Mercurial > repos > iuc > raceid_inspectclusters

annotate raceid_inspectclusters.xml @ 0:9fec5dd8fbb9 draft

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planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/raceid3 commit f880060c478d42202df5b78a81329f8af56b1138
author iuc
date Thu, 22 Nov 2018 04:45:41 -0500
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9fec5dd8fbb9 planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/raceid3 commit f880060c478d42202df5b78a81329f8af56b1138
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1 <tool id="raceid_inspectclusters" name="Cluster Inspection using RaceID" version="@VERSION_RACEID@.@VERSION_PACKAGE@.1" >
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2 <description>examines gene expression within clusters</description>
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3 <macros>
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4 <import>macros.xml</import>
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5 <import>macros_inspectclusters.xml</import>
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6 </macros>
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7 <expand macro="requirements" />
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8 <version_command><![CDATA[
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9 Rscript '$__tool_directory__/scripts/clusterinspect.R' @GET_VERSION@
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10 ]]></version_command>
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12 <command detect_errors="exit_code"><![CDATA[
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13 #set bin = 'clusterinspect.R'
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14 Rscript '$__tool_directory__/scripts/$bin' '$userconf' 2>&1 > '$outlog'
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15 ]]></command>
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17 <configfiles>
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18 <configfile name="userconf" ><![CDATA[
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19 @STRING2VECTOR@
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21 @INSPECTCLUSTERS_CHEETAH@
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22 ]]>
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23 </configfile>
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24 </configfiles>
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25 <inputs>
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26 <expand macro="inspectclusters_inputs" />
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27 </inputs>
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29 <outputs>
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30 <data name="outpdf" format="pdf" label="${tool.name} on ${on_string}: PDF Report" />
9fec5dd8fbb9 planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/raceid3 commit f880060c478d42202df5b78a81329f8af56b1138
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31 <data name="outlog" format="txt" label="${tool.name} on ${on_string}: Log" >
9fec5dd8fbb9 planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/raceid3 commit f880060c478d42202df5b78a81329f8af56b1138
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32 <filter>use_log</filter>
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33 </data>
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34 </outputs>
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35 <tests>
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36 <expand macro="inspectclusters_tests" />
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37 </tests>
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38 <help><![CDATA[
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40 RaceID3
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41 =========
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43 RaceID is a clustering algorithm for the identification of cell types from single-cell RNA-sequencing data. It was specifically designed for the detection of rare cells which correspond to outliers in conventional clustering methods.
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45 This module inspects the clusters generated from the previous clustering step (and requires the output RData file from it as input).
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47 The tool offers three modes of inspection which can all be activated at the same time, resulting in a single PDF report:
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48
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49 * Plot All Clusters:
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50 * Produces a tSNE of all clusters, as well as a force-directed Fruchterman-Reingold (F-R) layout which may have tidier plots.
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51 * Perform Subset Analysis:
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52 * tSNE and F-R plots with cells whose name match the specified regex highlighted
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53
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54 * Examine Genes of Interest:
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55 * Expression plots highlighting a gene or genes of interest across all clusters
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56
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57 * Differential Gene Testing:
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58 * Examining the expression between
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60 * A list of the most differentially expressed genes in each cluster
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61 * An output PDF that provides heatmaps for:
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62 * The initial and final clustering (as determined using random forest)
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63 * Heatmaps for each of the most differentially expressed genes in each cluster
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64
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65 The tool requires the RData input from the previous filtering / normalisation / confounder removal step to work.
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69 ]]></help>
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70 <expand macro="citations" />
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71 </tool>