Mercurial > repos > ebi-gxa > scanpy_compute_graph

annotate scanpy-neighbours.xml @ 8:fe740b19e3b9 draft

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planemo upload for repository https://github.com/ebi-gene-expression-group/container-galaxy-sc-tertiary/tree/develop/tools/tertiary-analysis/scanpy commit cbe12e02ee9ff5692be7547bdbe28fd1cd013a92
author ebi-gxa
date Thu, 20 Feb 2020 10:20:38 -0500
parents 75240f9a8c96
children c938bef2ca98
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3d242b0d97d0 planemo upload for repository https://github.com/ebi-gene-expression-group/container-galaxy-sc-tertiary/tree/develop/tools/tertiary-analysis/scanpy commit 9bf9a6e46a330890be932f60d1d996dd166426c4
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1 <?xml version="1.0" encoding="utf-8"?>
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fe740b19e3b9 planemo upload for repository https://github.com/ebi-gene-expression-group/container-galaxy-sc-tertiary/tree/develop/tools/tertiary-analysis/scanpy commit cbe12e02ee9ff5692be7547bdbe28fd1cd013a92
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2 <tool id="scanpy_compute_graph" name="Scanpy ComputeGraph" version="@TOOL_VERSION@+galaxy9" profile="@PROFILE@">
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3 <description>to derive kNN graph</description>
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4 <macros>
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5 <import>scanpy_macros2.xml</import>
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6 </macros>
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7 <expand macro="requirements"/>
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8 <command detect_errors="exit_code"><![CDATA[
3d242b0d97d0 planemo upload for repository https://github.com/ebi-gene-expression-group/container-galaxy-sc-tertiary/tree/develop/tools/tertiary-analysis/scanpy commit 9bf9a6e46a330890be932f60d1d996dd166426c4
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9 ln -s '${input_obj_file}' input.h5 &&
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10 PYTHONIOENCODING=utf-8 scanpy-neighbors
e7fd6981c0f0 "planemo upload for repository https://github.com/ebi-gene-expression-group/container-galaxy-sc-tertiary/tree/develop/tools/tertiary-analysis/scanpy commit 4846776f55931e176f7e77af7c185ec6fec7d142"
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11 #if $settings.default == "false"
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12 --n-neighbors '${settings.n_neighbours}'
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13 --method '${settings.method}'
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14 --random-state '${settings.random_seed}'
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15 #if $settings.use_rep != "auto"
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16 --use-rep '${settings.use_rep}'
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17 #end if
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18 #if $settings.n_pcs
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19 --n-pcs '${settings.n_pcs}'
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20 #end if
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21 ${settings.knn}
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22 #end if
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23 @INPUT_OPTS@
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24 @OUTPUT_OPTS@
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25 ]]></command>
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26
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27 <inputs>
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28 <expand macro="input_object_params"/>
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29 <expand macro="output_object_params"/>
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30 <conditional name="settings">
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31 <param name="default" type="boolean" checked="true" label="Use programme defaults"/>
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32 <when value="true"/>
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33 <when value="false">
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34 <param name="n_neighbours" argument="--n-neighbors" type="integer" value="15" label="Maximum number of neighbours used"/>
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35 <param name="use_rep" type="text" label="Use the indicated representation">
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36 <option value="X_pca" selected="true">X_pca, use PCs</option>
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37 <option value="X">X, use normalised expression values</option>
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38 </param>
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39 <param name="n_pcs" argument="--n-pcs" type="integer" value="50" optional="true" label="Number of PCs to use"/>
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40 <param name="knn" argument="--knn" type="boolean" truevalue="" falsevalue="--no-knn" checked="true"
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41 label="Use hard threshold to restrict neighbourhood size (otherwise use a Gaussian kernel to down weight distant neighbours)"/>
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42 <param name="method" argument="--method" type="select" label="Method for calculating connectivity">
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43 <option value="umap" selected="true">UMAP</option>
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44 <option value="gauss">Gaussian</option>
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45 </param>
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46 <param name="random_seed" argument="--random-seed" type="integer" value="0" label="Seed for random number generator"/>
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47 </when>
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48 </conditional>
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49 </inputs>
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50
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51 <outputs>
5
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52 <expand macro="output_data_obj" description="Graph object"/>
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53 </outputs>
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54
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55 <tests>
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56 <test>
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57 <param name="input_obj_file" value="run_pca.h5"/>
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58 <param name="input_format" value="anndata"/>
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59 <param name="output_format" value="anndata"/>
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60 <param name="default" value="false"/>
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61 <param name="n_neighbours" value="15"/>
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62 <param name="n_pcs" value="50"/>
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63 <param name="knn" value="true"/>
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64 <param name="random_seed" value="0"/>
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65 <param name="method" value="umap"/>
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66 <output name="output_h5" file="compute_graph.h5" ftype="h5" compare="sim_size"/>
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67 </test>
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68 </tests>
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69
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70 <help><![CDATA[
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71 =============================================================
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72 Compute a neighborhood graph of observations (`pp.neighbors`)
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73 =============================================================
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74
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75 The neighbor search efficiency of this heavily relies on UMAP (McInnes et al, 2018),
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76 which also provides a method for estimating connectivities of data points -
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77 the connectivity of the manifold (`method=='umap'`). If `method=='diffmap'`,
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78 connectivities are computed according to Coifman et al (2005), in the adaption of
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79 Haghverdi et al (2016).
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80
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81 @HELP@
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82
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83 @VERSION_HISTORY@
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84 ]]></help>
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85 <expand macro="citations"/>
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86 </tool>