{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Intermine-Python: Tutorial 6: Advanced Results Management" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the previous tutorials we learnt how to add filters to limit the query results. However, it's often useful to view the results without filters, to get an idea about the shape of the data. This tutorial will show you how you can process, sort and analyse the query results. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We begin by creating a query object. Our example for this tutorial is going to be related to RNA Sequences. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from intermine.webservice import Service" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "service = Service(\"https://www.flymine.org/flymine/service\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "query=service.new_query()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "query.add_views(\"RNASeqResult.expressionScore RNASeqResult.expressionLevel RNASeqResult.gene.symbol\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will now sort the results in descending order of their expression score. " ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "query.add_sort_order(\"RNASeqResult.expressionScore\",\"desc\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will now print the first ten rows. " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "RNASeqResult: expressionScore=273318 expressionLevel='Extremely high expression' gene.symbol='Sgs4'\n", "RNASeqResult: expressionScore=162095 expressionLevel='Extremely high expression' gene.symbol='Yp1'\n", "RNASeqResult: expressionScore=143098 expressionLevel='Extremely high expression' gene.symbol='Yp1'\n", "RNASeqResult: expressionScore=132342 expressionLevel='Extremely high expression' gene.symbol='lncRNA:CR40469'\n", "RNASeqResult: expressionScore=130637 expressionLevel='Extremely high expression' gene.symbol='Sgs7'\n", "RNASeqResult: expressionScore=119145 expressionLevel='Extremely high expression' gene.symbol='Yp1'\n", "RNASeqResult: expressionScore=116020 expressionLevel='Extremely high expression' gene.symbol='Yp1'\n", "RNASeqResult: expressionScore=115148 expressionLevel='Extremely high expression' gene.symbol='Yp1'\n", "RNASeqResult: expressionScore=114795 expressionLevel='Extremely high expression' gene.symbol='Yp1'\n", "RNASeqResult: expressionScore=111450 expressionLevel='Extremely high expression' gene.symbol='Yp1'\n" ] } ], "source": [ "for r in query.rows(size=10):\n", " print(r)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that we have not added any constraints and so we have extracted all the possible results. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, let's say that we want to sort all the results into three different dictionaries (or maps). The ones with expressionScores of greater than 25 into one map, the ones with expression scores greater than 10 but lesser than (or equal to 25) in another map and all the remaining ones into a separate map. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We begin by declaring these three dictionaries. " ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "high_dict={}\n", "medium_dict={}\n", "low_dict={}" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mr\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mquery\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrows\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m 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if(r[\"expressionScore\"]>25):\n", " high_dict[r[\"gene.symbol\"]]=r[\"expressionLevel\"]\n", " else:\n", " if(r[\"expressionScore\"]>10):\n", " medium_dict[r[\"gene.symbol\"]]=r[\"expressionLevel\"]\n", " else:\n", " low_dict[r[\"gene.symbol\"]]=r[\"expressionLevel\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you now want to view the items stored in the high_dict dicitionary, you can print them out as shown below. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for k,v in high_dict.items():\n", " print(k,v)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's say that you want to view the average score for extremely high expressions. This can be done as follows. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "total=0\n", "count=0\n", "for r in query.rows():\n", " if(r[\"expressionScore\"]>1000):\n", " total=total+r[\"expressionScore\"]\n", " count=count+1\n", "print(\"Average is\",total/count) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "An advantage of extracting all the results first and then processing them in the client side is that you do not need to keep re-running the query with different constraints. If you change your mind about the constraints, iterate over the same results and store them into a different list by simply changing the \"if\" condition. " ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.9" } }, "nbformat": 4, "nbformat_minor": 1 }