diff --git a/isa-cookbook/content/notebooks/investigation-datascriptor-crossover-study-on-humans.ipynb b/isa-cookbook/content/notebooks/investigation-datascriptor-crossover-study-on-humans.ipynb new file mode 100644 index 000000000..814c26f1d --- /dev/null +++ b/isa-cookbook/content/notebooks/investigation-datascriptor-crossover-study-on-humans.ipynb @@ -0,0 +1,1477 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Create ISA-API Investigation from Datascriptor Study Design configuration\n", + "# Crossover Study with two dietary treatments on dogs\n", + "\n", + "In this notebook I will show you how you can use a study design configuration is JSON format as produce by datascriptor (https://gitlab.com/datascriptor/datascriptor) to generate a single-study ISA investigation and how you can then serialise it in JSON and tabular (i.e. CSV) format.\n", + "\n", + "Or study design configuration consists of:\n", + "- a 4-arm study design. Each arm has 10 subjects\n", + "- subjects are humans. There is an observational factor, named \"status\" with two values: \"healthy\" and \"diseased\"\n", + "- a crossover of two drug treatments, a proper treatment (\"hypertena\" 20 mg/day for 14 days) and a control treatment (\"placebo\" 20 mg/day for 14 days)\n", + "- four non-treatment phases: screen (7 days), washout (14 days) and follow-up (180 days)\n", + "- three sample types collected: blood and saliva\n", + "- three assay types: \n", + " - DNA methylation profiling using nucleic acid sequencing on saliva samples\n", + " - clinical chemistry with marker on blood samples" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Setup\n", + "\n", + "Let's import all the required libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "from time import time\n", + "import os\n", + "import json" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "## ISA-API related imports\n", + "from isatools.model import Investigation, Study" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "## ISA-API create mode related imports\n", + "from isatools.create.model import StudyDesign\n", + "from isatools.create.connectors import generate_study_design\n", + "\n", + "# serializer from ISA Investigation to JSON\n", + "from isatools.isajson import ISAJSONEncoder\n", + "\n", + "# ISA-Tab serialisation\n", + "from isatools import isatab" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "## ISA-API create mode related imports\n", + "from isatools.create import model\n", + "from isatools import isajson" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Load the Study Design JSON configuration\n", + "\n", + "First of all we load the study design configurator with all the specs defined above" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "with open(os.path.abspath(os.path.join(\n", + " \"isa-study-design-as-json\", \"datascriptor\", \"crossover-study-human.json\"\n", + ")), \"r\") as config_file:\n", + " study_design_config = json.load(config_file)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Generate the ISA Study Design from the JSON configuration\n", + "To perform the conversion we just need to use the function `generate_isa_study_design()` (name possibly subject to change, should we drop the \"isa\" and \"datascriptor\" qualifiers?)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "study_design = generate_study_design(study_design_config)\n", + "assert isinstance(study_design, StudyDesign)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Generate the ISA Study from the StudyDesign and embed it into an ISA Investigation\n", + "\n", + "The `StudyDesign.generate_isa_study()` method returns the complete ISA-API `Study` object." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The generation of the study design took 2.02 s.\n" + ] + } + ], + "source": [ + "start = time()\n", + "study = study_design.generate_isa_study()\n", + "end = time()\n", + "print('The generation of the study design took {:.2f} s.'.format(end - start))\n", + "assert isinstance(study, Study)\n", + "investigation = Investigation(identifier='inv01', studies=[study])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Serialize and save the JSON representation of the generated ISA Investigation" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The JSON serialisation of the ISA investigation took 0.55 s.\n" + ] + } + ], + "source": [ + "start = time()\n", + "inv_json = json.dumps(investigation, cls=ISAJSONEncoder, sort_keys=True, indent=4, separators=(',', ': '))\n", + "end = time()\n", + "print('The JSON serialisation of the ISA investigation took {:.2f} s.'.format(end - start))" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "directory = os.path.abspath(os.path.join('output', 'crossover-2-treatments-mice'))\n", + "os.makedirs(directory, exist_ok=True)\n", + "with open(os.path.abspath(os.path.join(directory, 'isa-investigation-crossover-2-treatments-mice.json')), 'w') as out_fp:\n", + " json.dump(json.loads(inv_json), out_fp)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Dump the ISA Investigation to ISA-Tab" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The Tab serialisation of the ISA investigation took 21.58 s.\n" + ] + } + ], + "source": [ + "start = time()\n", + "isatab.dump(investigation, directory)\n", + "end = time()\n", + "print('The Tab serialisation of the ISA investigation took {:.2f} s.'.format(end - start))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To use them on the notebook we can also dump the tables to pandas DataFrames, using the `dump_tables_to_dataframes` function rather than dump" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "dataframes = isatab.dump_tables_to_dataframes(investigation)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(dataframes)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7. Check the correctness of the ISA-Tab DataFrames \n", + "\n", + "We have 1 study file and 2 assay files (one for MS and one for NMR). Let's check the names:" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'s_study_01.txt'" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "'a_AT5_DNA-methylation-profiling_nucleic-acid-sequencing.txt'" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "'a_AT11_clinical-chemistry_marker-panel.txt'" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for key in dataframes.keys():\n", + " display(key)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 7.1 Count of subjects and samples\n", + "\n", + "We have 10 subjects in the each of the 4 arms for a total of 40 subjects.\n", + "\n", + "We collect:\n", + "- 5 blood samples per subject (50 samples * 4 arms = 200 total samples)\n", + "- 2 blood samples per subject (20 samples * 4 arms = 80 total samples)\n", + "\n", + "Across the 4 study arms a total of 280 samples are collected (70 samples per arm)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There are 70 samples in the GRP0 arm (i.e. group)\n", + "There are 70 samples in the GRP1 arm (i.e. group)\n", + "There are 70 samples in the GRP2 arm (i.e. group)\n", + "There are 70 samples in the GRP3 arm (i.e. group)\n" + ] + } + ], + "source": [ + "study_frame = dataframes['s_study_01.txt']\n", + "count_arm0_samples = len(study_frame[study_frame['Source Name'].apply(lambda el: 'GRP0' in el)])\n", + "count_arm1_samples = len(study_frame[study_frame['Source Name'].apply(lambda el: 'GRP1' in el)])\n", + "count_arm2_samples = len(study_frame[study_frame['Source Name'].apply(lambda el: 'GRP2' in el)])\n", + "count_arm3_samples = len(study_frame[study_frame['Source Name'].apply(lambda el: 'GRP3' in el)])\n", + "print(\"There are {} samples in the GRP0 arm (i.e. group)\".format(count_arm0_samples))\n", + "print(\"There are {} samples in the GRP1 arm (i.e. group)\".format(count_arm1_samples))\n", + "print(\"There are {} samples in the GRP2 arm (i.e. group)\".format(count_arm2_samples))\n", + "print(\"There are {} samples in the GRP3 arm (i.e. group)\".format(count_arm3_samples))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 7.2 Study Table Overview\n", + "\n", + "The study table provides an overview of the subjects (sources) and samples" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Source NameCharacteristics[Study Subject]Term Accession NumberCharacteristics[status]Protocol REFParameter Value[Sampling order]Parameter Value[Study cell]DatePerformerSample NameCharacteristics[organism part]Term Accession Number.1Comment[study step with treatment]Factor Value[Sequence Order]Factor Value[DURATION]UnitFactor Value[AGENT]Factor Value[INTENSITY]Unit.1
0GRP0_SBJ01Homo sapienshttp://purl.obolibrary.org/obo/NCBITaxon_9606healthysample collection031A0E32021-06-30UnknownGRP0_SBJ01_A0E3_SMP-Blood-Sample-1Blood Samplehttp://purl.obolibrary.org/obo/NCIT_C17610YES314daysplacebo20.0mg/day
1GRP0_SBJ01Homo sapienshttp://purl.obolibrary.org/obo/NCBITaxon_9606healthysample collection001A0E12021-06-30UnknownGRP0_SBJ01_A0E1_SMP-Saliva-Sample-1Saliva Samplehttp://purl.obolibrary.org/obo/NCIT_C174119YES114dayshypertena20.0mg/day
2GRP0_SBJ01Homo sapienshttp://purl.obolibrary.org/obo/NCBITaxon_9606healthysample collection042A0E42021-06-30UnknownGRP0_SBJ01_A0E4_SMP-Blood-Sample-2Blood Samplehttp://purl.obolibrary.org/obo/NCIT_C17610NO4180days
3GRP0_SBJ01Homo sapienshttp://purl.obolibrary.org/obo/NCBITaxon_9606healthysample collection011A0E12021-06-30UnknownGRP0_SBJ01_A0E1_SMP-Blood-Sample-1Blood Samplehttp://purl.obolibrary.org/obo/NCIT_C17610YES114dayshypertena20.0mg/day
4GRP0_SBJ01Homo sapienshttp://purl.obolibrary.org/obo/NCBITaxon_9606healthysample collection043A0E42021-06-30UnknownGRP0_SBJ01_A0E4_SMP-Blood-Sample-3Blood Samplehttp://purl.obolibrary.org/obo/NCIT_C17610NO4180days
............................................................
275GRP3_SBJ10Homo sapienshttp://purl.obolibrary.org/obo/NCBITaxon_9606diseasedsample collection242A3E32021-06-30UnknownGRP3_SBJ10_A3E3_SMP-Blood-Sample-1Blood Samplehttp://purl.obolibrary.org/obo/NCIT_C17610YES314dayshypertena20.0mg/day
276GRP3_SBJ10Homo sapienshttp://purl.obolibrary.org/obo/NCBITaxon_9606diseasedsample collection256A3E42021-06-30UnknownGRP3_SBJ10_A3E4_SMP-Blood-Sample-3Blood Samplehttp://purl.obolibrary.org/obo/NCIT_C17610NO4180days
277GRP3_SBJ10Homo sapienshttp://purl.obolibrary.org/obo/NCBITaxon_9606diseasedsample collection254A3E42021-06-30UnknownGRP3_SBJ10_A3E4_SMP-Blood-Sample-1Blood Samplehttp://purl.obolibrary.org/obo/NCIT_C17610NO4180days
278GRP3_SBJ10Homo sapienshttp://purl.obolibrary.org/obo/NCBITaxon_9606diseasedsample collection232A3E32021-06-30UnknownGRP3_SBJ10_A3E3_SMP-Saliva-Sample-1Saliva Samplehttp://purl.obolibrary.org/obo/NCIT_C174119YES314dayshypertena20.0mg/day
279GRP3_SBJ10Homo sapienshttp://purl.obolibrary.org/obo/NCBITaxon_9606diseasedsample collection212A3E12021-06-30UnknownGRP3_SBJ10_A3E1_SMP-Saliva-Sample-1Saliva Samplehttp://purl.obolibrary.org/obo/NCIT_C174119YES114daysplacebo20.0mg/day
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280 rows × 19 columns

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" + ], + "text/plain": [ + " Source Name Characteristics[Study Subject] \\\n", + "0 GRP0_SBJ01 Homo sapiens \n", + "1 GRP0_SBJ01 Homo sapiens \n", + "2 GRP0_SBJ01 Homo sapiens \n", + "3 GRP0_SBJ01 Homo sapiens \n", + "4 GRP0_SBJ01 Homo sapiens \n", + ".. ... ... \n", + "275 GRP3_SBJ10 Homo sapiens \n", + "276 GRP3_SBJ10 Homo sapiens \n", + "277 GRP3_SBJ10 Homo sapiens \n", + "278 GRP3_SBJ10 Homo sapiens \n", + "279 GRP3_SBJ10 Homo sapiens \n", + "\n", + " Term Accession Number Characteristics[status] \\\n", + "0 http://purl.obolibrary.org/obo/NCBITaxon_9606 healthy \n", + "1 http://purl.obolibrary.org/obo/NCBITaxon_9606 healthy \n", + "2 http://purl.obolibrary.org/obo/NCBITaxon_9606 healthy \n", + "3 http://purl.obolibrary.org/obo/NCBITaxon_9606 healthy \n", + "4 http://purl.obolibrary.org/obo/NCBITaxon_9606 healthy \n", + ".. ... ... \n", + "275 http://purl.obolibrary.org/obo/NCBITaxon_9606 diseased \n", + "276 http://purl.obolibrary.org/obo/NCBITaxon_9606 diseased \n", + "277 http://purl.obolibrary.org/obo/NCBITaxon_9606 diseased \n", + "278 http://purl.obolibrary.org/obo/NCBITaxon_9606 diseased \n", + "279 http://purl.obolibrary.org/obo/NCBITaxon_9606 diseased \n", + "\n", + " Protocol REF Parameter Value[Sampling order] \\\n", + "0 sample collection 031 \n", + "1 sample collection 001 \n", + "2 sample collection 042 \n", + "3 sample collection 011 \n", + "4 sample collection 043 \n", + ".. ... ... \n", + "275 sample collection 242 \n", + "276 sample collection 256 \n", + "277 sample collection 254 \n", + "278 sample collection 232 \n", + "279 sample collection 212 \n", + "\n", + " Parameter Value[Study cell] Date Performer \\\n", + "0 A0E3 2021-06-30 Unknown \n", + "1 A0E1 2021-06-30 Unknown \n", + "2 A0E4 2021-06-30 Unknown \n", + "3 A0E1 2021-06-30 Unknown \n", + "4 A0E4 2021-06-30 Unknown \n", + ".. ... ... ... \n", + "275 A3E3 2021-06-30 Unknown \n", + "276 A3E4 2021-06-30 Unknown \n", + "277 A3E4 2021-06-30 Unknown \n", + "278 A3E3 2021-06-30 Unknown \n", + "279 A3E1 2021-06-30 Unknown \n", + "\n", + " Sample Name Characteristics[organism part] \\\n", + "0 GRP0_SBJ01_A0E3_SMP-Blood-Sample-1 Blood Sample \n", + "1 GRP0_SBJ01_A0E1_SMP-Saliva-Sample-1 Saliva Sample \n", + "2 GRP0_SBJ01_A0E4_SMP-Blood-Sample-2 Blood Sample \n", + "3 GRP0_SBJ01_A0E1_SMP-Blood-Sample-1 Blood Sample \n", + "4 GRP0_SBJ01_A0E4_SMP-Blood-Sample-3 Blood Sample \n", + ".. ... ... \n", + "275 GRP3_SBJ10_A3E3_SMP-Blood-Sample-1 Blood Sample \n", + "276 GRP3_SBJ10_A3E4_SMP-Blood-Sample-3 Blood Sample \n", + "277 GRP3_SBJ10_A3E4_SMP-Blood-Sample-1 Blood Sample \n", + "278 GRP3_SBJ10_A3E3_SMP-Saliva-Sample-1 Saliva Sample \n", + "279 GRP3_SBJ10_A3E1_SMP-Saliva-Sample-1 Saliva Sample \n", + "\n", + " Term Accession Number.1 \\\n", + "0 http://purl.obolibrary.org/obo/NCIT_C17610 \n", + "1 http://purl.obolibrary.org/obo/NCIT_C174119 \n", + "2 http://purl.obolibrary.org/obo/NCIT_C17610 \n", + "3 http://purl.obolibrary.org/obo/NCIT_C17610 \n", + "4 http://purl.obolibrary.org/obo/NCIT_C17610 \n", + ".. ... \n", + "275 http://purl.obolibrary.org/obo/NCIT_C17610 \n", + "276 http://purl.obolibrary.org/obo/NCIT_C17610 \n", + "277 http://purl.obolibrary.org/obo/NCIT_C17610 \n", + "278 http://purl.obolibrary.org/obo/NCIT_C174119 \n", + "279 http://purl.obolibrary.org/obo/NCIT_C174119 \n", + "\n", + " Comment[study step with treatment] Factor Value[Sequence Order] \\\n", + "0 YES 3 \n", + "1 YES 1 \n", + "2 NO 4 \n", + "3 YES 1 \n", + "4 NO 4 \n", + ".. ... ... \n", + "275 YES 3 \n", + "276 NO 4 \n", + "277 NO 4 \n", + "278 YES 3 \n", + "279 YES 1 \n", + "\n", + " Factor Value[DURATION] Unit Factor Value[AGENT] Factor Value[INTENSITY] \\\n", + "0 14 days placebo 20.0 \n", + "1 14 days hypertena 20.0 \n", + "2 180 days \n", + "3 14 days hypertena 20.0 \n", + "4 180 days \n", + ".. ... ... ... ... \n", + "275 14 days hypertena 20.0 \n", + "276 180 days \n", + "277 180 days \n", + "278 14 days hypertena 20.0 \n", + "279 14 days placebo 20.0 \n", + "\n", + " Unit.1 \n", + "0 mg/day \n", + "1 mg/day \n", + "2 \n", + "3 mg/day \n", + "4 \n", + ".. ... \n", + "275 mg/day \n", + "276 \n", + "277 \n", + "278 mg/day \n", + "279 mg/day \n", + "\n", + "[280 rows x 19 columns]" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "study_frame" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 7.3 First Assay: DNA Methylation Profiling using nucleic acid sequencing\n", + "\n", + "This assay takes urine samples as input" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Sample NameComment[study step with treatment]Protocol REFParameter Value[cross linking]Parameter Value[DNA fragmentation]Parameter Value[DNA fragment size]Parameter Value[immunoprecipitation antibody]PerformerExtract NameCharacteristics[extract type]Protocol REF.1Parameter Value[instrument]Parameter Value[library_orientation]Parameter Value[library_strategy]Parameter Value[library_selection]Parameter Value[multiplex identifier]Performer.1Raw Data File
0GRP0_SBJ01_A0E1_SMP-Saliva-Sample-1YESextractiondi-tert-butyl peroxidenebulizationadUnknownAT5-S81-Extract-R1gDNAlibrary_preparationGridIONsingleMBD-SeqMFfUnknownAT5-S81-raw_data_file-R2.raw
1GRP0_SBJ01_A0E1_SMP-Saliva-Sample-1YESextractionuv-lightnebulizationadUnknownAT5-S1-Extract-R1DNAlibrary_preparationGridIONpairedMBD-SeqMFfUnknownAT5-S1-raw_data_file-R1.raw
2GRP0_SBJ01_A0E1_SMP-Saliva-Sample-1YESextractiondi-tert-butyl peroxidenebulizationadUnknownAT5-S81-Extract-R1gDNAlibrary_preparationGridIONpairedMBD-SeqMFfUnknownAT5-S81-raw_data_file-R3.raw
3GRP0_SBJ01_A0E1_SMP-Saliva-Sample-1YESextractionuv-lightnebulizationadUnknownAT5-S1-Extract-R1DNAlibrary_preparationGridIONpairedMBD-SeqMFfUnknownAT5-S1-raw_data_file-R2.raw
4GRP0_SBJ01_A0E1_SMP-Saliva-Sample-1YESextractiondi-tert-butyl peroxidenebulizationadUnknownAT5-S81-Extract-R1gDNAlibrary_preparationGridIONpairedMBD-SeqMFfUnknownAT5-S81-raw_data_file-R4.raw
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1275GRP3_SBJ10_A3E3_SMP-Saliva-Sample-1YESextractiondi-tert-butyl peroxidenebulizationadUnknownAT5-S152-Extract-R1gDNAlibrary_preparationGridIONsingleMBD-SeqMFfUnknownAT5-S152-raw_data_file-R1.raw
1276GRP3_SBJ10_A3E3_SMP-Saliva-Sample-1YESextractiondi-tert-butyl peroxidenebulizationadUnknownAT5-S152-Extract-R2DNAlibrary_preparationGridIONsingleMBD-SeqMFfUnknownAT5-S152-raw_data_file-R5.raw
1277GRP3_SBJ10_A3E3_SMP-Saliva-Sample-1YESextractiondi-tert-butyl peroxidenebulizationadUnknownAT5-S152-Extract-R2DNAlibrary_preparationGridIONsingleMBD-SeqMFfUnknownAT5-S152-raw_data_file-R6.raw
1278GRP3_SBJ10_A3E3_SMP-Saliva-Sample-1YESextractiondi-tert-butyl peroxidenebulizationadUnknownAT5-S152-Extract-R1gDNAlibrary_preparationGridIONpairedMBD-SeqMFfUnknownAT5-S152-raw_data_file-R3.raw
1279GRP3_SBJ10_A3E3_SMP-Saliva-Sample-1YESextractionuv-lightnebulizationadUnknownAT5-S72-Extract-R2gDNAlibrary_preparationGridIONsingleMBD-SeqMFfUnknownAT5-S72-raw_data_file-R6.raw
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1280 rows × 18 columns

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" + ], + "text/plain": [ + " Sample Name Comment[study step with treatment] \\\n", + "0 GRP0_SBJ01_A0E1_SMP-Saliva-Sample-1 YES \n", + "1 GRP0_SBJ01_A0E1_SMP-Saliva-Sample-1 YES \n", + "2 GRP0_SBJ01_A0E1_SMP-Saliva-Sample-1 YES \n", + "3 GRP0_SBJ01_A0E1_SMP-Saliva-Sample-1 YES \n", + "4 GRP0_SBJ01_A0E1_SMP-Saliva-Sample-1 YES \n", + "... ... ... \n", + "1275 GRP3_SBJ10_A3E3_SMP-Saliva-Sample-1 YES \n", + "1276 GRP3_SBJ10_A3E3_SMP-Saliva-Sample-1 YES \n", + "1277 GRP3_SBJ10_A3E3_SMP-Saliva-Sample-1 YES \n", + "1278 GRP3_SBJ10_A3E3_SMP-Saliva-Sample-1 YES \n", + "1279 GRP3_SBJ10_A3E3_SMP-Saliva-Sample-1 YES \n", + "\n", + " Protocol REF Parameter Value[cross linking] \\\n", + "0 extraction di-tert-butyl peroxide \n", + "1 extraction uv-light \n", + "2 extraction di-tert-butyl peroxide \n", + "3 extraction uv-light \n", + "4 extraction di-tert-butyl peroxide \n", + "... ... ... \n", + "1275 extraction di-tert-butyl peroxide \n", + "1276 extraction di-tert-butyl peroxide \n", + "1277 extraction di-tert-butyl peroxide \n", + "1278 extraction di-tert-butyl peroxide \n", + "1279 extraction uv-light \n", + "\n", + " Parameter Value[DNA fragmentation] Parameter Value[DNA fragment size] \\\n", + "0 nebulization a \n", + "1 nebulization a \n", + "2 nebulization a \n", + "3 nebulization a \n", + "4 nebulization a \n", + "... ... ... \n", + "1275 nebulization a \n", + "1276 nebulization a \n", + "1277 nebulization a \n", + "1278 nebulization a \n", + "1279 nebulization a \n", + "\n", + " Parameter Value[immunoprecipitation antibody] Performer \\\n", + "0 d Unknown \n", + "1 d Unknown \n", + "2 d Unknown \n", + "3 d Unknown \n", + "4 d Unknown \n", + "... ... ... \n", + "1275 d Unknown \n", + "1276 d Unknown \n", + "1277 d Unknown \n", + "1278 d Unknown \n", + "1279 d Unknown \n", + "\n", + " Extract Name Characteristics[extract type] Protocol REF.1 \\\n", + "0 AT5-S81-Extract-R1 gDNA library_preparation \n", + "1 AT5-S1-Extract-R1 DNA library_preparation \n", + "2 AT5-S81-Extract-R1 gDNA library_preparation \n", + "3 AT5-S1-Extract-R1 DNA library_preparation \n", + "4 AT5-S81-Extract-R1 gDNA library_preparation \n", + "... ... ... ... \n", + "1275 AT5-S152-Extract-R1 gDNA library_preparation \n", + "1276 AT5-S152-Extract-R2 DNA library_preparation \n", + "1277 AT5-S152-Extract-R2 DNA library_preparation \n", + "1278 AT5-S152-Extract-R1 gDNA library_preparation \n", + "1279 AT5-S72-Extract-R2 gDNA library_preparation \n", + "\n", + " Parameter Value[instrument] Parameter Value[library_orientation] \\\n", + "0 GridION single \n", + "1 GridION paired \n", + "2 GridION paired \n", + "3 GridION paired \n", + "4 GridION paired \n", + "... ... ... \n", + "1275 GridION single \n", + "1276 GridION single \n", + "1277 GridION single \n", + "1278 GridION paired \n", + "1279 GridION single \n", + "\n", + " Parameter Value[library_strategy] Parameter Value[library_selection] \\\n", + "0 MBD-Seq MF \n", + "1 MBD-Seq MF \n", + "2 MBD-Seq MF \n", + "3 MBD-Seq MF \n", + "4 MBD-Seq MF \n", + "... ... ... \n", + "1275 MBD-Seq MF \n", + "1276 MBD-Seq MF \n", + "1277 MBD-Seq MF \n", + "1278 MBD-Seq MF \n", + "1279 MBD-Seq MF \n", + "\n", + " Parameter Value[multiplex identifier] Performer.1 \\\n", + "0 f Unknown \n", + "1 f Unknown \n", + "2 f Unknown \n", + "3 f Unknown \n", + "4 f Unknown \n", + "... ... ... \n", + "1275 f Unknown \n", + "1276 f Unknown \n", + "1277 f Unknown \n", + "1278 f Unknown \n", + "1279 f Unknown \n", + "\n", + " Raw Data File \n", + "0 AT5-S81-raw_data_file-R2.raw \n", + "1 AT5-S1-raw_data_file-R1.raw \n", + "2 AT5-S81-raw_data_file-R3.raw \n", + "3 AT5-S1-raw_data_file-R2.raw \n", + "4 AT5-S81-raw_data_file-R4.raw \n", + "... ... \n", + "1275 AT5-S152-raw_data_file-R1.raw \n", + "1276 AT5-S152-raw_data_file-R5.raw \n", + "1277 AT5-S152-raw_data_file-R6.raw \n", + "1278 AT5-S152-raw_data_file-R3.raw \n", + "1279 AT5-S72-raw_data_file-R6.raw \n", + "\n", + "[1280 rows x 18 columns]" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataframes['a_AT5_DNA-methylation-profiling_nucleic-acid-sequencing.txt']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 7.3.1 Nucleic acid sequencing stats Stats\n", + "\n", + "For this assay we have 280 urine samples. 280 DNA extracts are extracted from the samples. The 280 extracts are subsequently labeled. For each labeled extract, 4 mass.spec analyses are run (using Agilent QTQF 6510, positive acquisition mode, 2 replicates each for LC and FIA injection mode), for a total of 1120 mass. spec. processes and 1120 raw spectral data files" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Sample Name 80\n", + "Comment[study step with treatment] 1\n", + "Protocol REF 1\n", + "Parameter Value[cross linking] 2\n", + "Parameter Value[DNA fragmentation] 1\n", + "Parameter Value[DNA fragment size] 1\n", + "Parameter Value[immunoprecipitation antibody] 1\n", + "Performer 1\n", + "Extract Name 320\n", + "Characteristics[extract type] 2\n", + "Protocol REF.1 1\n", + "Parameter Value[instrument] 1\n", + "Parameter Value[library_orientation] 2\n", + "Parameter Value[library_strategy] 1\n", + "Parameter Value[library_selection] 1\n", + "Parameter Value[multiplex identifier] 1\n", + "Performer.1 1\n", + "Raw Data File 1280\n", + "dtype: int64" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataframes['a_AT5_DNA-methylation-profiling_nucleic-acid-sequencing.txt'].nunique(axis=0, dropna=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 7.4 Second Assay: Clinical Chemistry Marker Panel\n", + "\n", + "This assay takes blood samples as input" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Sample NameComment[study step with treatment]Protocol REFPerformerRaw Data File
0GRP0_SBJ01_A0E1_SMP-Blood-Sample-1YESsample preparationUnknownAT11-S1-raw_data_file-R1
1GRP0_SBJ01_A0E3_SMP-Blood-Sample-1YESsample preparationUnknownAT11-S11-raw_data_file-R1
2GRP0_SBJ01_A0E4_SMP-Blood-Sample-1NOsample preparationUnknownAT11-S21-raw_data_file-R1
3GRP0_SBJ01_A0E4_SMP-Blood-Sample-2NOsample preparationUnknownAT11-S22-raw_data_file-R1
4GRP0_SBJ01_A0E4_SMP-Blood-Sample-3NOsample preparationUnknownAT11-S23-raw_data_file-R1
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195GRP3_SBJ10_A3E1_SMP-Blood-Sample-1YESsample preparationUnknownAT11-S152-raw_data_file-R1
196GRP3_SBJ10_A3E3_SMP-Blood-Sample-1YESsample preparationUnknownAT11-S162-raw_data_file-R1
197GRP3_SBJ10_A3E4_SMP-Blood-Sample-1NOsample preparationUnknownAT11-S174-raw_data_file-R1
198GRP3_SBJ10_A3E4_SMP-Blood-Sample-2NOsample preparationUnknownAT11-S175-raw_data_file-R1
199GRP3_SBJ10_A3E4_SMP-Blood-Sample-3NOsample preparationUnknownAT11-S176-raw_data_file-R1
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\n", + "
" + ], + "text/plain": [ + " Sample Name Comment[study step with treatment] \\\n", + "0 GRP0_SBJ01_A0E1_SMP-Blood-Sample-1 YES \n", + "1 GRP0_SBJ01_A0E3_SMP-Blood-Sample-1 YES \n", + "2 GRP0_SBJ01_A0E4_SMP-Blood-Sample-1 NO \n", + "3 GRP0_SBJ01_A0E4_SMP-Blood-Sample-2 NO \n", + "4 GRP0_SBJ01_A0E4_SMP-Blood-Sample-3 NO \n", + ".. ... ... \n", + "195 GRP3_SBJ10_A3E1_SMP-Blood-Sample-1 YES \n", + "196 GRP3_SBJ10_A3E3_SMP-Blood-Sample-1 YES \n", + "197 GRP3_SBJ10_A3E4_SMP-Blood-Sample-1 NO \n", + "198 GRP3_SBJ10_A3E4_SMP-Blood-Sample-2 NO \n", + "199 GRP3_SBJ10_A3E4_SMP-Blood-Sample-3 NO \n", + "\n", + " Protocol REF Performer Raw Data File \n", + "0 sample preparation Unknown AT11-S1-raw_data_file-R1 \n", + "1 sample preparation Unknown AT11-S11-raw_data_file-R1 \n", + "2 sample preparation Unknown AT11-S21-raw_data_file-R1 \n", + "3 sample preparation Unknown AT11-S22-raw_data_file-R1 \n", + "4 sample preparation Unknown AT11-S23-raw_data_file-R1 \n", + ".. ... ... ... \n", + "195 sample preparation Unknown AT11-S152-raw_data_file-R1 \n", + "196 sample preparation Unknown AT11-S162-raw_data_file-R1 \n", + "197 sample preparation Unknown AT11-S174-raw_data_file-R1 \n", + "198 sample preparation Unknown AT11-S175-raw_data_file-R1 \n", + "199 sample preparation Unknown AT11-S176-raw_data_file-R1 \n", + "\n", + "[200 rows x 5 columns]" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataframes['a_AT11_clinical-chemistry_marker-panel.txt']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 7.4.1 Marker Panel Stats\n", + "\n", + "For this assay we use 320 blood samples. For each sample three chemical marker assays are run, producing a total of 960 sample preparation processes and 960 raw data files" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Sample Name 200\n", + "Comment[study step with treatment] 2\n", + "Protocol REF 1\n", + "Performer 1\n", + "Raw Data File 200\n", + "dtype: int64" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataframes['a_AT11_clinical-chemistry_marker-panel.txt'].nunique(axis=0, dropna=True)" + ] + } + ], + "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": "3.8.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/isa-cookbook/content/notebooks/investigation-from-datascriptor-config-observational-variables-and-ontology-annotation.ipynb b/isa-cookbook/content/notebooks/investigation-from-datascriptor-config-observational-variables-and-ontology-annotation.ipynb deleted file mode 100644 index a9cb09071..000000000 --- a/isa-cookbook/content/notebooks/investigation-from-datascriptor-config-observational-variables-and-ontology-annotation.ipynb +++ /dev/null @@ -1,386 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Create ISA-API Investigation from Datascriptor Study Design configuration\n", - "\n", - "In this notebook I will show you how you can use a study design configuration is JSON format as produce by datascriptor (https://gitlab.com/datascriptor/datascriptor) to generate a single-study ISA investigation and how you can then serialise it in JSON and tabular (i.e. CSV) format.\n", - "\n", - "Or study design configuration consists of:\n", - "- a 6-arm study design. Each arm has 10 subjects\n", - "- there is an observational factor with 3 values, which is age_group (young, middle-aged, elderly)\n", - "- a crossover of two treatments, a drug treatment and a biological treatment\n", - "- three non-treatment phases: screen, washout and follow-up\n", - "- two sample types colllected: blood and saliva\n", - "- two assay types: \n", - " - metabolite profiling through mass spectrometry on the saliva sample. The mass spec will be run on a \"Agilent QTQF 6510\" instrument, testing both \"FIA\" and \"LC\" injection modes, and \"positive\" acquisition mode.\n", - " - metabolite profiling through NMR spectroscopy on the blood samples. The NMR will be run on a \"Bruker Avance II 1 GHz\" instrument, on \"1D 1H NMR\" acquisition mode, testing both \"CPGM\" amd \"TOCSY\" pulse sequences." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Setup\n", - "\n", - "Let's import all the required libraries" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# If executing the notebooks on `Google Colab`,uncomment the following command \n", - "# and run it to install the required python libraries. Also, make the test datasets available.\n", - "\n", - "# !pip install -r requirements.txt" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from time import time\n", - "import os\n", - "import json" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "## ISA-API related imports\n", - "from isatools.model import Investigation, Study" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "## ISA-API create mode related imports\n", - "from isatools.create.model import StudyDesign\n", - "from isatools.create.connectors import generate_study_design\n", - "\n", - "# serializer from ISA Investigation to JSON\n", - "from isatools.isajson import ISAJSONEncoder\n", - "\n", - "# ISA-Tab serialisation\n", - "from isatools import isatab" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "## ISA-API create mode related imports\n", - "from isatools.create import model\n", - "from isatools import isajson" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Load the Study Design JSON configuration\n", - "\n", - "First of all we load the study design configurator with all the specs defined above" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "with open(os.path.abspath(os.path.join(\n", - " \"isa-study-design-as-json/datascriptor\", \"crossover-study-design-4-arms-blood-derma-nmr-ms-chipseq.json\")), \"r\") as config_file:\n", - " study_design_config = json.load(config_file)\n", - " \n", - "study_design_config" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Generate the ISA Study Design from the JSON configuration\n", - "To perform the conversion we just need to use the function `generate_isa_study_design_from_config` (name possibly subject to change, should we drop the \"isa\" and \"datascriptor\" qualifiers?)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "study_design = generate_study_design(study_design_config)\n", - "assert isinstance(study_design, StudyDesign)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Generate the ISA Study from the StudyDesign and embed it into an ISA Investigation\n", - "\n", - "The `StudyDesign.generate_isa_study()` method returns the complete ISA-API `Study` object." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "start = time()\n", - "study = study_design.generate_isa_study()\n", - "end = time()\n", - "print('The generation of the study design took {:.2f} s.'.format(end - start))\n", - "assert isinstance(study, Study)\n", - "investigation = Investigation(studies=[study])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5. Serialize and save the JSON representation of the generated ISA Investigation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "start = time()\n", - "inv_json = json.dumps(investigation, cls=ISAJSONEncoder, sort_keys=True, indent=4, separators=(',', ': '))\n", - "end = time()\n", - "print('The JSON serialisation of the ISA investigation took {:.2f} s.'.format(end - start))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "directory = os.path.abspath(os.path.join('output'))\n", - "if not os.path.exists(directory):\n", - " os.makedirs(directory)\n", - "with open(os.path.abspath(os.path.join('output','isa-investigation-2-arms-nmr-ms.json')), 'w') as out_fp:\n", - " json.dump(json.loads(inv_json), out_fp)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 6. Dump the ISA Investigation to ISA-Tab" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To use them on the notebook we can also dump the tables to pandas DataFrames, using the `dump_tables_to_dataframes` function rather than dump" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "start = time()\n", - "dataframes = isatab.dump_tables_to_dataframes(investigation)\n", - "end = time()\n", - "print('The Tab serialisation of the ISA investigation took {:.2f} s.'.format(end - start))\n", - "\n", - "# alternatively, if you just want to write the isatab files to file, you can run\n", - "\n", - "# isatab.dump(investigation, os.path.abspath(os.path.join('notebook-output/isa-study-from-design-config')))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "len(dataframes)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 7. Check the correctness of the ISA-Tab DataFrames \n", - "\n", - "We have 1 study file and 2 assay files (one for MS and one for NMR). Let's check the names:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for key in dataframes.keys():\n", - " display(key)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 7.1 Count of subjects and samples\n", - "\n", - "We have 10 subjects in the each of the six arms for a total of 60 subjects. 5 blood samples per subject are collected (1 in treatment 1 phase, 1 in treatment, and 3 in the follow-up phase) for a total of 300 blood samples. These will undergo the NMR assay. We have 4 saliva samples per subject (1 during screen and 3 during follow-up) for a total of 240 saliva samples. These will undergo the \"mass spcetrometry\" assay." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "study_frame = dataframes['s_study_01.txt']\n", - "count_arm0_samples = len(study_frame[study_frame['Source Name'].apply(lambda el: 'GRP0' in el)])\n", - "count_arm2_samples = len(study_frame[study_frame['Source Name'].apply(lambda el: 'GRP1' in el)])\n", - "count_arm3_samples = len(study_frame[study_frame['Source Name'].apply(lambda el: 'GRP2' in el)])\n", - "count_arm4_samples = len(study_frame[study_frame['Source Name'].apply(lambda el: 'GRP3' in el)])\n", - "count_arm3_samples = len(study_frame[study_frame['Source Name'].apply(lambda el: 'GRP4' in el)])\n", - "count_arm4_samples = len(study_frame[study_frame['Source Name'].apply(lambda el: 'GRP5' in el)])\n", - "print(\"There are {} samples in the GRP0 arm (i.e. group)\".format(count_arm0_samples))\n", - "print(\"There are {} samples in the GRP2 arm (i.e. group)\".format(count_arm2_samples))\n", - "print(\"There are {} samples in the GRP3 arm (i.e. group)\".format(count_arm3_samples))\n", - "print(\"There are {} samples in the GRP4 arm (i.e. group)\".format(count_arm4_samples))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 7.1 Overview of the Mass Spec assay table\n", - "\n", - "For the mass. spec. assay table, we have 240 (saliva) samples, 480 extracts (2 per sample, \"lipids\" and \"polar\" fractions), 960 labeled extracts (2 per extract, as \"#replicates\" is 2) and 3840 mass spec protocols + 3840 output files (4 per labeled extract as we do 2 technical replicates with 2 protocol parameter combinations `[\"Agilent QTQF 6510\", \"FIA\", \"positive mode\"]` and `[\"Agilent QTQF 6510\", \"LC\", \"positive mode\"]`)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dataframes['a_AT0_metabolite-profiling_mass-spectrometry.txt']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Overview of the NMR assay table\n", - "\n", - "For the NMR assay table, we have 300 (blood) samples, 1200 extracts (4 per sample, 2 extraction replicates of the \"supernatant\" and \"pellet\" fractions) and 4800 NMR protocols + 4800 output files (4 per extract as we do 2 technical replicates with 2 protocol parameter combinations `[\"Bruker Avance II 1 GHz\", \"1D 1H NMR\", \"CPGM\"]` and `[\"Bruker Avance II 1 GHz\", \"1D 1H NMR\", \"TOCSY\"]`)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dataframes['a_AT2_metabolite-profiling_NMR-spectroscopy.txt']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Overview of the Chip-Seq assay table\n", - "\n", - "For the Chip-Seq assay table, we have 300 (blood) samples, 1200 extracts (4 per sample, 2 extraction replicates of the \"supernatant\" and \"pellet\" fractions)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dataframes['a_AT16_protein-DNA-binding-site-identification_nucleic-acid-sequencing.txt']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dataframes['a_AT0_metabolite-profiling_mass-spectrometry.txt'].nunique(axis=0, dropna=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dataframes['a_AT2_metabolite-profiling_NMR-spectroscopy.txt'].nunique(axis=0, dropna=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## About this notebook\n", - "\n", - "- authors: philippe.rocca-serra@oerc.ox.ac.uk, massimiliano.izzo@oerc.ox.ac.uk\n", - "- license: CC-BY 4.0\n", - "- support: isatools@googlegroups.com\n", - "- issue tracker: https://github.com/ISA-tools/isa-api/issues" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.1" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/isa-cookbook/content/notebooks/isa-study-design-as-json/datascriptor/crossover-study-design-4-arms-blood-derma-nmr-ms-chipseq.json b/isa-cookbook/content/notebooks/isa-study-design-as-json/datascriptor/crossover-study-design-4-arms-blood-derma-nmr-ms-chipseq.json deleted file mode 100644 index 47cdbcfd5..000000000 --- a/isa-cookbook/content/notebooks/isa-study-design-as-json/datascriptor/crossover-study-design-4-arms-blood-derma-nmr-ms-chipseq.json +++ /dev/null @@ -1,1615 +0,0 @@ -{ - "name": "Chained protocols study", - "subjectType": "Homo sapiens", - "subjectSize": 10, - "designType": { - "term": "crossover design", - "id": "OBI:0500003", - "iri": "http://purl.obolibrary.org/obo/OBI_0500003", - "label": "Study subjects receive repeated treatments", - "value": "crossover" - }, - "observationalFactors": [ - { - "name": "sex", - "values": [ - "M", - "F" - ], - "isQuantitative": false, - "unit": null - }, - { - "name": "condition", - "values": [ - "healthy", - "diseased" - ], - "isQuantitative": false, - "unit": null - } - ], - "subjectGroups": { - "selected": [ - { - "name": "SubjectGroup_0", - "type": "Homo sapiens", - "characteristics": [ - { - "name": "sex", - "value": "M", - "unit": null, - "isQuantitative": false - }, - { - "name": "condition", - "value": "diseased", - "unit": null, - "isQuantitative": false - } - ] - }, - { - "name": "SubjectGroup_1", - "type": "Homo sapiens", - "characteristics": [ - { - "name": "sex", - "value": "M", - "unit": null, - "isQuantitative": false - }, - { - "name": "condition", - "value": "healthy", - "unit": null, - "isQuantitative": false - } - ] - }, - { - "name": "SubjectGroup_2", - "type": "Homo sapiens", - "characteristics": [ - { - "name": "sex", - "value": "F", - "unit": null, - "isQuantitative": false - }, - { - "name": "condition", - "value": "diseased", - "unit": null, - "isQuantitative": false - } - ] - }, - { - "name": "SubjectGroup_3", - "type": "Homo sapiens", - "characteristics": [ - { - "name": "sex", - "value": "F", - "unit": null, - "isQuantitative": false - }, - { - "name": "condition", - "value": "healthy", - "unit": null, - "isQuantitative": false - } - ] - } - ], - "unselected": [ - { - "name": "SubjectGroup_1", - "type": "Homo sapiens", - "characteristics": [ - { - "name": "sex", - "value": "M", - "unit": null, - "isQuantitative": false - }, - { - "name": "condition", - "value": "healthy", - "unit": null, - "isQuantitative": false - } - ] - }, - { - "name": "SubjectGroup_0", - "type": "Homo sapiens", - "characteristics": [ - { - "name": "sex", - "value": "M", - "unit": null, - "isQuantitative": false - }, - { - "name": "condition", - "value": "diseased", - "unit": null, - "isQuantitative": false - } - ] - }, - { - "name": "SubjectGroup_0", - "type": "Homo sapiens", - "characteristics": [ - { - "name": "sex", - "value": "M", - "unit": null, - "isQuantitative": false - }, - { - "name": "condition", - "value": "diseased", - "unit": null, - "isQuantitative": false - } - ] - }, - { - "name": "SubjectGroup_0", - "type": "Homo sapiens", - "characteristics": [ - { - "name": "sex", - "value": "M", - "unit": null, - "isQuantitative": false - }, - { - "name": "condition", - "value": "diseased", - "unit": null, - "isQuantitative": false - } - ] - } - ] - }, - "treatmentPlan": { - "screen": { - "selected": true, - "name": "screen", - "duration": 7, - "durationUnit": "days" - }, - "runIn": { - "selected": true, - "name": "run-in", - "duration": 2, - "durationUnit": "days" - }, - "washout": { - "selected": true, - "name": "washout", - "duration": 5, - "durationUnit": "days" - }, - "followUp": { - "selected": true, - "name": "follow-up", - "duration": 90, - "durationUnit": "days" - }, - "treatments": [ - { - "interventionType": { - "id": "ero:class:http://purl.obolibrary.org/obo/ERO_0000600", - "iri": "http://purl.obolibrary.org/obo/ERO_0000600", - "short_form": "ERO_0000600", - "obo_id": "ERO:0000600", - "label": "drug intervention", - "ontology_name": "ero", - "ontology_prefix": "ERO", - "type": "class" - }, - "agent": "ibuprofen", - "intensity": 8, - "intensityUnit": "mg/day", - "duration": 12, - "durationUnit": "days" - }, - { - "interventionType": { - "id": "ero:class:http://purl.obolibrary.org/obo/ERO_0001553", - "iri": "http://purl.obolibrary.org/obo/ERO_0001553", - "short_form": "ERO_0001553", - "obo_id": "ERO:0001553", - "label": "biologic intervention", - "ontology_name": "ero", - "ontology_prefix": "ERO", - "type": "class" - }, - "agent": "KpJH46Φ2 phage", - "intensity": 3, - "intensityUnit": "injections/day", - "duration": 14, - "durationUnit": "days" - } - ], - "elementParams": { - "interventionType": null, - 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], - [ - "blood" - ], - [], - [ - "blood" - ], - [ - "derma" - ] - ] - } - } - ] -} \ No newline at end of file diff --git a/isa-cookbook/content/notebooks/isa-study-design-as-json/datascriptor/crossover-study-design-4-arms-blood-derma-nmr-ms.json b/isa-cookbook/content/notebooks/isa-study-design-as-json/datascriptor/crossover-study-design-4-arms-blood-derma-nmr-ms.json deleted file mode 100644 index ca1846c2a..000000000 --- a/isa-cookbook/content/notebooks/isa-study-design-as-json/datascriptor/crossover-study-design-4-arms-blood-derma-nmr-ms.json +++ /dev/null @@ -1,1236 +0,0 @@ -{ - "name": "Chained protocols study", - "subjectType": "Homo sapiens", - "subjectSize": 10, - "designType": { - "term": "crossover design", - "id": "OBI:0500003", - "iri": "http://purl.obolibrary.org/obo/OBI_0500003", - "label": "Study subjects receive repeated treatments", - "value": "crossover" - }, - "observationalFactors": [ - { - "name": "sex", - "values": [ - "M", - "F" - ], - "isQuantitative": false, - 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Only dogs which will have an International Veterinary Epilepsy Task Force Tier II level like diagnosis of IE which satisfied the following inclusion criteria are included: age between 6 months and ≤ 12 years; weighing between 4 and ≤ 65 kg; unremarkable interictal neurological examinations; no clinically significant findings on routine laboratory diagnostics; unremarkable brain MRI scan; have had at least 3 seizures in the previous 3 months prior to enrolment; treated with at least one ASD and being classified as resistant. All dogs are fed initially for 90 ± 2 days with either the control oil or the MCT oil alongside their normal diet, followed by 97 ± 2 days with the other supplement including a 7-day washout period. Overall, the aim is to recruit thirty-six patients at five different centres and to investigate the effect of MCTs as DS on seizure activity, tolerability, behavioural comorbidities and quality of life (QoL).", + "design": { + "subjectType": "Dog", + "subjectSize": 12, + "designType": { + "term": "crossover design", + "id": "OBI:0500003", + "iri": "http://purl.obolibrary.org/obo/OBI_0500003", + "label": "Study subjects receive repeated treatments", + "value": "crossover" + }, + "observationalFactors": [ + { + "name": "health status", + "values": [ + "healthy", + "with idiopathic epilepsy" + ], + "isQuantitative": false, + "unit": null + } + ], + "subjectGroups": { + "selected": [ + { + "name": "SubjectGroup_1", + "type": "Dog", + "characteristics": [ + { + "name": "health status", + "value": "with idiopathic epilepsy", + "unit": null, + "isQuantitative": false + } + ] + }, + { + "name": "SubjectGroup_0", + "type": "Dog", + "characteristics": [ + { + "name": "health status", + "value": 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study", - "description": "This is a test factorial study with three agents (\"ibuprofen\", \"paracetamol\", \"hydroxyclorochine\") and three dosages (5, 10, 15 mg/day) and 2 durations (15 and 30 days).", - "subjectType": "Homo sapiens", - "subjectSize": 5, + "_id": "60d8b601b832df0008a19abc", + "name": "4-treatments factorial study", + "description": "Patients were randomised to one of 4 groups – intravenous streptokinase, oral aspirin, both or neither – and mortality was measured. (Lancet 1988;ii:349-60 )", + "design": { + "subjectType": { + "term": "Homo sapiens", + "iri": "http://purl.obolibrary.org/obo/NCBITaxon_9606" + }, + "subjectSize": 12, "designType": { "term": "full factorial design", "id": "STATO:0000270", @@ -15,17 +20,25 @@ "selected": [ { "name": "SubjectGroup_0", - "type": "Homo sapiens", + "type": { + "term": "Homo sapiens", + "iri": "http://purl.obolibrary.org/obo/NCBITaxon_9606" + }, "characteristics": [] } ], "unselected": [] }, "treatmentPlan": { + "observationPeriod": { + "name": "observation period", + "duration": null, + "durationUnit": "" + }, "screen": { "selected": true, "name": "screen", - "duration": 5, + "duration": 7, "durationUnit": "days" }, "runIn": { @@ -43,11 +56,40 @@ "followUp": { "selected": true, "name": "follow-up", - "duration": 60, + "duration": 90, + "durationUnit": "days" + }, + "elementParams": { + "agents": [ + "intravenous streptokinase", + "oral aspirin", + "intravenous streptokinase + oral aspirin", + "placebo" + 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