annotate raceid_filtnormconf.xml @ 7:d45f092caf24 draft

"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/raceid3 commit b461ba94158cf4cc793919470b78bd3eb91312eb"

<tool id="raceid_filtnormconf" name="Initial processing using RaceID" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" >
    <description>performs filtering, normalisation, and confounder removal to generate a normalised and filtered count matrix of single-cell RNA data</description>
    <macros>
        <import>macros.xml</import>
        <import>macros_cheetah.xml</import>
    </macros>
    <expand macro="requirements" />
    <expand macro="version_command_config" prog="cluster.R" cheetah="FILTNORM_CHEETAH" out="2&#62; '$outlog' &#62; /dev/null" />
    <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"  />
            <param name="hist_geq_one" type="boolean" checked="false" label="Count filtered features greater than or equal to 1" help="By default features are counted if they are above zero, but RaceID adds 0.1 to all counts after normalisation to create a non-zero dataset."  />
            <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>
            </expand>
        </section>
    </inputs>
    <outputs>
        <data name="outtable" format="tabular" label="${tool.name} on ${on_string}: Normalised Matrix" />
        <data name="outpdf" format="pdf" label="${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}: Metrics"  />
    </outputs>
    <tests>
        <test expect_num_outputs="4">
            <!-- 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" />
            <output name="outlog" value="intestinal.filter.log" />
            <output name="outtable" >
                <assert_contents>
                    <has_n_columns n="288" />
                    <has_text_matching expression="Amdhd2\s0\.000" />
                    <has_text_matching expression="Ammecr1(\s0\s0)+" />
                </assert_contents>
            </output>
        </test>
        <test expect_num_outputs="4">
            <!-- 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="1000" />
            <output name="outpdf" value="matrix.filter.pdf" compare="sim_size" delta="10" />
        </test>
        <test expect_num_outputs="4">
            <!-- 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 expect_num_outputs="4">
            <!-- defaults, but histogram adjustment  -->
            <param name="intable" value="use.intestinal" />
            <section name="filt" >
                <param name="hist_geq_one" value="true" />
            </section>
            <output name="outpdf" value="matrix.filter.geqone.pdf" compare="sim_size" delta="10" />
        </test>
        <test expect_num_outputs="4">
            <!-- 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>