annotate seurat_read10x.xml @ 1:265dcc5bc1e8 draft

planemo upload for repository https://github.com/ebi-gene-expression-group/container-galaxy-sc-tertiary/ commit 0463f230d18201c740851d72e31a5024f391207f

<tool id="seurat_read10x" name="Seurat Read10x" version="@SEURAT_VERSION@_@VERSION@+galaxy0">
<description>Loads 10x data into a serialized seurat R object</description>
  <macros>
      <import>seurat_macros.xml</import>
  </macros>
  <expand macro="requirements" />
<stdio>
<exit_code range="1:" />
</stdio>
<command><![CDATA[
ln -s "$matrix" matrix.mtx;
ln -s "$genes" genes.tsv;
ln -s "$barcodes" barcodes.tsv;

seurat-read-10x.R -d ./

#if $metadata
  --metadata '$metadata'
#end if
#if $min_cells
  --min-cells '$min_cells'
#end if
#if $min_features
  --min-features '$min_features'
#end if

@OUTPUT_OBJECT@

]]></command>
<inputs>
    <param type="data" name="matrix" help="Raw expression quantification as a sparse matrix in Matrix Market format, where each column is a gene and each row is a barcode/cell." format="txt" label="Expression matrix in sparse matrix format (.mtx)" />
    <param type="data" name="genes" help="Matrix market column file for genes" label="Gene table" format="tsv,tabular"/>
    <param type="data" name="barcodes" help="Matrix market row file for genes" label="Barcode/cell table" format="tsv,tabular"/>
    <param label="Cell Metadata" optional="true" name="metadata" argument="--metadata" type="data" format="tsv,tabular" help="File with metdata for the cells, first column should be cell identifiers as used in the cells 10x file."/>
    <param label="Minimum cells to include features" optional="true" name="min_cells" argument="--min-cells" type="integer" help="Include features detected in at least this many cells. Will subset the counts matrix as well. To reintroduce excluded features, create a new object with a lower cutoff."/>
    <param label="Minimum features to include cells" optional="true" name="min_features" argument="--min-features" type="integer" help="Include cells where at least this many features are detected."/>
    <expand macro="output_object_params"/>
</inputs>
<outputs>
    <expand macro="output_files"/>
</outputs>

<tests>
    <test>
       <param name="matrix" ftype="txt" value="test_matrix.txt"/>
       <param name="genes" ftype="txt" value="test_genes.txt"/>
       <param name="barcodes" ftype="txt" value="test_barcodes.txt"/>
       <output name="rds_seurat_file" ftype="rdata" value="out_scale.rds" compare="sim_size"/>
    </test>
</tests>


<help><![CDATA[
  .. class:: infomark

  **What it does**

  @SEURAT_INTRO@

  **Read 10x-Genomics-formatted mtx directory (`read_10x_mtx`)**

  The mtx directory should contain:

  1) Raw expression quantification as a sparse matrix in Matrix Market format, where the each column is a gene and each row is a barcode/cell.

  2) A gene table of at least two columns where the first column gives the gene IDs.

  3) A barcode/cell table of at least one column giving the barcode/cell IDs.

  The above-mentioned files can be obtained by running `EBI SCXA Data Retrieval`
  with a dataset accession. Otherwise, they need to be provided as separate Galaxy
  datasets.

  **Inputs**
      * Raw expression quantification as a sparse matrix in Matrix Market format, where the each column is a gene and each row is a barcode/cell.
      * A gene table of at least two columns where the first column gives the gene IDs.
      * A barcode/cell table of at least one column giving the barcode/cell IDs.
      * Optionally, a file with cell metadata.

  **Outputs**
      * R object for Seurat

  .. _Seurat: https://www.nature.com/articles/nbt.4096
  .. _Satija Lab: https://satijalab.org/seurat/
  .. _Seurat Guided Clustering tutorial: https://satijalab.org/seurat/pbmc3k_tutorial.html

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