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