{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Initializing a Connection\n", "\n", "We'll be using a MockConnection object and some sampledata for this example. Please feel free to follow along with your local credentials and switching the ids with the relevant object-ids." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from transcriptic import connect\n", "\n", "api = connect()\n", "\n", "# If you receive an `Unable to find .transcriptic` file error, please try\n", "# `transcriptic login` into the commandline." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the rest of the demo, we'll be using the sampledata. If using your credentials, please do not execute the cell below." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from transcriptic import connect\n", "\n", "api = connect(mocked=True)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'sample-org'" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "## Initialized organization\n", "api.organization_id" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[{'id': 'p123',\n", " 'name': 'sample project',\n", " 'created_at': '2020-10-01T00:00:00.000-07:00',\n", " 'updated_at': '2020-10-01T00:59:59.100-07:00'}]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "## List projects\n", "api.projects()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Project" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "from transcriptic.sampledata import load_sample_project" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "my_project = load_sample_project()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "## Bring up documentation\n", "my_project?" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'sample project'" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_project.name" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
idName
0r123Sample Run
\n", "
" ], "text/plain": [ " id Name\n", "0 r123 Sample Run" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "## View runs in the project as a pandas DataFrame\n", "my_project.runs()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Typically, you would initialize a project directly with the given Project-id." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "from transcriptic.jupyter import Project" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "my_project = Project(\"p123\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Run" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "from transcriptic.sampledata import load_sample_run" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "my_run = load_sample_run()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "## Bring up documentation\n", "my_run?" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'r123'" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_run.id" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "'p123'" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_run.project_id" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Instruction Exploration" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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NameIdStartedCompletedInstructions
0acoustic_transferi1232020-06-01T15:39:50.873-07:002020-06-01T15:39:55.049-07:00<transcriptic.jupyter.objects.Instruction obje...
1absorbancei1242020-06-01T15:40:50.873-07:002020-06-01T15:40:55.049-07:00<transcriptic.jupyter.objects.Instruction obje...
\n", "
" ], "text/plain": [ " Name Id Started \\\n", "0 acoustic_transfer i123 2020-06-01T15:39:50.873-07:00 \n", "1 absorbance i124 2020-06-01T15:40:50.873-07:00 \n", "\n", " Completed \\\n", "0 2020-06-01T15:39:55.049-07:00 \n", "1 2020-06-01T15:40:55.049-07:00 \n", "\n", " Instructions \n", "0 \n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
NameWarpIdCompletedStartedParams
0AcousticLiquidHandler.Transferw1232020-06-01T15:39:54.989-07:002020-06-01T15:39:50.791-07:00{'sourceContainer': {'id': 'ct123', 'cType': '...
\n", "" ], "text/plain": [ " Name WarpId Completed \\\n", "0 AcousticLiquidHandler.Transfer w123 2020-06-01T15:39:54.989-07:00 \n", "\n", " Started \\\n", "0 2020-06-01T15:39:50.791-07:00 \n", "\n", " Params \n", "0 {'sourceContainer': {'id': 'ct123', 'cType': '... " ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Examine warp-specifics\n", "my_inst.warps" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Container Exploration" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
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NameContainerIdTypeStatusStorage ConditionContainers
0Echo Source Platect123384-echoavailablecold_4Container(Echo Source Plate)
1VbottomPlatect12496-well-v-bottomavailablecold_4Container(VbottomPlate)
\n", "
" ], "text/plain": [ " Name ContainerId Type Status \\\n", "0 Echo Source Plate ct123 384-echo available \n", "1 VbottomPlate ct124 96-well-v-bottom available \n", "\n", " Storage Condition Containers \n", "0 cold_4 Container(Echo Source Plate) \n", "1 cold_4 Container(VbottomPlate) " ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_run.containers" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "# Access column of Containers\n", "my_containers = my_run.containers.Containers\n", "cont_123 = my_containers.loc[0]" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Container(Echo Source Plate)" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cont_123" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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NameIdVolume
Well Index
0Noneaq1egnpw5q5ythw99:microliter
\n", "
" ], "text/plain": [ " Name Id Volume\n", "Well Index \n", "0 None aq1egnpw5q5ythw 99:microliter" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Examine aliqutos of this container\n", "cont_123.aliquots" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "ContainerType(name='384-well Echo plate', is_tube=False, well_count=384, well_depth_mm=None, well_volume_ul=Unit(65.0, 'microliter'), well_coating=None, sterile=None, cover_types=['universal'], seal_types=['foil', 'ultra-clear'], capabilities=['liquid_handle', 'seal', 'spin', 'incubate', 'dispense', 'cover'], shortname='384-echo', col_count=24, dead_volume_ul=Unit(15, 'microliter'), safe_min_volume_ul=Unit(15, 'microliter'), true_max_vol_ul=Unit(135, 'microliter'), vendor='Labcyte', cat_no='PP-0200', prioritize_seal_or_cover='seal')" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# View the autoprotocol container-type\n", "cont_123.container_type" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'cold_4'" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# View storage status\n", "cont_123.storage" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Dataset Exploration" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Attempting to fetch 1 datasets...\n" ] }, { "data": { "text/html": [ "
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NameDataTypeOperationAnalysisToolDatasets
0OD600platereaderabsorbanceNone<transcriptic.jupyter.objects.Dataset object a...
\n", "
" ], "text/plain": [ " Name DataType Operation AnalysisTool \\\n", "0 OD600 platereader absorbance None \n", "\n", " Datasets \n", "0 \n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
A1A2A3B1B2B3C1C2C3
00.050.040.061.211.131.322.222.152.37
\n", "" ], "text/plain": [ " A1 A2 A3 B1 B2 B3 C1 C2 C3\n", "0 0.05 0.04 0.06 1.21 1.13 1.32 2.22 2.15 2.37" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_run.Datasets.loc[0].data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Some basic analysis is supported on datasets. See the Analysis notebook for more examples." ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "from transcriptic.analysis.spectrophotometry import Absorbance" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "Absorbance?" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "abs_dataset = Absorbance(\n", " my_run.Datasets.loc[0],\n", " [\"control\", \"sample1\", \"sample2\"],\n", " group_wells = [[0,1,2], [12, 13, 14], [24, 25, 26]]\n", ")\n", "abs_dataset" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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controlsample1sample2
00.051.212.22
10.041.132.15
20.061.322.37
\n", "
" ], "text/plain": [ " control sample1 sample2\n", "0 0.05 1.21 2.22\n", "1 0.04 1.13 2.15\n", "2 0.06 1.32 2.37" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "abs_dataset.df" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "abs_dataset.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Typically, you would initialize a project directly with the given Run-id." ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "from transcriptic.jupyter import Run" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "# You can select your desired run-id from the project runs\n", "my_run_id = my_project.runs().loc[0].id" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "my_run = Run(my_run_id)" ] } ], "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": "3.6.10" } }, "nbformat": 4, "nbformat_minor": 1 }