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annotate README @ 0:0b8ddf650752 draft
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author | eslerm |
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date | Wed, 02 May 2018 18:31:06 -0400 |
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planemo upload for repository https://github.com/HegemanLab/VKMZ commit 7c299d22bdce251ce599cd34df76919d297a7007-dirty
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1 # VKMZ version 1.0 |
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2 |
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3 VKMZ is a metabolomics vizualization tool which creates van Krevelen diagrams from mass spectrometry data. A van Krevelen diagram (VKD) plots a molecule on a scatterplot based on the molecule's oxygen to carbon ratio (O:C) against it's hydrogen to carbon ratio (H:C). Classes of metabolites cluster together on a VKD [0]. Plotting a complex mixture of metabolites on a VKD can be used to briefly convey untargeted metabolomics data. |
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4 |
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5 VKMZ can be used as a standalone tool or on the Galaxy Project web platform [1]. |
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6 ## Using VKMZ |
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7 |
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8 VKMZ is designed to use XCMS [2] data as input. Tabular data can also be used as input. For each feature in the data VKMZ attempts to predict it's molecular formula by comparing the features mass to a database of known formula masses. Heristically generated databases for unlabeled and labeled data is included with VKMZ. Users can define their own database. A VKD is created from formulas with predictions and outputed as a webpage and tabular file. |
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9 |
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10 ### Input modes |
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11 |
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12 VKMZ has three modes: |
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13 1. `tsv` mode reads a specially formatted tabular file |
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14 2. `xcms` mode reads features in [XCMS](https://bioconductor.org/packages/release/bioc/html/xcms.html) data |
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15 3. `plot` mode replots VKMZ tabular data |
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16 |
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17 Select a mode by declaring it as the first argument to `vkmz.py`. |
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18 |
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19 > **Example:** |
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20 > ``` |
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21 > python vkmz.py xcms [options] |
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22 > ``` |
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23 |
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24 Different modes take different parameters. |
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25 |
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26 All modes require an output parameter: |
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27 * `--output [FILENAME]` |
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28 * A `.tsv` and/or `.html` will be generated by VKMZ with this paraameter as the file name. |
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29 * A `.tsv` and `.html` files generated by VKMZ are named by this option |
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30 |
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31 All modes allow these options: |
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32 * `--plot-type [scatter-2d]` |
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33 * `--size [INTEGER]` |
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34 * Set base size of marker dots of the VKD |
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35 * `--size-algorithm [{1,2}]` |
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36 * Choose algorithm to modify marker size |
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37 1. Uniform base size |
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38 2. Intensity relative size |
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39 |
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40 #### xcms and tsv modes |
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41 |
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42 Both xcms and tsv mode require the mass error, in parts-per-million, of the mass spectrometer which generated the data: |
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43 * `--error [PPM_ERROR_NUMBER]` |
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44 |
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45 There are several options for xcms and tsv modes: |
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46 * `--database [DATABASE_FILE]` |
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47 * default is BMRB's monoisotopic heuristically generated database [3] |
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48 * `--directory [TOOL_PATH]` |
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49 * define tool directory |
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50 * `--no-plot` |
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51 * disable html plot generation |
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52 |
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53 #### xcms mode |
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54 |
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55 xcms mode requires tabular files generated by XCMS: |
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56 * `--data-matrix [XCMS_DATA_MATRIX_FILE]` |
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57 * `--sample-metadata [XCMS_SAMPLE_METADATAFILE]` |
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58 * `--variable-metadata [XCMS_VARIABLE_METADATAFILE]` |
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59 |
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60 ##### xcms mode example: |
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61 ``` |
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62 python vkmz.py xcms --data-matrix test-data/datamatrix.tabular --sample-metadata test-data/sampleMetadata.tabular --variable-metadata test-data/variableMetadata.tabular --output report --error 3 |
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63 ``` |
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64 |
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65 #### tsv mode |
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66 |
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67 tsv mode requires a tabular file of a specific format as input. |
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68 * `--input [TSV FILE]` |
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69 |
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70 The first five columns of the input tabular file must be: |
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71 |
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72 | sample ID | polarity | mz | retention time | intensity | |
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73 |-----------|----------|----|----------------|-----------| |
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74 |
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75 #### plot mode |
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76 |
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77 plot mode reads previously generated VKMZ tabular files to create VKD html files. |
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78 |
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79 Specifying the VKMZ tabular file is required: |
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80 * `--input [VKMZ_TSV_FILE]` |
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81 |
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82 ## Citations |
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83 |
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84 0. Brockman et al. [doi:10.1007/s11306-018-1343-y](https://doi.org/10.1007/s11306-018-1343-y) |
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85 1. Galaxy Project [Galaxy](https://github.com/galaxyproject/galaxy) |
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86 2. Giacomoni et al. [doi:10.1093/bioinformatics/btu813](https://doi.org/10.1093/bioinformatics/btu813) |
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87 3. Hegeman et al. [doi:10.1021/ac070346t](https://doi.org/10.1021/ac070346t) |