Mercurial > repos > eslerm > vkmz
comparison README @ 0:0b8ddf650752 draft
planemo upload for repository https://github.com/HegemanLab/VKMZ commit 7c299d22bdce251ce599cd34df76919d297a7007-dirty
author | eslerm |
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date | Wed, 02 May 2018 18:31:06 -0400 |
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children | d4a9e43aca74 |
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1 # VKMZ version 1.0 | |
2 | |
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. | |
4 | |
5 VKMZ can be used as a standalone tool or on the Galaxy Project web platform [1]. | |
6 ## Using VKMZ | |
7 | |
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. | |
9 | |
10 ### Input modes | |
11 | |
12 VKMZ has three modes: | |
13 1. `tsv` mode reads a specially formatted tabular file | |
14 2. `xcms` mode reads features in [XCMS](https://bioconductor.org/packages/release/bioc/html/xcms.html) data | |
15 3. `plot` mode replots VKMZ tabular data | |
16 | |
17 Select a mode by declaring it as the first argument to `vkmz.py`. | |
18 | |
19 > **Example:** | |
20 > ``` | |
21 > python vkmz.py xcms [options] | |
22 > ``` | |
23 | |
24 Different modes take different parameters. | |
25 | |
26 All modes require an output parameter: | |
27 * `--output [FILENAME]` | |
28 * A `.tsv` and/or `.html` will be generated by VKMZ with this paraameter as the file name. | |
29 * A `.tsv` and `.html` files generated by VKMZ are named by this option | |
30 | |
31 All modes allow these options: | |
32 * `--plot-type [scatter-2d]` | |
33 * `--size [INTEGER]` | |
34 * Set base size of marker dots of the VKD | |
35 * `--size-algorithm [{1,2}]` | |
36 * Choose algorithm to modify marker size | |
37 1. Uniform base size | |
38 2. Intensity relative size | |
39 | |
40 #### xcms and tsv modes | |
41 | |
42 Both xcms and tsv mode require the mass error, in parts-per-million, of the mass spectrometer which generated the data: | |
43 * `--error [PPM_ERROR_NUMBER]` | |
44 | |
45 There are several options for xcms and tsv modes: | |
46 * `--database [DATABASE_FILE]` | |
47 * default is BMRB's monoisotopic heuristically generated database [3] | |
48 * `--directory [TOOL_PATH]` | |
49 * define tool directory | |
50 * `--no-plot` | |
51 * disable html plot generation | |
52 | |
53 #### xcms mode | |
54 | |
55 xcms mode requires tabular files generated by XCMS: | |
56 * `--data-matrix [XCMS_DATA_MATRIX_FILE]` | |
57 * `--sample-metadata [XCMS_SAMPLE_METADATAFILE]` | |
58 * `--variable-metadata [XCMS_VARIABLE_METADATAFILE]` | |
59 | |
60 ##### xcms mode example: | |
61 ``` | |
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 | |
63 ``` | |
64 | |
65 #### tsv mode | |
66 | |
67 tsv mode requires a tabular file of a specific format as input. | |
68 * `--input [TSV FILE]` | |
69 | |
70 The first five columns of the input tabular file must be: | |
71 | |
72 | sample ID | polarity | mz | retention time | intensity | | |
73 |-----------|----------|----|----------------|-----------| | |
74 | |
75 #### plot mode | |
76 | |
77 plot mode reads previously generated VKMZ tabular files to create VKD html files. | |
78 | |
79 Specifying the VKMZ tabular file is required: | |
80 * `--input [VKMZ_TSV_FILE]` | |
81 | |
82 ## Citations | |
83 | |
84 0. Brockman et al. [doi:10.1007/s11306-018-1343-y](https://doi.org/10.1007/s11306-018-1343-y) | |
85 1. Galaxy Project [Galaxy](https://github.com/galaxyproject/galaxy) | |
86 2. Giacomoni et al. [doi:10.1093/bioinformatics/btu813](https://doi.org/10.1093/bioinformatics/btu813) | |
87 3. Hegeman et al. [doi:10.1021/ac070346t](https://doi.org/10.1021/ac070346t) |