Mercurial > repos > computational-metabolomics > dimspy_sample_filter
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/README.rst Wed Feb 17 10:49:33 2021 +0000 @@ -0,0 +1,82 @@ +Galaxy tool wrappers for DIMSpy +=============================== +|Build Status (last commit)| |Build Status (weekly global test)| |galaxy-eu| |Git| |Bioconda| |License| + +Galaxy tool wrappers for Python package DIMSpy: data processing of direct-infusion mass spectrometry (DIMS)-based metabolomics and lipidomics data + +Source code: https://github.com/computational-metabolomics/dimspy + +PyPI (The Python Package Index): https://pypi.org/project/dimspy + + +Version +-------- +v2.0.0+Galaxy0 (`DIMSpy v2.0.0 <https://pypi.org/project/dimspy/2.0.0/>`_) + + +Galaxy +------- +`Galaxy <https://galaxyproject.org>`_ is an open, web-based platform for data intensive biomedical research. Whether on the free public server or your own instance, you can perform, reproduce, and share complete analyses. + + +Credits +------- + +**Authors and contributors** + - Ralf J. M. Weber (r.j.weber@bham.ac.uk) - `University of Birmingham (UK) <https://www.birmingham.ac.uk/staff/profiles/biosciences/weber-ralf.aspx>`__ + - Thomas N. Lawson (t.n.lawson@bham.ac.uk) - `University of Birmingham (UK) <http://www.birmingham.ac.uk/index.aspx>`__ + - Martin R. Jones (martin.jones@eawag.ch) - `Eawag (Switzerland) <https://www.eawag.ch/en/aboutus/portrait/organisation/staff/profile/martin-jones/show/>`_ + + +**DIMSpy acknowledges support from the following funders:** + - BBSRC, grant number BB/M019985/1 + - European Commission's H2020 programme, grant agreement number 654241 + - Wellcome Trust, grant number 202952/Z/16/Z + + +Bugs +---- +Please report any bugs that you find `here <https://github.com/computational-metabolomics/dimspy-galaxy/issues>`_. +Or fork the repository on `GitHub <https://github.com/computational-metabolomics/dimspy-galaxy/>`_ +and create a pull request (PR). We welcome all contributions, and we +will help you to make the PR if you are new to `git`. + + +Changes +------- +v2.0.0+galaxy0 + - First release (Python 3-based version `DIMSpy <https://pypi.org/project/dimspy/2.0.0/>`_) + +v1.4.0+galaxy0 + - Final release (Python 2-based version `DIMSpy <https://pypi.org/project/dimspy/1.4.0/>`_) + + +Licenses +------- +DIMSpy and Galaxy tools are released under the GNU General Public License v3.0 (see `LICENSE file <https://github.com/computational-metabolomics/dimspy-galaxy/blob/master/LICENSE>`_) + +**Third-party licenses and copyright** + +RawFileReader reading tool. Copyright © 2016 by Thermo Fisher Scientific, Inc. All rights reserved. See `RawFileReaderLicense <https://github.com/computational-metabolomics/dimspy/blob/master/RawFileReaderLicense.rst>`_ for licensing information. +Using DIMSpy software for processing Thermo Fisher Scientific *.raw files implies the acceptance of the RawFileReader license terms. +Anyone receiving RawFileReader as part of a larger software distribution (in the current context, as part of DIMSpy) is considered an "end user" under +section 3.3 of the RawFileReader License, and is not granted rights to redistribute RawFileReader. + + +.. |Build Status (last commit)| image:: https://github.com/computational-metabolomics/dimspy-galaxy/workflows/Galaxy%20Tool%20Linting%20and%20Tests%20for%20push%20and%20PR/badge.svg + :target: https://github.com/computational-metabolomics/dimspy-galaxy/actions?query=workflow%3A%22Galaxy+Tool+Linting+and+Tests+for+push+and+PR%22 + +.. |Build Status (weekly global test)| image:: https://github.com/computational-metabolomics/dimspy-galaxy/workflows/Weekly%20global%20Tool%20Linting%20and%20Tests/badge.svg + :target: https://github.com/computational-metabolomics/dimspy-galaxy/actions?query=workflow%3A%22Weekly+global+Tool+Linting+and+Tests%22 + +.. |Git| image:: https://img.shields.io/badge/repository-GitHub-blue.svg?style=flat&maxAge=3600 + :target: https://github.com/computational-metabolomics/dimspy + +.. |galaxy-eu| image:: https://img.shields.io/badge/usegalaxy-.eu-brightgreen?logo=data:image/png;base64,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 + :target: http://usegalaxy.eu + +.. |Bioconda| image:: https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat&maxAge=3600 + :target: http://bioconda.github.io/recipes/dimspy/README.html + +.. |License| image:: https://img.shields.io/badge/License-GPL%20v3-blue.svg + :target: https://www.gnu.org/licenses/gpl-3.0.html
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/RawFileReaderLicense.md Wed Feb 17 10:49:33 2021 +0000 @@ -0,0 +1,51 @@ +**SOFTWARE LICENSE AGREEMENT ("License") FOR RawFileReader** + +These License terms are an agreement between you and Thermo Finnigan LLC ("Licensor"). They apply to Licensor's MSFileReader software program ("Software"), which includes documentation and any media on which you received it. These terms also apply to any updates or supplements for this Software, unless other terms accompany those items, in which case those terms apply. **If you use this Software, you accept this License. If you do not accept this License, you are prohibited from using this software. If you comply with these License terms, you have the rights set forth below.** + +1. Rights Granted: + +1.1. You may install and use this Software on any of your computing devices. + +1.2. You may distribute this Software to others, but only in combination with other software components and/or programs that you provide and subject to the distribution requirements and restrictions below. + +2. Use Restrictions: + +2.1. You may not decompile, disassemble, reverse engineer, use reflection or modify this Software. + +3. Distribution Requirements: + +If you distribute this Software to others, you agree to: + +3.1. Indemnify, defend and hold harmless the Licensor from any claims, including attorneys' fees, related to the distribution or use of this Software; + +3.2. Display the following text in your software's "About" box: " **RawFileReader reading tool. Copyright © 2016 by Thermo Fisher Scientific, Inc. All rights reserved**."; + +3.3. Require your end users to agree to a license agreement that prohibits them from redistributing this Software to others. + +4. Distribution Restrictions: + +4.1. You may not use the Licensor's trademarks in a way that suggests your software components and/or programs are provided by or are endorsed by the Licensor; and + +4.2. You may not commercially exploit this Software or products that incorporate this Software without the prior written consent of Licensor. Commercial exploitation includes, but is not limited to, charging a purchase price, license fee, maintenance fee, or subscription fee; or licensing, transferring or redistributing the Software in exchange for consideration of any kind. + +4.3. Your rights to this Software do not include any license, right, power or authority to subject this Software in whole or in part to any of the terms of an Excluded License. "Excluded License" means any license that requires as a condition of use, modification and/or distribution of software subject to the Excluded License, that such software or other software combined and/or distributed with such software be (a) disclosed or distributed in source code form; or (b) licensed for the purpose of making derivative works. Without limiting the foregoing obligation, you are specifically prohibited from distributing this Software with any software that is subject to the General Public License (GPL) or similar license in a manner that would create a combined work. + +5. Additional Terms Applicable to Software: + +5.1. This Software is licensed, not sold. This License only gives you some rights to use this Software; the Licensor reserves all other rights. Unless applicable law gives you more rights despite this limitation, you may use this Software only as expressly permitted in this License. + +5.2. Licensor has no obligation to fix, update, supplement or support this Software. + +5.3. This Software is not designed, manufactured or intended for any use requiring fail-safe performance in which the failure of this Software could lead to death, serious personal injury or severe physical and environmental damage ("High Risk Activities"), such as the operation of aircraft, medical or nuclear facilities. You agree not to use, or license the use of, this Software in connection with any High Risk Activities. + +5.4. Your rights under this License terminate automatically if you breach this License in any way. Termination of this License will not affect any of your obligations or liabilities arising prior to termination. The following sections of this License shall survive termination: 2.1, 3.1, 3.2, 3.3, 4.1, 4.2, 4.3, 5.1, 5.2, 5.3, 5.5, 5.6, 5.7, 5.8, and 5.9. + +5.5. This Software is subject to United States export laws and regulations. You agree to comply with all domestic and international export laws and regulations that apply to this Software. These laws include restrictions on destinations, end users and end use. + +5.6. This License shall be construed and controlled by the laws of the State of California, U.S.A., without regard to conflicts of law. You consent to the jurisdiction of the state and federal courts situated in the State of California in any action arising under this License. The application of the U.N. Convention on Contracts for the International Sale of Goods to this License is hereby expressly excluded. If any provision of this License shall be deemed unenforceable or contrary to law, the rest of this License shall remain in full effect and interpreted in an enforceable manner that most nearly captures the intent of the original language. + +5.7. THIS SOFTWARE IS LICENSED "AS IS". YOU BEAR ALL RISKS OF USING IT. LICENSOR GIVES NO AND DISCLAIMS ALL EXPRESS AND IMPLIED WARRANTIES, REPRESENTATIONS OR GUARANTEES. YOU MAY HAVE ADDITIONAL CONSUMER RIGHTS UNDER YOUR LOCAL LAWS WHICH THIS LICENSE CANNOT CHANGE. TO THE EXTENT PERMITTED UNDER YOUR LOCAL LAWS, LICENSOR EXCLUDES THE IMPLIED WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. + +5.8. LICENSOR'S TOTAL LIABILITY TO YOU FOR DIRECT DAMAGES ARISING UNDER THIS LICENSE IS LIMITED TO U.S. $1.00. YOU CANNOT RECOVER ANY OTHER DAMAGES, INCLUDING CONSEQUENTIAL, LOST PROFITS, SPECIAL, INDIRECT OR INCIDENTAL DAMAGES, EVEN IF LICENSOR IS EXPRESSLY MADE AWARE OF THE POSSIBILITY THEREOF OR IS NEGLIGENT. THIS LIMITATION APPLIES TO ANYTHING RELATED TO THIS SOFTWARE, SERVICES, CONTENT (INCLUDING CODE) ON THIRD PARTY INTERNET SITES, OR THIRD PARTY PROGRAMS, AND CLAIMS FOR BREACH OF CONTRACT, BREACH OF WARRANTY, GUARANTEE OR CONDITION, STRICT LIABILITY, NEGLIGENCE, OR OTHER TORT TO THE EXTENT PERMITTED BY APPLICABLE LAW. + +5.9. Use, duplication or disclosure of this Software by the U.S. Government is subject to the restricted rights applicable to commercial computer software (under FAR 52.227019 and DFARS 252.227-7013 or parallel regulations). The manufacturer for this purpose is Thermo Finnigan LLC, 355 River Oaks Parkway, San Jose, California 95134, U.S.A.
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/macros.xml Wed Feb 17 10:49:33 2021 +0000 @@ -0,0 +1,311 @@ +<macros> + <token name="@TOOL_VERSION@">2.0.0</token> + <token name="@GALAXY_TOOL_VERSION@">0</token> + + <xml name="requirements"> + <requirements> + <requirement type="package" version="@TOOL_VERSION@">dimspy</requirement> + </requirements> + </xml> + + + <token name="@HDF5_PM_TO_TXT@"> + dimspy hdf5-pm-to-txt + --input '$hdf5_file_out' + --output '$matrix_file_out' + --delimiter $delimiter + --attribute_name $hdf5_to_txt.matrix_attr + --representation-samples $hdf5_to_txt.representation_samples + </token> + + <token name="@HDF5_PM_TO_TXT_COMPREHENSIVE@"> + dimspy hdf5-pm-to-txt + --input '$hdf5_file_out' + --output '$matrix_comprehensive_file_out' + --delimiter $delimiter + --comprehensive + --attribute_name $hdf5_to_txt.matrix_attr + --representation-samples $hdf5_to_txt.representation_samples + </token> + + <xml name="hdf5_pm_to_txt"> + <section name="hdf5_to_txt" title="Show options for addtional output (*.tsv files)" expanded="False"> + <param name="standard" type="boolean" checked="false" truevalue="true" falsevalue="false" label="Standard output?" help="Set to "yes" to produce a "standard" Peak Matrix (i.e. m/z and intensity)."/> + <param name="comprehensive" type="boolean" checked="false" truevalue="true" falsevalue="false" label="Comprehensive output?" help="Set to "yes" to produce a Peak Matrix with additional information (e.g. m/z and intensity, rsd, missing values)."/> + <param name="representation_samples" type="select" label="Should the rows or columns represent the samples?" help=""> + <option value="rows" selected="true">Rows</option> + <option value="columns">Columns</option> + </param> + <param name="matrix_attr" type="select" label="The Peak Matrix should contain ... values" help=""> + <option value="intensity" selected="true">Intensity</option> + <option value="mz">m/z</option> + <option value="snr">Signal-to-noise ratio (SNR)</option> + </param> + </section> + </xml> + + <xml name="outputs_peak_intensity_matrix"> + <data name="hdf5_file_out" format="h5" label="${tool.name} on ${on_string}: Peak Intensity Matrix (HDF5 file)"/> + <data name="matrix_file_out" format="tsv" label="${tool.name} on ${on_string}: Peak Intensity Matrix"> + <filter>hdf5_to_txt["standard"] is True</filter> + </data> + <data name="matrix_comprehensive_file_out" format="tsv" label="${tool.name} on ${on_string}: Peak Intensity Matrix (comprehensive)" > + <filter>hdf5_to_txt["comprehensive"] is True</filter> + </data> + </xml> + + <xml name="citations"> + <citations> + <citation type="doi">10.1038/nprot.2016.156</citation> + <citation type="doi">10.1038/sdata.2014.12</citation> + <citation type="doi">10.1021/ac062446p</citation> + <citation type="doi">10.1021/ac2001803</citation> + <yield /> + </citations> + </xml> + + <token name="@example_filelist@"><![CDATA[ + + +-------------------------+----------------+---------------+-----------+--------------------+-----------+ + | **filename** | **classLabel** | **replicate** | **batch** | **injectionOrder** | **[...]** | + +-------------------------+----------------+---------------+-----------+--------------------+-----------+ + | sample_rep1.raw | sample | 1 | 1 | 1 | [...] | + +-------------------------+----------------+---------------+-----------+--------------------+-----------+ + | sample_rep2.raw | sample | 2 | 1 | 2 | [...] | + +-------------------------+----------------+---------------+-----------+--------------------+-----------+ + | sample_rep3.raw | sample | 3 | 1 | 3 | [...] | + +-------------------------+----------------+---------------+-----------+--------------------+-----------+ + | sample_rep4.raw | sample | 4 | 1 | 4 | [...] | + +-------------------------+----------------+---------------+-----------+--------------------+-----------+ + | blank_rep1.raw | blank | 1 | 1 | 5 | [...] | + +-------------------------+----------------+---------------+-----------+--------------------+-----------+ + | blank_rep2.raw | blank | 2 | 1 | 6 | [...] | + +-------------------------+----------------+---------------+-----------+--------------------+-----------+ + | blank_rep3.raw | blank | 3 | 1 | 7 | [...] | + +-------------------------+----------------+---------------+-----------+--------------------+-----------+ + | blank_rep4.raw | blank | 4 | 1 | 8 | [...] | + +-------------------------+----------------+---------------+-----------+--------------------+-----------+ + | ... | ... | ... | ... | ... | [...] | + +-------------------------+----------------+---------------+-----------+--------------------+-----------+ + + ]]></token> + + + <token name="@help_columns_peaklist@"> + + - **mz** - the mass-to-charge ratio of the extracted mass spectral peak + + - **intensity** - the intensity of the extracted mass spectral peak + + - **snr** - the signal-to-noise ratio of the extracted peak, which is defined as the ratio of the peak’s intensity value to that of the background noise intensity value + + - **present** - a numeric value greater than 0 that indicates the total number of scans at least one peak was detected in a given file + + - **fraction** - a proportion ranging from 0 to 1 that indicates the total number of times a peak was detected in a given scan event type, divided by the total number of occurrences of that scan event type recorded in a given file. + + - **purity** - a numeric value ranging from 0 to 1 that indicates the proportion of scans, for a given scan event type, that contained a single mass spectral peak following hierarchical clustering. A purity score less-than 1 indicates that in some proportion of scans, multiple peaks within a single scan were grouped together during the hierarchical clustering process. + + - **occurrence** - a numeric value greater than 0 that indicates the total number of peaks that were observed across scans within the user-defined ppm error tolerance. + + - **snr_flag** - a boolean value indicating whether to keep (“1”) or discard (“0”) a peak according to its signal-to-noise ratio value. + + - **fraction_flag** - a boolean value indicating whether a peak should be kept or discarded according to the ratio of the number of scans in which it was detected, to the the number of scans in which it was not detected. + + - **flags** - a boolean value indicating whether a peak should be retained or discarded based upon both its ‘snr_flag’ and ‘fraction_flag’ boolean values (if either is set to ‘0’ i.e. discard peak, then the ‘flags’ boolean should also be 0). + + </token> + + <token name="@example_peaklist@"> + + +**Example of an processed and filtered peaklist**: + + ++-----------+-----------+------+---------+----------+-------+------------+--------+----------+---------------+-------+ +| mz | intensity | snr | present | fraction | rsd | occurrence | purity | snr_flag | fraction_flag | flags | ++-----------+-----------+------+---------+----------+-------+------------+--------+----------+---------------+-------+ +| 90.44000 | 4744.0 | 3.06 | 1 | 0.063 | nan | 1 | 1 | 1 | 0 | 0 | ++-----------+-----------+------+---------+----------+-------+------------+--------+----------+---------------+-------+ +| 97.07380 | 5423.6 | 3.52 | 1 | 0.063 | nan | 1 | 1 | 1 | 0 | 0 | ++-----------+-----------+------+---------+----------+-------+------------+--------+----------+---------------+-------+ +| 99.04180 | 4105.8 | 3.60 | 1 | 0.063 | nan | 1 | 1 | 1 | 0 | 0 | ++-----------+-----------+------+---------+----------+-------+------------+--------+----------+---------------+-------+ +| 99.49800 | 4775.7 | 3.05 | 1 | 0.063 | nan | 1 | 1 | 1 | 0 | 0 | ++-----------+-----------+------+---------+----------+-------+------------+--------+----------+---------------+-------+ +| 99.95020 | 5657.8 | 3.63 | 1 | 0.063 | nan | 1 | 1 | 1 | 0 | 0 | ++-----------+-----------+------+---------+----------+-------+------------+--------+----------+---------------+-------+ +| 100.40660 | 5489.5 | 3.57 | 3 | 0.188 | 14.51 | 3 | 1 | 1 | 0 | 0 | ++-----------+-----------+------+---------+----------+-------+------------+--------+----------+---------------+-------+ +| 100.8672 | 4841.18 | 3.27 | 7 | 0.4375 | 16.36 | 7 | 1 | 1 | 0 | 0 | ++-----------+-----------+------+---------+----------+-------+------------+--------+----------+---------------+-------+ +| 101.0027 | 9047.79 | 5.99 | 16 | 1 | 21.53 | 19 | 0.8125 | 1 | 1 | 1 | ++-----------+-----------+------+---------+----------+-------+------------+--------+----------+---------------+-------+ +| 101.0033 | 271893.9 | 182 | 16 | 1 | 4.17 | 16 | 1 | 1 | 1 | 1 | ++-----------+-----------+------+---------+----------+-------+------------+--------+----------+---------------+-------+ +| 101.0038 | 8738.03 | 5.9 | 14 | 0.875 | 9.71 | 14 | 1 | 1 | 1 | 1 | ++-----------+-----------+------+---------+----------+-------+------------+--------+----------+---------------+-------+ +| 101.004 | 5166.67 | 3.5 | 5 | 0.3125 | 18.02 | 6 | 0.8 | 1 | 0 | 0 | ++-----------+-----------+------+---------+----------+-------+------------+--------+----------+---------------+-------+ +| 101.0599 | 5894.69 | 3.88 | 2 | 0.125 | 15.06 | 2 | 1 | 1 | 0 | 0 | ++-----------+-----------+------+---------+----------+-------+------------+--------+----------+---------------+-------+ +| 101.2728 | 6846.28 | 4.44 | 1 | 0.0625 | nan | 1 | 1 | 1 | 0 | 0 | ++-----------+-----------+------+---------+----------+-------+------------+--------+----------+---------------+-------+ + + </token> + + <token name="@help_options_addtional_output@"> + +**Show options for additional output(s)** (OPTIONAL): + +- **Standard output** (default = **No**) - boolean toggle where selection of: + + - **No** - prevent the export of a .txt formatted peak matrix to the active Galaxy history. + + - **Yes** - export a .txt formatted peak matrix to the active Galaxy history that includes only those peaks from the input peak intensity matrix that passed the filtering procedure. + +- **Comprehensive output** (default = "No") - boolean toggle where selection of: + + - **No** - prevents export of a .txt formatted comprehensive peak matrix. + + - **Yes** - exports a .txt formatted comprehensive peak matrix to the active Galaxy history that contains the m/z, missing values and other metrics associated with all peaks included in the input peak intensity matrix, including the metric defined by the "The peak matrix should contain intensity | m/z | SNR values" parameter. + +- **Should rows or columns represent the samples?** (default = **rows**) - binary toggle where selection of: + + - **rows** - sample information is presented in the rows and m/z values (for aligned mass spectral peaks) in the columns of any output peak matrix. + + - **columns** - sample information is presented in the columns and m/z values (for aligned mass spectral peaks) in the rows of any output peak matrix. + +- **The peak matrix should contain intensity | m/z | SNR values** - use this option to define which peak metric is inserted in to the cells of any optionally-output peak matrix: + + - **Intensity** - writes the absolute peak intensity to the cells of the peak matrix + + - **m/z** - writes the mass-to-charge ratio to the cells of the peak matrix + + - **signal-to-noise ratio (SNR)** - writes the signal-to-noise ratio to the cells of the peak matrix + + </token> + + <token name="@help_outputs_matrix@"> + +**Default output** - a HDF5 file containing the aligned peak intensity matrix. + +| + +**Optional outputs** - the metric recorded in any optionally output peak matrix/matrices is defined using the parameter "The peak matrix should contain intensity | m/z | SNR values". By default, study samples are listed row-wise, while mass-to-charge ratios of the aligned mass spectral peaks are presented in columns (to adjust, users must adjust the "Should rows or columns represent samples" toggle to “columns”). + + +- **Standard output** - an aligned peak matrix in tab-delimited format (“.” as decimal and NA for missing values). + + **Example of a standard peak intensity matrix**: + + +------------+----------+----------+----------+----------+---------+ + | mz | 96.04216 | 99.08062 | 100.0759 | 100.8672 | ... | + +------------+----------+----------+----------+----------+---------+ + | QC_1 | 0 | 0 | 0 | 0 | ... | + +------------+----------+----------+----------+----------+---------+ + | Blank_1 | 3342.626 | 0 | 0 | 0 | ... | + +------------+----------+----------+----------+----------+---------+ + | Control_10 | 0 | 0 | 45432.2 | 0 | ... | + +------------+----------+----------+----------+----------+---------+ + | Sample_2 | 0 | 3423.3 | 0 | 0 | ... | + +------------+----------+----------+----------+----------+---------+ + | Control_5 | 0 | 0 | 49759 | 0 | ... | + +------------+----------+----------+----------+----------+---------+ + | Control_10 | 0 | | 39890.5 | 0 | ... | + +------------+----------+----------+----------+----------+---------+ + | Sample_20 | 0 | 14563.7 | 0 | 0 | ... | + +------------+----------+----------+----------+----------+---------+ + | Sample_2 | 0 | 34676.4 | 0 | 0 | ... | + +------------+----------+----------+----------+----------+---------+ + | Sample_14 | 0 | 13134.9 | 0 | 521.4 | ... | + +------------+----------+----------+----------+----------+---------+ + | ... | ... | ... | ... | ... | ... | + +------------+----------+----------+----------+----------+---------+ + + | + +- **Comprehensive output** - an aligned peak matrix, as described for the "standard output" (above), including all metadata from the "Process Scans" Filelist/samplelist and the following additional mass spectral peak metrics: + + - **present** - a positive integer value (0 <![CDATA[ < ]]> value <![CDATA[ < ]]> total number of study samples in the filelist / samplelist) that indicates the total number of study samples in which a peak was detected with the specified mass-to-charge ratio, plus or minus the user-defined ppm error tolerance. + + - **occurrence** - a positive integer value indicating the number of peaks that were grouped together during the alignment procedure and thus, that were used to calculate the average mass-to-charge ratio indicated for the aligned peak. A value greater than given in the “Present” metric indicates that one or more peaklists contained more-than one mass spectral peak with the specified mass-to-charge, plus or minus the user-defined ppm error tolerance. + + - **purity** - a proportion ranging from 0 to 1 that indicates the number of scans in which only a single peak was detected during the peaklist alignment process. If the value in the “occurrence” metric is greater than the “present” metric, purity will be <![CDATA[ < ]]> 1. A purity <![CDATA[ < ]]> 1 means that in at least one peaklist there was more-than one mass spectral peak with the specified mass-to-charge, plus or minus the user-defined ppm error tolerance. + + - **rsd_all** - a numeric value indicating the percent relative standard deviation (otherwise termed the percent coefficient of variation) of peak intensities for peaks aligned together using the Align Samples tool. If fewer than 2 peaks were aligned across samples, then the rsd_all column will be filled in with ‘nan’ + + - **blank_flag** (may be absent if "Blank filter” tool was not applied) - a boolean value where 0 = reject peak, 1 = accept peak. A peak is accepted during blank filtering if a user-defined minimum proportion of study samples had peak intensity values greater-than the product of the average of “reference” sample peak intensities and the “min_fold_change” parameter. + + - **fraction_flag** (may be absent if "Sample filter” tool was not applied)- a boolean value where 0 = reject peak, 1 = accept peak. If greater-than a user-defined minimum fraction of samples (whether checked across ALL experimental classes, or within ANY of the individual experimental classes) had recorded intensity values for a given peak, then this peak is accepted, i.e. it is considered in downstream processing procedures, while rejected peaks are not. + + - **flags** - a boolean value indicating whether a peak should be included (“1”) or excluded (“0”) from downstream processing procedures. Exclusion of a peak occurs if the thresholds for “relative standard deviation” and/or “minimum number of technical replicates a peak has to be present in” were not met. + + | + + **Example of a comprehensive peak intensity matrix**: + + +-------------+----------------+------------+-----------------+----------------+---------------------+-----------------+--------------+----------+----------+----------+----------+---------+ + | mz | missing values | tags_batch | tags_replicates | tags_replicate | tags_injectionOrder | tags_classLabel | tags_untyped | 96.04216 | 99.08062 | 100.0759 | 100.8672 | ... | + +-------------+----------------+------------+-----------------+----------------+---------------------+-----------------+--------------+----------+----------+----------+----------+---------+ + | present* | | | | | | | | 1 | 4 | 3 | 1 | ... | + +-------------+----------------+------------+-----------------+----------------+---------------------+-----------------+--------------+----------+----------+----------+----------+---------+ + | occurrence* | | | | | | | | 1 | 4 | 4 | 1 | ... | + +-------------+----------------+------------+-----------------+----------------+---------------------+-----------------+--------------+----------+----------+----------+----------+---------+ + | purity* | | | | | | | | 1 | 1 | 1 | 1 | ... | + +-------------+----------------+------------+-----------------+----------------+---------------------+-----------------+--------------+----------+----------+----------+----------+---------+ + | rsd_all* | | | | | | | | nan | nan | 10.98 | nan | ... | + +-------------+----------------+------------+-----------------+----------------+---------------------+-----------------+--------------+----------+----------+----------+----------+---------+ + | flags* | | | | | | | | 1 | 1 | 1 | 1 | ... | + +-------------+----------------+------------+-----------------+----------------+---------------------+-----------------+--------------+----------+----------+----------+----------+---------+ + | QC_1 | 2901 | 1 | 2_3_4 | 2 | 2 | QC | | 0 | 0 | 0 | 0 | ... | + +-------------+----------------+------------+-----------------+----------------+---------------------+-----------------+--------------+----------+----------+----------+----------+---------+ + | Blank_1 | 2948 | 1 | 1_2_4 | 1 | 5 | Blank | | 3342.626 | 0 | 0 | 0 | ... | + +-------------+----------------+------------+-----------------+----------------+---------------------+-----------------+--------------+----------+----------+----------+----------+---------+ + | Control_10 | 2921 | 1 | 2_3_4 | 2 | 10 | Control | | 0 | 0 | 45432.2 | 0 | ... | + +-------------+----------------+------------+-----------------+----------------+---------------------+-----------------+--------------+----------+----------+----------+----------+---------+ + | Sample_2 | 2819 | 1 | 1_2_4 | 1 | 13 | Exposed | | 0 | 3423.3 | 0 | 0 | ... | + +-------------+----------------+------------+-----------------+----------------+---------------------+-----------------+--------------+----------+----------+----------+----------+---------+ + | Control_5 | 2877 | 1 | 2_3_4 | 2 | 18 | Control | | 0 | 0 | 49759 | 0 | ... | + +-------------+----------------+------------+-----------------+----------------+---------------------+-----------------+--------------+----------+----------+----------+----------+---------+ + | Control_10 | 2856 | 1 | 1_2_3 | 1 | 21 | Control | | 0 | | 39890.5 | 0 | ... | + +-------------+----------------+------------+-----------------+----------------+---------------------+-----------------+--------------+----------+----------+----------+----------+---------+ + | Sample_20 | 2855 | 1 | 1_2_4 | 1 | 25 | Exposed | | 0 | 14563.7 | 0 | 0 | ... | + +-------------+----------------+------------+-----------------+----------------+---------------------+-----------------+--------------+----------+----------+----------+----------+---------+ + | Sample_2 | 2814 | 1 | 1_2_4 | 1 | 29 | Exposed | | 0 | 34676.4 | 0 | 0 | ... | + +-------------+----------------+------------+-----------------+----------------+---------------------+-----------------+--------------+----------+----------+----------+----------+---------+ + | Sample_14 | 2870 | 1 | 1_2_3 | 1 | 33 | Exposed | | 0 | 13134.9 | 0 | 521.4 | ... | + +-------------+----------------+------------+-----------------+----------------+---------------------+-----------------+--------------+----------+----------+----------+----------+---------+ + | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | + +-------------+----------------+------------+-----------------+----------------+---------------------+-----------------+--------------+----------+----------+----------+----------+---------+ + + </token> + + + <token name="@github_developers_contributors@"> +Github respository (source code) +-------------------------------- + +Galaxy Tool Wrappers: https://github.com/computational-metabolomics/dimspy-galaxy/ +DIMSpy package: https://github.com/computational-metabolomics/dimspy/ + + +Developers and contributors +--------------------------- + +- Ralf Weber (r.j.weber@bham.ac.uk) - University of Birmingham (UK) +- Martin R. Jones (m.r.jones.1@bham.ac.uk) - University of Birmingham (UK) +- Thomas Lawson (tnl495@bham.ac.uk) - University of Birmingham (UK) + + </token> + + <token name="@license@"> +License +------- +DIMSpy is released under the GNU General Public License v3.0 (see `LICENSE file`_) + +.. _`LICENSE file`: https://github.com/computational-metabolomics/dimspy-galaxy/blob/master/LICENSE + + </token> + + +</macros>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/sample_filter.xml Wed Feb 17 10:49:33 2021 +0000 @@ -0,0 +1,155 @@ +<tool id="dimspy_sample_filter" name="Sample Filter" version="@TOOL_VERSION@+galaxy@GALAXY_TOOL_VERSION@"> + <description>Remove peaks that fail to appear in x-out-of-n (biological) samples</description> + <macros> + <import>macros.xml</import> + </macros> + <expand macro="requirements" /> + <command detect_errors="exit_code"> + <![CDATA[ + dimspy sample-filter + --input '$hdf5_file_in' + --output '$hdf5_file_out' + --min-fraction $min_fraction + ${within} + #if $rsd_threshold + --rsd-threshold $rsd_threshold + #end if + #if $qc_label + --qc-label '$qc_label' + #end if + #if $hdf5_to_txt.standard + && + @HDF5_PM_TO_TXT@ + #end if + #if $hdf5_to_txt.comprehensive + && + @HDF5_PM_TO_TXT_COMPREHENSIVE@ + #end if + ]]> + </command> + <inputs> + <param name="hdf5_file_in" argument="--input" type="data" format="h5" label="Peak Intensity Matrix (HDF5 file)" help="" /> + <param name="within" argument="--within" type="boolean" checked="false" truevalue="--within" falsevalue="" label="Apply sample filter within each sample class." help="" /> + <param name="min_fraction" argument="--min-fraction" type="float" min="0" max="1.0" value="0.5" label="Minimum fraction" help="Minimum fraction (i.e. percentage) of samples a peak has to be present" /> + <param name="rsd_threshold" type="hidden" value="" label="Relative standard deviation threshold" help="Leave empty to skip this step" argument="--rsd-threshold"/> + <param name="qc_label" argument="--qc-label" type="hidden" value="" label="Label for the QC sample (RSD filter only)" help="Peaks that have a larger RSD than the QC peaks are removed (Leave empty to skip this step)." > + <validator type="regex" message="Value may include alphanumeric characters, underscores, dashes and spaces.">[A-Za-z0-9_\- ]*</validator> + </param> + <param name="delimiter" argument="--delimiter" type="hidden" value="tab" label="" help=""/> + <expand macro="hdf5_pm_to_txt" /> + </inputs> + <outputs> + <expand macro="outputs_peak_intensity_matrix" /> + </outputs> + <tests> + <test> + <param name="hdf5_file_in" value="pm_as_bf.h5" ftype="h5"/> + <param name="within" value=""/> + <param name="min_fraction" value="0.5"/> + <param name="rsd_threshold" value=""/> + <param name="qc_label" value=""/> + <param name="delimiter" value="tab"/> + <conditional name="hdf5_to_txt"> + <param name="standard" value="True"/> + <param name="comprehensive" value="True"/> + <param name="representation_samples" value="rows"/> + <param name="matrix_attr" value="intensity"/> + </conditional> + <output name="hdf5_file_out" file="pm_as_bf_sf.h5" ftype="h5" compare="sim_size"/> + <output name="matrix_file_out" file="peak_matrix_as_bf_sf.txt" ftype="tsv" compare="sim_size"/> + <output name="matrix_comprehensive_file_out" file="peak_matrix_as_bf_sf_compr.txt" ftype="tsv"/> + </test> + <test> + <param name="hdf5_file_in" value="pm_as_bf.h5" ftype="h5"/> + <param name="within" value=""/> + <param name="min_fraction" value="0.5"/> + <param name="rsd_threshold" value=""/> + <param name="qc_label" value=""/> + <param name="delimiter" value="tab"/> + <conditional name="hdf5_to_txt"> + <param name="standard" value="True"/> + <param name="comprehensive" value="True"/> + <param name="representation_samples" value="columns"/> + <param name="matrix_attr" value="mz"/> + </conditional> + <output name="hdf5_file_out" file="pm_as_bf_sf.h5" ftype="h5" compare="sim_size"/> + <output name="matrix_file_out" file="peak_matrix_as_bf_sf_mz.txt" ftype="tsv"/> + <output name="matrix_comprehensive_file_out" file="peak_matrix_as_bf_sf_mz_compr.txt" ftype="tsv"/> + </test> + <test> + <param name="hdf5_file_in" value="pm_as_bf.h5" ftype="h5"/> + <param name="within" value="--within"/> + <param name="min_fraction" value="0.5"/> + <param name="rsd_threshold" value=""/> + <param name="qc_label" value=""/> + <param name="delimiter" value="tab"/> + <conditional name="hdf5_to_txt"> + <param name="standard" value="True"/> + <param name="comprehensive" value="True"/> + <param name="representation_samples" value="rows"/> + <param name="matrix_attr" value="intensity"/> + </conditional> + <output name="hdf5_file_out" file="pm_as_bf_sf_within.h5" ftype="h5" compare="sim_size"/> + <output name="matrix_file_out" file="peak_matrix_as_bf_sf_within.txt" ftype="tsv"/> + <output name="matrix_comprehensive_file_out" file="peak_matrix_as_bf_sf_within_compr.txt" ftype="tsv"/> + </test> + </tests> + <help> +------------- +Sample Filter +------------- + +.. + +---------------------------------------------- + +Description +----------- + +Standard DIMS processing workflow: Process Scans -> [Replicate Filter] -> Align Samples -> Blank Filter -> **Sample Filter** -> [Missing values sample filter] -> Pre-processing -> Statistics + +| + +There are many and varied reasons why a peak may not have been detected in all study samples, including: due to having an intensity (concentration) close to the signal-to-noise limit of the system; due to having been present in only one of the study classes (e.g. a drug administered to the ‘treatment’ class samples); due to ion suppression/enhancement effects in the mass spectrometer source region; etc. The Sample Filter tool allows users to remove peaks from the input peak intensity matrix that were detected in fewer-than a user-defined minimum number of study samples. + +------------------------------------ + +Parameters +----------- + +**Peak Intensity Matrix (HDF5 file)** (REQUIRED) - a HDF5 file containing the peak intensity matrix to undergo sample filtering. + + +**Apply sample filter within each sample class** (REQUIRED; default **No**) + + - **No** - check across ALL classes simultaneously whether greater-than the user-defined “Minimum fraction” of samples contained an intensity value for a specific mass spectral peak. + + - **Yes** - check within EACH class separately whether greater-than the user-defined “Minimum fraction” of samples contained an intensity value for a specific mass spectral peak. + + **IMPORTANT**: if in ANY class a peak is detected in greater-than the user-defined minimum fraction of samples, then the peak is retained in the output peak matrix. For classes in which this condition is not met, the peak intensity recorded for that peak (if any) will still be presented in the output peak matrix. If no peak intensity was recorded in a sample, then a ‘0’ is inserted in to the peak matrix. + + +**Minimum fraction** (REQUIRED, default 0.5) - a numeric value between 0 and 1 indicating the proportion of study samples in which a peak must have a recorded intensity value in order for it to be retained in the output peak intensity matrix; e.g. 0.5 means that at least 50% of samples (whether assessed across all classes, or within each class individually) must have a recorded intensity value for a specific peak in order for it to be retained in the output peak matrix. + +| + +@help_options_addtional_output@ + +------------------------------- + + +Output file(s) +-------------- + +**IMPORTANT** - in all outputs except for the (optional) comprehensive output, if fewer-than the user-defined “Minimum fraction” of samples (whether assessed within a class, or across all classes) had a recorded intensity value (either within or across classes) for a given peak, then that peak will be removed from the output peak matrix. Note that when assessing within classes, only one class needs to have recorded intensity values in greater-than the "Minimum fraction" of samples in order for the peak to be retained in the output peak matrix. + +@help_outputs_matrix@ + +------------------------------------- + +@github_developers_contributors@ +@license@ + </help> + <expand macro="citations" /> +</tool> +
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/filelist_merge.txt Wed Feb 17 10:49:33 2021 +0000 @@ -0,0 +1,14 @@ +filename classLabel +batch04_B02_rep01_301.mzML blank +batch04_B02_rep02_302.mzML blank +batch04_B02_rep03_303.mzML blank +batch04_S01_rep01_247.mzML sample +batch04_S01_rep02_248.mzML sample +batch04_S01_rep03_249.mzML sample +batch04_QC17_rep01_262.mzML QC +batch04_QC17_rep02_263.mzML QC +batch04_QC17_rep03_264.mzML QC +batch04_QC18_rep01_280.mzML QC +batch04_QC18_rep02_281.mzML QC +batch04_QC18_rep03_282.mzML QC +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/filelist_multilist_merge.txt Wed Feb 17 10:49:33 2021 +0000 @@ -0,0 +1,14 @@ +filename classLabel multilist +batch04_B02_rep01_301.mzML blank 1 +batch04_B02_rep02_302.mzML blank 2 +batch04_B02_rep03_303.mzML blank 3 +batch04_S01_rep01_247.mzML sample 1 +batch04_S01_rep02_248.mzML sample 2 +batch04_S01_rep03_249.mzML sample 3 +batch04_QC17_rep01_262.mzML QC 1 +batch04_QC17_rep02_263.mzML QC 2 +batch04_QC17_rep03_264.mzML QC 3 +batch04_QC18_rep01_280.mzML QC 1 +batch04_QC18_rep02_281.mzML QC 2 +batch04_QC18_rep03_282.mzML QC 3 +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/filelist_mzml_QC17_triplicate.txt Wed Feb 17 10:49:33 2021 +0000 @@ -0,0 +1,5 @@ +filename replicate injectionOrder classLabel +batch04_QC17_rep01_262.mzML 1 1 QC +batch04_QC17_rep02_263.mzML 2 2 QC +batch04_QC17_rep03_264.mzML 3 3 QC +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/filelist_mzml_triplicates.txt Wed Feb 17 10:49:33 2021 +0000 @@ -0,0 +1,10 @@ +filename replicate injectionOrder classLabel +batch04_B02_rep01_301.mzML 1 1 blank +batch04_B02_rep02_302.mzML 2 2 blank +batch04_B02_rep03_303.mzML 3 3 blank +batch04_QC17_rep01_262.mzML 1 4 QC +batch04_QC17_rep02_263.mzML 2 5 QC +batch04_QC17_rep03_264.mzML 3 6 QC +batch04_S01_rep01_247.mzML 1 7 sample +batch04_S01_rep02_248.mzML 2 8 sample +batch04_S01_rep03_249.mzML 3 9 sample
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/peak_matrix_as.txt Wed Feb 17 10:49:33 2021 +0000 @@ -0,0 +1,4 @@ +mz 95.08548671366543 96.04772304614822 97.06475291143175 97.10113840655873 98.99549986905885 99.08040117675267 101.00311801679446 101.05928426198216 101.0596673366772 101.77730040746692 101.78152400621897 102.12768606337795 102.97022355467323 103.03895120603583 103.07531883392421 104.10697250871583 105.00323419476476 105.09096431099911 107.07023024629058 107.08506232821624 107.08548831437565 109.10070305258999 109.10113731393284 111.08040236020433 111.11678604513918 112.0523012825977 113.05966680810211 113.09605148446333 114.0656779694334 114.06617048671613 114.09130290870944 115.03655729951394 115.07531829295569 115.11170521576067 116.07059901131294 116.98538214067723 116.9858715396252 117.05458339511324 117.09096864546488 118.08625365345154 119.08548531097144 120.98026726080117 120.98078653145228 121.00756219037551 121.06475400897882 123.01384531619902 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/peak_matrix_as_bf_compr.txt Wed Feb 17 10:49:33 2021 +0000 @@ -0,0 +1,10 @@ +mz missing values tags_classLabel tags_injectionOrder tags_replicates tags_untyped 95.08548671366543 96.04772304614822 97.06475291143175 97.10113840655873 98.99549986905885 99.08040117675267 101.00311801679446 101.05928426198216 101.0596673366772 101.77730040746692 101.78152400621897 102.12768606337795 102.97022355467323 103.03895120603583 103.07531883392421 104.10697250871583 105.00323419476476 105.09096431099911 107.07023024629058 107.08506232821624 107.08548831437565 109.10070305258999 109.10113731393284 111.08040236020433 111.11678604513918 112.0523012825977 113.05966680810211 113.09605148446333 114.0656779694334 114.06617048671613 114.09130290870944 115.03655729951394 115.07531829295569 115.11170521576067 116.07059901131294 116.98538214067723 116.9858715396252 117.05458339511324 117.09096864546488 118.08625365345154 119.08548531097144 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/peak_matrix_as_bf_sf_compr.txt Wed Feb 17 10:49:33 2021 +0000 @@ -0,0 +1,11 @@ +mz missing values tags_classLabel tags_injectionOrder tags_replicates tags_untyped 95.08548671366543 96.04772304614822 97.06475291143175 97.10113840655873 98.99549986905885 99.08040117675267 101.00311801679446 101.05928426198216 101.0596673366772 101.77730040746692 101.78152400621897 102.12768606337795 102.97022355467323 103.03895120603583 103.07531883392421 104.10697250871583 105.00323419476476 105.09096431099911 107.07023024629058 107.08506232821624 107.08548831437565 109.10070305258999 109.10113731393284 111.08040236020433 111.11678604513918 112.0523012825977 113.05966680810211 113.09605148446333 114.0656779694334 114.06617048671613 114.09130290870944 115.03655729951394 115.07531829295569 115.11170521576067 116.07059901131294 116.98538214067723 116.9858715396252 117.05458339511324 117.09096864546488 118.08625365345154 119.08548531097144 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/peak_matrix_as_bf_sf_mz.txt Wed Feb 17 10:49:33 2021 +0000 @@ -0,0 +1,238 @@ +mz batch04_B02_rep01_301_2_302_3_303 batch04_QC17_rep01_262_2_263_3_264 batch04_S01_rep01_247_2_248_3_249 +98.99549986905885 0.0 98.9955011572127 98.995498580905 +104.10697250871583 0.0 104.10697545483367 104.10696956259798 +112.0523012825977 0.0 112.05230129558926 112.05230126960613 +118.08625365345154 0.0 118.08625298204298 118.08625432486008 +123.01384531619902 0.0 123.01384410416192 123.01384652823613 +128.01084347694388 0.0 128.01084448835746 128.0108424655303 +128.9948583362488 0.0 128.99486394491046 128.99485272758713 +131.00983680839596 0.0 131.00983275926734 131.00984085752455 +131.01051782481363 0.0 131.0105163639754 131.0105192856519 +131.01894203525586 0.0 131.0189422814459 131.01894178906585 +132.0745839879842 0.0 132.07457964278774 132.07458833318066 +132.07500896236533 0.0 132.07500253214462 132.07501539258604 +132.07556731753854 0.0 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/peak_matrix_as_bf_sf_within_compr.txt Wed Feb 17 10:49:33 2021 +0000 @@ -0,0 +1,11 @@ +mz missing values tags_classLabel tags_injectionOrder tags_replicates tags_untyped 95.08548671366543 96.04772304614822 97.06475291143175 97.10113840655873 98.99549986905885 99.08040117675267 101.00311801679446 101.05928426198216 101.0596673366772 101.77730040746692 101.78152400621897 102.12768606337795 102.97022355467323 103.03895120603583 103.07531883392421 104.10697250871583 105.00323419476476 105.09096431099911 107.07023024629058 107.08506232821624 107.08548831437565 109.10070305258999 109.10113731393284 111.08040236020433 111.11678604513918 112.0523012825977 113.05966680810211 113.09605148446333 114.0656779694334 114.06617048671613 114.09130290870944 115.03655729951394 115.07531829295569 115.11170521576067 116.07059901131294 116.98538214067723 116.9858715396252 117.05458339511324 117.09096864546488 118.08625365345154 119.08548531097144 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/peak_matrix_as_mz_compr.txt Wed Feb 17 10:49:33 2021 +0000 @@ -0,0 +1,9 @@ +mz missing values tags_classLabel tags_injectionOrder tags_replicates tags_untyped 95.08548671366543 96.04772304614822 97.06475291143175 97.10113840655873 98.99549986905885 99.08040117675267 101.00311801679446 101.05928426198216 101.0596673366772 101.77730040746692 101.78152400621897 102.12768606337795 102.97022355467323 103.03895120603583 103.07531883392421 104.10697250871583 105.00323419476476 105.09096431099911 107.07023024629058 107.08506232821624 107.08548831437565 109.10070305258999 109.10113731393284 111.08040236020433 111.11678604513918 112.0523012825977 113.05966680810211 113.09605148446333 114.0656779694334 114.06617048671613 114.09130290870944 115.03655729951394 115.07531829295569 115.11170521576067 116.07059901131294 116.98538214067723 116.9858715396252 117.05458339511324 117.09096864546488 118.08625365345154 119.08548531097144 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/peak_matrix_hdf5_to_txt_comprehensive_snr.txt Wed Feb 17 10:49:33 2021 +0000 @@ -0,0 +1,11 @@ +mz missing values tags_classLabel tags_injectionOrder tags_replicates tags_untyped 95.08548671366543 96.04772304614822 97.06475291143175 97.10113840655873 98.99549986905885 99.08040117675267 101.00311801679446 101.05928426198216 101.0596673366772 101.77730040746692 101.78152400621897 102.12768606337795 102.97022355467323 103.03895120603583 103.07531883392421 104.10697250871583 105.00323419476476 105.09096431099911 107.07023024629058 107.08506232821624 107.08548831437565 109.10070305258999 109.10113731393284 111.08040236020433 111.11678604513918 112.0523012825977 113.05966680810211 113.09605148446333 114.0656779694334 114.06617048671613 114.09130290870944 115.03655729951394 115.07531829295569 115.11170521576067 116.07059901131294 116.98538214067723 116.9858715396252 117.05458339511324 117.09096864546488 118.08625365345154 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53.24978023564191 19.035632043933415 14.866557870963199 154.7408318971431 42.938331617576985 +batch04_S01_rep01_247_2_248_3_249 29.145804446154347 11.403257356162111 26.671321816272354 65.91472721557282 12.836481344761841 12.589077627524283 18.174340947535672 12.503269485340411 39.24309334626613 17.524176897031357 14.55250874417292 33.702422128429816 172.92629507894276 783.0955480760176 7890.742288561613 908.0847163397217 19.80037708341843 31.007337461395068 106.32318791039548 14.163165916988133 48.508016460110696 15.46594197170891 57.00207791406385 220.00847934187422 20.265189887912243 23.64314762466461 15.73938622044489 22.474819764854853 153.8046490125126 12.902672684596872 13.529289950527245 35.18005005483794 17.803237013058293 22.91072216920635 29.072511801888158 10.435581029054788 26.001983006564117 162.1758993249092 20.523978854006582 11.734970488934207 68.04667417044196 15.209997366072061 35.70823481568423 141.61804148226892 995.5159480566981 15.360056761719074 19.626739294854946 54.80574407550248 332.4198233912416 27.57146068694726 23.082504376575656 132.4055492901498 11.352536889069903 34.19246759587594 52.709599633192404 32.6931251553414 16.732183143852165 129.39223683334504 12.735173804314831 37.664226924806435 20.178098052354798 32.63022033422411 1650.12475841556 144.59618318393495 11.819937252289463 11.459531719137752 95.46652178986943 45.98850818681185 392.4324624347464 28.685306330470123 19.66277923411009 45.57751242122212 65.03369856223578 27.663583323038484 34.45283078135637 26.24089257798373 259.14730100803024 16.993272580010395 24.579201187435473 45.057380086635504 19.769360109557685 12.091783154572036 29.63671646056521 64.42305757073076 28.296074014644727 23.609207062634766 155.1323669617276 117.52891526562009 12.774927068360968 14.761445762695951 67.41343877441024 26.656919047591074 177.0126155010977 17.569721569882333 11.817399598394326 223.0044571955293 3526.0382083293966 263.4147490754906 22.028991460975703 41.00222805238901 249.86976272590354 11.42237162300078 18.59791776995712 35.34156945436025 14.238011877702322 11.289302661995595 20.939675362480823 11.52938652876898 14.148670284636568 113.37395153991798 30.49617058273766 358.7286777554346 41.224347513824036 44.238918822376824 30.905327105476847 18.246005079669917 32.321157990536726 11.764751030543003 17.96943139409716 161.90416918265475 19.544153742229074 16.42734118048151 70.83017548262067 646.9591141860177 71.68063879447885 11.671268297746115 49.64731982379532 37.00565159265913 42.83286034871188 11.52932426628371 22.962090677201655 27.44859289176055 290.6535913733465 31.68773047161383 62.67680088531491 11.468196944790535 121.15358078229941 19.171553035559757 23.262259705804468 223.97304645610822 19.119746382461162 13.010191891585634 12.533160144732772 84.52665085186352 11.999358657595645 23.38385451885196 21.708123951976614 54.443280861491054 32.58749038238159 27.284490940069862 13.252732897966391 14.525702481993328 23.3838038584064 20.32707051546949 14.538039938005511 16.828676369044985 220.24146495745495 22.09430838853324 30.024820758808584 16.170785108828955 15.862493269798813 42.28732580139026 13.52399702995943 51.33591276439305 395.2946071417642 68.84312568682064 28.643986350999295 3630.999610028613 31.991042660590484 55.450056633369876 546.0755933641905 182.0840733025534 41.59250978520138 31.80990575010578 32.195811531274494 11.590697418912091 11.395825193240068 45.26159311145779 58.49763388576406 33.46273357461692 12.998941237893646 13.038885147447058 13.509903069259614 21.08013837227028 18.311317666309577 89.9265129734 106.53471945494805 23.37536311599015 76.69939647108087 200.49225230573367 47.78644233041623 66.62160077707276 24.940088941581887 11.386196899968327 42.54436534925234 15.237082099161327 13.63603576582579 93.23941825083688 650.3651900055743 18.777507029959892 138.035814562545 38.08176346730567 12.20169222371279 131.63761137189343 39.203173988624656 14.97075981434991 25.978261386245816 19.270181242818552 21.128630855630842 54.76136776189306 15.192120491000578 16.121940831468994 12.98590780483278 13.687328272431008 22.461615744812274 24.53821393065536 16.372367422051937 55.74055916007458 16.286049449096613 12.412335455408789 11.25626577802412 552.476527126291 247.71627501660913 153.7884429505165 46.70596168901866 37.75444408713959 2497.7825988364466 82.13199686979547 17.502397199854265 777.6885047154927 181.7680519611231 95.84847856691711 46.76710816318723 14.433467449312426 11.59975438288337 306.0211220254601 83.72515304002712 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/report_pls_01.xt Wed Feb 17 10:49:33 2021 +0000 @@ -0,0 +1,73 @@ +filename event scans peaks median_rsd +batch04_B02_rep01_301.mzML FTMS + p ESI w SIM ms [70.00-170.00] 10 125 8.164744871334845 +batch04_B02_rep01_301.mzML FTMS + p ESI w SIM ms [140.00-240.00] 10 118 7.9168945392902135 +batch04_B02_rep01_301.mzML FTMS + p ESI w SIM ms [210.00-310.00] 14 66 7.686071240864687 +batch04_B02_rep01_301.mzML FTMS + p ESI w SIM ms [280.00-380.00] 14 59 7.357182338903599 +batch04_B02_rep01_301.mzML FTMS + p ESI w SIM ms [350.00-450.00] 14 38 7.810733023874823 +batch04_B02_rep01_301.mzML FTMS + p ESI w SIM ms [420.00-520.00] 12 31 7.113040692029466 +batch04_B02_rep01_301.mzML FTMS + p ESI w SIM ms [490.00-590.00] 14 45 6.300051388534287 +batch04_B02_rep01_301.mzML SIM-Stitch NA 482 7.768584373738055 +batch04_B02_rep02_302.mzML FTMS + p ESI w SIM ms [70.00-170.00] 10 128 8.076009915741135 +batch04_B02_rep02_302.mzML FTMS + p ESI w SIM ms [140.00-240.00] 10 125 8.038649267793415 +batch04_B02_rep02_302.mzML FTMS + p ESI w SIM ms [210.00-310.00] 14 65 7.448888733736318 +batch04_B02_rep02_302.mzML FTMS + p ESI w SIM ms [280.00-380.00] 14 61 6.296572607068351 +batch04_B02_rep02_302.mzML FTMS + p ESI w SIM ms [350.00-450.00] 14 47 7.012719313185922 +batch04_B02_rep02_302.mzML FTMS + p ESI w SIM ms [420.00-520.00] 12 31 7.350041999095771 +batch04_B02_rep02_302.mzML FTMS + p ESI w SIM ms [490.00-590.00] 14 43 7.375664473847967 +batch04_B02_rep02_302.mzML SIM-Stitch NA 500 7.687154590676263 +batch04_B02_rep03_303.mzML FTMS + p ESI w SIM ms [70.00-170.00] 10 123 9.169351544578852 +batch04_B02_rep03_303.mzML FTMS + p ESI w SIM ms [140.00-240.00] 10 118 7.554443758914416 +batch04_B02_rep03_303.mzML FTMS + p ESI w SIM ms [210.00-310.00] 15 66 7.783516194269935 +batch04_B02_rep03_303.mzML FTMS + p ESI w SIM ms [280.00-380.00] 14 57 6.270569975489961 +batch04_B02_rep03_303.mzML FTMS + p ESI w SIM ms [350.00-450.00] 13 44 8.203805443231127 +batch04_B02_rep03_303.mzML FTMS + p ESI w SIM ms [420.00-520.00] 12 32 8.263278003056694 +batch04_B02_rep03_303.mzML FTMS + p ESI w SIM ms [490.00-590.00] 14 45 8.21592686599179 +batch04_B02_rep03_303.mzML SIM-Stitch NA 485 8.035526771916881 +batch04_QC17_rep01_262.mzML FTMS + p ESI w SIM ms [70.00-170.00] 10 79 7.031966967936938 +batch04_QC17_rep01_262.mzML FTMS + p ESI w SIM ms [140.00-240.00] 11 80 7.7676656135167175 +batch04_QC17_rep01_262.mzML FTMS + p ESI w SIM ms [210.00-310.00] 14 61 8.153633358875439 +batch04_QC17_rep01_262.mzML FTMS + p ESI w SIM ms [280.00-380.00] 14 54 6.064659199205902 +batch04_QC17_rep01_262.mzML FTMS + p ESI w SIM ms [350.00-450.00] 13 38 6.249704967143267 +batch04_QC17_rep01_262.mzML FTMS + p ESI w SIM ms [420.00-520.00] 13 19 7.423632652950013 +batch04_QC17_rep01_262.mzML FTMS + p ESI w SIM ms [490.00-590.00] 13 38 7.08865209918093 +batch04_QC17_rep01_262.mzML SIM-Stitch NA 369 7.1854609699699745 +batch04_QC17_rep02_263.mzML FTMS + p ESI w SIM ms [70.00-170.00] 10 77 7.679712527427614 +batch04_QC17_rep02_263.mzML FTMS + p ESI w SIM ms [140.00-240.00] 11 76 7.4095987002251995 +batch04_QC17_rep02_263.mzML FTMS + p ESI w SIM ms [210.00-310.00] 14 61 8.070077166892615 +batch04_QC17_rep02_263.mzML FTMS + p ESI w SIM ms [280.00-380.00] 15 53 6.56434715809076 +batch04_QC17_rep02_263.mzML FTMS + p ESI w SIM ms [350.00-450.00] 13 43 6.0120617575086035 +batch04_QC17_rep02_263.mzML FTMS + p ESI w SIM ms [420.00-520.00] 13 19 6.894796224726954 +batch04_QC17_rep02_263.mzML FTMS + p ESI w SIM ms [490.00-590.00] 13 41 6.823399230382235 +batch04_QC17_rep02_263.mzML SIM-Stitch NA 370 7.3215340368881066 +batch04_QC17_rep03_264.mzML FTMS + p ESI w SIM ms [70.00-170.00] 10 76 7.066844757552882 +batch04_QC17_rep03_264.mzML FTMS + p ESI w SIM ms [140.00-240.00] 11 77 7.708127247443593 +batch04_QC17_rep03_264.mzML FTMS + p ESI w SIM ms [210.00-310.00] 14 62 7.273819325368941 +batch04_QC17_rep03_264.mzML FTMS + p ESI w SIM ms [280.00-380.00] 14 56 6.85168250470795 +batch04_QC17_rep03_264.mzML FTMS + p ESI w SIM ms [350.00-450.00] 13 44 8.136951844903859 +batch04_QC17_rep03_264.mzML FTMS + p ESI w SIM ms [420.00-520.00] 13 22 9.043103879106212 +batch04_QC17_rep03_264.mzML FTMS + p ESI w SIM ms [490.00-590.00] 13 44 8.17119973043174 +batch04_QC17_rep03_264.mzML SIM-Stitch NA 381 7.638539899322612 +batch04_S01_rep01_247.mzML FTMS + p ESI w SIM ms [70.00-170.00] 10 67 6.94493393729959 +batch04_S01_rep01_247.mzML FTMS + p ESI w SIM ms [140.00-240.00] 12 74 7.565621413380442 +batch04_S01_rep01_247.mzML FTMS + p ESI w SIM ms [210.00-310.00] 14 63 7.250184527315313 +batch04_S01_rep01_247.mzML FTMS + p ESI w SIM ms [280.00-380.00] 15 51 6.834026481611651 +batch04_S01_rep01_247.mzML FTMS + p ESI w SIM ms [350.00-450.00] 13 46 6.8631168803092875 +batch04_S01_rep01_247.mzML FTMS + p ESI w SIM ms [420.00-520.00] 13 30 7.5643366272894745 +batch04_S01_rep01_247.mzML FTMS + p ESI w SIM ms [490.00-590.00] 14 47 7.383285889388576 +batch04_S01_rep01_247.mzML SIM-Stitch NA 378 7.204818271465472 +batch04_S01_rep02_248.mzML FTMS + p ESI w SIM ms [70.00-170.00] 10 68 7.6140232663257965 +batch04_S01_rep02_248.mzML FTMS + p ESI w SIM ms [140.00-240.00] 11 74 6.913828093334823 +batch04_S01_rep02_248.mzML FTMS + p ESI w SIM ms [210.00-310.00] 15 63 7.810206749993205 +batch04_S01_rep02_248.mzML FTMS + p ESI w SIM ms [280.00-380.00] 14 52 6.671167067945765 +batch04_S01_rep02_248.mzML FTMS + p ESI w SIM ms [350.00-450.00] 14 49 7.47235757298909 +batch04_S01_rep02_248.mzML FTMS + p ESI w SIM ms [420.00-520.00] 13 29 9.180929326732146 +batch04_S01_rep02_248.mzML FTMS + p ESI w SIM ms [490.00-590.00] 13 50 7.4974332114539 +batch04_S01_rep02_248.mzML SIM-Stitch NA 385 7.447728173693162 +batch04_S01_rep03_249.mzML FTMS + p ESI w SIM ms [70.00-170.00] 10 70 8.140596594185261 +batch04_S01_rep03_249.mzML FTMS + p ESI w SIM ms [140.00-240.00] 11 72 6.7023463423858916 +batch04_S01_rep03_249.mzML FTMS + p ESI w SIM ms [210.00-310.00] 14 60 8.312209996466285 +batch04_S01_rep03_249.mzML FTMS + p ESI w SIM ms [280.00-380.00] 15 51 6.643019115940489 +batch04_S01_rep03_249.mzML FTMS + p ESI w SIM ms [350.00-450.00] 14 48 6.599696065499092 +batch04_S01_rep03_249.mzML FTMS + p ESI w SIM ms [420.00-520.00] 13 30 7.38454762189305 +batch04_S01_rep03_249.mzML FTMS + p ESI w SIM ms [490.00-590.00] 13 50 9.086547438353602 +batch04_S01_rep03_249.mzML SIM-Stitch NA 381 7.563062708333447
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/report_pls_02.xt Wed Feb 17 10:49:33 2021 +0000 @@ -0,0 +1,25 @@ +filename event scans peaks median_rsd +batch04_QC18_rep01_280.mzML FTMS + p ESI w SIM ms [70.00-170.00] 10 62 9.106795765105987 +batch04_QC18_rep01_280.mzML FTMS + p ESI w SIM ms [140.00-240.00] 11 66 8.376338250469699 +batch04_QC18_rep01_280.mzML FTMS + p ESI w SIM ms [210.00-310.00] 14 30 13.860951930094206 +batch04_QC18_rep01_280.mzML FTMS + p ESI w SIM ms [280.00-380.00] 14 43 11.995016644120462 +batch04_QC18_rep01_280.mzML FTMS + p ESI w SIM ms [350.00-450.00] 13 32 10.18681141686221 +batch04_QC18_rep01_280.mzML FTMS + p ESI w SIM ms [420.00-520.00] 12 15 11.232655948051354 +batch04_QC18_rep01_280.mzML FTMS + p ESI w SIM ms [490.00-590.00] 13 24 14.164121165835418 +batch04_QC18_rep01_280.mzML SIM-Stitch NA 272 10.833932763925075 +batch04_QC18_rep02_281.mzML FTMS + p ESI w SIM ms [70.00-170.00] 10 56 10.02227469296428 +batch04_QC18_rep02_281.mzML FTMS + p ESI w SIM ms [140.00-240.00] 11 47 16.50748950248718 +batch04_QC18_rep02_281.mzML FTMS + p ESI w SIM ms [210.00-310.00] 14 47 10.323122529981012 +batch04_QC18_rep02_281.mzML FTMS + p ESI w SIM ms [280.00-380.00] 14 46 7.969584657178587 +batch04_QC18_rep02_281.mzML FTMS + p ESI w SIM ms [350.00-450.00] 14 30 8.390974309393076 +batch04_QC18_rep02_281.mzML FTMS + p ESI w SIM ms [420.00-520.00] 12 16 9.67349503755467 +batch04_QC18_rep02_281.mzML FTMS + p ESI w SIM ms [490.00-590.00] 12 27 9.466647675517853 +batch04_QC18_rep02_281.mzML SIM-Stitch NA 269 10.06252290827951 +batch04_QC18_rep03_282.mzML FTMS + p ESI w SIM ms [70.00-170.00] 10 69 7.52328763290241 +batch04_QC18_rep03_282.mzML FTMS + p ESI w SIM ms [140.00-240.00] 11 71 7.905522565392167 +batch04_QC18_rep03_282.mzML FTMS + p ESI w SIM ms [210.00-310.00] 14 50 7.010930764188746 +batch04_QC18_rep03_282.mzML FTMS + p ESI w SIM ms [280.00-380.00] 14 49 7.247286414579351 +batch04_QC18_rep03_282.mzML FTMS + p ESI w SIM ms [350.00-450.00] 14 39 8.26255528557101 +batch04_QC18_rep03_282.mzML FTMS + p ESI w SIM ms [420.00-520.00] 13 20 8.483947267451931 +batch04_QC18_rep03_282.mzML FTMS + p ESI w SIM ms [490.00-590.00] 13 41 5.4612510154689105 +batch04_QC18_rep03_282.mzML SIM-Stitch NA 339 7.428907429907003
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/report_pls_rf_01.txt Wed Feb 17 10:49:33 2021 +0000 @@ -0,0 +1,4 @@ +name peaks peaks_3oo3 median_rsd_3oo3 +batch04_B02_rep01_301_2_302_3_303 487 421 12.335333490423352 +batch04_QC17_rep01_262_2_263_3_264 364 321 7.908538366075825 +batch04_S01_rep01_247_2_248_3_249 383 331 5.344215585775828
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/report_pls_rf_02.txt Wed Feb 17 10:49:33 2021 +0000 @@ -0,0 +1,2 @@ +name peaks peaks_3oo3 median_rsd_3oo3 +batch04_QC17_rep01_262_2_263_3_264 15 15 8.602469347435797
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/report_pls_scan5.txt Wed Feb 17 10:49:33 2021 +0000 @@ -0,0 +1,4 @@ +filename event scans peaks median_rsd +batch04_QC17_rep01_262.mzML FTMS + p ESI w SIM ms [70.00-170.00] 1 15 nan +batch04_QC17_rep02_263.mzML FTMS + p ESI w SIM ms [70.00-170.00] 1 15 nan +batch04_QC17_rep03_264.mzML FTMS + p ESI w SIM ms [70.00-170.00] 1 15 nan
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/sample_list_after_bf.txt Wed Feb 17 10:49:33 2021 +0000 @@ -0,0 +1,3 @@ +filename injectionOrder replicates classLabel +batch04_QC17_rep01_262_2_263_3_264 4 1-2-3 QC +batch04_S01_rep01_247_2_248_3_249 7 1-2-3 sample
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/sample_list_after_mv_filter.txt Wed Feb 17 10:49:33 2021 +0000 @@ -0,0 +1,3 @@ +filename injectionOrder replicates classLabel +batch04_QC17_rep01_262_2_263_3_264 4 1-2-3 QC +batch04_S01_rep01_247_2_248_3_249 7 1-2-3 sample
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/samplelist_1.txt Wed Feb 17 10:49:33 2021 +0000 @@ -0,0 +1,4 @@ +filename injectionOrder replicates classLabel +batch04_B02_rep01_301_2_302_3_303 1 1-2-3 blank +batch04_QC17_rep01_262_2_263_3_264 4 1-2-3 QC +batch04_S01_rep01_247_2_248_3_249 7 1-2-3 sample