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October 27, 2020 22:55
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| { | |
| "cells": [ | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "# Using KNIME Analytics Platform from Python\n\nThe new [knime module for Python](https://pypi.org/project/knime/), released at the same time as [v3.7 of KNIME Analytics Platform](https://www.knime.com/knime-analytics-platform-37-highlights), allows KNIME workflows to be called from Python and integrates nicely with the Jupyter notebook.\n\nHere we'll demonstrate using a KNIME workflow that performs a complicated database query (the query is against a PostgreSQL instance of [ChEMBL24](https://www.ebi.ac.uk/chembl/) that we have running in AWS) that is parameterized on user-supplied information. \n\nThe results of the query are summarized in a couple of different ways and made available in two separate output tables." | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "Start by importing the knime(py) module, which can be installed from pip: `pip install knime` and then tell it where to find the KNIME executable. If you have KNIME installed in a standard location this second step isn't necessary, but I tend to have KNIME installed on my desktop." | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "import pandas as pd\nimport knime\nknime.executable_path = \"c:/Users/glandrum/Desktop/KNIME_3.7/knime.exe\"", | |
| "execution_count": 1, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "Define the location of the workspace:" | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "workspace = \"c:/Users/glandrum/KNIME workspaces/KNIME nightly\"", | |
| "execution_count": 2, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "Now pick a workflow and make sure it's the right one by displaying an SVG of the workflow:" | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "workflow = \"KNIME Stories/Jupyter KNIME Jupyter/Fetch ChEMBL Target Data\"\nknime.Workflow(workflow_path=workflow,workspace_path=workspace)", | |
| "execution_count": 3, | |
| "outputs": [ | |
| { | |
| "data": { | |
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| "text/plain": "<knime.LocalWorkflow at 0x1ecc011b6c8>" | |
| }, | |
| "execution_count": 3, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "We need to provide the workflow with a table containing the terms we want to query for. For this workflow we are providing ChEMBL target IDs.\n\nWe'll do this by providing a Pandas DataFrame:" | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "interesting_targets = {'target_chembl_id':[\"CHEMBL214\",\"CHEMBL219\",\"CHEMBL1983\"]}\ndf = pd.DataFrame(interesting_targets)", | |
| "execution_count": 4, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "Now execute the workflow with that DataFrame as one of the inputs:" | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "with knime.Workflow(workflow_path=workflow,workspace_path=workspace) as wf:\n wf.data_table_inputs[0] = df\n wf.execute()\n", | |
| "execution_count": 5, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "The two output tables are also present as Pandas DataFrames:" | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "len(wf.data_table_outputs)", | |
| "execution_count": 6, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": "2" | |
| }, | |
| "execution_count": 6, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "The first data table has one summary level of the data - the number of compounds for which there is data for each readout of each assay that was run against each target:" | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "len(wf.data_table_outputs[0])", | |
| "execution_count": 7, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": "2067" | |
| }, | |
| "execution_count": 7, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "That's too many rows to look at all at once, so let's just dispay the first few:" | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "wf.data_table_outputs[0].head()", | |
| "execution_count": 8, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>standard_type</th>\n <th>description</th>\n <th>assay_type</th>\n <th>assay_chembl_id</th>\n <th>pref_name</th>\n <th>organism</th>\n <th>target_chembl_id</th>\n <th>COUNT(molregno)</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>Ki</td>\n <td>Antagonist activity at 5HT1A</td>\n <td>F</td>\n <td>CHEMBL1106423</td>\n <td>Serotonin 1a (5-HT1a) receptor</td>\n <td>Homo sapiens</td>\n <td>CHEMBL214</td>\n <td>88</td>\n </tr>\n <tr>\n <th>1</th>\n <td>Ki</td>\n <td>Binding affinity to human 5-hydroxytryptamine ...</td>\n <td>B</td>\n <td>CHEMBL615772</td>\n <td>Serotonin 1a (5-HT1a) receptor</td>\n <td>Homo sapiens</td>\n <td>CHEMBL214</td>\n <td>81</td>\n </tr>\n <tr>\n <th>2</th>\n <td>Emax</td>\n <td>Intrinsic activity at human 5-HT1A receptor ex...</td>\n <td>F</td>\n <td>CHEMBL1259257</td>\n <td>Serotonin 1a (5-HT1a) receptor</td>\n <td>Homo sapiens</td>\n <td>CHEMBL214</td>\n <td>75</td>\n </tr>\n <tr>\n <th>3</th>\n <td>EC50</td>\n <td>Intrinsic activity at human 5-HT1A receptor ex...</td>\n <td>F</td>\n <td>CHEMBL1259255</td>\n <td>Serotonin 1a (5-HT1a) receptor</td>\n <td>Homo sapiens</td>\n <td>CHEMBL214</td>\n <td>74</td>\n </tr>\n <tr>\n <th>4</th>\n <td>Ki</td>\n <td>Binding affinity to 5HT1A receptor</td>\n <td>B</td>\n <td>CHEMBL1785228</td>\n <td>Serotonin 1a (5-HT1a) receptor</td>\n <td>Homo sapiens</td>\n <td>CHEMBL214</td>\n <td>66</td>\n </tr>\n </tbody>\n</table>\n</div>", | |
| "text/plain": " standard_type description assay_type \\\n0 Ki Antagonist activity at 5HT1A F \n1 Ki Binding affinity to human 5-hydroxytryptamine ... B \n2 Emax Intrinsic activity at human 5-HT1A receptor ex... F \n3 EC50 Intrinsic activity at human 5-HT1A receptor ex... F \n4 Ki Binding affinity to 5HT1A receptor B \n\n assay_chembl_id pref_name organism \\\n0 CHEMBL1106423 Serotonin 1a (5-HT1a) receptor Homo sapiens \n1 CHEMBL615772 Serotonin 1a (5-HT1a) receptor Homo sapiens \n2 CHEMBL1259257 Serotonin 1a (5-HT1a) receptor Homo sapiens \n3 CHEMBL1259255 Serotonin 1a (5-HT1a) receptor Homo sapiens \n4 CHEMBL1785228 Serotonin 1a (5-HT1a) receptor Homo sapiens \n\n target_chembl_id COUNT(molregno) \n0 CHEMBL214 88 \n1 CHEMBL214 81 \n2 CHEMBL214 75 \n3 CHEMBL214 74 \n4 CHEMBL214 66 " | |
| }, | |
| "execution_count": 8, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "The second data table rolls the data up in even further, it shows the number of compounds that were tested in all assays that were run against each target:" | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "wf.data_table_outputs[1]", | |
| "execution_count": 9, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>pref_name</th>\n <th>organism</th>\n <th>target_chembl_id</th>\n <th>COUNT(molregno)</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>Serotonin 1a (5-HT1a) receptor</td>\n <td>Homo sapiens</td>\n <td>CHEMBL214</td>\n <td>8089</td>\n </tr>\n <tr>\n <th>1</th>\n <td>Dopamine D4 receptor</td>\n <td>Homo sapiens</td>\n <td>CHEMBL219</td>\n <td>6090</td>\n </tr>\n <tr>\n <th>2</th>\n <td>Serotonin 1d (5-HT1d) receptor</td>\n <td>Homo sapiens</td>\n <td>CHEMBL1983</td>\n <td>2497</td>\n </tr>\n </tbody>\n</table>\n</div>", | |
| "text/plain": " pref_name organism target_chembl_id \\\n0 Serotonin 1a (5-HT1a) receptor Homo sapiens CHEMBL214 \n1 Dopamine D4 receptor Homo sapiens CHEMBL219 \n2 Serotonin 1d (5-HT1d) receptor Homo sapiens CHEMBL1983 \n\n COUNT(molregno) \n0 8089 \n1 6090 \n2 2497 " | |
| }, | |
| "execution_count": 9, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "That's it!" | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "", | |
| "execution_count": null, | |
| "outputs": [] | |
| } | |
| ], | |
| "metadata": { | |
| "_draft": { | |
| "nbviewer_url": "https://gist.github.com/9c563ac4eaafff99929c5a4c2e39ed9e" | |
| }, | |
| "gist": { | |
| "id": "9c563ac4eaafff99929c5a4c2e39ed9e", | |
| "data": { | |
| "description": "KNIME from Jupyter_demo.ipynb", | |
| "public": true | |
| } | |
| }, | |
| "kernelspec": { | |
| "name": "python3", | |
| "display_name": "Python 3", | |
| "language": "python" | |
| }, | |
| "language_info": { | |
| "name": "python", | |
| "version": "3.6.6", | |
| "mimetype": "text/x-python", | |
| "codemirror_mode": { | |
| "name": "ipython", | |
| "version": 3 | |
| }, | |
| "pygments_lexer": "ipython3", | |
| "nbconvert_exporter": "python", | |
| "file_extension": ".py" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 2 | |
| } |
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