{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ExtIdentifierSubjectIDagegendercombinedANACentromereSCL_70RNA_Polymerase_3Lung_Fibrosis_binaryLung_FibrosisTotal_mRssImmunosupression_binImmunosupressionoverallcategoryconditionlow_sscDisease_durationLocal_skin_score
0EXID40000009256847COO-36350Female50_F10100No0No0SclerodermaVEDOSSVEDOSSno00
1EXID40000009257105PDAR-033542Female42_F00000No0No0HealthyHealthyHealthyno00
2EXID40000009257119COO-00551Male51_M10001Yes2Yes1SclerodermaSclerodermaSSC_lowlow1560
3EXID40000009257121COO-42970Female70_F10011Yes33No0SclerodermaSclerodermaSSC_highno02
4EXID40000009257122COO-42578Female78_F10101Yes18Yes1SclerodermaSclerodermaSSC_lowlow601
\n", "
" ], "text/plain": [ " ExtIdentifier SubjectID age gender combined ANA Centromere \\\n", "0 EXID40000009256847 COO-363 50 Female 50_F 1 0 \n", "1 EXID40000009257105 PDAR-0335 42 Female 42_F 0 0 \n", "2 EXID40000009257119 COO-005 51 Male 51_M 1 0 \n", "3 EXID40000009257121 COO-429 70 Female 70_F 1 0 \n", "4 EXID40000009257122 COO-425 78 Female 78_F 1 0 \n", "\n", " SCL_70 RNA_Polymerase_3 Lung_Fibrosis_binary Lung_Fibrosis Total_mRss \\\n", "0 1 0 0 No 0 \n", "1 0 0 0 No 0 \n", "2 0 0 1 Yes 2 \n", "3 0 1 1 Yes 33 \n", "4 1 0 1 Yes 18 \n", "\n", " Immunosupression_bin Immunosupression overall category condition \\\n", "0 No 0 Scleroderma VEDOSS VEDOSS \n", "1 No 0 Healthy Healthy Healthy \n", "2 Yes 1 Scleroderma Scleroderma SSC_low \n", "3 No 0 Scleroderma Scleroderma SSC_high \n", "4 Yes 1 Scleroderma Scleroderma SSC_low \n", "\n", " low_ssc Disease_duration Local_skin_score \n", "0 no 0 0 \n", "1 no 0 0 \n", "2 low 156 0 \n", "3 no 0 2 \n", "4 low 60 1 " ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "METADATA_PATH = \"../Core data/somalogic_metadata.csv\"\n", "PROTEINS_PATH = \"../Core data/proteins_plot.csv\"\n", "metadata = pd.read_csv(METADATA_PATH)\n", "proteins = pd.read_csv(PROTEINS_PATH)\n", "valid_columns = {\n", " \"TargetFullName\": \"TargetFullName\",\n", " \"Target\": \"Target\",\n", " \"EntrezGeneID\": \"EntrezGeneID\",\n", " \"EntrezGeneSymbol\": \"EntrezGeneSymbol\"\n", " }\n", "metadata.head()\n" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Unnamed: 0SeqIdPlateIdPlateRunDateScannerIDPlatePositionSlideIdSubarraySampleIdSampleType...TargetFullNameTargetUniProtEntrezGeneIDEntrezGeneSymbolOrganismUnitsTypeDilutionPlateScale_Reference
0110000-28PLT246842023-08-13SG16064525, SG17164580C82586338888453COO-363Sample...Beta-crystallin B2CRBB2P433201415CRYBB2HumanRFUProtein2.5292.15
1210000-28PLT246842023-08-13SG16064525, SG17164580C92586338888463PDAR-0335Sample...Beta-crystallin B2CRBB2P433201415CRYBB2HumanRFUProtein2.5292.15
2310000-28PLT246842023-08-13SG16064525, SG17164580E82586338888455COO-005Sample...Beta-crystallin B2CRBB2P433201415CRYBB2HumanRFUProtein2.5292.15
3410000-28PLT246842023-08-13SG16064525, SG17164580F82586338888456COO-429Sample...Beta-crystallin B2CRBB2P433201415CRYBB2HumanRFUProtein2.5292.15
4510000-28PLT246842023-08-13SG16064525, SG17164580F72586338888446COO-425Sample...Beta-crystallin B2CRBB2P433201415CRYBB2HumanRFUProtein2.5292.15
..................................................................
94752947539999-1PLT246842023-08-13SG16064525, SG17164580A92586338888461COO-428Sample...Interferon regulatory factor 6IRF6O148963664IRF6HumanRFUProtein2.5409.75
94753947549999-1PLT246842023-08-13SG16064525, SG17164580D82586338888454COO-403Sample...Interferon regulatory factor 6IRF6O148963664IRF6HumanRFUProtein2.5409.75
94754947559999-1PLT246842023-08-13SG16064525, SG17164580D92586338888464PDAR-0343Sample...Interferon regulatory factor 6IRF6O148963664IRF6HumanRFUProtein2.5409.75
94755947569999-1PLT246842023-08-13SG16064525, SG17164580G92586338888467COO-180Sample...Interferon regulatory factor 6IRF6O148963664IRF6HumanRFUProtein2.5409.75
94756947579999-1PLT246842023-08-13SG16064525, SG17164580E72586338888445PDAR-0344Sample...Interferon regulatory factor 6IRF6O148963664IRF6HumanRFUProtein2.5409.75
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94757 rows × 43 columns

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" ], "text/plain": [ " Unnamed: 0 SeqId PlateId PlateRunDate ScannerID \\\n", "0 1 10000-28 PLT24684 2023-08-13 SG16064525, SG17164580 \n", "1 2 10000-28 PLT24684 2023-08-13 SG16064525, SG17164580 \n", "2 3 10000-28 PLT24684 2023-08-13 SG16064525, SG17164580 \n", "3 4 10000-28 PLT24684 2023-08-13 SG16064525, SG17164580 \n", "4 5 10000-28 PLT24684 2023-08-13 SG16064525, SG17164580 \n", "... ... ... ... ... ... \n", "94752 94753 9999-1 PLT24684 2023-08-13 SG16064525, SG17164580 \n", "94753 94754 9999-1 PLT24684 2023-08-13 SG16064525, SG17164580 \n", "94754 94755 9999-1 PLT24684 2023-08-13 SG16064525, SG17164580 \n", "94755 94756 9999-1 PLT24684 2023-08-13 SG16064525, SG17164580 \n", "94756 94757 9999-1 PLT24684 2023-08-13 SG16064525, SG17164580 \n", "\n", " PlatePosition SlideId Subarray SampleId SampleType ... \\\n", "0 C8 258633888845 3 COO-363 Sample ... \n", "1 C9 258633888846 3 PDAR-0335 Sample ... \n", "2 E8 258633888845 5 COO-005 Sample ... \n", "3 F8 258633888845 6 COO-429 Sample ... \n", "4 F7 258633888844 6 COO-425 Sample ... \n", "... ... ... ... ... ... ... \n", "94752 A9 258633888846 1 COO-428 Sample ... \n", "94753 D8 258633888845 4 COO-403 Sample ... \n", "94754 D9 258633888846 4 PDAR-0343 Sample ... \n", "94755 G9 258633888846 7 COO-180 Sample ... \n", "94756 E7 258633888844 5 PDAR-0344 Sample ... \n", "\n", " TargetFullName Target UniProt EntrezGeneID \\\n", "0 Beta-crystallin B2 CRBB2 P43320 1415 \n", "1 Beta-crystallin B2 CRBB2 P43320 1415 \n", "2 Beta-crystallin B2 CRBB2 P43320 1415 \n", "3 Beta-crystallin B2 CRBB2 P43320 1415 \n", "4 Beta-crystallin B2 CRBB2 P43320 1415 \n", "... ... ... ... ... \n", "94752 Interferon regulatory factor 6 IRF6 O14896 3664 \n", "94753 Interferon regulatory factor 6 IRF6 O14896 3664 \n", "94754 Interferon regulatory factor 6 IRF6 O14896 3664 \n", "94755 Interferon regulatory factor 6 IRF6 O14896 3664 \n", "94756 Interferon regulatory factor 6 IRF6 O14896 3664 \n", "\n", " EntrezGeneSymbol Organism Units Type Dilution \\\n", "0 CRYBB2 Human RFU Protein 2.5 \n", "1 CRYBB2 Human RFU Protein 2.5 \n", "2 CRYBB2 Human RFU Protein 2.5 \n", "3 CRYBB2 Human RFU Protein 2.5 \n", "4 CRYBB2 Human RFU Protein 2.5 \n", "... ... ... ... ... ... \n", "94752 IRF6 Human RFU Protein 2.5 \n", "94753 IRF6 Human RFU Protein 2.5 \n", "94754 IRF6 Human RFU Protein 2.5 \n", "94755 IRF6 Human RFU Protein 2.5 \n", "94756 IRF6 Human RFU Protein 2.5 \n", "\n", " PlateScale_Reference \n", "0 292.15 \n", "1 292.15 \n", "2 292.15 \n", "3 292.15 \n", "4 292.15 \n", "... ... \n", "94752 409.75 \n", "94753 409.75 \n", "94754 409.75 \n", "94755 409.75 \n", "94756 409.75 \n", "\n", "[94757 rows x 43 columns]" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "proteins" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "TargetFullName\n", "Filtered Data for Interferon regulatory factor 6:\n" ] }, { "data": { "text/html": [ "
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Unnamed: 0SeqIdPlateIdPlateRunDateScannerIDPlatePositionSlideIdSubarraySampleIdSampleType...TargetFullNameTargetUniProtEntrezGeneIDEntrezGeneSymbolOrganismUnitsTypeDilutionPlateScale_Reference
94744947459999-1PLT246842023-08-13SG16064525, SG17164580C82586338888453COO-363Sample...Interferon regulatory factor 6IRF6O148963664IRF6HumanRFUProtein2.5409.75
94745947469999-1PLT246842023-08-13SG16064525, SG17164580C92586338888463PDAR-0335Sample...Interferon regulatory factor 6IRF6O148963664IRF6HumanRFUProtein2.5409.75
94746947479999-1PLT246842023-08-13SG16064525, SG17164580E82586338888455COO-005Sample...Interferon regulatory factor 6IRF6O148963664IRF6HumanRFUProtein2.5409.75
94747947489999-1PLT246842023-08-13SG16064525, SG17164580F82586338888456COO-429Sample...Interferon regulatory factor 6IRF6O148963664IRF6HumanRFUProtein2.5409.75
94748947499999-1PLT246842023-08-13SG16064525, SG17164580F72586338888446COO-425Sample...Interferon regulatory factor 6IRF6O148963664IRF6HumanRFUProtein2.5409.75
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5 rows × 43 columns

\n", "
" ], "text/plain": [ " Unnamed: 0 SeqId PlateId PlateRunDate ScannerID \\\n", "94744 94745 9999-1 PLT24684 2023-08-13 SG16064525, SG17164580 \n", "94745 94746 9999-1 PLT24684 2023-08-13 SG16064525, SG17164580 \n", "94746 94747 9999-1 PLT24684 2023-08-13 SG16064525, SG17164580 \n", "94747 94748 9999-1 PLT24684 2023-08-13 SG16064525, SG17164580 \n", "94748 94749 9999-1 PLT24684 2023-08-13 SG16064525, SG17164580 \n", "\n", " PlatePosition SlideId Subarray SampleId SampleType ... \\\n", "94744 C8 258633888845 3 COO-363 Sample ... \n", "94745 C9 258633888846 3 PDAR-0335 Sample ... \n", "94746 E8 258633888845 5 COO-005 Sample ... \n", "94747 F8 258633888845 6 COO-429 Sample ... \n", "94748 F7 258633888844 6 COO-425 Sample ... \n", "\n", " TargetFullName Target UniProt EntrezGeneID \\\n", "94744 Interferon regulatory factor 6 IRF6 O14896 3664 \n", "94745 Interferon regulatory factor 6 IRF6 O14896 3664 \n", "94746 Interferon regulatory factor 6 IRF6 O14896 3664 \n", "94747 Interferon regulatory factor 6 IRF6 O14896 3664 \n", "94748 Interferon regulatory factor 6 IRF6 O14896 3664 \n", "\n", " EntrezGeneSymbol Organism Units Type Dilution \\\n", "94744 IRF6 Human RFU Protein 2.5 \n", "94745 IRF6 Human RFU Protein 2.5 \n", "94746 IRF6 Human RFU Protein 2.5 \n", "94747 IRF6 Human RFU Protein 2.5 \n", "94748 IRF6 Human RFU Protein 2.5 \n", "\n", " PlateScale_Reference \n", "94744 409.75 \n", "94745 409.75 \n", "94746 409.75 \n", "94747 409.75 \n", "94748 409.75 \n", "\n", "[5 rows x 43 columns]" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "id_type = \"TargetFullName\"\n", "column_name = valid_columns[id_type]\n", "print(column_name)\n", "protein_id = \"Interferon regulatory factor 6\"\n", "filtered_data = proteins[proteins[column_name] == protein_id]\n", "print(f\"Filtered Data for {protein_id}:\")\n", "filtered_data.head()" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ExtIdentifierSubjectIDagegendercombinedANACentromereSCL_70RNA_Polymerase_3Lung_Fibrosis_binaryLung_FibrosisTotal_mRssImmunosupression_binImmunosupressionoverallcategoryconditionlow_sscDisease_durationLocal_skin_score
0EXID40000009256847COO-36350Female50_F10100No0No0SclerodermaVEDOSSVEDOSSno00
1EXID40000009257105PDAR-033542Female42_F00000No0No0HealthyHealthyHealthyno00
2EXID40000009257119COO-00551Male51_M10001Yes2Yes1SclerodermaSclerodermaSSC_lowlow1560
3EXID40000009257121COO-42970Female70_F10011Yes33No0SclerodermaSclerodermaSSC_highno02
4EXID40000009257122COO-42578Female78_F10101Yes18Yes1SclerodermaSclerodermaSSC_lowlow601
\n", "
" ], "text/plain": [ " ExtIdentifier SubjectID age gender combined ANA Centromere \\\n", "0 EXID40000009256847 COO-363 50 Female 50_F 1 0 \n", "1 EXID40000009257105 PDAR-0335 42 Female 42_F 0 0 \n", "2 EXID40000009257119 COO-005 51 Male 51_M 1 0 \n", "3 EXID40000009257121 COO-429 70 Female 70_F 1 0 \n", "4 EXID40000009257122 COO-425 78 Female 78_F 1 0 \n", "\n", " SCL_70 RNA_Polymerase_3 Lung_Fibrosis_binary Lung_Fibrosis Total_mRss \\\n", "0 1 0 0 No 0 \n", "1 0 0 0 No 0 \n", "2 0 0 1 Yes 2 \n", "3 0 1 1 Yes 33 \n", "4 1 0 1 Yes 18 \n", "\n", " Immunosupression_bin Immunosupression overall category condition \\\n", "0 No 0 Scleroderma VEDOSS VEDOSS \n", "1 No 0 Healthy Healthy Healthy \n", "2 Yes 1 Scleroderma Scleroderma SSC_low \n", "3 No 0 Scleroderma Scleroderma SSC_high \n", "4 Yes 1 Scleroderma Scleroderma SSC_low \n", "\n", " low_ssc Disease_duration Local_skin_score \n", "0 no 0 0 \n", "1 no 0 0 \n", "2 low 156 0 \n", "3 no 0 2 \n", "4 low 60 1 " ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sample_ids = filtered_data[\"SampleId\"].unique()\n", "metadata_info = metadata[metadata[\"SubjectID\"].isin(sample_ids)]\n", "metadata_info.head()" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Unnamed: 0SeqIdPlateIdPlateRunDateScannerIDPlatePositionSlideIdSubarraySampleIdSampleType...Lung_Fibrosis_yTotal_mRssImmunosupression_bin_yImmunosupressionoverallcategoryconditionlow_sscDisease_duration_yLocal_skin_score_y
0947459999-1PLT246842023-08-13SG16064525, SG17164580C82586338888453COO-363Sample...No0No0SclerodermaVEDOSSVEDOSSno00
1947469999-1PLT246842023-08-13SG16064525, SG17164580C92586338888463PDAR-0335Sample...No0No0HealthyHealthyHealthyno00
2947479999-1PLT246842023-08-13SG16064525, SG17164580E82586338888455COO-005Sample...Yes2Yes1SclerodermaSclerodermaSSC_lowlow1560
3947489999-1PLT246842023-08-13SG16064525, SG17164580F82586338888456COO-429Sample...Yes33No0SclerodermaSclerodermaSSC_highno02
4947499999-1PLT246842023-08-13SG16064525, SG17164580F72586338888446COO-425Sample...Yes18Yes1SclerodermaSclerodermaSSC_lowlow601
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5 rows × 63 columns

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" ], "text/plain": [ " Unnamed: 0 SeqId PlateId PlateRunDate ScannerID \\\n", "0 94745 9999-1 PLT24684 2023-08-13 SG16064525, SG17164580 \n", "1 94746 9999-1 PLT24684 2023-08-13 SG16064525, SG17164580 \n", "2 94747 9999-1 PLT24684 2023-08-13 SG16064525, SG17164580 \n", "3 94748 9999-1 PLT24684 2023-08-13 SG16064525, SG17164580 \n", "4 94749 9999-1 PLT24684 2023-08-13 SG16064525, SG17164580 \n", "\n", " PlatePosition SlideId Subarray SampleId SampleType ... \\\n", "0 C8 258633888845 3 COO-363 Sample ... \n", "1 C9 258633888846 3 PDAR-0335 Sample ... \n", "2 E8 258633888845 5 COO-005 Sample ... \n", "3 F8 258633888845 6 COO-429 Sample ... \n", "4 F7 258633888844 6 COO-425 Sample ... \n", "\n", " Lung_Fibrosis_y Total_mRss Immunosupression_bin_y Immunosupression \\\n", "0 No 0 No 0 \n", "1 No 0 No 0 \n", "2 Yes 2 Yes 1 \n", "3 Yes 33 No 0 \n", "4 Yes 18 Yes 1 \n", "\n", " overall category condition low_ssc Disease_duration_y \\\n", "0 Scleroderma VEDOSS VEDOSS no 0 \n", "1 Healthy Healthy Healthy no 0 \n", "2 Scleroderma Scleroderma SSC_low low 156 \n", "3 Scleroderma Scleroderma SSC_high no 0 \n", "4 Scleroderma Scleroderma SSC_low low 60 \n", "\n", " Local_skin_score_y \n", "0 0 \n", "1 0 \n", "2 0 \n", "3 2 \n", "4 1 \n", "\n", "[5 rows x 63 columns]" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "merged_data = pd.merge(\n", " filtered_data,\n", " metadata_info,\n", " left_on=\"SampleId\",\n", " right_on=\"SubjectID\",\n", " how=\"inner\"\n", " )\n", "merged_data.head()" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 402.5\n", "1 276.0\n", "2 272.3\n", "3 317.7\n", "4 277.5\n", "Name: Intensity, dtype: float64" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "custom_palette = {\n", " \"Healthy\": \"green\",\n", " \"VEDOSS\": \"violet\",\n", " \"SSC_low\": \"cyan\",\n", " \"SSC_high\": \"red\"\n", " }\n", "intensity.head()" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Unnamed: 0SeqIdPlateIdPlateRunDateScannerIDPlatePositionSlideIdSubarraySampleIdSampleType...Lung_Fibrosis_yTotal_mRssImmunosupression_bin_yImmunosupressionoverallcategoryconditionlow_sscDisease_duration_yLocal_skin_score_y
1947469999-1PLT246842023-08-13SG16064525, SG17164580C92586338888463PDAR-0335Sample...No0No0HealthyHealthyHealthyno00
10947559999-1PLT246842023-08-13SG16064525, SG17164580D92586338888464PDAR-0343Sample...No0No0HealthyHealthyHealthyno00
12947579999-1PLT246842023-08-13SG16064525, SG17164580E72586338888445PDAR-0344Sample...No0No0HealthyHealthyHealthyno00
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3 rows × 63 columns

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" ], "text/plain": [ " Unnamed: 0 SeqId PlateId PlateRunDate ScannerID \\\n", "1 94746 9999-1 PLT24684 2023-08-13 SG16064525, SG17164580 \n", "10 94755 9999-1 PLT24684 2023-08-13 SG16064525, SG17164580 \n", "12 94757 9999-1 PLT24684 2023-08-13 SG16064525, SG17164580 \n", "\n", " PlatePosition SlideId Subarray SampleId SampleType ... \\\n", "1 C9 258633888846 3 PDAR-0335 Sample ... \n", "10 D9 258633888846 4 PDAR-0343 Sample ... \n", "12 E7 258633888844 5 PDAR-0344 Sample ... \n", "\n", " Lung_Fibrosis_y Total_mRss Immunosupression_bin_y Immunosupression \\\n", "1 No 0 No 0 \n", "10 No 0 No 0 \n", "12 No 0 No 0 \n", "\n", " overall category condition low_ssc Disease_duration_y \\\n", "1 Healthy Healthy Healthy no 0 \n", "10 Healthy Healthy Healthy no 0 \n", "12 Healthy Healthy Healthy no 0 \n", "\n", " Local_skin_score_y \n", "1 0 \n", "10 0 \n", "12 0 \n", "\n", "[3 rows x 63 columns]" ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_healthy = merged_data[merged_data['condition']== \"Healthy\"]\n", "data_VEDOSS =merged_data[merged_data['condition']== \"VEDOSS\"]\n", "data_SSClow = merged_data[merged_data['condition']== \"SSC_low\"]\n", "data_SSChigh = merged_data[merged_data['condition']== \"SSC_high\"]\n", "data_healthy.head()" ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data_healthy = data_healthy[\"Intensity\"]\n", "data_VEDOSS =data_VEDOSS[\"Intensity\"]\n", "data_SSClow = data_SSClow[\"Intensity\"]\n", "data_SSChigh = data_SSChigh[\"Intensity\"]\n", "data = [data_healthy, data_VEDOSS, data_SSClow, data_SSChigh]\n", "fig = plt.figure(figsize =(10, 7))\n", "\n", "ax = fig.add_axes([0, 0, 1, 1])\n", "\n", "# Creating plot\n", "bp = ax.boxplot(data)\n", "\n", "# show plot\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [], "source": [ "# # Create scatter plot\n", "# plt.figure(figsize=(10, 8))\n", "# sns.scatterplot(\n", "# x=mrss, y=intensity_log, hue=condition, s=100, palette=custom_palette, edgecolor=\"black\"\n", "# )\n", "\n", "# # Add color-coded annotations for each point\n", "# for i in range(len(merged_data)):\n", "# plt.text(\n", "# mrss.iloc[i],\n", "# intensity_log.iloc[i],\n", "# condition.iloc[i], # Text is the condition\n", "# fontsize=10,\n", "# ha=\"center\",\n", "# bbox=dict(\n", "# boxstyle=\"round,pad=0.3\",\n", "# edgecolor=\"black\",\n", "# facecolor=custom_palette[condition.iloc[i]], # Custom color for tag\n", "# alpha=0.7\n", "# ),\n", "# )\n", "\n", "# # Set title and labels\n", "# plt.title(f\"Correlation Plot for {protein_name}\", fontsize=16)\n", "# plt.xlabel(\"MRSS (Linear Scale)\", fontsize=12)\n", "# plt.ylabel(\"Intensity (Logarithmic Scale)\", fontsize=12)\n", "# plt.grid(visible=True, linestyle=\"--\", alpha=0.6)\n", "# plt.legend(title=\"Condition\", loc=\"best\")\n", "# plt.tight_layout()\n", "\n", "# return plt\n", "\n", "\n", "\n", "# Creating dataset" ] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [], "source": [ "# # Load necessary libraries\n", "# library(ggplot2)\n", "\n", "# # Define the colors for the boxplot\n", "# mat_colors <- c('turquoise2', 'red')\n", "\n", "# # Filter data for the specific protein of interest\n", "# SCUBE3 <- subset_proteins_plot %>%\n", "# subset(SeqId == \"16773-29\") %>% # Select the specific protein\n", "# ggplot(aes(Lung_Fibrosis, Intensity, label = SampleId)) + # Map variables to axes\n", "# facet_wrap(~ EntrezGeneSymbol) + # Facet by protein name (optional)\n", "# scale_y_log10() + # Apply log10 scale to y-axis\n", "# geom_boxplot(fill = mat_colors) + # Create boxplot with specified colors\n", "# theme_bw() + # Apply a clean theme\n", "# theme(legend.position = \"top\") + # Adjust legend position\n", "# labs(color = \"Sample\", x = \"Lung Fibrosis\") # Add axis labels\n", "# SCUBE3\n" ] } ], "metadata": { "kernelspec": { "display_name": ".venv", "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": "3.11.4" } }, "nbformat": 4, "nbformat_minor": 2 }