annotate scran_normalize.xml @ 2:6864acb21714 draft default tip

planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/main/tools/gsc_scran_normalize commit a14fb3d106b647c4f1dea2c8d3ac7c1e8848b21c

<tool id="scran_normalize" name="scran_normalize" version="1.28.1+galaxy0">
    <description>Normalize raw counts expression values using deconvolution size factors</description>
    <requirements>
        <requirement type="package" version="1.28.1">bioconductor-scran</requirement>
        <requirement type="package" version="1.63_1">r-dynamictreecut</requirement>
        <requirement type="package" version="1.7.3">r-optparse</requirement>
    </requirements>
    <stdio>
        <exit_code range="1:" level="fatal" description="Tool exception" />
    </stdio>
    <command detect_errors="exit_code"><![CDATA[ 
        Rscript $__tool_directory__/scran-normalize.R 
            --data '$input'
            #if $metacell.cluster == "Yes":
                --cluster
                --method '$metacell.method'
                --size '$metacell.size'
            #end if
            -o '${output}'
]]></command>
    <inputs>
        <param name="input" type="data" format="tabular" label="Raw counts of expression data" help = "A tsv file that must have an header line"/>
        <conditional name="metacell">
            <param name="cluster" type="select" label = "Do you want to cluster cells ?" help="Perform scaling method on metacell, see Details">
                <option value="Yes">Yes</option>
                <option value="No" selected="true">No</option>
            </param>
            <when value="Yes">
                <param name="method" type="select" label="Clustering method">
                    <option value="hclust" selected="true">hclust</option>
                    <option value="igraph">igprah</option>
                </param>
                <param name="size" type="integer" value="100" label="Minimum size of each cluster"/>
            </when>
            <when value="No"/>
        </conditional>
    </inputs>
    <outputs>
        <data name="output" format="tabular" label="Normalized Log counts of ${on_string}">
        </data>
    </outputs>
    <tests>
        <test>
            <param name="input" value="counts.tsv" ftype="tabular"/>
            <output name="output" file="logcounts.tsv" ftype="tabular"/>
        </test>
        <test>
            <param name="input" value="counts.tsv" ftype="tabular"/>
            <param name="cluster" value="Yes"/>
            <param name="method" value="igraph"/>
            <param name="size" value="25"/>
            <output name="output" file="logcounts_igraph.tsv" ftype="tabular"/>
        </test>
        <test>
            <param name="input" value="counts.tsv" ftype="tabular"/>
            <param name="cluster" value="Yes"/>
            <param name="method" value="hclust"/>
            <param name="size" value="25"/>
            <output name="output" file="logcounts_hclust.tsv" ftype="tabular"/>
        </test>
    </tests>
    <help>

**What it does**

Takes a raw count expression matrix and returns a table of log transformed scran-normalized expression values.

This computes size factors that are used to scale the counts in each cell. The assumption is that 
most genes are not differentially expressed (DE) between cells, such that any differences in 
expression across the majority of genes represents some technical bias that should be removed.

Cell-specific biases are normalized using the computeSumFactors method, which implements the
deconvolution strategy for scaling normalization (A. T. Lun, Bach, and Marioni 2016). It creates a reference : 
  - if no clustering step : the average count of all transcriptomes 
  - if you choose to cluster your cells : the average count of each cluster. 
Then it pools cells and then sum their expression profiles. The size factor is described as the median ration
between the count sums and the average across all genes. Finally it constructs a linear distribution (deconvolution method)
of size factors by taking multiple pools of cells.

You can apply this method on cell cluster instead of your all set of cells by using quickCluster.
It defines cluster using distances based on Spearman correlation on counts between cells, there is two available methods : 

  - *hclust* : hierarchical clustering on the distance matrix and dynamic tree cut.
  - *igraph* : constructs a Shared Nearest Neighbor graph (SNN) on the distance matrix and identifies highly connected communities.


Note: First header row must NOT start with a '#' comment character

    </help>
    <citations>
        <citation type="doi">10.12688/f1000research.9501.2</citation>
    </citations>
</tool>