Mercurial > repos > computational-metabolomics > dimspy_replicate_filter

comparison replicate_filter.xml @ 1:0cdf340364ed draft

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"planemo upload for repository https://github.com/computational-metabolomics/dimspy-galaxy commit 680116d0cf6a6d7246cba655452dea43269aeba4"
author computational-metabolomics
date Tue, 28 Apr 2020 17:43:04 -0400
parents cb2acfaec200
children 6cb796aa12c8
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0:cb2acfaec200 1:0cdf340364ed
61 <output_collection name="peaklists_txt" type="list"> 61 <output_collection name="peaklists_txt" type="list">
62 <element name="batch04_QC17_rep01_262_2_263_3_264" file="batch04_QC17_rep01_262_2_263_3_264.txt" ftype="tsv"/> 62 <element name="batch04_QC17_rep01_262_2_263_3_264" file="batch04_QC17_rep01_262_2_263_3_264.txt" ftype="tsv"/>
63 </output_collection> 63 </output_collection>
64 </test> 64 </test>
65 <test> 65 <test>
66 <param name="hdf5_file_in" value="pls.h5" ftype="h5"/>
67 <param name="filelist" value="filelist_mzml_triplicates.txt" ftype="tsv"/>
68 <param name="replicates" value="3"/>
69 <param name="min_peaks" value="2"/>
70 <param name="ppm" value="2.0"/>
71 <param name="rsd_threshold" value=""/>
72 <output name="hdf5_file_out" value="pls_rf.h5" ftype="h5" compare="sim_size"/>
73 <output name="report" value="report_pls_rf_02.txt" ftype="txt"/>
74 <output name="samplelist" value="samplelist_2.txt" ftype="tsv"/>
75 <output_collection name="peaklists_txt" type="list">
76 <element name="batch04_QC17_rep01_262_2_263_3_264" file="batch04_QC17_rep01_262_2_263_3_264.txt" ftype="tsv"/>
77 </output_collection>
78 </test>
79 <test>
80 <param name="hdf5_file_in" value="pls_scan5.h5" ftype="h5"/> 66 <param name="hdf5_file_in" value="pls_scan5.h5" ftype="h5"/>
81 <param name="replicates" value="3"/> 67 <param name="replicates" value="3"/>
82 <param name="min_peaks" value="2"/> 68 <param name="min_peaks" value="2"/>
83 <param name="ppm" value="2.0"/> 69 <param name="ppm" value="2.0"/>
84 <param name="rsd_threshold" value=""/> 70 <param name="rsd_threshold" value=""/>
85 <output name="report" value="report_pls_rf_03.txt" ftype="txt"/> 71 <output name="report" value="report_pls_rf_02.txt" ftype="txt"/>
86 </test> 72 </test>
87 </tests> 73 </tests>
88 <help> 74 <help>
89 ---------------- 75 ----------------
90 Replicate filter 76 Replicate filter
95 -------------------- 81 --------------------
96 82
97 Description 83 Description
98 ----------- 84 -----------
99 85
100 Standard DIMS processing workflow: Process Scans -> **Replicate Filter** -> Align Samples -> [Missing value sample filter] -> Blank Filter -> Sample Filter -> Matrix processing -> Statistics 86 Standard DIMS processing workflow: Process Scans -> **[Replicate Filter]** -> Align Samples -> Blank Filter -> Sample Filter -> [Missing value sample filter] -> Pre-processing -> Statistics
101 87
102 | 88 |
103 89
104 To draw robust conclusions from DIMS-based metabolomics datasets, the data itself must be collected and processed in a robust and reproducible way. To support this aim, study samples are often divided in to a set of equivalent aliquots, each of which is analysed under defined and consistent analytical conditions. As aliquots of the same sample are assumed to comprise identical biological material, differences in their resulting spectra are assumed to arise due to technical variability. Removing artifacts associated with this technical variability, and removing mass spectral peaks that are detected irreproducible, is possible by filtering across these technical replicate spectra. The Replicate Filter tools facilitates this process, combining the peaklists extracted (using the Process Scans tool) from each technical replicate of a given study in to a single merged peaklist, before applying a series of user-defined filters to yield a replicate-filtered peaklist. 90 To draw robust conclusions from DIMS-based metabolomics datasets, the data itself must be collected and processed in a robust and reproducible way. To support this aim, study samples are often divided in to a set of equivalent aliquots, each of which is analysed under defined and consistent analytical conditions. As aliquots of the same sample are assumed to comprise identical biological material, differences in their resulting spectra are assumed to arise due to technical variability. Removing artifacts associated with this technical variability, and removing mass spectral peaks that are detected irreproducible, is possible by filtering across these technical replicate spectra. The Replicate Filter tools facilitates this process, combining the peaklists extracted (using the Process Scans tool) from each technical replicate of a given study in to a single merged peaklist, before applying a series of user-defined filters to yield a replicate-filtered peaklist.
105 91