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New release (#140)
* deleting old test data * add empress example plot * add empress image from paper * update screenshot path * remove plot description * Update README.md * update empress plot * Update README.md * add markdown image specs * add markdown image specs * add markdown image specs * add image with legend * deprecate plot.py * update paper link * Update README.md * Update README.md Co-authored-by: Yoshiki Vázquez Baeza <[email protected]>
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README.md

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[![Build Status](https://travis-ci.org/biocore/q2-qemistree.svg?branch=master)](https://travis-ci.org/biocore/q2-qemistree) [![Coverage Status](https://coveralls.io/repos/github/biocore/q2-qemistree/badge.svg?branch=master)](https://coveralls.io/github/biocore/q2-qemistree?branch=master)
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A tool to build a tree of mass-spectrometry (LC-MS/MS) features to perform chemically-informed comparison of untargeted metabolomic profiles. The preprint describing q2-qemistree is available [here](https://www.biorxiv.org/content/10.1101/2020.05.04.077636v1)!
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A tool to build a tree of mass-spectrometry (LC-MS/MS) features to perform chemically-informed comparison of untargeted metabolomic profiles. The manuscript describing q2-qemistree is available [here](https://www.nature.com/articles/s41589-020-00677-3).
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![Qemistree manuscript](q2_qemistree/img/paper-ncb.png)
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## Installation
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qiime qemistree make-hierarchy
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qiime qemistree get-classyfire-taxonomy
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qiime qemistree prune-hierarchy
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qiime qemistree plot
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```
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To generate a tree that relates the MS1 features in your experiment, we need to pre-process mass-spectrometry data (.mzXML, .mzML or .mzDATA files) using [MZmine2](http://mzmine.github.io) and produce the following inputs:
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--o-visualization empress-tree.qzv
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```
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The output empress QZV can be visualized using [Qiime2 Viewer](https://view.qiime2.org); EMPress can be used to interactively modify the tree visualization.
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For example, if the user has sample metadata columns to compare groups of samples, Empress enables them to visualize feature abundance barcharts at the tips of the tree (abundance or relative abundance) of the feature stratified by the sample metadata column of interest. Please visit [Empress tutorial](https://github.com/biocore/empress) for all the currently supported tree visualization features that can be leveraged to explore the chemical diversity of your metabolomics dataset.
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The output empress QZV can be visualized using [Qiime2 Viewer](https://view.qiime2.org); EMPress can be used to interactively modify the tree visualization.
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Below is an example visualization from Empress' preprint. Here, the user has sample metadata columns (food sources) to compare groups of food samples; Empress enables them to visualize metabolite relative prevalence as barcharts at the tips of the tree.
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![Empress plot](q2_qemistree/img/gfop-empress-plot-wlegend.png)
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Please visit the [Empress tutorial](https://github.com/biocore/empress) for all the currently supported tree visualization features that can be leveraged to explore the chemical diversity of your metabolomics dataset.

q2_qemistree/__init__.py

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from ._classyfire import get_classyfire_taxonomy
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from ._hierarchy import make_hierarchy
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from ._prune_hierarchy import prune_hierarchy
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from ._plot import plot
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from ._semantics import (MassSpectrometryFeatures, MGFDirFmt,
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CSIFolder, CSIDirFmt, ZodiacFolder, ZodiacDirFmt,
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SiriusFolder, SiriusDirFmt, OutputDirs)

q2_qemistree/_plot.py

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q2_qemistree/img/paper-ncb.png

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q2_qemistree/plugin_setup.py

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ZodiacFolder, ZodiacDirFmt,
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CSIFolder, CSIDirFmt,
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FeatureData, TSVMoleculesFormat, Molecules)
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from ._plot import plot
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from qiime2.plugin import (Plugin, Str, Range, Choices, Float, Int, Bool, List,
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Citations)
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'tips that are in feature data'}
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)
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plugin.visualizers.register_function(
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function=plot,
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name='Generate an annotated qemistree plot in iTOL',
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description=('Plots the phenetic tree in iTOL with clade colors, '
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'feature labels and relative abundance per sample group'),
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inputs={'grouped_table': FeatureTable[Frequency],
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'tree': Phylogeny[Rooted],
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'feature_metadata': FeatureData[Molecules]
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},
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parameters={
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'category': Str,
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'color_palette': Str % Choices(['Pastel1', 'Pastel2', 'Paired',
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'Accent', 'Dark2', 'Set1', 'Set2',
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'Set3', 'tab10', 'tab20', 'tab20b',
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'tab20c', 'Greys', 'Purples', 'Blues',
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'Greens', 'Oranges', 'Reds', 'YlOrBr',
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'YlOrRd', 'OrRd', 'PuRd', 'RdPu',
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'BuPu', 'GnBu', 'PuBu', 'YlGnBu',
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'PuBuGn', 'BuGn', 'YlGn']),
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'ms2_label': Bool,
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'parent_mz': Bool,
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'normalize_features': Bool
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},
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input_descriptions={'grouped_table': 'Feature table of samples '
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'grouped by categories. We recommend '
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'collapsing feature table by a '
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'sample metadata category using '
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'`qiime feature-table group`. '
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'We can then plot the prevalence '
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'of these categories for '
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'each molecule on the tree',
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'tree': 'Phenetic tree',
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'feature_metadata': 'Feature metadata'
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},
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parameter_descriptions={
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'category': 'The feature data column used to color and label the tips',
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'color_palette': 'The color palette to use for coloring tips. '
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'For examples, see: https://matplotlib.org/'
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'tutorials/colors/colormaps.html',
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'ms2_label': 'Whether to label the tips with the MS2 value',
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'parent_mz': 'If the feature is unclassified, label the tips using '
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'this parent mass of the molecule',
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'normalize_features': 'Whether to normalize feature abundance to '
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'a constant sum i.e convert to '
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'relative abundance'
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},
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citations=[citations['letunic2019itol']])
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importlib.import_module('q2_qemistree._transformer')

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