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planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/main/tools/fastspar commit 0e305d21d0634a1788b9105ec4d0ab1c2da62359
author iuc
date Thu, 19 Jun 2025 21:51:32 +0000
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1 <tool id="fastspar" name="FastSpar" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="@PROFILE@">
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2 <description>
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3 correlation estimation for compositional data
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4 </description>
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5 <macros>
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6 <import>macros.xml</import>
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7 </macros>
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8 <expand macro="biotools"/>
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9 <expand macro="requirements"/>
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10 <command detect_errors="exit_code"><![CDATA[
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11 fastspar
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12 --otu_table '$otu_table'
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13 --iterations $iterations
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14 --exclude_iterations $exclude_iterations
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15 --threshold $threshold
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16 --seed $seed
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17 --correlation '$correlation'
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18 --covariance '$covariance'
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19 --threads \${GALAXY_SLOTS:-1}
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20 ## Skip warning prompt and continue analysis even if the input contains OTUs with just one permutation.
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21 --yes
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22 ]]></command>
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23 <inputs>
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24 <param argument="--otu_table" type="data" format="tabular" label="Input OTU table"
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25 help="The table must contain absolute OTU counts in plain tabular (TSV) format, with OTUs as rows and samples as columns. Do not include any metadata rows or columns."/>
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26 <expand macro="fastspar_tool_parameters"/>
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27 <param argument="--seed" type="integer" value="1" label="Random number seed"/>
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28 </inputs>
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29 <outputs>
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30 <data name="correlation" format="tabular" label="${tool.name} on ${on_string}: median_correlation.tsv"/>
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31 <data name="covariance" format="tabular" label="${tool.name} on ${on_string}: median_covariance.tsv"/>
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32 </outputs>
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33 <tests>
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34 <test expect_num_outputs="2">
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35 <param name="otu_table" ftype="tabular" value="fake_data.tsv"/>
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36 <output name="correlation" file="fake_data_cor.tsv" compare="diff"/>
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37 <output name="covariance" file="fake_data_cov.tsv" compare="diff"/>
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38 </test>
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39 <test expect_num_outputs="2">
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40 <param name="otu_table" ftype="tabular" value="fake_data.tsv"/>
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41 <param name="exclude_iterations" value="20"/>
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42 <param name="threshold" value="0.2"/>
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43 <output name="correlation" ftype="tabular">
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44 <assert_contents>
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45 <has_n_columns n="51"/>
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46 <has_text text="1.0000"/>
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47 </assert_contents>
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48 </output>
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49 <output name="covariance" ftype="tabular">
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50 <assert_contents>
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51 <has_n_columns n="51"/>
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52 <has_text text="OTU ID"/>
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53 </assert_contents>
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54 </output>
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55 </test>
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56 </tests>
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57 <help><![CDATA[
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58 What it does
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59 ============
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60
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61 FastSpar is a C++ implementation of the SparCC algorithm for estimating correlations from compositional data.
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62 This tool performs the **initial correlation and covariance matrix estimation** as the first step in the FastSpar pipeline.
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63 **If you also want to estimate p-values** you might want to use `fastspar_pvalues` with "Recalculate the correlation matrix".
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64
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65 Required Inputs
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66 ===============
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67
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68 - **OTU table** (TSV format): Contains absolute OTU counts (not relative abundances). Must be a plain tabular file with samples in columns and OTUs in rows. Metadata is not supported.
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69
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70 Main Parameters
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71 ===============
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72
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73 - **Iterations** (`--iterations`): Number of correlation estimation rounds. More iterations improve stability.
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74 - **Exclude iterations** (`--exclude_iterations`): Number of times highly correlated OTU pairs are removed.
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75 - **Correlation threshold** (`--threshold`): Correlation strength above which to exclude OTU pairs.
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76 - **Seed** (`--seed`): Random seed for reproducibility.
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77
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78 Main Features
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79 ===============
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80
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81 - Efficient and fast computation of sparse correlations.
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82 - Customizable exclusion and thresholding strategy.
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83 - Designed to handle compositional count data from microbiome studies.
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84
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85 Generated Outputs
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86 =================
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87
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88 - `median_correlation.tsv`: Correlation matrix between all OTUs.
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89 - `median_covariance.tsv`: Covariance matrix between all OTUs.
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90
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91 Additional Resources
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92 ====================
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93
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94 - FastSpar GitHub: [https://github.com/scwatts/fastspar]
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95
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96 For a complete FastSpar analysis, follow up with:
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97
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98 1. `fastspar_pvalues`: Estimate empirical p-values from bootstrap correlations.
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99 2. `fastspar_reduce`: Filter correlation and p-value matrices to produce sparse networks.
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100 ]]></help>
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101 <expand macro="citations"/>
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102 </tool>