view raceid_filtnormconf.xml @ 0:8dc8ff057b0f draft

planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/raceid3 commit f880060c478d42202df5b78a81329f8af56b1138

<tool id="raceid_filtnormconf" name="Filtering, Normalisation, and Confounder Removal using RaceID" version="@VERSION_RACEID@.@VERSION_PACKAGE@.1" >
    <description>generates a normalised and filtered count matrix of single-cell RNA data</description>
    <macros>
        <import>macros.xml</import>
        <import>macros_cluster.xml</import>
    </macros>
    <expand macro="requirements" />
    <version_command><![CDATA[
Rscript '$__tool_directory__/scripts/cluster.R' @GET_VERSION@
]]></version_command>

    <command detect_errors="exit_code"><![CDATA[
#set bin = 'cluster.R'
Rscript '$__tool_directory__/scripts/$bin' '$userconf' 2> '$outlog' > /dev/null
    ]]></command>
  
    <configfiles>
        <configfile name="userconf" ><![CDATA[
@STRING2VECTOR@

@FILTNORM_CHEETAH@
]]>
        </configfile>
    </configfiles>
    <inputs>
        <param name="intable" type="data" format="tabular" label="Count Matrix" />
        <section name="filt" title="Filtering" expanded="true" >
            <param name="mintotal" type="integer" min="1" value="3000" label="Min Transcripts" help="The minimum total transcripts required. Cells with less than mintotal transcripts are filtered out." />
            <param name="minexpr" type="integer" min="1" value="5" label="Min Expression" help="The minimum required transcript counts of a gene in the minimum number of cells (below)" />
            <param name="minnumber" type="integer" min="1" value="5" label="Min Cells" help="The minumum number of cells for gene expression to be counted"  />
            <expand macro="use_defaults_no" >
                <param name="knn" type="integer" min="0" value="10" label="K-nearest-neighbours" help="Number of nearest neighbors used to infer corresponding cell types in different batches" />
                <param name="CGenes" type="text" optional="true" label="CGenes" help="Filter out genes with correlated expression for cell type inference" >
                    <expand macro="sanitize_string_vector" />
                </param>
                <param name="FGenes" type="text" optional="true" label="FGenes" help="Explicitly filter out genes for cell type inference" >
                    <expand macro="sanitize_string_vector" />
                </param>
                <param name="LBatch_regexes" type="text" optional="true" label="Batch Regex" help="List of regexes to capture experimental batches for batch effect correction" >
                    <expand macro="sanitize_string_vector" />
                </param>
                <param name="ccor" type="float" value="0.4" label="CCor" help="Correlation coefficient used as a threshold for determining correlated genes" />
                <param name="bmode" type="select" label="Batch Mode" help="Method to regress out batch effects" >
                    <option value="RaceID" selected="true" >RaceID</option>
                    <option value="scran">SCRAN</option>
                </param>
                <conditional name="ccc" >
                    <param name="use" type="select" label="Perform Cell-cycle correction?" >
                        <option value="yes" >Yes</option>
                        <option value="no" selected="true" >No</option>
                    </param>
                    <when value="no" />
                    <when value="yes" >
                        <param name="vset" type="text" optional="true" label="List of Gene Sets" >
                            <expand macro="sanitize_string_vector" />
                        </param>
                        <param name="pvalue" type="float" value="0.01" min="0" max="1" label="P-value Cutoff" help="P-value cutoff for determining enriched components" />
                        <param name="quant" type="float" value="0.01" min="0" max="1" label="Quantification Fraction" help="Upper and lower fraction of gene loadings use for determining enriched components"  />
                        <param name="ncomp" type="integer" min="0" optional="true" label="Number of components to use" help="If left blank, the maximum number of components are used" /><!-- 0 = NULL -->
                        <param name="dimr" type="boolean" value="true" label="Derive Components from saturation criterion"  />
                        <param name="mode" type="select" label="Type of Component Analysis" help="If ICA is selected, ensure that the number of components value above is sufficiently high" >
                            <option value="pca" selected="true">PCA</option>
                            <option value="ica">ICA</option>
                        </param>
                        <param name="logscale" type="boolean" value="false" label="Log-transform data prior to PCA or ICA" help="" />
                    </when>
                </conditional>
                <param name="use_log" type="boolean" checked="false" label="Output Log?" />
            </expand>
        </section>                    
    </inputs>
    <outputs>
        <data name="outpdf" format="pdf" label="${tool.name} on ${on_string}: PDF Report" />
        <data name="outrdat" format="rdata" label="${tool.name} on ${on_string}: RDS" />
        <data name="outlog" format="txt" label="${tool.name} on ${on_string}: Log" >
            <filter>use_log</filter>
        </data>
    </outputs>
    <tests>
        <test>
            <!-- This is a file with a single word 'test', which prompts the scripts to use the test intestinalData in the library -->
            <param name="intable" value="use.intestinal" />
            <output name="outpdf" value="intestinal.filter.pdf" compare="sim_size" delta="50" />
        </test>
        <test>
            <!-- defaults, feeding in a matrix with reduced filtering -->
            <param name="intable" value="matrix.tabular" />
            <section name="filt" >
                <param name="mintotal" value="1050" />
                <param name="minexpr" value="1" />
                <param name="minnumber" value="3" />
            </section>
            <output name="outrdat" value="matrix.filter.rdat" compare="sim_size" delta="300" />
            <output name="outpdf" value="matrix.filter.pdf" compare="sim_size" delta="10" />
        </test>
        <test>
            <!-- defaults, but manually specified. No opts, no CC. Generates identical to above -->
            <param name="intable" value="use.intestinal" />
            <section name="filt" >
                <param name="mintotal" value="3000" />
                <param name="minexpr" value="5" />
                <param name="minnumber" value="5" />
                <expand macro="test_nondef" >
                    <param name="knn" value="10" />
                    <param name="ccor" value="0.4" />
                    <param name="bmode" value="RaceID" />
                </expand>
            </section>
            <output name="outpdf" value="intestinal.filter.pdf" compare="sim_size" delta="50" />
        </test>
        <test>
            <!-- Advanced. Opts, CC used  -->
            <param name="intable" value="use.intestinal" />
            <section name="filt" >
                <param name="mintotal" value="2000" />
                <param name="minexpr" value="3" />
                <param name="minnumber" value="2" />
                <expand macro="test_nondef" >
                    <param name="knn" value="5" />
                    <param name="ccor" value="0.5" />
                    <param name="CGenes" value="Gga3,Ggact,Ggct" />
                    <param name="FGenes" value="Zxdc,Zyg11a,Zyg11b,Zyx" />
                    <param name="LBatch_regexes" value="^I5,^II5,^III5,^IV5d,^V5d" />
                    <param name="bmode" value="scran" />
                    <conditional name="ccc" >
                        <param name="use" value="yes" />
                        <param name="pvalue" value="0.05" />
                        <param name="quant" value="0.05" />
                        <param name="ncomp" value="3" />
                        <param name="dimr" value="true" />
                        <param name="mode" value="pca" />
                        <param name="logscale" value="true" />
                    </conditional>
                </expand>
            </section>
            <output name="outpdf" value="intestinal_advanced.filter.pdf" compare="sim_size" delta="150" />
        </test>
    </tests>
        <help><![CDATA[
RaceID3
=======

RaceID is a clustering algorithm for the identification of cell types from single-cell RNA-sequencing data. It was specifically designed for the detection of rare cells which correspond to outliers in conventional clustering methods.

This module performs filtering, normalisation, and batch effect removal in the same step.


Example Usage: Inspecting the Aggregated Expression for a Group of Genes
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Our cells come from 5 different batches (I5,II5,III5,IV5,V5) and are labelled to reflect this (i.e. "I5_1", "I5_2", ..., "I5_129", "II5_1", ..., "V5_236" )

We wish to filter out the gene Lpca5 and Atk2 which we know in advance will saturate our analysis with unwanted expression.

We will also be interested in the cluster that contains significant expression for Apoa genes (Apoa1, Apoa1bp, Apoa2, Apoa4, Apoa5).

First, we must load in our count matrix in order to correct for batch effects, filter out unwanted genes, and compute our clusters and outliers.

 * *Mode of Analysis* → **Cluster**

   * *Count Matrix* → [input tabular]

   * Filtering:

     * *Use Defaults?* → **No**

     * *Batch Regex* → "^I5,^II5,^III5,^IV5,^V5"

     * *FGenes* → "Lpca5,Atk2"

A PDF report will be generated giving metrics about the library size and number of features as histograms, and additional metrics relating to cell-cycle correction will be produced if that option has been selected.

]]>
        </help>
        <expand macro="citations" />
</tool>