{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "slideshow": { "slide_type": "skip" } }, "outputs": [ { "data": { "text/plain": [ "{'start_slideshow_at': 'selected', 'theme': 'serif', 'transition': 'fade'}" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# setup the presentation settings\n", "from notebook.services.config import ConfigManager\n", "cm = ConfigManager()\n", "cm.update('livereveal', {\n", " 'theme': 'serif',\n", " 'start_slideshow_at': 'selected',\n", " 'transition': 'fade'\n", "})" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true, "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "# some good ol' imports\n", "import pandas as pd, numpy as np\n", "from emperor import Emperor, nbinstall\n", "from skbio import OrdinationResults\n", "\n", "from emperor.qiime_backports.parse import parse_mapping_file\n", "from emperor.qiime_backports.format import format_mapping_file\n", "\n", "from skbio.io.util import open_file\n", "\n", "nbinstall()\n", "\n", "def load_mf(fn):\n", " with open_file(fn) as f:\n", " mapping_data, header, _ = parse_mapping_file(f)\n", " _mapping_file = pd.DataFrame(mapping_data, columns=header)\n", " _mapping_file.set_index('SampleID', inplace=True)\n", " return _mapping_file\n", "\n", "def write_mf(f, _df):\n", " with open(f, 'w') as fp:\n", " lines = format_mapping_file(['SampleID'] + _df.columns.tolist(),\n", " list(_df.itertuples()))\n", " fp.write(lines+'\\n')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true, "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "def one_more_thing():\n", " return 'We are hiring, contact robknight@ucsd.edu'" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Emperor: interactive $\\beta$-diversity visualization" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "slideshow": { "slide_type": "-" } }, "outputs": [], "source": [ "# @ElDeveloper on GitHub\n", "__presenter__ = 'Yoshiki Vazquez-Baeza'\n", "__email__ = 'yoshiki@ucsd.edu'\n", "\n", "\n", "__license__ = 'BSD-3'\n", "__url__ = 'https://github.com/biocore/emperor'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "\n", "\n", "\n", "
\n", "\"ucsd\"\n", "\n", "\"knight-lab\"\n", "
" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Outline\n", "\n", "- Background (why $\\beta$-diversity).\n", "\n", "- What is Emperor.\n", "\n", "- How can we use Emperor.\n", " \n", "- Analyzing a use case." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Outline\n", "\n", "- **Background (why $\\beta$-diversity).**\n", "\n", "- What is Emperor.\n", "\n", "- How can we use Emperor.\n", " \n", "- Analyzing a use case." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Microscopic worlds on and inside our bodies\n", "\n", "\n", "\n", "Photograph by Martin Oeggerli, Micronaut, Supported by School of Life Sciences." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# How do we make sense out of them?\n", "\n", "![microbe](./images/microbes.png)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# How do we make sense out of them?\n", "\n", "![microbe](./images/microbes-metric.png)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "source": [ "# What is $\\beta$-diversity\n", "\n", "- Comparison of two individual communities to determine how similar they are.\n", "\n", "\"ucsd\"" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "source": [ "# What is $\\beta$-diversity\n", "\n", "- Comparison of two individual communities to determine how similar they are.\n", "\n", "\"dm\"/" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# What is $\\beta$-diversity\n", "\n", "- Comparison of two individual samples to determine how similar they are.\n", "- Elucidate patterns.\n", "\n", "![costello](./images/costello.png)\n", "\n", "

\n", " Bacterial community variation in human body habitats across space and time.\n", " Costello EK et al. 2009.\n", "

" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "# What is $\\beta$-diversity\n", "\n", "- Comparison of two individual samples to determine how similar they are.\n", "- Elucidate patterns.\n", "\n", "![costello](./images/costello-colored.png)\n", "\n", "

\n", " Bacterial community variation in human body habitats across space and time.\n", " Costello EK et al. 2009.\n", "

" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "# What is $\\beta$-diversity\n", "\n", "- Comparison of two individual samples to determine how similar they are.\n", "- Elucidate patterns.\n", "\n", "![costello](./images/costello-all.png)\n", "\n", "

\n", " Bacterial community variation in human body habitats across space and time.\n", " Costello EK et al. 2009.\n", "

" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "from skbio import OrdinationResults\n", "\n", "coordinates = OrdinationResults.read('costello/unweighted_unifrac_pc.txt')\n", "metadata = load_mf('costello/mapping-file.txt')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": 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mM6tWreKRRx5hx45dbN++jUBgMHb7yyhKTGQjkfNpa5vB/fffz6233grA3r17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3QdFouFrCwLFRU7kaTjsrAlJdtITlaRkpJy7F4dv4dJSYno9bXU1JRgs5VTV6fBZjtCQoJChw4d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\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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n" ], "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "coordinates" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "from emperor import Emperor" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "
\n", "
Emperor resources missing. Expected them to be found in /nbextensions/emperor/support_files
\n", "
\n", "\n", "\n", "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Emperor(coordinates, metadata, remote=False)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "-" } }, "source": [ "\n", "
" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Outline\n", "\n", "- ~~Background (why $\\beta$-diversity).~~\n", "\n", "- **What is Emperor.**\n", "\n", "- How can we use Emperor.\n", " \n", "- Analyzing a use case." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "# What is Emperor?\n", "\n", "- A Python 2/3 package that powers a JavaScript UI.\n", " - https://github.com/biocore/emperor\n", " \n", "- Originated in the context of Quantitative Insights Into Microbial Ecology http://2.qiime.org" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "fragment" } }, "source": [ "- Scatter plot viewer.\n", "\n", " - Visualize\n", " - Interact\n", " - Share" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Other applications" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- Qiita\n", " - https://qiita.ucsd.edu\n", " ![qiita](https://raw.githubusercontent.com/biocore/qiita/master/qiita_pet/static/img/logo.png)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- American Gut (participant's results).\n", " - http://americangut.org/\n", " " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- Illumina's BaseSpace processing results for the QIIME app.\n", "- Metabolomic analysis, IPython notebook ..." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ " ... kinda" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Kinda?\n", "\n", "- https://github.com/jupyter/nbviewer/issues/316\n", "\n", "" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Nowadays" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- Python API.\n", " - Python 2 and 3.\n", " - Integration with scikit-bio.\n", " - Jupyter integration.\n", " - Pandas 🐼 Integration." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "fragment" } }, "source": [ "- JavaScript API." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "fragment" } }, "source": [ "- Command Line Interface (powered by QIIME 2)." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Outline\n", "\n", "- ~~Background (why $\\beta$-diversity).~~\n", "\n", "- ~~What is Emperor.~~\n", "\n", "- **How can we use Emperor**.\n", " \n", "- Analyzing a use case." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Python" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- 1 main class `Emperor`, depends on `scikit-bio` and `pandas`.\n", "\n", "- Format Python data into JSON and display it using JavaScript." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from emperor import Emperor\n", "\n", "Emperor?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# http://emperor.microbio.me/uno/\n", "\n", "![emperor](./images/main-doc.png)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# http://emperor.microbio.me/uno/\n", "\n", "![emperor](./images/python-doc.png)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# http://emperor.microbio.me/uno/\n", "\n", "![emperor](./images/python-doc-2.png)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# 🐼s integration - experimental" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df = pd.read_csv('./tips.csv')" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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total_billtipsexsmokerdaytimesize
016.991.01FemaleNoSunDinner2
110.341.66MaleNoSunDinner3
221.013.50MaleNoSunDinner3
323.683.31MaleNoSunDinner2
424.593.61FemaleNoSunDinner4
525.294.71MaleNoSunDinner4
68.772.00MaleNoSunDinner2
726.883.12MaleNoSunDinner4
815.041.96MaleNoSunDinner2
914.783.23MaleNoSunDinner2
1010.271.71MaleNoSunDinner2
1135.265.00FemaleNoSunDinner4
1215.421.57MaleNoSunDinner2
1318.433.00MaleNoSunDinner4
1414.833.02FemaleNoSunDinner2
1521.583.92MaleNoSunDinner2
1610.331.67FemaleNoSunDinner3
1716.293.71MaleNoSunDinner3
1816.973.50FemaleNoSunDinner3
1920.653.35MaleNoSatDinner3
2017.924.08MaleNoSatDinner2
2120.292.75FemaleNoSatDinner2
2215.772.23FemaleNoSatDinner2
2339.427.58MaleNoSatDinner4
2419.823.18MaleNoSatDinner2
2517.812.34MaleNoSatDinner4
2613.372.00MaleNoSatDinner2
2712.692.00MaleNoSatDinner2
2821.704.30MaleNoSatDinner2
2919.653.00FemaleNoSatDinner2
........................
21428.176.50FemaleYesSatDinner3
21512.901.10FemaleYesSatDinner2
21628.153.00MaleYesSatDinner5
21711.591.50MaleYesSatDinner2
2187.741.44MaleYesSatDinner2
21930.143.09FemaleYesSatDinner4
22012.162.20MaleYesFriLunch2
22113.423.48FemaleYesFriLunch2
2228.581.92MaleYesFriLunch1
22315.983.00FemaleNoFriLunch3
22413.421.58MaleYesFriLunch2
22516.272.50FemaleYesFriLunch2
22610.092.00FemaleYesFriLunch2
22720.453.00MaleNoSatDinner4
22813.282.72MaleNoSatDinner2
22922.122.88FemaleYesSatDinner2
23024.012.00MaleYesSatDinner4
23115.693.00MaleYesSatDinner3
23211.613.39MaleNoSatDinner2
23310.771.47MaleNoSatDinner2
23415.533.00MaleYesSatDinner2
23510.071.25MaleNoSatDinner2
23612.601.00MaleYesSatDinner2
23732.831.17MaleYesSatDinner2
23835.834.67FemaleNoSatDinner3
23929.035.92MaleNoSatDinner3
24027.182.00FemaleYesSatDinner2
24122.672.00MaleYesSatDinner2
24217.821.75MaleNoSatDinner2
24318.783.00FemaleNoThurDinner2
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244 rows × 7 columns

\n", "
" ], "text/plain": [ " total_bill tip sex smoker day time size\n", "0 16.99 1.01 Female No Sun Dinner 2\n", "1 10.34 1.66 Male No Sun Dinner 3\n", "2 21.01 3.50 Male No Sun Dinner 3\n", "3 23.68 3.31 Male No Sun Dinner 2\n", "4 24.59 3.61 Female No Sun Dinner 4\n", "5 25.29 4.71 Male No Sun Dinner 4\n", "6 8.77 2.00 Male No Sun Dinner 2\n", "7 26.88 3.12 Male No Sun Dinner 4\n", "8 15.04 1.96 Male No Sun Dinner 2\n", "9 14.78 3.23 Male No Sun Dinner 2\n", "10 10.27 1.71 Male No Sun Dinner 2\n", "11 35.26 5.00 Female No Sun Dinner 4\n", "12 15.42 1.57 Male No Sun Dinner 2\n", "13 18.43 3.00 Male No Sun Dinner 4\n", "14 14.83 3.02 Female No Sun Dinner 2\n", "15 21.58 3.92 Male No Sun Dinner 2\n", "16 10.33 1.67 Female No Sun Dinner 3\n", "17 16.29 3.71 Male No Sun Dinner 3\n", "18 16.97 3.50 Female No Sun Dinner 3\n", "19 20.65 3.35 Male No Sat Dinner 3\n", "20 17.92 4.08 Male No Sat Dinner 2\n", "21 20.29 2.75 Female No Sat Dinner 2\n", "22 15.77 2.23 Female No Sat Dinner 2\n", "23 39.42 7.58 Male No Sat Dinner 4\n", "24 19.82 3.18 Male No Sat Dinner 2\n", "25 17.81 2.34 Male No Sat Dinner 4\n", "26 13.37 2.00 Male No Sat Dinner 2\n", "27 12.69 2.00 Male No Sat Dinner 2\n", "28 21.70 4.30 Male No Sat Dinner 2\n", "29 19.65 3.00 Female No Sat Dinner 2\n", ".. ... ... ... ... ... ... ...\n", "214 28.17 6.50 Female Yes Sat Dinner 3\n", "215 12.90 1.10 Female Yes Sat Dinner 2\n", "216 28.15 3.00 Male Yes Sat Dinner 5\n", "217 11.59 1.50 Male Yes Sat Dinner 2\n", "218 7.74 1.44 Male Yes Sat Dinner 2\n", "219 30.14 3.09 Female Yes Sat Dinner 4\n", "220 12.16 2.20 Male Yes Fri Lunch 2\n", "221 13.42 3.48 Female Yes Fri Lunch 2\n", "222 8.58 1.92 Male Yes Fri Lunch 1\n", "223 15.98 3.00 Female No Fri Lunch 3\n", "224 13.42 1.58 Male Yes Fri Lunch 2\n", "225 16.27 2.50 Female Yes Fri Lunch 2\n", "226 10.09 2.00 Female Yes Fri Lunch 2\n", "227 20.45 3.00 Male No Sat Dinner 4\n", "228 13.28 2.72 Male No Sat Dinner 2\n", "229 22.12 2.88 Female Yes Sat Dinner 2\n", "230 24.01 2.00 Male Yes Sat Dinner 4\n", "231 15.69 3.00 Male Yes Sat Dinner 3\n", "232 11.61 3.39 Male No Sat Dinner 2\n", "233 10.77 1.47 Male No Sat Dinner 2\n", "234 15.53 3.00 Male Yes Sat Dinner 2\n", "235 10.07 1.25 Male No Sat Dinner 2\n", "236 12.60 1.00 Male Yes Sat Dinner 2\n", "237 32.83 1.17 Male Yes Sat Dinner 2\n", "238 35.83 4.67 Female No Sat Dinner 3\n", "239 29.03 5.92 Male No Sat Dinner 3\n", "240 27.18 2.00 Female Yes Sat Dinner 2\n", "241 22.67 2.00 Male Yes Sat Dinner 2\n", "242 17.82 1.75 Male No Sat Dinner 2\n", "243 18.78 3.00 Female No Thur Dinner 2\n", "\n", "[244 rows x 7 columns]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# 🐼s integration - experimental" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Coordinates are inferred from the numerical data, see additional `x`, `y` and `z` parameters." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "
\n", "
Emperor resources missing. Expected them to be found in /nbextensions/emperor/support_files
\n", "
\n", "\n", "\n", "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from emperor import scatterplot\n", "scatterplot(df, remote=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Outline\n", "\n", "- ~~Background (why $\\beta$-diversity).~~\n", "\n", "- ~~What is Emperor.~~\n", "\n", "- **How can we use Emperor.**\n", " \n", "- Analyzing a use case." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Emperor and Jupyter 📓\n", "\n", "- nbviewer\n", " See the examples folder in our repo: https://github.com/biocore/emperor/tree/new-api/examples\n", "\n", "- Jupyter notebook\n", "\n", "- Standalone HTML plot\n", " - Generate a standalone HTML file with the needed resources." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# JavaScript" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- Isn't this Sci**Py**." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ " SciJS?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- Thoroughly unit tested." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- Public API ready to be used:\n", "\n", " http://emperor.microbio.me/uno/build/jsdoc/index.html" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# JavaScript" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![JS-DOC](./images/js-doc.png)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# JavaScript" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Integration with SAGE2:\n", "\n", " http://sage2.sagecommons.org/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# JavaScript" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Integration with SAGE2:\n", "\n", " http://sage2.sagecommons.org/\n", "\n", "![sage](./images/vroom.png)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# QIIME 2 integration" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- 1.5 hours to implement." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- In less than 80 lines of code we got a CLI, GUI and provenance tracking." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- Consider QIIME 2 as a gateway to an expanded user base." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- CLI Provided through QIIME 2\n", "\n", " https://github.com/qiime2/qiime2\n", " \n", " https://github.com/qiime2/q2-emperor/\n", "\n", "```bash\n", "qiime emperor plot --help\n", "```" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Outline\n", "\n", "- ~~Background (why $\\beta$-diversity).~~\n", "\n", "- ~~What is Emperor.~~\n", "\n", "- ~~How can we use Emperor.~~\n", " \n", "- **Analyzing a use case.**" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Use case" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- Let's leverage the technology we have available." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- Create a small interface to:\n", "\n", " - Subsample.\n", " - Compute a distance matrix.\n", " * Use all the cores in our machine.\n", " - Visualize.\n", " \n", " - This was kinda possible through E-vident https://github.com/biocore/evident" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "# biocore\n", "from emperor.qiime_backports.parse import parse_mapping_file\n", "from emperor import Emperor, nbinstall\n", "\n", "nbinstall()\n", "\n", "from skbio.stats.ordination import pcoa\n", "from skbio.diversity import beta_diversity\n", "from skbio import TreeNode\n", "from skbio.io.util import open_file\n", "\n", "from biom import load_table\n", "from biom.util import biom_open\n", "\n", "import qiime_default_reference\n", "\n", "# pydata/scipy\n", "import pandas as pd\n", "import numpy as np\n", "\n", "from scipy.spatial.distance import braycurtis, canberra\n", "from ipywidgets import interact\n", "from sklearn.metrics import pairwise_distances\n", "from functools import partial" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "import warnings\n", "# don't try this at home\n", "warnings.filterwarnings(action='ignore', category=Warning)\n", "\n", "# -1 means all the processors available\n", "pw_dists = partial(pairwise_distances, n_jobs=-1)\n", "\n", "def load_mf(fn):\n", " with open_file(fn) as f:\n", " mapping_data, header, _ = parse_mapping_file(f)\n", " _mapping_file = pd.DataFrame(mapping_data, columns=header)\n", " _mapping_file.set_index('SampleID', inplace=True)\n", " return _mapping_file" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Load the data (table and tree)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mf = load_mf('keyboard/mapping-file.txt')\n", "bt = load_table('keyboard/otu-table.biom')" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tree = TreeNode.read(qiime_default_reference.get_reference_tree())\n", "\n", "for n in tree.traverse():\n", " if n.length is None:\n", " n.length = 0" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Interaction function" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def evident(n, metric):\n", " rarefied = bt.subsample(n)\n", " data = np.array([rarefied.data(i) for i in rarefied.ids()], dtype='int64')\n", " \n", " # phylogenetic\n", " if metric in ['unweighted_unifrac', 'weighted_unifrac']:\n", " res = pcoa(beta_diversity(metric, data, rarefied.ids(),\n", " otu_ids=rarefied.ids('observation'),\n", " tree=tree, pairwise_func=pw_dists))\n", " # non-phylogenetic\n", " else:\n", " res = pcoa(beta_diversity(metric, data, rarefied.ids(),\n", " pairwise_func=pw_dists))\n", " return Emperor(res, mf, remote=True)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "
\n", "
Emperor resources missing. Expected them to be found in https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files
\n", "
\n", "\n", "\n", "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "interact(evident, n=(200, 2000, 50),\n", " metric=['unweighted_unifrac', 'weighted_unifrac', 'braycurtis', 'euclidean'],\n", " __manual=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Summarizing" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Ready to be used!" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "👍\n" ] } ], "source": [ "print(b'\\xF0\\x9F\\x91\\x8D'.decode('utf-8'))" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "## version 1.0 $\\beta$ is out!" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "```bash\n", "pip install jupyter\n", "pip install emperor --pre\n", "\n", "\n", "conda install -c biocore emperor jupyter\n", "```" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Acknowledments" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "-" } }, "source": [ "- Thanks to all our users (**cited 99 times since 2013**)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "from knightlab.members import current\n", "from knightlab.members import past\n", "from caporasolab.members import current as c_current\n", "\n", "__credits__ = ['Antonio Gonzalez', 'Joshua Shorenstein', 'Jamie Morton',\n", " 'Jose Navas', 'Rob Knight'] + current + past + c_current" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "'We are hiring, contact robknight@ucsd.edu'" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "one_more_thing()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "\n", "\n", "\n", "\n", "\n", "
\n", "\"ucsd\"\n", "\n", "\"knight-lab\"\n", "
" ] } ], "metadata": { "celltoolbar": "Slideshow", "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": "3.5.1" }, "widgets": { "state": { "17677a6304674f019c1ee7d53f747793": { "views": [] }, "2d01207ced7f414484f16f56e1ba830e": { "views": [] }, "3538694b46744935ae560c76ceb8c1ed": { "views": [] }, "6d68824a4ae94551844e3566bec2f449": { "views": [] }, "ae42cd6d165947cd9085cca86055cedc": { "views": [] }, "f47a2fe4ecb04d6892d275e10c00c878": { "views": [] }, "f7d8683df9304a15bc6403d42363c1e0": { "views": [ { "cell_index": 83 } ] }, "fe964521953048ce80e0910bba898c47": { "views": [] } }, "version": "1.1.2" } }, "nbformat": 4, "nbformat_minor": 0 }