmzMxgeEHiX+MUbgL5H3Ccqg74eu5gSyA1dT4eer6RlRuTp9bRE08KHLFvDicdkUN6egKexg1A7e3K6g3tfLS2jdWb2mhK0hOUcGWmCdMnZDBnegZ7TU8ne4D13YWUQETkcvfXPYEZwDPA50N6VPXGCMcZdYMlgfj9yktvt/LMGw20tdsZYqBh+R4WnziEvaanxzuUQcXnU9Zs9LJsZSufrGujJQGWAEhkaanCrMkZzN0zkzkz0kmLwfUa0RLqdSCdCzNsdW/p7s3E0ZoNXh5+oYFtVTaKpjs7anzc+kANs6dmsnh+LiOHJX4TwUDg8Qizp6Uze1o6Xq/y/qo23lrRymdb26zPJMCEMWkcNCeLeXMyycocuEkjWL02Ybmjr65T1R/FLqToGcg1kB01Ph54upEP1lpzVbDSPMLRB+Sw8NhsMjMG/z9zPFTu9PHiWy28/XELTS3JWSvJSBPmzsriuIOzmFA0+Prhwu0DeUlVj4lKZDE2EBNIU7Ofx15s5n/Lmgf90NxoGZKTwilH5nLMQZl4PJZIoqG9XXl9eRsvv9tMSWVy1I5HFHg4cm42xxyUSU72wOrX6I9wE8gfcZaM/Tfw+ZwYqvqfSAYZCwMpgXi9yvOvt/D0q01Je2YXaaNHpHLWCbnsv3dGQg6XHCxWr/fy/OvNrFjbin+QfXRFYI9JGRx3cBZz90qOz1G4CeTubjarqn4jEsHF0kBIID6f8r932nj0xUZ21tmsqtEwdVw655ycy+zpiXOx1mC0s8bHM6+08uryZhqaB3YmycoQDp6TxVeOymbc6OTqVwv7SvTBIpETiCq8+X4bDz3bSFnVgB8xPSDsNS2DJSfnMn3S4Gu3TiRer/LKu208+1ozW8oHVvNW4bBUTjg0mxMOS45O8e6EWwMZB9yCMxeWAm8Al6pqaaQDjbZETSDvrmjngWca2bxtYP1zDQYisO/MTL56as6g7ABNNB+t8fLoC018sj6xR29NK05nwTHZHLJfcjRT9SYSa6LfD5zp3j/X3XZcZMJLXitWe7nv8UbWbbXptuNp2cpWlq9qZd7eWXz11ByKkqyJIpbm7JHGnD0K2LClg4eeaeLdT1oTKpHsNS2DxV/JYa+Z1rwZjGBqICtUdZ++tg0EiVIDWb3Oy13/bmLlhsEx1fpg4kmBI+Zmc/7p2YwaYYkk2raW+fjHY028tSK+a7vvMyODr56aw6xplji6CrcGskNEzgUecO+fDQz4ebHiYVNJB397sIllq+xajkTl88GLbzfzyrIWjj84m/PPyKZgyOAdohlvxUUefnnxEDaVZHPXv5t49+NWYplH9pySzjfOzGVvq3GEJJgaSDFwK3CQu+lNnD6QLVGOLeLiVQMpr/Tx94eaeG15y4BbkzzZZWcKpxydw7kLs8nOSvLG8BhYs66DOx5s5KPPols7n1yUxkWLczlgH5tcoy82CssV6wTS1Kz8/cEmnn61Ga83ed7nwaggL4XzFuay8Hi7GDEW3vmgnf+7v5EtER5YMnKYhwvOyOWEIzKTvnM8WOGOwpoM3ATMwxmF9TbwfVXdGOlAoy1WCcTnUx5+upV7H2ukvsmqHINJ8ehULv5qHofub2eu0ebzKY8+18qdjzTSEOb/UWa6cNb8HL5+ZjYZNq1Nv4SbQN4BbuOLPpDFwCWqemBEo4yBWCSQD1d6uf6OBjaW2pDcweyQL2Xyw4tyGVtoHe3RVlPn54bbG3npnZaQnr/vHhn88pI8G10XonATyLtdk4WIvKOq8yIYY0xEM4HUN/r5/V8a+e9bLQk1LNFET2a68NVTc7lgUZY1a8XA0jfa+O1f6oOu1WekC5ecm8eiU7KsuSoMoa4HMsz99cdALfAgThPWIiBDVa+JQqxRFa0E8vKb7Vx7az07a23qkWQ0c3Iav/1RPlMm2hlutJWW+/jOr2rZUtb7bA3DCzzcfGUBs2faxaHhCjWBbMJJGN3lblXVyZELMTYinUDa2pRrbm7k8aXNEdunGZgy0oTLvp7HeadnxTuUQa+uwc/Xvl/L+pLum4lHDfXwjz8NZdwYS+iRYKOwXJFMINu2+7j4ijrWbLS+DvOFLx+exW9/kmcdtVFWucPPwot2UlO/a3NWRppw/03DmDXdah6REu6FhIjIwcDEwPKq+o+IRDcArf6sg6/9oIaaOhthZXb1xNIWNmzxce+f88nPswsQo2XUiBR++M08fnxd3S7bLzwz15JHDAXTiX4fMAVYAXQ28quqfi/KsUVcJGogK1Y5yaO+wZKH6dmMyWn86+YChg+1JBItqnDM2TvZVOL0hwzJTeGdx0eQZRd8RlS4NZC5wCxNprauHmyv9HO+1TxMEFav9/LNH9XxyO0FNkIrSkTg5KOz+NPdDQAceWCmJY8YCyaBrARGA+VRjiWhqcLXL6+josqShwnOOx+2c+Ufmrj2J7nxDmXQOvm4DK6/3UkgJxyREedokk+PCUREnsIZhZUHrBaR94DPJ6hR1VOiH17iePCJVt5fadOum/65+5EmLlySxYRxNiIoGqZO8jC8wEN1nY8jD7IJEWOttxrIH2IWxQBwy93N+OwyD9NPPh/88fZmbr4mL96hDFp7TE2jrDyF7Gxrvoq1HhOIqr4ay0ASWX2Dsma9N67rFZiB650PbKh3NO05LZXsJF1uNt767AMRkQbYbYr+OmA58IOBOKlif61Z10G7tV6ZEG0utTXuo2n65FSk2+udTbQF04l+I7ANZ1lbwZlMcTSwFrgLODJawSWKMaMEVes8N6HJz7X+j2iaOF5oa7MEEg/BJJATu0ymeIc7meLVIvLzaAWWSIrGpDBiqLCjxjpBTP/Nmmajg6Jp5PAUdlTbCV48BJNA/CJyFvCIe/+MgMeSolfA40lhycJMbrq7ru/CxnRx8dez4x3CoJabI2TbFGRxEUwCWYKzoNRfcBLGO8C5IpIFXBzF2BLKTy7J45HnGijbbu3ZJnjHHpLNsYdbDSSacnPEJk6ME5tMsR/e/aCNE5aU0W7L05ogjBuTynvPjKNgiH25mYErpKlMROTHqnq9iNxCN01VA3EurHAduG8GD942mvMuK6ep2ZKI6dmEojSevW+sJQ8zqPXWhLXG/RnxFZjcxaoewpnhdzNwlqrWdFPua8Av3bvXquq9XR5/EpisqrMjHWNPTjw6m6fuHstZ3yplR401Z5nd7TUji6fuLWLUCJsV1gxuQTdhiUiOqjZF5KAi1wPVqnqdiPwUGKqqP+lSZhhO8pqLUwN6H9ivM9GIyGk4Hfp7B5tAIrkeSOWODr5xeRkvv9UYkf2ZgS8lRfj2ucP57c9GkpZms/CawaG3Jqw+P+UicpCIrMatkYjIHBH5S5gxLQA6axP3Agu7KXMC8KKqVrtJ40XgRDeGXOBy4Now4wjZqBGpPHVvMTf8YjT5eQL47ZbEt0njU3nizmJuuKLQkodJGsF80v+M82W+E0BVPwIOD/O4hapa7u6vHBjVTZkioCTgfqm7DeAa4I9An2vJishFIrJcRJZXVVWFF/Xu++a7Xx/OJy9N57xTh+LxKE5lyW7JcsvLEa66rJCPXpzGMYfZrLsmuQTVSKuqJSK7XOnZ5xV1IrIU54r1rn4RXGjdr8UuIvsAU1X1+yIysa+dqOodwB3gNGEFeex+GTEsldtvGMd3zh/OFTeUsfTNepJocFtSyswQFp88nKt+OIZRI2wWWJOcgkkgJe6Stioi6cD3+KKDvUeqemxPj4lIhYiMUdVyERkDVHZTrJRdp0kZB7wCHATsJyKb3fhHicgrqnokcTZnzyyeuGcqK1Y2cfWN23jh9TpLJINMVqZwzoIR/OLSMYwelR7vcIyJq2CWtB2BcyHhsTi1gv8Cl6rqzpAPKnIDsDOgE32Yqv64S5lhOB3n+7qbPsDpRK8OKDMReDoenejBWPlpM3/4v3KeWFpNW5s/Zsc1kTd8aBrnnTqSyy4abTUOk1TCWtJWVXfgXI0eSdcBD4vIBcBW4EwAEZkLfEtVv6mq1SJyDbDMfc7VgcljIJg9M5t7bppC1c7x3HJnOfc+UklVtU3tPZDMmJzFt84dzfmLCsnIsM5xYwL1WAMRkSt6eZ6q6jXRCSl6Yl0D6crr9fPwkzu488HtvLvC+kkSVUZ6CiccPpRvf3UMRxxcEO9wjImrUGsg3V3zkQNcAAzHGQll+iEtLYUlp49iyemj+GxDM7feXcajz1VRU2cXJCaCSeMzOffUQi48dwwjhln/hjF9CepCQhHJAy7FSR4PA39U1e46vhNavGsg3fF6/fznmSru+Xc5byyvw+ezakks5WR7mH/kMC48p4hDD8yny2hDY5JeyH0gbkf25Th9IPcC+3Y35YgJXVpaCosWFrJoYSHlFa3c8c8y/v1MBRu39nmJiwmRCMzdawjnnjaWJaeNJjvbphwxJhS99YHcAJyGcw3Fbao64OfsSMQaSE/eeb+Gux4o4+mXq6itt473SCguyuKM+YVccM44JhXbGh3GBKO3GkhvCcQPtAEdsMtsvILTiT4k0oFG20BKIJ28Xh//eaaC+/6zjdff24m3w5q4+mNIbirzjxzJNxaP59ADh1oTlTH9FFITlqramMUEkJbmYdHCsSxaOJaqnW3c+1AJDz21jVXrGuIdWsJKTRUO2W8YS04dx5knjyEjw6ZUNyYabEGpAeqTNXX8/f4tPP5COVXVbfEOJyFMm5TLopOKuOCcCRSOzIx3OMYMCiE1YQ1GgymBdPL5/Dz133LueXgr/3unCq83ua54z89L4+RjxvDNJRM54EvD4h2OMYNOWFeim8Tm8aSwcH4RC+cXsaO6jb//ayP/eryEDVsG/JiHHonAAXOGcf6ZE1m8cLw1URkTJ1YDGaRee6eS2/+xgede3UbrIJmHa/jQDM6YP47vnD+VaZMH3BgOYwYkq4EkocPnjeLweaOorm3lL/es595HNlK2vSXeYYVkzh4F/L8lU1ly+kTS0qy2YUyisBpIkvD5/Dz+XAm33rOWd1eEPJFyzKSlCl8+aiyXXbgHB+47Mt7hGJO0rAZi8HhSOP2kCZx+0gTe/aCS6279hBff2Ibfn1gnEFlZqSw+aRI/vXgvxhfZCn/GJDJLIEnowH1H8dhdx/Dpulqu+fMKnly6Ne5zcOXmpHLBWdP5wbdnM2JYVlxjMcYExxJIEps5rYB/3XYkKz+t5pe/X8aLb2yL+RTzGRkpnHfqVK64fF9LHMYMMJZADLNnDuPxu0/gnfe3c+kVb/LJ2tis23XyMcX84YqDGV+UF5PjGWMiy6YrMZ+bt99o3nn6NK776QEMyfUA/qjcJo3P4fG/H89Dt59gycOYAcwSiNmFiPC9b85hxX8Xcdj+o4lk4hBRLjp7Jh88fxbHHzkhxq/MGBNplkBMt0aPyuH5+0/hysvmkpYmhJs8hg9N5+G/HM+frzmCjAxrOTVmMLAEYnokIvzk4v15/G/zGZKXgjOzf/9v0ybl8tbjZ/KV4ybH42UYY6LEEojp01GHTmDpA2cwemQW/a15HDBnFK//ZzHji2zqEWMGG0sgJiizZ47k+X+ezrCCdIJNHnP2GMGz953OkLyMeIVtjIkiSyAmaNOnDOfROxaQmQF9NVtNKMrhqXtPIzs7PW7xGmOiyxKI6ZcD9x3HVd8/hN5qHmlpcN/NJzNiWE4cIzXGRJslENNvl1xwIIftX0RPCeTyCw5g7pyieIZojIkBSyCm30SEP155Ah4PgO5yKyrM5aeXHBbX+IwxsWED8k1IZs8s5IV/LqGmrnWX7ZOLh9p1HsYkCftPNyE7eP+J8Q7BGBNH1oRljDEmJJZAjDHGhMQSiDHGmJBYAjHGGBMSSyDGGGNCYgnEGGNMSCyBGGOMCYklEGOMMSGxBGKMMSYklkCMMcaExBKIMcaYkFgCMcYYExJLIMYYY0JiCcQYY0xILIEYY4wJiSUQY4wxIbEEYowxJiSWQIwxxoQkLglERIaJyIsiss79ObSHcl9zy6wTka8FbD9bRD4RkY9F5HkRGRG76I0xxkD8aiA/BV5S1WnAS+79XYjIMOBK4EDgAOBKERkqIqnATcBRqro38DFwccwiN8YYA8QvgSwA7nV/vxdY2E2ZE4AXVbVaVWuAF4ETAXFvOSIiwBBgW/RDNsYYEyg1TsctVNVyAFUtF5FR3ZQpAkoC7pcCRarqFZFvA58ATcA64Ls9HUhELgIucu82isjaSLwAs5sRwI54B2GSln3+omdCTw9ELYGIyFJgdDcP/SLYXXSzTUUkDfg28CVgI3AL8DPg2u52oqp3AHcEeUwTIhFZrqpz4x2HSU72+YuPqCUQVT22p8dEpEJExri1jzFAZTfFSoEjA+6PA14B9nH3v8Hd18N004dijDEmuuLVB/Ik0Dmq6mvAE92UeQE43u04Hwoc724rA2aJyEi33HHAmijHa4wxpot49YFcBzwsIhcAW4EzAURkLvAtVf2mqlaLyDXAMvc5V6tqtVvu18BrIuIFtgDnx/oFmN1YM6GJJ/v8xYGoarxjMMYYMwDZlejGGGNCYgnEGGNMSCyBmLCIyIkislZE1ouIjYYzMSUid4lIpYisjHcsycgSiAmZiHiA24D5wCzgbBGZFd+oTJK5B2eGChMHlkBMOA4A1qvqRlVtBx7EmabGmJhQ1deA6njHkawsgZhwdDvdTJxiMcbEmCUQE45up5uJeRTGmLiwBGLCUQqMD7g/DpsZ2ZikYQnEhGMZME1EJolIOrAYZ5oaY0wSsARiQqaqHTiLeb2AMx/Zw6q6Kr5RmWQiIg8AbwMzRKTUnR7JxIhNZWKMMSYkVgMxxhgTEksgxhhjQmIJxBhjTEgsgRhjjAmJJRBjjDEhsQRiTASIyGgReVBENojIahF5VkSmhzpLrIicLyJjIx2nMZFkCcSYMImIAI8Br6jqFFWdBfwcKAxjt+cD/UogIhKvJapNkrIEYkz4jgK8qvrXzg2quoKAiSbdGsWtAfefFpEjRcQjIveIyEoR+UREvi8iZwBzgX+JyAoRyRKR/UTkVRF5X0ReEJEx7n5eEZHfisirwKUxe8XGAHbGYkz4ZgPvh/jcfYAiVZ0NICIFqlorIhcDP1TV5SKSBtwCLFDVKhFZBPwG+Ia7jwJVPSLM12BMv1kCMSa+NgKTReQW4Bngv92UmYGTpF50WsvwAOUBjz8U7SCN6Y4lEGPCtwo4o48yHezaZJwJoKo1IjIHOAH4LnAWX9QsOgmwSlUP6mHfTf2O2JgIsD4QY8L3MpAhIhd2bhCR/YEJAWU2A/uISIqIjMdZzRERGQGkqOqjwK+Afd3yDUCe+/taYKSIHOQ+J01E9ozi6zEmKFYDMSZMqqoicirwZxH5KdCKkzAuCyj2JrAJ+ARYCXzgbi8C7haRzpO5n7k/7wH+KiItwEE4NZybRSQf5//2zzg1H2PixmbjNcYYExJrwjLGGBMSSyDGGGNCYgnEGGNMSCyBGGOMCYklEGOMMSGxBGKMMSYklkCMMcaE5P8DKYAGJTNc8rsAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax = cna.pl.violinplot(d, # dataset\n", " res, # CNA results object\n", " 'leiden') # name of the column of obs that contains the clustering\n", "ax.set_title('Within-Cluster Heterogeneity')\n", "ax.set_yticks([-0.08, -0.04, 0, 0.04, 0.08])\n", "ax.set_xlabel('Cluster')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "Collapsed": "false" }, "source": [ "### 1.5 relate results to genes" ] }, { "cell_type": "markdown", "metadata": { "Collapsed": "false" }, "source": [ "To characterize the populations driving the association, we can examine per-gene correlations to neighborhood coefficient. These per-gene values may be informative on their own, or they could be further analyzed using techniques like gene set enrichment analysis." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "Collapsed": "false" }, "outputs": [], "source": [ "# compute correlation between neighborhood coefficients and genes\n", "d.var['corr_case'] = np.corrcoef(res.ncorrs.reshape(1,-1), d.X, rowvar=False)[0,1:]" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "Collapsed": "false" }, "outputs": [ { "data": { "text/html": [ "
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corr_case
gene
b'gene_10'0.186798
b'gene_9'0.181042
b'gene_4'0.179933
b'gene_11'0.179853
b'gene_1'0.173886
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" ], "text/plain": [ " corr_case\n", "gene \n", "b'gene_10' 0.186798\n", "b'gene_9' 0.181042\n", "b'gene_4' 0.179933\n", "b'gene_11' 0.179853\n", "b'gene_1' 0.173886" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# view top 5 most strongly correlated genes\n", "d.var.sort_values('corr_case', ascending=False).head()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "Collapsed": "false" }, "outputs": [ { "data": { "text/html": [ "
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corr_case
gene
b'gene_20'-0.100297
b'gene_21'-0.097420
b'gene_22'-0.089048
b'gene_14'-0.085773
b'gene_19'-0.079371
\n", "
" ], "text/plain": [ " corr_case\n", "gene \n", "b'gene_20' -0.100297\n", "b'gene_21' -0.097420\n", "b'gene_22' -0.089048\n", "b'gene_14' -0.085773\n", "b'gene_19' -0.079371" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# view top 5 most negatively correlated genes\n", "d.var.sort_values('corr_case', ascending=True).head()" ] }, { "cell_type": "markdown", "metadata": { "Collapsed": "false" }, "source": [ "## 2. data formats and preprocessing " ] }, { "cell_type": "markdown", "metadata": { "Collapsed": "false" }, "source": [ "Let's now understand better the data format used by `cna` and what pre-processing is necessary to run `cna`." ] }, { "cell_type": "markdown", "metadata": { "Collapsed": "false" }, "source": [ "### 2.1 the `MultiAnnData` object" ] }, { "cell_type": "markdown", "metadata": { "Collapsed": "false" }, "source": [ "The data format used by `cna` a python object called `MultiAnnData`, which is a derivative of the common `AnnData` object used by `scanpy` that is modified to explicitly accommodate multi-sample single-cell datasets by including sample-level metadata." ] }, { "cell_type": "markdown", "metadata": { "Collapsed": "false" }, "source": [ "There are three ways to create `MultiAnnData` objects. Reading from `h5ad` format using `cna.read`, conversion from an `AnnData` object, and manual construction." ] }, { "cell_type": "markdown", "metadata": { "Collapsed": "false" }, "source": [ "#### 2.1.1 read a `MultiAnnData` object from a file" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "Collapsed": "false" }, "outputs": [], "source": [ "d = cna.read('data_multianndata.h5ad')" ] }, { "cell_type": "markdown", "metadata": { "Collapsed": "false" }, "source": [ "Like an `AnnData` object, the `MultiAnnData` object `d` stores raw data in `.X`, per-cell data in `.obs`, and per-marker data in `.var`.\n", "\n", "However, `d` additional stores per-sample data in `d.samplem`. Let's take a look." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "Collapsed": "false" }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " case male batch\n", "id \n", "0 0 0 0\n", "1 0 0 1\n", "2 0 0 2\n", "3 0 0 3\n", "4 0 0 4" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d.samplem.head()" ] }, { "cell_type": "markdown", "metadata": { "Collapsed": "false" }, "source": [ "#### 2.1.2 build a `MultiAnnData` object from an `AnnData` object" ] }, { "cell_type": "markdown", "metadata": { "Collapsed": "false" }, "source": [ "The second way to create a `MultiAnnData` object is to create one from an `AnnData` object. In this case, you need to explicitly supply the sample-level metadata for `.samplem`. The easiest way to do this is typically to take this information from `.obs` if it is already stored there, as is often the case in single-cell datasets. Another option is to explicitly set `.samplem` to a `DataFrame` that's been manually created.\n", "\n", "**Either way, `cna` needs to know which column of `.obs` contains the sample id of each cell. `cna` assumes that this columns is named `id`, but this behavior can be changed by passing `sampleid='someothername'` into the `MultiAnnData` function. The name of this column also needs to match the name of the index of `.samplem`.**" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "Collapsed": "false" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "d.samplem has 0 columns\n", "now d.samplem has 3 columns\n" ] }, { "data": { "text/html": [ "
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" ], "text/plain": [ " case male batch\n", "id \n", "0 0 0 0\n", "1 0 0 1\n", "2 0 0 2\n", "3 0 0 3\n", "4 0 0 4" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# read in AnnData object from h5ad file and convert it to MultiAnnData\n", "d = ad.read_h5ad('data_anndata.h5ad')\n", "d = MultiAnnData(d, sampleid='id') # the default value of sampleid is 'id', here we just repeat it for clarity\n", "print('d.samplem has', len(d.samplem.columns), 'columns')\n", "\n", "# Since our metadata are stored per-cell, we need to copy the information that is actually per-sampl\n", "# (case status, batch and covariates) to the per-sample metadata DataFrame `d.samplem`\n", "d.obs_to_sample(['case','male','batch'])\n", "print('now d.samplem has', len(d.samplem.columns), 'columns')\n", "d.samplem.head()" ] }, { "cell_type": "markdown", "metadata": { "Collapsed": "false" }, "source": [ "#### 2.1.3 build a `MultiAnnData` object manually" ] }, { "cell_type": "markdown", "metadata": { "Collapsed": "false" }, "source": [ "Building a `MultiAnnData` object manually works just like building an `AnnData` object manually. The main difference is that you also need to tell `cna` which column of `.obs` contains the sample id of each cell. **`cna` will assume that this column is named `id` unless it is told otherwise.**" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "Collapsed": "false" }, "outputs": [], "source": [ "import multianndata as mad\n", "\n", "# let's pretend we had X, obs, and var as standalone objects...\n", "X, obs, var = d.X, d.obs, d.var\n", "\n", "# we can create a MultiAnnData object from these\n", "d = mad.MultiAnnData(X=X, # expression matrix\n", " obs=obs, # cell metadata matrix\n", " sampleid='id') # name of column of cell_meta that contains sample id for each cell\n", " # default value is 'id' but we repeat here for clarity\n", "# convert per-cell metadata into per-sample metadata as before\n", "d.obs_to_sample(['batch', 'case', 'male'])" ] }, { "cell_type": "markdown", "metadata": { "Collapsed": "false" }, "source": [ "As above, if instead our raw metadata table already contained information per-sample, we could have created `d.samplem` directly." ] }, { "cell_type": "markdown", "metadata": { "Collapsed": "false" }, "source": [ "### 2.2 pre-processing" ] }, { "cell_type": "markdown", "metadata": { "Collapsed": "false" }, "source": [ "The main pre-processing step that is required before running `cna` is to compute a cell-cell similarity graph. Here we'll use `scanpy` to compute the standard UMAP similarity graph. (In principle, any graph can be computed here.)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "Collapsed": "false" }, "outputs": [], "source": [ "%%capture --no-stdout\n", "\n", "# compute the UMAP cell-cell similarity graph\n", "sc.pp.neighbors(d)\n", "\n", "# compute UMAP coordinates for plotting\n", "sc.tl.umap(d)\n", "\n", "# the following line would save the pre-processed data as a h5ad file\n", "# d.write('test.h5ad')" ] }, { "cell_type": "markdown", "metadata": { "Collapsed": "false" }, "source": [ "## 3. additional `cna` features" ] }, { "cell_type": "markdown", "metadata": { "Collapsed": "false" }, "source": [ "### 3.1 additional results produced by the `cna` `association` function " ] }, { "cell_type": "markdown", "metadata": { "Collapsed": "false" }, "source": [ "Let's re-do the `cna` analysis for case status and go over the results in more detail" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "Collapsed": "false" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "qcd NAM not found; computing and saving\n", "\ttaking step 1\n", "\tmedian kurtosis: 16.40326205754668\n", "\ttaking step 2\n", "\tmedian kurtosis: 12.75799245750447\n", "\ttaking step 3\n", "\tmedian kurtosis: 6.1154849181504485\n", "\ttaking step 4\n", "\tmedian kurtosis: 3.177490071480447\n", "stopping after 4 steps\n", "throwing out neighborhoods with batch kurtosis >= 6\n", "keeping 10000 neighborhoods\n", "covariate-adjusted NAM not found; computing and saving\n", "\twith ridge 100000.0 median batch kurtosis = 1.981293621817167\n", "computing SVD\n", "performing association test\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/data/srlab1/yakir/py/cna/cna/tools/_association.py:74: UserWarning: global association p-value attained minimal possible value. Consider increasing Nnull\n", " warnings.warn('global association p-value attained minimal possible value. '+\\\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "computing neighborhood-level FDRs\n" ] } ], "source": [ "d = cna.read('data_multianndata.h5ad')\n", "res = cna.tl.association(d, d.samplem.case, covs=d.samplem[['male']], batches=d.samplem.batch)" ] }, { "cell_type": "markdown", "metadata": { "Collapsed": "false" }, "source": [ "The `res` object returned by the association function contains the following fields:\n", "1. `p` is the global p-value for association\n", "1. `k` is the number of NAM PCs used for the association test (automatically selected in a data-dependent way)\n", "1. `ncorrs` is the vector of neighborhood coefficients\n", "1. `fdr_10p_t` and `fdr_5p_t` contain the thresholds on `np.abs(ncorrs)` needed to provide FDRs of 10% and 5% respectively\n", "1. `fdrs` contains information on neighborhood-level FDRs at other thresholds\n", "1. `kept` is a boolean vector that specifies which cells passed qc. So, e.g., `ncorrs` is of length `kept.sum()`.\n", "1. `r2` is the global prediction r-squared achieved by the model\n", "1. `r2_perpc` is the prediction r-squared achieved by each pc on its own\n", "1. `beta` is the coefficient vector, one coefficient per PC\n", "1. `yresid` and `yresid_hat` are the phenotype and the predicted phenotype, both with covariates and batch residualized out" ] }, { "cell_type": "markdown", "metadata": { "Collapsed": "false" }, "source": [ "The kurtosis numbers in the output above say how 'outlier-y' the neighborhood abundances are at each step in the diffusion that `cna` does to define its neighborhoods. The algorithm stops when these numbers stop going down quickly. But if you see that `cna` stops when kurtosis is still very high (like above 10), that's likely a sign that the data aren't qc'd properly or are otherwise unsuitable." ] }, { "cell_type": "markdown", "metadata": { "Collapsed": "false" }, "source": [ "### 3.2 working directly with the NAM and NAM PCs " ] }, { "cell_type": "markdown", "metadata": { "Collapsed": "false" }, "source": [ "The association function above also automatically computes the NAM and its SVD as a prerequisite to the association testing. These can be found in the following objects\n", "1. `d.uns['NAM.T']` contains the transpose of the NAM (neighborhoods by samples)\n", "1. `d.uns['NAM_sampleXpc']` contains the sample loadings of the principal components of the NAM\n", "1. `d.uns['NAM_nbhdXpc']` contains the neighborhood loadings of the principal components of the NAM\n", "1. `d.uns['NAM_svs']` contains the squared singular values of the NAM\n", "\n", "These objects can also be computed independent of any association test using the function `cna.tl.nam`, demonstrated below" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "Collapsed": "false" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "covariate-adjusted NAM not found; computing and saving\n", "\twith ridge 100000.0 median batch kurtosis = 1.9949274142866247\n", "computing SVD\n" ] } ], "source": [ "cna.tl.nam(d, batches=d.samplem.batch) #note: this NAM is being computed *without* adjusting for sex or case/ctrl status" ] }, { "cell_type": "markdown", "metadata": { "Collapsed": "false" }, "source": [ "Let's visualize a few of the results..." ] }, { "cell_type": "markdown", "metadata": { "Collapsed": "false" }, "source": [ "#### 3.2.1 neighborhood loadings of NAM PCs" ] }, { "cell_type": "markdown", "metadata": { "Collapsed": "false" }, "source": [ "First, we can plot the neighborhood loadings of each NAM PC to see which abundance patterns they capture" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "Collapsed": "false" }, "outputs": [ { "data": { "image/png": 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#plot neighborhood loadings of NAM PC 1\n", "d.obs['NAMPC1'] = np.nan\n", "d.obs.loc[d.uns['keptcells'], 'NAMPC1'] = d.uns['NAM_nbhdXpc'].PC1\n", "sc.pl.umap(d, color='NAMPC1', cmap='seismic', title='NAM PC1 neighborhood loadings')\n", "plt.show()\n", "\n", "#plot neighborhood loadings of NAM PC 2\n", "d.obs['NAMPC2'] = np.nan\n", "d.obs.loc[d.uns['keptcells'], 'NAMPC2'] = d.uns['NAM_nbhdXpc'].PC2\n", "sc.pl.umap(d, color='NAMPC2', cmap='seismic', title='NAM PC2 neighborhood loadings')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "Collapsed": "false" }, "source": [ "We can see here that NAM PC 1 primarily captures the extent to which a sample has cells in the left-hand cluster vs the right-hand cluster, and that NAM PC 2 primarily additionall captures the extent to which a sample has cells in the top of the left-hand cluster vs the bottom of the left-hand cluster." ] }, { "cell_type": "markdown", "metadata": { "Collapsed": "false" }, "source": [ "#### 3.2.2 sample loadings of NAM PCs" ] }, { "cell_type": "markdown", "metadata": { "Collapsed": "false" }, "source": [ "We can also examine the sample loadings of each NAM PC to explicitly see which samples have high and low values of each NAM PC. Below we'll examine how this relates to the sample-level metadata in our toy dataset." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "Collapsed": "false" }, "outputs": [ { "data": { "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "def sample_loading_plot_by_binary_pheno(pheno, case_label, ctrl_label): #assumes pheno is binary\n", " pcs = d.uns['NAM_sampleXpc']\n", " cases = pheno.astype(np.bool)\n", " plt.scatter(pcs.loc[cases].PC1, pcs.loc[cases].PC2, s=40, alpha=0.8, label=case_label)\n", " plt.scatter(pcs.loc[~cases].PC1, pcs.loc[~cases].PC2, s=40, alpha=0.8, label=ctrl_label)\n", " plt.legend()\n", " plt.xlabel('NAM PC1')\n", " plt.ylabel('NAM PC2')\n", "\n", "#plot sample loadings of NAM PC1 versus NAM PC2, colored by case/control status\n", "sample_loading_plot_by_binary_pheno(d.samplem.case, 'case', 'ctrl')\n", "plt.title('NAM PC1 vs NAM PC2, colored by case/control status')\n", "plt.show()\n", "\n", "#plot sample loadings of NAM PC1 versus NAM PC2, colored by male/female sex\n", "sample_loading_plot_by_binary_pheno(d.samplem.male, 'male', 'female')\n", "plt.title('NAM PC1 vs NAM PC2, colored by male/female sex')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "Collapsed": "false" }, "source": [ "You can see in these plots that NAM PC 1 roughly corresponds to case/control status and NAM PC1 roughly corresponds to sex." ] }, { "cell_type": "markdown", "metadata": { "Collapsed": "false" }, "source": [ "#### 3.2.3 variance explained by NAM PCs" ] }, { "cell_type": "markdown", "metadata": { "Collapsed": "false" }, "source": [ "Like ordinary PCs, each NAM PC also comes with a quantification of how much dataset-wide variance it explains. These are stored in the `NAM_svs` field. Let's visualize them in the traditional way below." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "Collapsed": "false" }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# plot variance explained by each NAM PC\n", "plt.plot(d.uns['NAM_svs']/d.uns['NAM_svs'].sum(), marker='o', linestyle='--')\n", "plt.xlabel('NAM PC')\n", "plt.ylabel('fraction of variance explained')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "Collapsed": "false" }, "source": [ "As this plot demonstrates, PC1 explains ~40% of the variance in the dataset, PC2 explains ~12%, and the rest really don't contribute much." ] } ], "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": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 4 }