diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 38fbde4..4c18d90 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -6,6 +6,7 @@ repos: - id: end-of-file-fixer - id: trailing-whitespace - id: check-added-large-files + args: ['--maxkb=6000'] - repo: https://github.com/PyCQA/docformatter rev: v1.7.5 hooks: diff --git a/docs/_static/cayonu_results/bootstrap/bootstrap_0.eval b/docs/_static/cayonu_results/bootstrap/bootstrap_0.eval new file mode 100644 index 0000000..f3ff0aa --- /dev/null +++ b/docs/_static/cayonu_results/bootstrap/bootstrap_0.eval @@ -0,0 +1,759 @@ + 6.414185 + 3.342736 + 2.575268 + 2.187306 + 1.955826 + 1.713444 + 1.704925 + 1.629545 + 1.614243 + 1.601670 + 1.596601 + 1.592426 + 1.578978 + 1.576199 + 1.567953 + 1.561259 + 1.557027 + 1.552208 + 1.540400 + 1.536444 + 1.529153 + 1.525430 + 1.517870 + 1.513280 + 1.511213 + 1.506637 + 1.503176 + 1.496237 + 1.490032 + 1.486830 + 1.486292 + 1.476696 + 1.475609 + 1.462499 + 1.457800 + 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0.0186 0.0043 Basque + HGDP01365 0.0318 0.0473 -0.0338 -0.0327 0.0157 -0.0030 0.0005 -0.0000 0.0135 0.0024 Basque + HGDP01366 0.0341 0.0461 -0.0315 -0.0318 0.0237 -0.0025 0.0002 0.0015 0.0135 0.0028 Basque + HGDP01367 0.0360 0.0434 -0.0371 -0.0331 0.0227 -0.0023 -0.0000 0.0009 0.0136 0.0042 Basque + HGDP01368 0.0316 0.0386 -0.0348 -0.0308 0.0273 -0.0025 0.0000 0.0044 0.0133 0.0028 Basque + HGDP01370 0.0337 0.0486 -0.0345 -0.0334 0.0215 -0.0024 0.0001 0.0024 0.0176 0.0033 Basque + HGDP01371 0.0362 0.0427 -0.0292 -0.0344 0.0264 -0.0030 0.0002 0.0018 0.0103 0.0041 Basque + HGDP01373 0.0329 0.0468 -0.0304 -0.0349 0.0258 -0.0043 -0.0001 0.0014 0.0120 0.0020 Basque + HGDP01374 0.0339 0.0517 -0.0301 -0.0337 0.0262 -0.0035 0.0001 0.0031 0.0099 0.0022 Basque + HGDP01375 0.0321 0.0453 -0.0382 -0.0328 0.0370 -0.0035 0.0002 0.0019 0.0150 0.0031 Basque + HGDP01377 0.0344 0.0458 -0.0389 -0.0382 0.0210 -0.0032 0.0001 0.0034 0.0115 0.0026 Basque + HGDP01378 0.0331 0.0455 -0.0283 -0.0334 0.0227 -0.0025 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0.0056 0.0019 0.0303 -0.0019 0.0007 0.0004 0.0007 0.0008 Adygei + HGDP01399 -0.0111 -0.0572 0.0064 -0.0058 0.0269 -0.0020 0.0012 0.0022 -0.0025 -0.0013 Adygei + HGDP01400 -0.0111 -0.0569 0.0095 -0.0045 0.0272 -0.0022 0.0006 0.0006 0.0035 0.0008 Adygei + HGDP01401 -0.0114 -0.0589 0.0062 -0.0068 0.0168 -0.0030 0.0008 0.0020 0.0039 0.0009 Adygei + HGDP01402 -0.0053 -0.0576 0.0038 -0.0107 0.0285 -0.0026 0.0011 0.0049 0.0035 0.0038 Adygei + HGDP01403 -0.0091 -0.0462 0.0088 -0.0059 0.0287 -0.0021 0.0001 0.0000 0.0072 0.0020 Adygei + HGDP01404 -0.0135 -0.0615 0.0157 -0.0044 0.0286 -0.0028 0.0001 0.0025 -0.0053 0.0024 Adygei + BulgarianD6 0.0175 -0.0010 0.0023 -0.0038 0.0052 0.0006 -0.0002 -0.0042 -0.0009 -0.0006 Bulgarian + BulgarianA4 0.0195 -0.0008 0.0089 -0.0053 -0.0083 0.0007 -0.0007 -0.0001 0.0019 -0.0020 Bulgarian + hungary3 0.0275 0.0022 0.0136 0.0003 -0.0053 0.0012 -0.0004 -0.0035 0.0007 0.0027 Hungarian + LithuanianF1 0.0534 -0.0036 0.0563 0.0015 -0.0330 0.0020 -0.0001 -0.0026 0.0037 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0.0238 0.0143 Syrian + Lebanon4 -0.0525 0.0130 -0.0096 -0.0024 -0.0410 0.0015 -0.0048 -0.0045 0.0175 -0.0058 Lebanese + SaudiA1 -0.0701 0.0548 0.0424 -0.0108 -0.0365 0.0837 0.0066 -0.2729 -0.2422 -0.1702 Saudi + Jordan646 -0.0453 0.0036 -0.0035 -0.0028 -0.0449 0.0018 -0.0037 -0.0053 0.0338 -0.0043 Jordanian + Jordan384 -0.0469 0.0069 -0.0086 -0.0057 -0.0410 0.0015 -0.0034 -0.0020 0.0087 -0.0045 Jordanian + bal102 -0.0080 -0.0585 0.0183 -0.0064 0.0206 -0.0014 0.0001 0.0019 0.0083 0.0011 Balkar + bal64 -0.0088 -0.0622 0.0153 -0.0024 0.0205 -0.0012 -0.0002 -0.0006 0.0054 0.0015 Balkar + mg43 -0.0198 -0.0625 -0.0053 -0.0053 0.0378 -0.0029 0.0001 0.0019 -0.0026 -0.0016 Georgian + NorthOssetia19 -0.0102 -0.0648 0.0210 -0.0092 0.0215 -0.0018 -0.0006 0.0007 0.0007 0.0002 North_Ossetian + ch126 -0.0068 -0.0640 0.0205 0.0028 0.0200 -0.0023 -0.0005 0.0025 0.0149 0.0002 Chechen + abh27 -0.0183 -0.0589 0.0043 -0.0088 0.0449 -0.0027 -0.0002 -0.0006 0.0074 0.0004 Abkhasian + armenia176 -0.0267 -0.0304 -0.0139 -0.0092 0.0253 -0.0009 -0.0007 -0.0031 0.0031 -0.0020 Armenian + lez38 -0.0099 -0.0637 0.0306 -0.0043 -0.0020 -0.0314 0.0240 -0.0667 0.0060 0.0677 Lezgin + bal124 -0.0088 -0.0600 0.0187 -0.0093 0.0223 -0.0025 -0.0000 -0.0001 0.0046 -0.0021 Balkar + bal97 -0.0137 -0.0618 0.0216 -0.0091 0.0200 -0.0019 -0.0004 0.0020 -0.0002 0.0027 Balkar + mg47 -0.0212 -0.0584 -0.0130 -0.0112 0.0367 -0.0033 0.0003 0.0007 0.0011 0.0015 Georgian + NorthOssetia2 -0.0126 -0.0625 0.0152 -0.0057 0.0191 -0.0014 0.0003 0.0017 -0.0024 0.0003 North_Ossetian + ch16 -0.0086 -0.0563 0.0183 0.0010 0.0144 -0.0014 0.0003 0.0025 0.0005 -0.0023 Chechen + abh107 -0.0196 -0.0615 -0.0003 -0.0069 0.0369 -0.0020 0.0008 -0.0010 -0.0011 -0.0002 Abkhasian + abh41 -0.0177 -0.0708 0.0163 -0.0076 0.0318 -0.0021 0.0009 -0.0034 0.0175 0.0365 Abkhasian + armenia191 -0.0243 -0.0340 -0.0132 -0.0075 0.0297 -0.0013 0.0000 -0.0032 0.0027 -0.0009 Armenian + lez40 -0.0046 -0.0641 0.0191 -0.0018 0.0099 -0.0012 0.0001 -0.0003 0.0081 -0.0025 Lezgin + kumyks44 -0.0110 -0.0598 0.0226 -0.0041 0.0158 -0.0015 -0.0004 -0.0020 0.0071 -0.0043 Kumyk + bal136 -0.0092 -0.0554 0.0207 0.0019 0.0294 -0.0021 0.0007 0.0029 0.0004 -0.0022 Balkar + mg22 -0.0214 -0.0533 -0.0102 -0.0113 0.0479 -0.0026 0.0008 0.0031 0.0029 0.0017 Georgian + mg49 -0.0241 -0.0562 -0.0089 -0.0079 0.0390 -0.0030 0.0003 -0.0006 0.0050 -0.0018 Georgian + NorthOssetia3 -0.0071 -0.0618 0.0175 -0.0015 0.0138 -0.0025 -0.0002 0.0023 0.0033 -0.0015 North_Ossetian + ch174 -0.0048 -0.0552 0.0194 -0.0050 0.0158 -0.0016 -0.0002 0.0000 0.0073 0.0013 Chechen + abh119 -0.0219 -0.0605 -0.0040 -0.0101 0.0371 -0.0029 0.0003 0.0012 0.0092 0.0012 Abkhasian + armenia86 -0.0236 -0.0325 -0.0142 -0.0054 0.0281 -0.0010 0.0007 -0.0016 0.0022 0.0003 Armenian + armenia279 -0.0325 -0.0296 -0.0163 -0.0074 0.0170 0.0002 0.0004 0.0019 0.0055 -0.0016 Armenian + bal22 -0.0088 -0.0570 0.0126 -0.0078 0.0301 -0.0025 0.0002 0.0043 -0.0026 0.0025 Balkar + mg23 -0.0224 -0.0520 -0.0038 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0.0007 -0.0012 0.0015 -0.0015 BedouinA + HGDP00641 -0.0796 -0.0917 -0.1121 -0.0020 -0.0169 -0.0042 0.0002 -0.0003 0.0006 -0.0011 BedouinB + HGDP00642 -0.0550 -0.0229 0.0015 0.0010 0.0114 0.0351 0.0000 -0.0005 -0.0006 0.0009 BedouinA + HGDP00643 -0.0797 -0.0982 -0.1062 -0.0025 -0.0182 -0.0076 0.0001 0.0003 0.0002 -0.0002 BedouinB + HGDP00644 -0.0482 -0.0166 0.0061 0.0042 0.0274 0.0290 -0.0085 -0.0142 0.0157 -0.0264 BedouinA + HGDP00645 -0.0549 -0.0201 0.0178 -0.0030 0.0340 0.0516 0.0005 -0.0007 -0.0016 0.0006 BedouinA + HGDP00646 -0.0683 -0.0452 0.0127 0.0137 0.0617 0.0817 0.0006 -0.0009 -0.0007 -0.0009 BedouinA + HGDP00647 -0.0539 -0.0268 0.0057 0.0042 0.0347 0.0555 0.0009 0.0009 -0.0001 -0.0028 BedouinA + HGDP00648 -0.0792 -0.0984 -0.1227 -0.0018 -0.0210 -0.0072 0.0010 -0.0025 -0.0010 0.0009 BedouinB + HGDP00649 -0.0872 -0.1182 -0.1543 -0.0051 -0.0263 -0.0103 0.0010 -0.0026 0.0049 -0.0027 BedouinB + HGDP00650 -0.0768 -0.0931 -0.1181 -0.0009 -0.0232 -0.0133 0.0005 -0.0003 0.0020 0.0029 BedouinB + HGDP00651 -0.0787 -0.0860 -0.0769 -0.0001 -0.0026 0.0129 -0.0001 0.0038 -0.0091 0.0132 BedouinB + HGDP00653 -0.0823 -0.0889 -0.1149 -0.0034 -0.0182 0.0019 -0.0005 0.0004 0.0004 0.0013 BedouinB + HGDP00654 -0.0432 -0.0066 -0.0068 -0.0004 0.0090 0.0276 -0.0056 0.0074 0.0172 0.0054 BedouinA + HGDP00666 0.0078 -0.0523 0.0729 0.0190 -0.0416 -0.0148 -0.0003 0.0002 -0.0000 0.0003 Sardinian + HGDP00667 0.0128 -0.0513 0.0651 0.0099 -0.0327 -0.0151 -0.0005 0.0010 -0.0005 -0.0004 Sardinian + HGDP00668 0.0092 -0.0579 0.0667 0.0200 -0.0478 -0.0151 -0.0001 -0.0001 -0.0013 -0.0002 Sardinian + HGDP00669 0.0085 -0.0571 0.0637 0.0186 -0.0458 -0.0182 0.0001 0.0002 -0.0002 -0.0002 Sardinian + HGDP00670 0.0091 -0.0582 0.0769 0.0151 -0.0372 -0.0155 0.0000 0.0005 -0.0015 -0.0005 Sardinian + HGDP00671 0.0098 -0.0544 0.0703 0.0215 -0.0437 -0.0147 0.0001 0.0002 -0.0010 -0.0004 Sardinian + HGDP00672 0.0060 -0.0530 0.0677 0.0147 -0.0477 -0.0103 0.0000 -0.0001 0.0001 -0.0005 Sardinian + HGDP00673 0.0056 -0.0521 0.0622 0.0153 -0.0421 -0.0095 -0.0001 -0.0001 -0.0011 -0.0006 Sardinian + HGDP00674 0.0099 -0.0562 0.0718 0.0193 -0.0437 -0.0125 -0.0003 0.0004 -0.0016 0.0001 Sardinian + HGDP00675 -0.0526 -0.0144 0.0462 0.1258 0.1828 0.0490 0.0004 0.0006 0.0010 -0.0239 Palestinian + HGDP00676 -0.0468 -0.0114 0.0275 0.0115 0.0486 0.0066 -0.0006 -0.0001 0.0005 0.0032 Palestinian + HGDP00677 -0.0525 -0.0173 0.0696 0.2118 0.2775 0.0013 -0.0003 0.0026 0.0896 0.1101 Palestinian + HGDP00679 -0.0485 -0.0109 0.0256 0.0470 0.0821 -0.0120 0.0107 -0.0333 -0.0315 -0.0345 Palestinian + HGDP00680 -0.0590 -0.0291 -0.0080 0.0063 0.0408 0.0437 -0.0003 -0.0009 -0.0015 -0.0009 Palestinian + HGDP00683 -0.0556 -0.0158 0.0213 0.0188 0.1005 0.0122 -0.0003 -0.0008 0.0005 -0.0040 Palestinian + HGDP00684 -0.0500 -0.0229 0.0127 0.0022 0.0219 0.0258 0.0002 -0.0013 0.0037 0.0019 Palestinian + HGDP00685 -0.0592 -0.0291 0.0290 0.0132 0.1059 0.0249 0.0014 -0.0004 -0.0002 0.0020 Palestinian + HGDP00686 -0.0507 -0.0215 0.0148 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b/docs/_static/require.min.js @@ -0,0 +1 @@ +var requirejs,require,define;!function(global,setTimeout){var req,s,head,baseElement,dataMain,src,interactiveScript,currentlyAddingScript,mainScript,subPath,version="2.3.6",commentRegExp=/\/\*[\s\S]*?\*\/|([^:"'=]|^)\/\/.*$/gm,cjsRequireRegExp=/[^.]\s*require\s*\(\s*["']([^'"\s]+)["']\s*\)/g,jsSuffixRegExp=/\.js$/,currDirRegExp=/^\.\//,op=Object.prototype,ostring=op.toString,hasOwn=op.hasOwnProperty,isBrowser=!("undefined"==typeof window||"undefined"==typeof navigator||!window.document),isWebWorker=!isBrowser&&"undefined"!=typeof importScripts,readyRegExp=isBrowser&&"PLAYSTATION 3"===navigator.platform?/^complete$/:/^(complete|loaded)$/,defContextName="_",isOpera="undefined"!=typeof opera&&"[object Opera]"===opera.toString(),contexts={},cfg={},globalDefQueue=[],useInteractive=!1;function commentReplace(e,t){return t||""}function isFunction(e){return"[object Function]"===ostring.call(e)}function isArray(e){return"[object Array]"===ostring.call(e)}function each(e,t){var i;if(e)for(i=0;i EIGEN-based interface - Plotting + Plotting and further insights Working with R exports diff --git a/docs/examples/inspections.ipynb b/docs/examples/inspections.ipynb new file mode 100644 index 0000000..8c992a5 --- /dev/null +++ b/docs/examples/inspections.ipynb @@ -0,0 +1,63582 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# Plotting and further insights\n", + "The raw results of Pandora might be enough for you, but sometimes you really want to know what is going on with your data and why the results are the way they are. This is probably especially true for the support values of single samples.\n", + "\n", + "In this jupyter notebook, we provide some examples on how you can inspect your results with a focus on the support values.\n", + "\n", + "These examples are also intended to demonstrate the plotting functionalities of Pandora " + ], + "metadata": { + "collapsed": false + }, + "id": "25026758a9700c7d" + }, + { + "cell_type": "code", + "execution_count": 1, + "outputs": [], + "source": [ + "# First import some Pandora library functionality and other packages we are going to need for the following inspection\n", + "import pandas as pd\n", + "import pathlib\n", + "\n", + "from pandora.embedding import from_smartpca\n", + "from pandora.embedding_comparison import EmbeddingComparison\n", + "from pandora.plotting import plot_embedding_comparison_rogue_samples, plot_support_values, plot_populations, plot_projections\n", + "\n", + "# and setup the paths to the exemplary results we are going to inspect\n", + "results_dir = pathlib.Path(\"../_static/cayonu_results\")\n", + "bootstrap_results = results_dir / \"bootstrap\"\n", + "support_values_file = results_dir / \"pandora.supportValues.csv\"\n", + "projected_support_values_file = results_dir / \"pandora.supportValues.projected.csv\"" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-12-12T12:50:38.172767Z", + "start_time": "2023-12-12T12:50:36.866361Z" + } + }, + "id": "3f0a00d2f014444d" + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [], + "source": [ + "# we next load the EigenDataset for the Cayonu dataset\n", + "# note that we only use a pickled object for this as the EIGENSTRAT files would be too large for uploading to GitHub\n", + "import pickle\n", + "\n", + "cayonu_dataset = pickle.load((results_dir / \"dataset.pickle\").open(\"rb\"))\n", + "\n", + "# here is how you would normally initialize this dataset:\n", + "# cayonu_dataset = EigenDataset(\n", + "# file_prefix=pathlib.Path(prefix/to/cayonu_files), \n", + "# embedding_populations=pathlib.Path(prefix/to/embedding_populations.txt)\n", + "# ) \n", + "\n", + "# load the pre-computed PCA object\n", + "cayonu_dataset.pca = from_smartpca(\n", + " evec=results_dir / \"cayonu.evec\",\n", + " eval=results_dir / \"cayonu.eval\"\n", + ")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-12-12T12:50:38.617382Z", + "start_time": "2023-12-12T12:50:38.173416Z" + } + }, + "id": "9598b7d558b3a2a1" + }, + { + "cell_type": "markdown", + "source": [ + "First, we visualize the dataset's unbootstrapped PCA using the precomputed results and annotate all populations" + ], + "metadata": { + "collapsed": false + }, + "id": "7749423be091d808" + }, + { + "cell_type": "code", + "execution_count": 3, + "outputs": [ + { + "data": { + "text/html": " \n " + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.plotly.v1+json": { + "data": [ + { + "customdata": [ + [ + "ALB191", + "Albanian", + 0.0106, + 0.0023 + ], + [ + "ALB202", + "Albanian", + 0.0069, + 0.0015 + ], + [ + "ALB212", + "Albanian", + 0.0065, + -0.0099 + ], + [ + "ALB213", + "Albanian", + 0.006, + -0.0017 + ], + [ + "ALB220", + "Albanian", + 0.0031, + -0.0045 + ], + [ + "ALB230", + "Albanian", + 0.0095, + -0.0056 + ] + ], + "hovertemplate": "Sample: %{customdata[0]}
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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plot_projections(cayonu_dataset.pca, cayonu_dataset.embedding_populations)\n", + "fig.update_layout(height=750, width=750)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-12-12T12:50:39.024596Z", + "start_time": "2023-12-12T12:50:38.984568Z" + } + }, + "id": "e050331a54163613" + }, + { + "cell_type": "markdown", + "source": [ + "Next, let's have a look at the support values for all samples, and for the projected samples" + ], + "metadata": { + "collapsed": false + }, + "id": "c68b1663a5c7bd47" + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [ + { + "data": { + "text/plain": " PSV\nITS2 0.424616\nITS5 0.424936\nJordan445 0.476375\nHGDP00625 0.487708\ncay033 0.492772\n... ...\nHGDP00887 0.942582\nUkrBel618 0.942695\nMordovians28 0.942815\nUkrBel614 0.943761\nHGDP00894 0.944473\n\n[826 rows x 1 columns]", + "text/html": "
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......
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" + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "psv_all_samples = pd.read_csv(support_values_file, index_col=[0])\n", + "psv_all_samples.sort_values(\"PSV\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-12-12T12:50:39.024850Z", + "start_time": "2023-12-12T12:50:38.987939Z" + } + }, + "id": "a1ee0b20b07d2641" + }, + { + "cell_type": "code", + "execution_count": 6, + "outputs": [ + { + "data": { + "text/plain": " PSV\ncay033 0.492772\ncay014 0.537452\ncay015 0.539045\ncay022 0.561840\ncay020 0.579423\ncay027 0.584384\ncay012 0.590237\ncay011 0.609492\ncay016 0.627005\ncay018 0.627732\ncay1820 0.654619\ncay013 0.712516\ncay007 0.806723", + "text/html": "
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" + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "psv_projected_samples = pd.read_csv(projected_support_values_file, index_col=[0])\n", + "psv_projected_samples.sort_values(\"PSV\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-12-12T12:50:39.071411Z", + "start_time": "2023-12-12T12:50:39.000831Z" + } + }, + "id": "959461aa1d9108a6" + }, + { + "cell_type": "markdown", + "source": [ + "A table is nice, but a plot is nicer for sure!\n", + "\n", + "The following plot color-codes all samples according to their support, and additionally annotates all samples with a support value below 0.5" + ], + "metadata": { + "collapsed": false + }, + "id": "3d1109bf3efeb906" + }, + { + "cell_type": "code", + "execution_count": 7, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "data": [ + { + "customdata": [ + [ + "ALB191", + "Albanian", + 0.7867532346641544, + 0.0106, + 0.0023 + ], + [ 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_support_values(cayonu_dataset.pca, psv_all_samples.PSV, support_value_rogue_cutoff=0.5)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-12-12T12:50:39.073234Z", + "start_time": "2023-12-12T12:50:39.015022Z" + } + }, + "id": "13099e4972700902" + }, + { + "cell_type": "markdown", + "source": [ + "We can also make some more sense about why these samples have such a low PSV. To do so, we plot two bootstrapped PCA results against each other and annotate the \"rogue\" samples.\n", + "\n", + "To demonstrate the rogueness of rogue samples, we need to match two PCA results (using Procrustes Transformation) and then highlight the positions of the respective samples in both plots. Since we can only match two embeddings at once, we can only plot the rogue samples for pairs of embeddings.\n", + "\n", + "A few notes regarding the following plot: \n", + "- The shape of the PCA does not match the shape of the previous plots anymore. This is a direct consequence of the Procrustes Transformation.\n", + "- Each color corresponds to a single sample. Each color has two points: a dot and a star representing the two bootstrap replicates. \n", + "- Here we only plot samples with a PSV <= 0.45 to better demonstrate how to use this \"rogueness\" plot" + ], + "metadata": { + "collapsed": false + }, + "id": "547745de26a868db" + }, + { + "cell_type": "code", + "execution_count": 8, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "data": [ + { + "customdata": [ + [ + "ALB191", + "Albanian", + -0.685321629459982, + 0.24904481309973267 + ], + [ + "ALB202", + "Albanian", + -0.4110902243812413, + 0.08984050020987053 + ], + [ + "ALB212", + "Albanian", + -0.4039230013434312, + -0.29928641878272727 + ], + [ + "ALB213", + "Albanian", + -0.38441973071416385, + -0.055060767576877646 + ], + [ + "ALB220", + "Albanian", + -0.3638227089036435, + -0.13929715820112168 + ], + [ + "ALB230", + "Albanian", + -0.5695636054660645, + -0.1985465061100383 + ], + [ + "Adana23108", + "Turkish", + 0.6122499836704004, + 0.11886599144307182 + ], + 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# first, we load two bootstrap PCA results\n", + "# for samples with low support you can basically use any two bootstraps and chances are the results will be as obvious as the example belpw \n", + "\n", + "bootstrap0 = from_smartpca(\n", + " evec=bootstrap_results / \"bootstrap_0.evec\",\n", + " eval=bootstrap_results / \"bootstrap_0.eval\"\n", + ")\n", + "\n", + "bootstrap1 = from_smartpca(\n", + " evec=bootstrap_results / \"bootstrap_1.evec\",\n", + " eval=bootstrap_results / \"bootstrap_1.eval\"\n", + ")\n", + "\n", + "# next, we create a EmbeddingComparison object that handles all the Procrustes stuff for us\n", + "comparison = EmbeddingComparison(bootstrap0, bootstrap1)\n", + "\n", + "# finally, we can plot the comparison and highlight samples with a PSV <= 0.45 \n", + "plot_embedding_comparison_rogue_samples(comparison, psv_all_samples.PSV, support_value_rogue_cutoff=0.45)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-12-12T12:50:39.219439Z", + "start_time": "2023-12-12T12:50:39.068223Z" + } + }, + "id": "319be19291465884" + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "You can now make sense of why ITS2 and ITS5 (both South-Italian samples) have such low PSVs: both seem to be projected to substantially different points in the embedding space depending on the bootstrap we are looking at. \n", + "If we plot both bootstrap replicates separately (and untransformed), we can see that ITS2's cloest neighbor in Bootstrap #0 is an Ashkenazi-Jew samples, whereas in Bootstrap #1 it is a Greek sample. Similarly, for ITS5 the closest neighbors are a Maltese sample or another Italian-South sample. 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