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…bolomics [IBCDPE-688] Great Expectations Implementation for Metabolomics Data
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| # Ensure that /great_expectations and all of its contents are included when the package is installed | ||
| graft src/agoradatatools/great_expectations/ |
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| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import synapseclient\n", | ||
| "\n", | ||
| "import great_expectations as gx\n", | ||
| "\n", | ||
| "context = gx.get_context(project_root_dir='../src/agoradatatools/great_expectations')\n", | ||
| "\n", | ||
| "from expectations.expect_column_values_to_have_list_length import ExpectColumnValuesToHaveListLength\n", | ||
| "from expectations.expect_column_values_to_have_list_members import ExpectColumnValuesToHaveListMembers\n", | ||
| "from expectations.expect_column_values_to_have_list_members_of_type import ExpectColumnValuesToHaveListMembersOfType\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Create Expectation Suite for Metabolomics Data" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Get Example Data File" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "syn = synapseclient.Synapse()\n", | ||
| "syn.login()\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "metabolomics_data_file = syn.get(\"syn19276330\").path\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Create Validator Object on Data File" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "validator = context.sources.pandas_default.read_json(\n", | ||
| " metabolomics_data_file\n", | ||
| ")\n", | ||
| "validator.expectation_suite_name = \"metabolomics\"\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Add Expectations to Validator Object For Each Column" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# ad_diagnosis_p_value\n", | ||
| "validator.expect_column_values_to_be_of_type(\"ad_diagnosis_p_value\", \"list\")\n", | ||
| "validator.expect_column_values_to_not_be_null(\"ad_diagnosis_p_value\")\n", | ||
| "# for custom and experimental expectations you have to pass args as kwargs\n", | ||
| "validator.expect_column_values_to_have_list_length(column=\"ad_diagnosis_p_value\", list_length=1)\n", | ||
| "validator.expect_column_values_to_have_list_members_of_type(column=\"ad_diagnosis_p_value\", member_type=\"float\")\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# associated gene name\n", | ||
| "validator.expect_column_values_to_be_of_type(\"associated_gene_name\", \"str\")\n", | ||
| "validator.expect_column_values_to_not_be_null(\"associated_gene_name\")\n", | ||
| "validator.expect_column_value_lengths_to_be_between(\"associated_gene_name\", min_value=2, max_value=100)\n", | ||
| "# allows all alphanumeric characters, underscores, periods, and dashes\n", | ||
| "validator.expect_column_values_to_match_regex(\"associated_gene_name\", \"^[A-Za-z0-9_.-]+$\")\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# association p\n", | ||
| "validator.expect_column_values_to_be_of_type(\"association_p\", \"float\")\n", | ||
| "validator.expect_column_values_to_not_be_null(\"association_p\")\n", | ||
| "validator.expect_column_values_to_be_between(\"association_p\", strict_min_value=0, max_value=1)\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# boxplot_group_names\n", | ||
| "validator.expect_column_values_to_be_of_type(\"boxplot_group_names\", \"list\")\n", | ||
| "validator.expect_column_values_to_not_be_null(\"boxplot_group_names\")\n", | ||
| "validator.expect_column_values_to_have_list_length(column=\"boxplot_group_names\", list_length=2)\n", | ||
| "validator.expect_column_values_to_have_list_members(column=\"boxplot_group_names\", list_members={\"AD\", \"CN\"})\n", | ||
| "validator.expect_column_values_to_have_list_members_of_type(column=\"boxplot_group_names\", member_type=\"str\")\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# ensembl gene id\n", | ||
| "validator.expect_column_values_to_be_of_type(\"ensembl_gene_id\", \"str\")\n", | ||
| "validator.expect_column_values_to_not_be_null(\"ensembl_gene_id\")\n", | ||
| "validator.expect_column_value_lengths_to_equal(\"ensembl_gene_id\", 15)\n", | ||
| "# checks format and allowed chatacters\n", | ||
| "validator.expect_column_values_to_match_regex(\"ensembl_gene_id\", \"^ENSG\\d{11}$\")\n", | ||
| "validator.expect_column_values_to_be_unique(\"ensembl_gene_id\")\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# gene_wide_p_threshold_1kgp\n", | ||
| "validator.expect_column_values_to_be_of_type(\"gene_wide_p_threshold_1kgp\", \"float\")\n", | ||
| "validator.expect_column_values_to_not_be_null(\"gene_wide_p_threshold_1kgp\")\n", | ||
| "validator.expect_column_values_to_be_between(\"gene_wide_p_threshold_1kgp\", strict_min_value=0, max_value=0.05)\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# metabolite full name\n", | ||
| "validator.expect_column_values_to_be_of_type(\"metabolite_full_name\", \"str\")\n", | ||
| "validator.expect_column_values_to_not_be_null(\"metabolite_full_name\")\n", | ||
| "validator.expect_column_value_lengths_to_be_between(\"metabolite_full_name\", min_value=2, max_value=100) \n", | ||
| "# allows all alphanumeric characters, dashes, parentheses, hyphens and spaces\n", | ||
| "validator.expect_column_values_to_match_regex(\"metabolite_full_name\", \"^[A-Za-z0-9\\s\\-:.()+]+$\")\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# metabolite ID\n", | ||
| "validator.expect_column_values_to_be_of_type(\"metabolite_id\", \"str\")\n", | ||
| "validator.expect_column_values_to_not_be_null(\"metabolite_id\")\n", | ||
| "validator.expect_column_value_lengths_to_be_between(\"metabolite_id\", min_value=2, max_value=100)\n", | ||
| "# allows all alphanumeric characters and periods\n", | ||
| "validator.expect_column_values_to_match_regex(\"metabolite_id\", \"^[A-Za-z0-9.]+$\")\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# n_per_group\n", | ||
| "validator.expect_column_values_to_be_of_type(\"n_per_group\", \"list\")\n", | ||
| "validator.expect_column_values_to_not_be_null(\"n_per_group\")\n", | ||
| "validator.expect_column_values_to_have_list_length(column=\"n_per_group\", list_length=2)\n", | ||
| "validator.expect_column_values_to_have_list_members_of_type(column=\"n_per_group\", member_type=\"int\")\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# transposed_boxplot_stats\n", | ||
| "validator.expect_column_values_to_be_of_type(\"transposed_boxplot_stats\", \"list\")\n", | ||
| "validator.expect_column_values_to_not_be_null(\"transposed_boxplot_stats\")\n", | ||
| "validator.expect_column_values_to_have_list_length(column=\"transposed_boxplot_stats\", list_length=2)\n", | ||
| "validator.expect_column_values_to_have_list_members_of_type(column=\"transposed_boxplot_stats\", member_type=\"list\")\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Save Expectation Suite" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "validator.save_expectation_suite(discard_failed_expectations=False)\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Create Checkpoint and View Results" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "checkpoint = context.add_or_update_checkpoint(\n", | ||
| " name=\"agora-test-checkpoint\",\n", | ||
| " validator=validator,\n", | ||
| ")\n", | ||
| "checkpoint_result = checkpoint.run()\n", | ||
| "context.view_validation_result(checkpoint_result)\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Build Data Docs - Click on Expectation Suite to View All Expectations" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "context.build_data_docs()\n", | ||
| "context.open_data_docs()\n" | ||
| ] | ||
| } | ||
| ], | ||
| "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": "3.9.13" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 2 | ||
| } |
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