diff seurat_find_clusters.xml @ 1:bdabb6af06e4 draft

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

--- a/seurat_find_clusters.xml	Wed Apr 03 11:17:04 2019 -0400
+++ b/seurat_find_clusters.xml	Mon Nov 25 06:09:15 2019 -0500
@@ -1,4 +1,4 @@
-<tool id="seurat_find_clusters" name="Seurat FindClusters" version="2.3.1+galaxy1">
+<tool id="seurat_find_clusters" name="Seurat FindClusters" version="@SEURAT_VERSION@_@VERSION@+galaxy0">
     <description>find clusters of cells</description>
     <macros>
         <import>seurat_macros.xml</import>
@@ -8,30 +8,10 @@
     <command detect_errors="exit_code"><![CDATA[
         seurat-find-clusters.R
 
-        --input-object-file '$input'
-        --output-object-file '$output'
+        @INPUT_OBJECT@
+        @OUTPUT_OBJECT@
         --output-text-file output_tab
 
-        #if $genes_use:
-          --genes-use '$genes_use'
-        #end if
-
-        #if str($adv.reduction_type):
-          --reduction-type '$adv.reduction_type'
-        #end if
-
-        #if str($adv.dims_use):
-          --dims-use \$(seq -s , 1 '$adv.dims_use')
-        #end if
-
-        #if str($adv.k_num_clusters):
-          --k-param '$adv.k_num_clusters'
-        #end if
-
-        #if str($adv.prune_snn):
-          --prune-snn '$adv.prune_snn'
-        #end if
-
         #if str($adv.resolution):
           --resolution '$adv.resolution'
         #end if
@@ -40,31 +20,58 @@
           --algorithm '$adv.algorithm'
         #end if
 
+        #if str($adv.modularity_fxn):
+          --modularity-fxn '$adv.modularity_fxn'
+        #end if
+
+        #if str($adv.method):
+          --method '$adv.method'
+        #end if
+
+        #if str($adv.graph_name):
+          --graph-name '$adv.graph_name'
+        #end if
+
+        #if str($adv.nrandom_starts):
+          --nrandom-starts '$adv.nrandom_starts'
+        #end if
+
+        $adv.group_singletons
+
+
+
   ## TODO add pdf support as optional
   ]]></command>
 
       <inputs>
-        <param name="input" argument="--input-object-file" type="data" format="rdata" label="Seurat RDS object" help="Seurat object produced by Seurat run PCA or other." />
-        <expand macro="genes-use-input"/>
+        <expand macro="input_object_params"/>
+        <expand macro="output_object_params"/>
           <section name="adv" title="Advanced Options">
-            <param name="reduction_type" argument="--reduction-type" optional="true" type="select" label="Dimensional reduction type" help="dimensional reduction technique to use in construction of SNN graph. (e.g. 'pca', 'ica'). PCA by default.">
-              <option value="pca" selected="true">PCA</option>
-              <option value="ica">ICA</option>
-            </param>
-            <expand macro="dims-use-input"/>
-            <param name="k_num_clusters" argument="--k-param" optional="true" type="integer" label="Number of clusters (k) to compute" help="Defines k for the k-nearest neighbor algorithm."/>
-            <param name="prune_snn" argument="--prune-snn" optional="true" type="float" label="Prune SNN cutoff" help="Sets the cutoff for acceptable Jaccard distances when computing the neighborhood overlap for the SNN construction. Any edges with values less than or equal to this will be set to 0 and removed from the SNN graph. Essentially sets the strigency of pruning (0 — no pruning, 1 — prune everything). Defaults to 1/15."/>
             <param name="resolution" argument="--resolution" optional="true" type="float" label="Resolution" help="Value of the resolution parameter, use a value above (below) 1.0 if you want to obtain a larger (smaller) number of communities. Defaults to 0.8."/>
             <param name="algorithm" argument="--algorithm" optional="true" type="select" label="Modularity organization algorithm">
               <option value="1" selected="true">Louvain</option>
               <option value="2">Louvain algorithm with multilevel refinement</option>
               <option value="3">SLM algorithm</option>
+              <option value="4">Leiden</option>
             </param>
+            <param name="modularity_fxn" argument="--modularity-fxn" optional="true" type="select" label="Modularity function">
+              <option value="1" selected="true">Standard</option>
+              <option value="2">Alternative</option>
+            </param>
+            <param name="method" argument="--method" type="select" label="Method for Leiden" help="Method for leiden  (defaults to matrix which is fast for small datasets). Select iGraph to avoid casting large data to a dense matrix.">
+              <option value="matrix" selected="true">Matrix</option>
+              <option value="igraph">iGraph</option>
+            </param>
+            <param name="graph_name" argument="--graph-name" type="text" value="RNA_nn" label="Graph Name" help="Name of graph to use for the clustering algorith."/>
+            <param name="nrandom_starts" argument="--nrandom-starts" type="integer" optional="true" label="Random starts" help="Number of random starts, 10 by default."/>
+            <param name="group_singletons" argument="--group-singletons" type="boolean" truevalue="--group-singletons" falsevalue="" checked="false" label="Group singletons" help="Group singletons into nearest cluster. If FALSE, assign all singletons to a 'singleton' group."/>
+            <param name="random_seed" argument="--random-seed" type="integer" optional="true" label="Random seed" help="Seed of the random number generator"/>
           </section>
+
       </inputs>
       <outputs>
           <!-- <data name="out_pdf" format="pdf" from_work_dir="out.pdf" label="${tool.name} on ${on_string}: Plots" /> -->
-          <data name="output" format="rdata" from_work_dir="*.rds" label="${tool.name} on ${on_string}: Seurat RDS"/>
+          <expand macro="output_files"/>
           <data name="output_tab" format="csv" from_work_dir="output_tab" label="${tool.name} on ${on_string}: CSV Seurat Clusters"/>
       </outputs>
 
@@ -72,7 +79,7 @@
         <!-- Ensure count matrix input works -->
         <test>
             <param name="input" ftype="rdata" value="out_runpca.rds"/>
-            <output name="output" ftype="rdata" value="out_findclust.rds" compare="sim_size"/>
+            <output name="rds_seurat_file" ftype="rdata" value="out_findclust.rds" compare="sim_size"/>
         </test>
     </tests>
     <help><![CDATA[
@@ -80,16 +87,14 @@
 
 **What it does**
 
-Seurat_ is a toolkit for quality control, analysis, and exploration of single cell RNA sequencing data.
-It is developed and maintained by the `Satija Lab`_ at NYGC. Seurat aims to enable users to identify and
-interpret sources of heterogeneity from single cell transcriptomic measurements, and to integrate diverse
-types of single cell data.
+Identify clusters of cells by a shared nearest neighbor (SNN) modularity optimization
+based clustering algorithm. First calculate k-nearest neighbors and construct t
+he SNN graph (using Seurat find neighbours).
+Then optimize the modularity function to determine clusters.
+For a full description of the algorithms, see Waltman and van Eck (2013)
+The European Physical Journal B.
 
-Seurat clustering use SNN method to determine different clusters in your dataset. In order to construct a
-SNN graph, you must have perform a PCA before launch this tool (you can use Seurat dimensional reduction).
-It will search k (30) nearest neighbors for each cells and link cells to each other if they shared the
-same neighbors. You can modulate the resolution in order to get larger (resolution superior to 1) or smaller
-(inferior to 1) clusters.
+@SEURAT_INTRO@
 
 -----