diff scanpy-run-umap.xml @ 0:88c1516e25e0 draft

planemo upload for repository https://github.com/ebi-gene-expression-group/container-galaxy-sc-tertiary/tree/develop/tools/tertiary-analysis/scanpy commit 9bf9a6e46a330890be932f60d1d996dd166426c4

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/scanpy-run-umap.xml	Wed Apr 03 11:10:27 2019 -0400
@@ -0,0 +1,133 @@
+<?xml version="1.0" encoding="utf-8"?>
+<tool id="scanpy_run_umap" name="Scanpy RunUMAP" version="@TOOL_VERSION@+galaxy1">
+  <description>visualise cell clusters using UMAP</description>
+  <macros>
+    <import>scanpy_macros.xml</import>
+  </macros>
+  <expand macro="requirements"/>
+  <command detect_errors="exit_code"><![CDATA[
+ln -s '${input_obj_file}' input.h5 &&
+PYTHONIOENCODING=utf-8 scanpy-run-umap.py
+    -i input.h5
+    -f '${input_format}'
+    -o output.h5
+    -F '${output_format}'
+    #if $embeddings
+        --output-embeddings-file embeddings.csv
+    #end if
+    #if $settings.default == "false"
+        -n '${settings.n_components}'
+        --min-dist '${settings.min_dist}'
+        --spread '${settings.spread}'
+        --alpha '${settings.alpha}'
+        --gamma '${settings.gamma}'
+        --negative-sample-rate '${settings.negative_sample_rate}'
+        #if $settings.init_pos
+            --init-pos '${settings.init_pos}'
+        #end if
+        #if $settings.maxiter
+            --maxiter '${settings.maxiter}'
+        #end if
+        #if $settings.a
+            -a '${settings.a}'
+        #end if
+        #if $settings.b
+            -b '${settings.b}'
+        #end if
+        #if $settings.random_seed is not None
+            -s '${settings.random_seed}'
+        #end if
+   #end if
+
+@PLOT_OPTS@
+]]></command>
+
+  <inputs>
+    <expand macro="input_object_params"/>
+    <expand macro="output_object_params"/>
+    <param name="embeddings" type="boolean" checked="true" label="Output embeddings in csv format"/>
+    <conditional name="settings">
+      <param name="default" type="boolean" checked="true" label="Use programme defaults"/>
+      <when value="true"/>
+      <when value="false">
+        <param name="n_components" argument="--n-components" type="integer" value="2" label="The number of dimensions of the embedding"/>
+        <param name="min_dist" argument="--min-dist" type="float" value="0.5" label="The effective minimum distance between embedded points"/>
+        <param name="spread" argument="--spread" type="float" value="1.0" label="The effective spread of embedded points"/>
+        <param name="alpha" argument="--alpha" type="float" value="1.0" label="Initial learning rate"/>
+        <param name="gamma" argument="--gamma" type="float" value="1.0" label="Weighting applied to negative samples"/>
+        <param name="negative_sample_rate" argument="--negative-sample-rate" type="integer" value="5" label="The ratio of negative to positive edge in optimisation"/>
+        <param name="init_pos" argument="--init-pos" type="text" label="Method to initialise embedding, any key for adata.obsm or choose from the preset methods">
+          <option value="spectral" selected="true">spectral</option>
+          <option value="paga">paga</option>
+          <option value="random">random</option>
+        </param>
+        <param name="maxiter" argument="--maxiter" type="integer" optional="true" label="Number of iterations of optimisation"/>
+        <param name="a" argument="-a" type="float" optional="true" label="More specific parameter controlling embedding, automatically determined from --min-dist and --spread if unset"/>
+        <param name="b" argument="-b" type="float" optional="true" label="More specific parameter controlling embedding, automatically determined from --min-dist and --spread if unset"/>
+        <param name="random_seed" argument="--random-seed" type="integer" value="0" label="Seed for numpy random number generator"/>
+      </when>
+    </conditional>
+    <conditional name="do_plotting">
+      <param name="plot" type="boolean" checked="false" label="Make UMAP plot"/>
+      <when value="true">
+        <expand macro="output_plot_params"/>
+        <param name="color_by" argument="--color-by" type="text" value="louvain" label="Color by attributes, comma separated strings"/>
+      </when>
+      <when value="false"/>
+    </conditional>
+  </inputs>
+
+  <outputs>
+    <data name="output_h5" format="h5" from_work_dir="output.h5" label="${tool.name} on ${on_string}: UMAP object"/>
+    <data name="output_png" format="png" from_work_dir="output.png" label="${tool.name} on ${on_string}: UMAP plot">
+      <filter>do_plotting['plot']</filter>
+    </data>
+    <data name="output_embed" format="csv" from_work_dir="embeddings.csv" label="${tool.name} on ${on_string}: UMAP embeddings">
+      <filter>embeddings</filter>
+    </data>
+  </outputs>
+
+  <tests>
+    <test>
+      <param name="input_obj_file" value="find_cluster.h5"/>
+      <param name="input_format" value="anndata"/>
+      <param name="output_format" value="anndata"/>
+      <param name="default" value="false"/>
+      <param name="embeddings" value="true"/>
+      <param name="random_seed" value="0"/>
+      <param name="plot" value="true"/>
+      <param name="color_by" value="louvain"/>
+      <output name="output_h5" file="run_umap.h5" ftype="h5" compare="sim_size"/>
+      <output name="output_png" file="run_umap.png" ftype="png" compare="sim_size"/>
+      <output name="output_embed" file="run_umap.embeddings.csv" ftype="csv" compare="sim_size">
+        <assert_contents>
+          <has_n_columns n="2" sep=","/>
+        </assert_contents>
+      </output>
+    </test>
+  </tests>
+
+  <help><![CDATA[
+========================================================
+Embed the neighborhood graph using UMAP (`tl.umap`)
+========================================================
+
+UMAP (Uniform Manifold Approximation and Projection) is a manifold learning
+technique suitable for visualizing high-dimensional data. Besides tending to
+be faster than tSNE, it optimizes the embedding such that it best reflects
+the topology of the data, which we represent throughout Scanpy using a
+neighborhood graph. tSNE, by contrast, optimizes the distribution of
+nearest-neighbor distances in the embedding such that these best match the
+distribution of distances in the high-dimensional space.  We use the
+implementation of `umap-learn <https://github.com/lmcinnes/umap>`__
+(McInnes et al, 2018). For a few comparisons of UMAP with tSNE, see this `preprint
+<https://doi.org/10.1101/298430>`__.
+
+It yields `X_umap`, UMAP coordinates of data.
+
+@HELP@
+
+@VERSION_HISTORY@
+]]></help>
+  <expand macro="citations"/>
+</tool>