From a64c036bad618580e39b77076fcd5831fd58961e Mon Sep 17 00:00:00 2001 From: Julius Booth Date: Wed, 15 Jan 2020 10:45:00 -0800 Subject: [PATCH] added data_for_paper --- data_for_paper/beijing_advntr.csv | 109 + data_for_paper/beijing_results.csv | 110 + data_for_paper/expansion_hunter.ipynb | 3565 +++++++++++++++++++++++++ data_for_paper/kelowna_advntr.csv | 24 + data_for_paper/kelowna_results.csv | 25 + data_for_paper/training_data.csv | 1920 +++++++++++++ data_for_paper/training_genomes.zip | Bin 0 -> 184 bytes 7 files changed, 5753 insertions(+) create mode 100644 data_for_paper/beijing_advntr.csv create mode 100644 data_for_paper/beijing_results.csv create mode 100644 data_for_paper/expansion_hunter.ipynb create mode 100644 data_for_paper/kelowna_advntr.csv create mode 100644 data_for_paper/kelowna_results.csv create mode 100644 data_for_paper/training_data.csv create mode 100644 data_for_paper/training_genomes.zip diff --git a/data_for_paper/beijing_advntr.csv 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+ "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Expansion Hunter: Results" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# prep data\n", + "%matplotlib inline\n", + "\n", + "list_of_results = [\"Beijing-391_c100.json\", \"Beijing-like-1104_c200.json\",\n", + "\"Beijing-391_c150.json\", \"Beijing-like-1104_c250.json\",\n", + "\"Beijing-391_c150_PCR-free.json\", \"Beijing-like-1104_c50.json\",\n", + "\"Beijing-391_c200.json\", \"Beijing-like-35049_c100.json\",\n", + "\"Beijing-391_c250.json\", \"Beijing-like-35049_c150.json\",\n", + "\"Beijing-391_c50.json\", \"Beijing-like-35049_c150_PCR-free.json\",\n", + "\"Beijing-like-1104_c100.json\", \"Beijing-like-35049_c200.json\",\n", + "\"Beijing-like-1104_c150.json\", \"Beijing-like-35049_c250.json\",\n", + "\"Beijing-like-1104_c150_PCR-free.json\", \"Beijing-like-35049_c50.json\"]\n", + "\n", + "# \"A9s008\" NOT INCLUDED\n", + "\n", + "list_of_kelowna_results = [\"10s165\",\"A9s272\",\"A9s314\",\"10s177\",\"A8s358\",\"A9s089\",\"A8s221\",\"10s379\",\n", + "\"A9s005\",\"A7s291\",\"A6s116\",\"A6s239\",\"A6s426\",\"A6s283\",\"A6s158\",\"A7s317\",\"A6s320\",\"A6s218\",\"A5s120\",\"10s255\",\n", + "\"12s074\",\"A7s146\",\"10s342\",\"A8s182\"]\n", + "\n", + "\n", + "\n", + "\n", + "list_of_templates = [\"154\",\"424\",\"577\",\"580\",\"802\",\"960\",\"1664\",\"1955\",\"2059\",\n", + " \"2165\",\"2347\",\"2401\",\"2461\",\"2531\",\"2687\",\"2996\",\"3007\",\n", + " \"3171\",\"3192\",\"3690\",\"4052\",\"4156\",\"4348\",\"2163b\"]\n", + "\n", + "\n", + "\n", + "\n", + "# Indeces of copy numbers correspond to those in list_of_templates\n", + "\n", + "true_391 = [2,2.5,3.5,2.5,1,3,3,4.5,2,4,1.7,1.7,2,5,1.5,7,1.5,3,4,3,8,2.7,2.7,9]\n", + "true_1104 = [2,2.5,3.5,2.5,1,3,3,4.5,2,4,1.7,1.7,2,5,1.5,7,1.5,3,4,3,1,2.7,2.7,9]\n", + "true_35049 = [2,2.5,3.5,2.5,1.5,3,3,4.5,2,4,2,1.5,2,5,1.5,7,1.5,3,4,3,1,3,3,8]\n", + "\n", + "\n", + "true_10s165=[2,4,4,2,3,4,3,4,2,3,4,4,2,5,1,5,3,3,3,3,7,3,2,2]\n", + "true_A9s008=[2,4,4,2,3,4,3,4,2,3,4,4,2,5,1,5,3,3,3,3,7,3,2,2]\n", + "true_A9s272=[2,4,4,2,3,4,3,4,2,3,4,4,2,5,1,5,3,3,3,3,7,3,2,2]\n", + "true_A9s314=[2,4,4,2,3,4,3,4,2,3,4,4,2,5,1,5,3,3,3,3,7,3,2,2]\n", + "true_10s177=[2,4,4,2,3,4,3,4,2,3,4,4,2,5,1,5,3,3,3,3,7,3,2,2]\n", + "true_A8s358=[2,4,4,3,3,4,3,1,1,4,4,2,2,5,1,5,3,3,3,3,5,2,2,4]\n", + "true_A9s089=[2,4,4,3,3,4,3,1,1,4,4,2,2,5,1,5,3,3,3,3,5,2,2,4]\n", + "true_A8s221=[2,4,4,3,3,4,3,1,1,4,4,2,2,5,1,5,3,3,3,3,5,2,2,4]\n", + "true_10s379=[2,4,4,3,3,4,3,1,1,4,4,2,2,5,1,5,3,3,3,3,5,2,2,4]\n", + "true_A9s005=[2,4,4,3,3,4,3,1,1,4,4,2,2,5,1,5,3,3,3,3,5,2,2,4]\n", + "true_A7s291=[2,2,4,2,3,3,3,5,2,4,4,4,2,5,1,6,3,3,5,3,8,2,3,5]\n", + "true_A6s116=[2,2,4,2,3,3,3,5,2,4,4,4,2,5,1,6,3,3,5,3,8,2,3,5]\n", + "true_A6s239=[2,2,4,2,3,3,3,5,2,4,4,4,2,5,1,6,3,3,5,3,8,2,3,5]\n", + "true_A6s426=[2,2,4,2,3,3,3,5,2,4,4,4,2,5,1,6,3,3,5,3,8,2,3,5]\n", + "true_A6s283=[2,2,4,2,3,3,3,5,2,4,4,4,2,5,1,6,3,3,5,3,8,2,3,5]\n", + "true_A6s158=[2,3,4,2,1,8,2,3,2,4,4,4,2,5,1,1,3,3,2,3,8,3,2,2]\n", + "true_A7s317=[2,3,4,2,1,8,2,3,2,4,4,4,2,5,1,1,3,3,2,3,8,3,2,2]\n", + "true_A6s320=[2,3,4,2,1,8,2,3,2,4,4,4,2,5,1,1,3,3,2,3,8,3,2,2]\n", + "true_A6s218=[2,3,4,2,1,8,2,3,2,4,4,4,2,5,1,1,3,3,2,3,8,3,2,2]\n", + "true_A5s120=[2,3,4,2,1,8,2,3,2,4,4,4,2,5,1,1,3,3,2,3,8,3,2,2]\n", + "true_10s255=[2,5,2,2,3,5,4,4,2,4,4,2,2,5,1,7,3,3,5,3,8,4,3,2]\n", + "true_12s074=[2,5,2,2,3,5,4,4,2,4,4,2,2,5,1,7,3,3,5,3,8,4,3,2]\n", + "true_A7s146=[2,5,2,2,3,5,4,4,2,4,4,2,2,5,1,7,3,3,5,3,8,4,3,2]\n", + "true_10s342=[2,5,2,2,3,5,4,4,2,4,4,2,2,5,1,7,3,3,5,3,8,4,3,2]\n", + "true_A8s182=[2,5,2,2,3,5,4,4,2,4,4,2,2,5,1,7,3,3,5,3,8,4,3,2]\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fake data " + ] + }, + { + "cell_type": "code", + "execution_count": 51, + 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genometemplateread_lengthpredicted_copy_numberactual_copy_numberdifference
0Beijing-39115410032.01.0
1Beijing-39142410032.50.5
2Beijing-39157710053.51.5
3Beijing-39180210011.00.0
4Beijing-39196010043.01.0
5Beijing-391195510084.53.5
6Beijing-391205910032.01.0
7Beijing-391216510079.02.0
8Beijing-391234710024.02.0
9Beijing-391240110021.70.3
10Beijing-391246110021.70.3
11Beijing-391253110082.06.0
12Beijing-391268710015.04.0
13Beijing-391299610031.51.5
14Beijing-391300710017.06.0
15Beijing-391317110031.51.5
16Beijing-391319210073.04.0
17Beijing-391369010044.00.0
18Beijing-391405210063.03.0
19Beijing-391415610048.04.0
20Beijing-391434810042.71.3
21Beijing-3912163b10042.71.3
22Beijing-like-110415420022.00.0
23Beijing-like-110442420022.50.5
24Beijing-like-110457720043.50.5
25Beijing-like-110480220011.00.0
26Beijing-like-110496020033.00.0
27Beijing-like-1104195520064.51.5
28Beijing-like-1104205920022.00.0
29Beijing-like-1104216520079.02.0
.....................
351Beijing-like-11043007150_PCR-free17.06.0
352Beijing-like-11043171150_PCR-free31.51.5
353Beijing-like-11043192150_PCR-free43.01.0
354Beijing-like-11043690150_PCR-free44.00.0
355Beijing-like-11044156150_PCR-free31.02.0
356Beijing-like-11044348150_PCR-free32.70.3
357Beijing-like-11042163b150_PCR-free42.71.3
358Beijing-like-350491545032.01.0
359Beijing-like-350494245042.51.5
360Beijing-like-350495775063.52.5
361Beijing-like-350495805022.50.5
362Beijing-like-350498025021.50.5
363Beijing-like-350499605053.02.0
364Beijing-like-3504919555084.53.5
365Beijing-like-3504920595032.01.0
366Beijing-like-3504921655078.01.0
367Beijing-like-3504923475034.01.0
368Beijing-like-3504924015032.01.0
369Beijing-like-3504924615031.51.5
370Beijing-like-3504925315082.06.0
371Beijing-like-3504926875015.04.0
372Beijing-like-3504929965031.51.5
373Beijing-like-3504930075027.05.0
374Beijing-like-3504931715041.52.5
375Beijing-like-3504931925073.04.0
376Beijing-like-3504936905054.01.0
377Beijing-like-3504940525013.02.0
378Beijing-like-3504941565051.04.0
379Beijing-like-3504943485043.01.0
380Beijing-like-350492163b5023.01.0
\n", + "

381 rows × 6 columns

\n", + "
" + ], + "text/plain": [ + " genome template read_length predicted_copy_number \\\n", + "0 Beijing-391 154 100 3 \n", + "1 Beijing-391 424 100 3 \n", + "2 Beijing-391 577 100 5 \n", + "3 Beijing-391 802 100 1 \n", + "4 Beijing-391 960 100 4 \n", + "5 Beijing-391 1955 100 8 \n", + "6 Beijing-391 2059 100 3 \n", + "7 Beijing-391 2165 100 7 \n", + "8 Beijing-391 2347 100 2 \n", + "9 Beijing-391 2401 100 2 \n", + "10 Beijing-391 2461 100 2 \n", + "11 Beijing-391 2531 100 8 \n", + "12 Beijing-391 2687 100 1 \n", + "13 Beijing-391 2996 100 3 \n", + "14 Beijing-391 3007 100 1 \n", + "15 Beijing-391 3171 100 3 \n", + "16 Beijing-391 3192 100 7 \n", + "17 Beijing-391 3690 100 4 \n", + "18 Beijing-391 4052 100 6 \n", + "19 Beijing-391 4156 100 4 \n", + "20 Beijing-391 4348 100 4 \n", + "21 Beijing-391 2163b 100 4 \n", + "22 Beijing-like-1104 154 200 2 \n", + "23 Beijing-like-1104 424 200 2 \n", + "24 Beijing-like-1104 577 200 4 \n", + "25 Beijing-like-1104 802 200 1 \n", + "26 Beijing-like-1104 960 200 3 \n", + "27 Beijing-like-1104 1955 200 6 \n", + "28 Beijing-like-1104 2059 200 2 \n", + "29 Beijing-like-1104 2165 200 7 \n", + ".. ... ... ... ... \n", + "351 Beijing-like-1104 3007 150_PCR-free 1 \n", + "352 Beijing-like-1104 3171 150_PCR-free 3 \n", + "353 Beijing-like-1104 3192 150_PCR-free 4 \n", + "354 Beijing-like-1104 3690 150_PCR-free 4 \n", + "355 Beijing-like-1104 4156 150_PCR-free 3 \n", + "356 Beijing-like-1104 4348 150_PCR-free 3 \n", + "357 Beijing-like-1104 2163b 150_PCR-free 4 \n", + "358 Beijing-like-35049 154 50 3 \n", + "359 Beijing-like-35049 424 50 4 \n", + "360 Beijing-like-35049 577 50 6 \n", + "361 Beijing-like-35049 580 50 2 \n", + "362 Beijing-like-35049 802 50 2 \n", + "363 Beijing-like-35049 960 50 5 \n", + "364 Beijing-like-35049 1955 50 8 \n", + "365 Beijing-like-35049 2059 50 3 \n", + "366 Beijing-like-35049 2165 50 7 \n", + "367 Beijing-like-35049 2347 50 3 \n", + "368 Beijing-like-35049 2401 50 3 \n", + "369 Beijing-like-35049 2461 50 3 \n", + "370 Beijing-like-35049 2531 50 8 \n", + "371 Beijing-like-35049 2687 50 1 \n", + "372 Beijing-like-35049 2996 50 3 \n", + "373 Beijing-like-35049 3007 50 2 \n", + "374 Beijing-like-35049 3171 50 4 \n", + "375 Beijing-like-35049 3192 50 7 \n", + "376 Beijing-like-35049 3690 50 5 \n", + "377 Beijing-like-35049 4052 50 1 \n", + "378 Beijing-like-35049 4156 50 5 \n", + "379 Beijing-like-35049 4348 50 4 \n", + "380 Beijing-like-35049 2163b 50 2 \n", + "\n", + " actual_copy_number difference \n", + "0 2.0 1.0 \n", + "1 2.5 0.5 \n", + "2 3.5 1.5 \n", + "3 1.0 0.0 \n", + "4 3.0 1.0 \n", + "5 4.5 3.5 \n", + "6 2.0 1.0 \n", + "7 9.0 2.0 \n", + "8 4.0 2.0 \n", + "9 1.7 0.3 \n", + "10 1.7 0.3 \n", + "11 2.0 6.0 \n", + "12 5.0 4.0 \n", + "13 1.5 1.5 \n", + "14 7.0 6.0 \n", + "15 1.5 1.5 \n", + "16 3.0 4.0 \n", + "17 4.0 0.0 \n", + "18 3.0 3.0 \n", + "19 8.0 4.0 \n", + "20 2.7 1.3 \n", + "21 2.7 1.3 \n", + "22 2.0 0.0 \n", + "23 2.5 0.5 \n", + "24 3.5 0.5 \n", + "25 1.0 0.0 \n", + "26 3.0 0.0 \n", + "27 4.5 1.5 \n", + "28 2.0 0.0 \n", + "29 9.0 2.0 \n", + ".. ... ... \n", + "351 7.0 6.0 \n", + "352 1.5 1.5 \n", + "353 3.0 1.0 \n", + "354 4.0 0.0 \n", + "355 1.0 2.0 \n", + "356 2.7 0.3 \n", + "357 2.7 1.3 \n", + "358 2.0 1.0 \n", + "359 2.5 1.5 \n", + "360 3.5 2.5 \n", + "361 2.5 0.5 \n", + "362 1.5 0.5 \n", + "363 3.0 2.0 \n", + "364 4.5 3.5 \n", + "365 2.0 1.0 \n", + "366 8.0 1.0 \n", + "367 4.0 1.0 \n", + "368 2.0 1.0 \n", + "369 1.5 1.5 \n", + "370 2.0 6.0 \n", + "371 5.0 4.0 \n", + "372 1.5 1.5 \n", + "373 7.0 5.0 \n", + "374 1.5 2.5 \n", + "375 3.0 4.0 \n", + "376 4.0 1.0 \n", + "377 3.0 2.0 \n", + "378 1.0 4.0 \n", + "379 3.0 1.0 \n", + "380 3.0 1.0 \n", + "\n", + "[381 rows x 6 columns]" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import json\n", + "import pandas as pd\n", + "\n", + "df = pd.DataFrame(columns=[\"genome\", \"template\",\"read_length\",\"predicted_copy_number\"])\n", + "\n", + "for file in list_of_results:\n", + " \n", + " #load file\n", + " json_file = open(\"/Users/ritavityaz/PycharmProjects/PythonPRINCE/results/\" + file)\n", + " json_str = json_file.read()\n", + " json_data = json.loads(json_str)\n", + " \n", + " \n", + " #assign correct genome\n", + " if \"391\" in file:\n", + " genome = \"Beijing-391\"\n", + " if \"1104\" in file:\n", + " genome = \"Beijing-like-1104\"\n", + " if \"35049\" in file:\n", + " genome = \"Beijing-like-35049\"\n", + " \n", + " \n", + " #assign correct read length\n", + " if \"c150.json\" in file:\n", + " read_length = \"150\"\n", + " if \"c150_PCR-free\" in file:\n", + " read_length = \"150_PCR-free\"\n", + " if \"c200\" in file:\n", + " read_length = \"200\"\n", + " if \"c250\" in file:\n", + " read_length = \"250\"\n", + " if \"c50\" in file:\n", + " read_length = \"50\"\n", + " if \"c100\" in file:\n", + " read_length = \"100\"\n", + " \n", + "# Put all the data in a Pandas dataframe \n", + " for i, template in enumerate(list_of_templates):\n", + " if template in json_data:\n", + " \n", + " #assign correct true copy number\n", + " if \"391\" in file:\n", + " acp = true_391[i]\n", + " if \"1104\" in file:\n", + " acp = true_1104[i]\n", + " if \"35049\" in file:\n", + " acp = true_35049[i]\n", + "\n", + "\n", + " df = df.append({\n", + " \"genome\": genome,\n", + " \"template\": template,\n", + " \"read_length\": read_length,\n", + " \"predicted_copy_number\":json_data[template][\"Genotype\"][-1],\n", + " \"actual_copy_number\": acp,\n", + " \"difference\":abs(int(json_data[template][\"Genotype\"][-1])-acp)\n", + " }, ignore_index=True)\n", + "\n", + " \n", + "df\n", + "\n", + "\n", + " \n", + " \n", + "# # print(template)\n", + "# print(json_data[\"154\"][\"Genotype\"])\n", + "# # print(json_data[\"154\"][\"GenotypeCi\"])\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fake data V2" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": {}, + "outputs": [], + "source": [ + "#UPDATED\n", + "import json\n", + "import pandas as pd\n", + "\n", + "df_fake2 = pd.DataFrame(columns=[\"genome\", \"template\",\"read_length\",\"predicted_copy_number\"])\n", + "\n", + "for file in list_of_results:\n", + " \n", + " #load file\n", + " json_file = open(\"/Users/ritavityaz/Desktop/results_upd2/\" + file)\n", + " json_str = json_file.read()\n", + " json_data = json.loads(json_str)\n", + " \n", + " \n", + " #assign correct genome\n", + " if \"391\" in file:\n", + " genome = \"Beijing-391\"\n", + " if \"1104\" in file:\n", + " genome = \"Beijing-like-1104\"\n", + " if \"35049\" in file:\n", + " genome = \"Beijing-like-35049\"\n", + " \n", + " \n", + " #assign correct read length\n", + " if \"c150.json\" in file:\n", + " read_length = \"150\"\n", + " if \"c150_PCR-free\" in file:\n", + " read_length = \"150_PCR-free\"\n", + " if \"c200\" in file:\n", + " read_length = \"200\"\n", + " if \"c250\" in file:\n", + " read_length = \"250\"\n", + " if \"c50\" in file:\n", + " read_length = \"50\"\n", + " if \"c100\" in file:\n", + " read_length = \"100\"\n", + " \n", + "# Put all the data in a Pandas dataframe \n", + " for i, template in enumerate(list_of_templates):\n", + " if template in json_data:\n", + " \n", + " #assign correct true copy number\n", + " if \"391\" in file:\n", + " acp = true_391[i]\n", + " if \"1104\" in file:\n", + " acp = true_1104[i]\n", + " if \"35049\" in file:\n", + " acp = true_35049[i]\n", + " \n", + " #Find largest interval and find it's median\n", + " CI = (json_data[template][\"GenotypeCi\"]).split(\"/\")\n", + " predicted_from_CI_1 = CI[0]\n", + " \n", + " CI_1_split = predicted_from_CI_1.split(\"-\")\n", + " CI_1_L = int(CI_1_split[0])\n", + " CI_1_R = int(CI_1_split[1])\n", + " range_1 = CI_1_R - CI_1_L \n", + " \n", + " predicted_from_CI_2 = CI[1]\n", + " CI_2_split = predicted_from_CI_2.split(\"-\")\n", + " CI_2_L = int(CI_2_split[0])\n", + " CI_2_R = int(CI_2_split[1])\n", + " range_2 = CI_2_R -CI_2_L \n", + " \n", + " \n", + " if range_1 > range_2:\n", + " pred_Ci = CI_1_L + range_1/2\n", + " \n", + " if range_1 < range_2:\n", + " pred_Ci = CI_2_L + range_2/2\n", + "\n", + "\n", + " df_fake2 = df_fake2.append({\n", + " \"genome\": genome,\n", + " \"template\": template,\n", + " \"read_length\": read_length,\n", + " \"predicted_copy_number\":json_data[template][\"Genotype\"][-1],\n", + " \"predicted_copy_number_range\":pred_Ci,\n", + " \"actual_copy_number\": acp,\n", + " \"difference\":abs(int(json_data[template][\"Genotype\"][-1])-acp),\n", + " \"diiference_range\":abs(int(json_data[template][\"Genotype\"][-1])-pred_Ci)\n", + " }, ignore_index=True)\n", + " \n", + " \n", + " df_fake2[\"abs_diff\"] = (df_fake2['actual_copy_number'] - df_fake2[\"predicted_copy_number_range\"]).abs()\n", + " df_fake2[\"sq_diff\"] = (df_fake2['actual_copy_number'] - df_fake2[\"predicted_copy_number_range\"])**2\n", + "\n", + "\n", + " \n", + "df_fake2\n", + "\n", + "from sklearn.metrics import mean_squared_error,mean_absolute_error\n", + "\n", + "mae_f= df_fake2.groupby(\"template\").apply(\n", + " lambda x: mean_absolute_error(x.actual_copy_number, x.predicted_copy_number_range))\n", + "\n", + "from sklearn.metrics import mean_squared_error\n", + "\n", + "mrse_f = df_fake2.groupby(\"template\").apply(\n", + " lambda x: mean_squared_error(x.actual_copy_number, x.predicted_copy_number_range) ** .5)\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "temp = pd.DataFrame({'templates':mae_f.index, 'mae_expHunter':mae_f.values, 'mrse_expHunter':mrse_f.values })\n", + "\n", + "temp\n", + "temp.to_csv(\"fake_expHunter.csv\")\n", + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + "# # print(template)\n", + "# print(json_data[\"154\"][\"Genotype\"])\n", + "# # print(json_data[\"154\"][\"GenotypeCi\"])\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 118, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "from sklearn.metrics import mean_squared_error,mean_absolute_error\n", + "\n", + "df_fake2.groupby(\"template\").apply(\n", + " lambda x: mean_absolute_error(x.actual_copy_number, x.predicted_copy_number_range)).plot(kind='bar')\n", + "\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 121, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "from sklearn.metrics import mean_squared_error\n", + "\n", + "df_fake2.groupby(\"template\").apply(\n", + " lambda x: mean_squared_error(x.actual_copy_number, x.predicted_copy_number_range) ** .5).plot(kind='bar')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.5 90\n", + "1.5 59\n", + "3.5 41\n", + "1.0 38\n", + "2.5 28\n", + "4.5 22\n", + "0.0 22\n", + "2.0 22\n", + "3.0 15\n", + "5.5 11\n", + "4.0 11\n", + "5.0 5\n", + "7.5 3\n", + "8.5 3\n", + "6.5 3\n", + "7.0 2\n", + "dtype: int64" + ] + }, + "execution_count": 119, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#total counts\n", + "counts = pd.value_counts(df_fake2[\"diiference_range\"].values, sort=True)\n", + "pd.value_counts(df_fake2[\"diiference_range\"].values, sort=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "22\n", + "375\n", + "Percent perfect answers 5.86666666667\n", + "Percent answers off by less than(or equal to) one 21.8666666667\n", + "Percent answers off by less than(or equal to) two 28.8\n", + "MAE: 1.8016\n", + "RMSE: 2.39865517878\n" + ] + } + ], + "source": [ + "total = 0\n", + "for count in counts:\n", + " total +=count\n", + "# total = total - counts[0]\n", + "print(counts[0])\n", + "print(total)\n", + "\n", + "print(\"Percent perfect answers \" + str((counts[0]/total)*100))\n", + "print(\"Percent answers off by less than(or equal to) one \" + str(((counts[0] + counts[1] + counts[2])/total)*100))\n", + "print(\"Percent answers off by less than(or equal to) two \" + str(((counts[0] + counts[1] + counts[2]+ counts[3]+ counts[4])/total)*100))\n", + "\n", + "abs_dif_sum = df_fake2[\"abs_diff\"].sum()\n", + "MAE = abs_dif_sum/total\n", + "print(\"MAE: \" + str(MAE))\n", + "\n", + "sq_dif_sum = df_fake2[\"sq_diff\"].sum()\n", + "RMSE = (sq_dif_sum/total)**(0.5)\n", + "print(\"RMSE: \" + str(RMSE))\n", + " \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Kelowna results" + ] + }, + { + "cell_type": "code", + "execution_count": 168, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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191.5625002.616056577
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\n", + "
" + ], + "text/plain": [ + " mae_expHunter mrse_expHunter templates\n", + "0 0.333333 1.050793 154\n", + "1 0.804348 1.026814 1955\n", + "2 0.916667 1.198958 2059\n", + "3 2.687500 5.879874 2163b\n", + "4 4.250000 6.110101 2165\n", + "5 1.562500 2.048882 2347\n", + "6 0.571429 0.859125 2401\n", + "7 1.375000 2.309401 2461\n", + "8 0.875000 1.030776 2531\n", + "9 4.791667 4.836924 2687\n", + "10 3.208333 3.567796 2996\n", + "11 0.854167 0.940966 3007\n", + "12 0.354167 0.420813 3171\n", + "13 0.875000 1.136515 3192\n", + "14 0.562500 0.653516 3690\n", + "15 2.166667 2.508319 4052\n", + "16 1.520833 1.782437 4156\n", + "17 1.312500 1.571226 424\n", + "18 0.729167 1.211920 4348\n", + "19 1.562500 2.616056 577\n", + "20 2.395833 2.624008 802\n", + "21 1.729167 2.551552 960" + ] + }, + "execution_count": 168, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#UPDATED\n", + "%matplotlib inline\n", + "import json\n", + "import pandas as pd\n", + "\n", + "df_kelowna = pd.DataFrame(columns=[\"genome\",\"template\",\"predicted_copy_number\"])\n", + "\n", + "for file in list_of_kelowna_results:\n", + " \n", + " #load file\n", + " json_file = open(\"/Users/ritavityaz/Desktop/kelowna_results_fixed/\" + file + \".json\")\n", + " json_str = json_file.read()\n", + " json_data = json.loads(json_str)\n", + " \n", + "# Put all the data in a Pandas dataframe \n", + " for i, template in enumerate(list_of_templates):\n", + " if template in json_data:\n", + " \n", + " #assign correct true copy number\n", + " acp = (eval(\"true_\" + file))[i]\n", + " \n", + " \n", + " #Find largest interval and find it's median\n", + " CI = (json_data[template][\"GenotypeCi\"]).split(\"/\")\n", + " predicted_from_CI_1 = CI[0]\n", + " \n", + " CI_1_split = predicted_from_CI_1.split(\"-\")\n", + " CI_1_L = int(CI_1_split[0])\n", + " CI_1_R = int(CI_1_split[1])\n", + " range_1 = CI_1_R - CI_1_L \n", + " \n", + " predicted_from_CI_2 = CI[1]\n", + " CI_2_split = predicted_from_CI_2.split(\"-\")\n", + " CI_2_L = int(CI_2_split[0])\n", + " CI_2_R = int(CI_2_split[1])\n", + " range_2 = CI_2_R -CI_2_L \n", + " \n", + " \n", + " if range_1 > range_2:\n", + " pred_Ci = CI_1_L + range_1/2\n", + " \n", + " if range_1 < range_2:\n", + " pred_Ci = CI_2_L + range_2/2\n", + " \n", + " \n", + " \n", + " \n", + "# print(CI)\n", + "# print(predicted_from_CI_1)\n", + "# print(predicted_from_CI_2)\n", + "# print(CI_1_L)\n", + "# print(CI_1_R)\n", + "# print(range_1)\n", + " \n", + " \n", + " \n", + "\n", + " df_kelowna = df_kelowna.append({\n", + " \"genome\": file,\n", + " \"template\": template,\n", + " \"predicted_copy_number\":json_data[template][\"Genotype\"][-1],\n", + " \"predicted_copy_number_range\":pred_Ci,\n", + " \"actual_copy_number\": acp,\n", + " \"difference\":abs(int(json_data[template][\"Genotype\"][-1])-acp),\n", + " \"diiference_range\":abs(int(json_data[template][\"Genotype\"][-1])-pred_Ci) \n", + " }, ignore_index=True)\n", + " \n", + " df_kelowna[\"abs_diff\"] = (df_kelowna['actual_copy_number'] - df_kelowna[\"predicted_copy_number_range\"]).abs()\n", + " df_kelowna[\"sq_diff\"] = (df_kelowna['actual_copy_number'] - df_kelowna[\"predicted_copy_number_range\"])**2\n", + "\n", + " \n", + "# df\n", + "\n", + "%matplotlib inline\n", + "from sklearn.metrics import mean_squared_error,mean_absolute_error\n", + "\n", + "mae_k= df_kelowna.groupby(\"template\").apply(\n", + " lambda x: mean_absolute_error(x.actual_copy_number, x.predicted_copy_number_range))\n", + "\n", + "from sklearn.metrics import mean_squared_error\n", + "\n", + "mrse_k = df_kelowna.groupby(\"template\").apply(\n", + " lambda x: mean_squared_error(x.actual_copy_number, x.predicted_copy_number_range) ** .5)\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "temp = pd.DataFrame({'templates':mae_k.index, 'mae_expHunter':mae_k.values, 'mrse_expHunter':mrse_k.values })\n", + "\n", + "\n", + "temp.to_csv(\"Kelowna_expHunter.csv\")\n", + "# df_kelowna.groupby(\"template\").apply(\n", + "# lambda x: mean_absolute_error(x.actual_copy_number, x.predicted_copy_number_range)).plot(kind='bar')\n", + "\n", + "temp " + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 115, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "from sklearn.metrics import mean_squared_error\n", + "\n", + "df_kelowna.groupby(\"template\").apply(\n", + " lambda x: mean_squared_error(x.actual_copy_number, x.predicted_copy_number_range) ** .5).plot(kind='bar')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.5 170\n", + "0.0 88\n", + "1.0 81\n", + "1.5 61\n", + "7.0 17\n", + "2.0 15\n", + "4.5 14\n", + "7.5 13\n", + "5.0 12\n", + "3.5 12\n", + "6.5 11\n", + "2.5 8\n", + "8.0 5\n", + "5.5 4\n", + "4.0 3\n", + "3.0 3\n", + "6.0 3\n", + "10.0 1\n", + "13.5 1\n", + "15.5 1\n", + "30.5 1\n", + "dtype: int64" + ] + }, + "execution_count": 100, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#total counts\n", + "counts = pd.value_counts(df[\"diiference_range\"].values, sort=True)\n", + "pd.value_counts(df[\"diiference_range\"].values, sort=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "88\n", + "524\n", + "Percent perfect answers 16.7938931298\n", + "Percent answers off by less than(or equal to) one 35.1145038168\n", + "Percent answers off by less than(or equal to) two 36.2595419847\n", + "MAE: 1.61832061069\n", + "RMSE: 2.70072085373\n" + ] + } + ], + "source": [ + "total = 0\n", + "for count in counts:\n", + " total +=count\n", + "# total = total - counts[0]\n", + "print(counts[0])\n", + "print(total)\n", + "\n", + "print(\"Percent perfect answers \" + str((counts[0]/total)*100))\n", + "print(\"Percent answers off by less than(or equal to) one \" + str(((counts[0] + counts[1] + counts[2])/total)*100))\n", + "print(\"Percent answers off by less than(or equal to) two \" + str(((counts[0] + counts[1] + counts[2]+ counts[3]+ counts[4])/total)*100))\n", + "\n", + "abs_dif_sum = df[\"abs_diff\"].sum()\n", + "MAE = abs_dif_sum/total\n", + "print(\"MAE: \" + str(MAE))\n", + "\n", + "sq_dif_sum = df[\"sq_diff\"].sum()\n", + "RMSE = (sq_dif_sum/total)**(0.5)\n", + "print(\"RMSE: \" + str(RMSE))\n", + " \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Beijing results" + ] + }, + { + "cell_type": "code", + "execution_count": 163, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Mean Coveragegenome15458096016442059253126872996...19552163b21652347240124613171369040524156
072.8ERR55064322332517...4744423381
157.5ERR55065822332515...5644423382
273.4ERR55067022132517...4644423392
398.9ERR55072422332515...5644423382
457.4ERR55073822332517...5644423372
572.5ERR55073922332517...5242423382
667.2ERR55077922332515...5624423382
783.8ERR55078222332515...5644423382
884.5ERR55088722332515...5644423382
9267.1ERR55091022332517...5544423382
1052.9ERR55092722332517...3642413371
1198.0ERR55094022332617...5544423282
12107.9ERR55094622332516...4644423372
13111.8ERR55095722232519...5644423462
1453.5ERR55098422332515...5644423382
15141.7ERR55100722332517...5544423382
1693.0ERR55107122232517...5644423462
1750.4ERR55107922332517...5544423382
1880.6ERR55108622332517...4344423383
1952.7ERR55109022332517...5644423372
20110.8ERR55115532232517...5544423392
2155.6ERR55115922332515...5644423372
2272.5ERR55116822332515...5644423382
2359.4ERR55118422332516...4444423383
24146.6ERR55120122332515...4644423382
2564.4ERR55121222332517...5644423372
2671.1ERR55121422332515...7644423382
27104.5ERR55122522332517...47444233104
28154.5ERR55129322332515...5442423392
2991.0ERR55130522332515...5644423382
..................................................................
7758.6ERR55258022332517...5644423372
7952.3ERR55269022332517...5444424382
8053.4ERR55274322332517...4344423383
8263.9ERR55276022332617...5624423382
8361.3ERR55278722332517...5644423372
8481.1ERR55283022332517...4644423383
8586.9ERR55283822232517...5644423382
86109.1ERR55289422332515...5644423382
8788.6ERR55290722332517...5644423362
88164.5ERR55291122332517...4344423383
8952.8ERR55291222332515...5644423382
9049.7ERR55293922332616...5444423382
9173.1ERR55294922322517...4644423374
9262.7ERR55295422332517...5644423372
9396.8ERR55306822322515...5644423382
94190.4ERR55308124332517...5444423382
95162.0ERR55308622332515...5644423382
9654.5ERR55309822332517...4744423374
97122.0ERR55310720332517...5544423382
9865.6ERR55311622332514...4644423382
9954.2ERR55313922332517...5534423382
10069.3ERR55315622332517...5644423382
10153.0ERR55317122332517...5644423372
102105.3ERR55323722332515...5644423382
10380.0ERR55325122332617...4334223483
10453.4ERR55327522332517...5544423382
10551.2ERR55327722332517...5644423382
10653.3ERR55329122232516...5544423372
10767.8ERR55330322332517...5544423382
10857.5ERR55331322332519...5544423382
\n", + "

103 rows × 26 columns

\n", + "
" + ], + "text/plain": [ + " Mean Coverage genome 154 580 960 1644 2059 2531 2687 2996 \\\n", + "0 72.8 ERR550643 2 2 3 3 2 5 1 7 \n", + "1 57.5 ERR550658 2 2 3 3 2 5 1 5 \n", + "2 73.4 ERR550670 2 2 1 3 2 5 1 7 \n", + "3 98.9 ERR550724 2 2 3 3 2 5 1 5 \n", + "4 57.4 ERR550738 2 2 3 3 2 5 1 7 \n", + "5 72.5 ERR550739 2 2 3 3 2 5 1 7 \n", + "6 67.2 ERR550779 2 2 3 3 2 5 1 5 \n", + "7 83.8 ERR550782 2 2 3 3 2 5 1 5 \n", + "8 84.5 ERR550887 2 2 3 3 2 5 1 5 \n", + "9 267.1 ERR550910 2 2 3 3 2 5 1 7 \n", + "10 52.9 ERR550927 2 2 3 3 2 5 1 7 \n", + "11 98.0 ERR550940 2 2 3 3 2 6 1 7 \n", + "12 107.9 ERR550946 2 2 3 3 2 5 1 6 \n", + "13 111.8 ERR550957 2 2 2 3 2 5 1 9 \n", + "14 53.5 ERR550984 2 2 3 3 2 5 1 5 \n", + "15 141.7 ERR551007 2 2 3 3 2 5 1 7 \n", + "16 93.0 ERR551071 2 2 2 3 2 5 1 7 \n", + "17 50.4 ERR551079 2 2 3 3 2 5 1 7 \n", + "18 80.6 ERR551086 2 2 3 3 2 5 1 7 \n", + "19 52.7 ERR551090 2 2 3 3 2 5 1 7 \n", + "20 110.8 ERR551155 3 2 2 3 2 5 1 7 \n", + "21 55.6 ERR551159 2 2 3 3 2 5 1 5 \n", + "22 72.5 ERR551168 2 2 3 3 2 5 1 5 \n", + "23 59.4 ERR551184 2 2 3 3 2 5 1 6 \n", + "24 146.6 ERR551201 2 2 3 3 2 5 1 5 \n", + "25 64.4 ERR551212 2 2 3 3 2 5 1 7 \n", + "26 71.1 ERR551214 2 2 3 3 2 5 1 5 \n", + "27 104.5 ERR551225 2 2 3 3 2 5 1 7 \n", + "28 154.5 ERR551293 2 2 3 3 2 5 1 5 \n", + "29 91.0 ERR551305 2 2 3 3 2 5 1 5 \n", + ".. ... ... ... ... ... ... ... ... ... ... \n", + "77 58.6 ERR552580 2 2 3 3 2 5 1 7 \n", + "79 52.3 ERR552690 2 2 3 3 2 5 1 7 \n", + "80 53.4 ERR552743 2 2 3 3 2 5 1 7 \n", + "82 63.9 ERR552760 2 2 3 3 2 6 1 7 \n", + "83 61.3 ERR552787 2 2 3 3 2 5 1 7 \n", + "84 81.1 ERR552830 2 2 3 3 2 5 1 7 \n", + "85 86.9 ERR552838 2 2 2 3 2 5 1 7 \n", + "86 109.1 ERR552894 2 2 3 3 2 5 1 5 \n", + "87 88.6 ERR552907 2 2 3 3 2 5 1 7 \n", + "88 164.5 ERR552911 2 2 3 3 2 5 1 7 \n", + "89 52.8 ERR552912 2 2 3 3 2 5 1 5 \n", + "90 49.7 ERR552939 2 2 3 3 2 6 1 6 \n", + "91 73.1 ERR552949 2 2 3 2 2 5 1 7 \n", + "92 62.7 ERR552954 2 2 3 3 2 5 1 7 \n", + "93 96.8 ERR553068 2 2 3 2 2 5 1 5 \n", + "94 190.4 ERR553081 2 4 3 3 2 5 1 7 \n", + "95 162.0 ERR553086 2 2 3 3 2 5 1 5 \n", + "96 54.5 ERR553098 2 2 3 3 2 5 1 7 \n", + "97 122.0 ERR553107 2 0 3 3 2 5 1 7 \n", + "98 65.6 ERR553116 2 2 3 3 2 5 1 4 \n", + "99 54.2 ERR553139 2 2 3 3 2 5 1 7 \n", + "100 69.3 ERR553156 2 2 3 3 2 5 1 7 \n", + "101 53.0 ERR553171 2 2 3 3 2 5 1 7 \n", + "102 105.3 ERR553237 2 2 3 3 2 5 1 5 \n", + "103 80.0 ERR553251 2 2 3 3 2 6 1 7 \n", + "104 53.4 ERR553275 2 2 3 3 2 5 1 7 \n", + "105 51.2 ERR553277 2 2 3 3 2 5 1 7 \n", + "106 53.3 ERR553291 2 2 2 3 2 5 1 6 \n", + "107 67.8 ERR553303 2 2 3 3 2 5 1 7 \n", + "108 57.5 ERR553313 2 2 3 3 2 5 1 9 \n", + "\n", + " ... 1955 2163b 2165 2347 2401 2461 3171 3690 4052 4156 \n", + "0 ... 4 7 4 4 4 2 3 3 8 1 \n", + "1 ... 5 6 4 4 4 2 3 3 8 2 \n", + "2 ... 4 6 4 4 4 2 3 3 9 2 \n", + "3 ... 5 6 4 4 4 2 3 3 8 2 \n", + "4 ... 5 6 4 4 4 2 3 3 7 2 \n", + "5 ... 5 2 4 2 4 2 3 3 8 2 \n", + "6 ... 5 6 2 4 4 2 3 3 8 2 \n", + "7 ... 5 6 4 4 4 2 3 3 8 2 \n", + "8 ... 5 6 4 4 4 2 3 3 8 2 \n", + "9 ... 5 5 4 4 4 2 3 3 8 2 \n", + "10 ... 3 6 4 2 4 1 3 3 7 1 \n", + "11 ... 5 5 4 4 4 2 3 2 8 2 \n", + "12 ... 4 6 4 4 4 2 3 3 7 2 \n", + "13 ... 5 6 4 4 4 2 3 4 6 2 \n", + "14 ... 5 6 4 4 4 2 3 3 8 2 \n", + "15 ... 5 5 4 4 4 2 3 3 8 2 \n", + "16 ... 5 6 4 4 4 2 3 4 6 2 \n", + "17 ... 5 5 4 4 4 2 3 3 8 2 \n", + "18 ... 4 3 4 4 4 2 3 3 8 3 \n", + "19 ... 5 6 4 4 4 2 3 3 7 2 \n", + "20 ... 5 5 4 4 4 2 3 3 9 2 \n", + "21 ... 5 6 4 4 4 2 3 3 7 2 \n", + "22 ... 5 6 4 4 4 2 3 3 8 2 \n", + "23 ... 4 4 4 4 4 2 3 3 8 3 \n", + "24 ... 4 6 4 4 4 2 3 3 8 2 \n", + "25 ... 5 6 4 4 4 2 3 3 7 2 \n", + "26 ... 7 6 4 4 4 2 3 3 8 2 \n", + "27 ... 4 7 4 4 4 2 3 3 10 4 \n", + "28 ... 5 4 4 2 4 2 3 3 9 2 \n", + "29 ... 5 6 4 4 4 2 3 3 8 2 \n", + ".. ... ... ... ... ... ... ... ... ... ... ... \n", + "77 ... 5 6 4 4 4 2 3 3 7 2 \n", + "79 ... 5 4 4 4 4 2 4 3 8 2 \n", + "80 ... 4 3 4 4 4 2 3 3 8 3 \n", + "82 ... 5 6 2 4 4 2 3 3 8 2 \n", + "83 ... 5 6 4 4 4 2 3 3 7 2 \n", + "84 ... 4 6 4 4 4 2 3 3 8 3 \n", + "85 ... 5 6 4 4 4 2 3 3 8 2 \n", + "86 ... 5 6 4 4 4 2 3 3 8 2 \n", + "87 ... 5 6 4 4 4 2 3 3 6 2 \n", + "88 ... 4 3 4 4 4 2 3 3 8 3 \n", + "89 ... 5 6 4 4 4 2 3 3 8 2 \n", + "90 ... 5 4 4 4 4 2 3 3 8 2 \n", + "91 ... 4 6 4 4 4 2 3 3 7 4 \n", + "92 ... 5 6 4 4 4 2 3 3 7 2 \n", + "93 ... 5 6 4 4 4 2 3 3 8 2 \n", + "94 ... 5 4 4 4 4 2 3 3 8 2 \n", + "95 ... 5 6 4 4 4 2 3 3 8 2 \n", + "96 ... 4 7 4 4 4 2 3 3 7 4 \n", + "97 ... 5 5 4 4 4 2 3 3 8 2 \n", + "98 ... 4 6 4 4 4 2 3 3 8 2 \n", + "99 ... 5 5 3 4 4 2 3 3 8 2 \n", + "100 ... 5 6 4 4 4 2 3 3 8 2 \n", + "101 ... 5 6 4 4 4 2 3 3 7 2 \n", + "102 ... 5 6 4 4 4 2 3 3 8 2 \n", + "103 ... 4 3 3 4 2 2 3 4 8 3 \n", + "104 ... 5 5 4 4 4 2 3 3 8 2 \n", + "105 ... 5 6 4 4 4 2 3 3 8 2 \n", + "106 ... 5 5 4 4 4 2 3 3 7 2 \n", + "107 ... 5 5 4 4 4 2 3 3 8 2 \n", + "108 ... 5 5 4 4 4 2 3 3 8 2 \n", + "\n", + "[103 rows x 26 columns]" + ] + }, + "execution_count": 163, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "beijing_data = pd.read_csv('/Users/ritavityaz/Downloads/BeijingCopynumbers.csv')\n", + "problem = [\"ERR551979\",\"ERR552141\",\"ERR552194\",\"ERR552358\",\"ERR552668\",\"ERR552755\"]\n", + "\n", + "beijing_data = beijing_data[~beijing_data['genome'].isin(problem)]\n", + "\n", + "beijing_data" + ] + }, + { + "cell_type": "code", + "execution_count": 173, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import json\n", + "import pandas as pd\n", + "\n", + "df_beijing = pd.DataFrame(columns=[\"genome\",\"template\",\"predicted_copy_number\"])\n", + "\n", + "for index, file in beijing_data.iterrows():\n", + " #load file\n", + " json_file = open(\"/Users/ritavityaz/Desktop/beijing_results/\" + file[\"genome\"] + \".json\")\n", + " json_str = json_file.read()\n", + " json_data = json.loads(json_str)\n", + " \n", + "# Put all the data in a Pandas dataframe \n", + " for i, template in enumerate(list_of_templates):\n", + "\n", + " if template in json_data:\n", + " \n", + " #assign correct true copy number\n", + " acp = file[template]\n", + " \n", + " #Find largest interval and find it's median\n", + " CI = (json_data[template][\"GenotypeCi\"]).split(\"/\")\n", + " predicted_from_CI_1 = CI[0]\n", + " \n", + " CI_1_split = predicted_from_CI_1.split(\"-\")\n", + " CI_1_L = int(CI_1_split[0])\n", + " CI_1_R = int(CI_1_split[1])\n", + " range_1 = CI_1_R - CI_1_L \n", + " \n", + " predicted_from_CI_2 = CI[1]\n", + " CI_2_split = predicted_from_CI_2.split(\"-\")\n", + " CI_2_L = int(CI_2_split[0])\n", + " CI_2_R = int(CI_2_split[1])\n", + " range_2 = CI_2_R -CI_2_L \n", + " \n", + " if range_1 > range_2:\n", + " pred_Ci = CI_1_L + range_1/2\n", + " \n", + " if range_1 < range_2:\n", + " pred_Ci = CI_2_L + range_2/2\n", + " \n", + " df_beijing = df_beijing.append({\n", + " \"genome\": file[\"genome\"],\n", + " \"template\": template,\n", + " \"predicted_copy_number\":json_data[template][\"Genotype\"][-1],\n", + " \"predicted_copy_number_range\":pred_Ci,\n", + " \"actual_copy_number\": acp,\n", + " \"difference\":abs(int(json_data[template][\"Genotype\"][-1])-acp),\n", + " \"diiference_range\":abs(int(json_data[template][\"Genotype\"][-1])-pred_Ci) \n", + " }, ignore_index=True)\n", + " \n", + " \n", + " df_beijing[\"abs_diff\"] = (df_beijing['actual_copy_number'] - df_beijing[\"predicted_copy_number_range\"]).abs()\n", + " df_beijing[\"sq_diff\"] = (df_beijing['actual_copy_number'] - df_beijing[\"predicted_copy_number_range\"])**2\n", + "\n", + " \n", + "df_beijing\n", + "\n", + "%matplotlib inline\n", + "from sklearn.metrics import mean_squared_error,mean_absolute_error\n", + "\n", + "mae_b= df_beijing.groupby(\"template\").apply(\n", + " lambda x: mean_absolute_error(x.actual_copy_number, x.predicted_copy_number_range))\n", + "\n", + "from sklearn.metrics import mean_squared_error\n", + "\n", + "mrse_b = df_beijing.groupby(\"template\").apply(\n", + " lambda x: mean_squared_error(x.actual_copy_number, x.predicted_copy_number_range) ** .5)\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "temp = pd.DataFrame({'templates':mae_b.index, 'mae_expHunter':mae_b.values, 'mrse_expHunter':mrse_b.values })\n", + "\n", + "temp\n", + "temp.to_csv(\"beijing_expHunter.csv\")\n", + "\n", + "# df_kelowna.groupby(\"template\").apply(\n", + "# lambda x: mean_absolute_error(x.actual_copy_number, x.predicted_copy_number_range)).plot(kind='bar')\n", + "\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 174, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 174, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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H5+B0AA1mzuUP5nkdXtu3Ju3WpFrVWm3P79kg32VpSZ0n2zRN27JfxxPwj25b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\njAO3mVlh/hd/8cBKWsQRyAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "from sklearn.metrics import mean_squared_error\n", + "\n", + "df_beijing.groupby(\"template\").apply(\n", + " lambda x: mean_squared_error(x.actual_copy_number, x.predicted_copy_number_range) ** .5).plot(kind='bar')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 175, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.5 684\n", + "1.0 315\n", + "0.0 296\n", + "1.5 183\n", + "7.5 89\n", + "2.0 76\n", + "4.0 66\n", + "2.5 63\n", + "8.0 61\n", + "3.5 59\n", + "4.5 51\n", + "7.0 49\n", + "3.0 44\n", + "6.0 43\n", + "8.5 41\n", + "5.0 37\n", + "6.5 31\n", + "5.5 20\n", + "9.0 19\n", + "10.0 7\n", + "10.5 7\n", + "9.5 6\n", + "11.5 5\n", + "11.0 5\n", + "15.5 2\n", + "14.0 2\n", + "16.0 2\n", + "15.0 2\n", + "13.0 2\n", + "12.5 2\n", + "13.5 1\n", + "dtype: int64" + ] + }, + "execution_count": 175, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#total counts\n", + "counts = pd.value_counts(df_beijing[\"diiference_range\"].values, sort=True)\n", + "pd.value_counts(df_beijing[\"diiference_range\"].values, sort=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 176, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "296\n", + "2270\n", + "Percent perfect answers 13.0396475771\n", + "Percent answers off by less than(or equal to) one 30.2643171806\n", + "Percent answers off by less than(or equal to) two 35.1101321586\n", + "MAE: 2.06651982379\n", + "RMSE: 3.05125091702\n" + ] + } + ], + "source": [ + "total = 0\n", + "for count in counts:\n", + " total +=count\n", + "# total = total - counts[0]\n", + "print(counts[0])\n", + "print(total)\n", + "\n", + "print(\"Percent perfect answers \" + str((counts[0]/total)*100))\n", + "print(\"Percent answers off by less than(or equal to) one \" + str(((counts[0] + counts[1] + counts[2])/total)*100))\n", + "print(\"Percent answers off by less than(or equal to) two \" + str(((counts[0] + counts[1] + counts[2]+ counts[3]+ counts[4])/total)*100))\n", + "\n", + "abs_dif_sum = df_beijing[\"abs_diff\"].sum()\n", + "MAE = abs_dif_sum/total\n", + "print(\"MAE: \" + str(MAE))\n", + "\n", + "sq_dif_sum = df_beijing[\"sq_diff\"].sum()\n", + "RMSE = (sq_dif_sum/total)**(0.5)\n", + "print(\"RMSE: \" + str(RMSE))\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "CNVeM: Issues" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "http://genetics.cs.ucla.edu/cnvem/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "ritas-MacBook-Pro:CNVem1.0 ritavityaz$ ./splitReads\n", + "./splitReads./splitReads\n", + "Segmentation fault: 11\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "CNVnator" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "https://github.com/abyzovlab/CNVnator" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the one that is hard to set up. It Requires to install the ROOT package, but doesn't specify explicitly how to set the variable. On the ROOT website, and in the package documentation, several completely different methods are described, so I can't be certain that what I was doing is what I was expected to do. The documentation is not explicit about the installation overall. Many things were attempted, and none of them worked." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Summary: https://docs.google.com/spreadsheets/d/14CwRhsLzBuSUOoqah4sBYYMCTL0xKHmt6yXehmUm0iI/edit?usp=sharing" + ] + } + ], + "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": "3.6.3" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/data_for_paper/kelowna_advntr.csv b/data_for_paper/kelowna_advntr.csv new file mode 100644 index 0000000..3bc09f9 --- /dev/null +++ b/data_for_paper/kelowna_advntr.csv @@ -0,0 +1,24 @@ +10s165,3,2,3,3,2,3,2,3,3,3,2,3,3,3,3,2,2,3,1,0,3,3,2,3 +A9s272,3,2,3,3,2,3,2,3,3,3,2,3,3,3,3,2,2,3,1,3,3,3,2,3 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