comparison raceid_inspectclusters.xml @ 8:2b0d3e2f402a draft

"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/raceid3 commit 455bad7eca54164f95755174904842907846bb42"

7:be4646256624

<tool id="raceid_inspectclusters" name="Cluster Inspection using RaceID" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" >
    <description>examines gene expression within clusters</description>
    <macros>
        <import>macros.xml</import>
        <import>macros_cheetah.xml</import>
        <macro name="select_cells" token_sectionname="@SECTIONNAME@" token_sectiontitle="@SECTIONTITLE@" >

        </macro>
    </macros>
    <expand macro="requirements" />
    <expand macro="version_command_config" prog="clusterinspect.R" cheetah="INSPECTCLUSTERS_CHEETAH" out="&#38;&#62; '$outlog'" />
    <inputs>
        <param name="inputrds" type="data" format="rdata" label="Input RaceID RDS" help="Requires the RDS output from the cluster analysis" />
        <conditional name="plotgen">
            <param name="do_opt" type="select" label="Plot Clusters?" help="Generates tSNE and F-R plots" >
                <option value="yes" selected="true" >Yes</option>
                <option value="no" >No</option>
            </param>

RaceID3
=========

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.

This module inspects the clusters generated from the previous clustering step (and requires the output RData file from it as input).

The tool offers three modes of inspection which can all be activated at the same time, resulting in a single PDF report:

 * Plot All Clusters:
   * Produces a tSNE of all clusters, as well as a force-directed Fruchterman-Reingold (F-R) layout which may have tidier plots.

 * A list of the most differentially expressed genes in each cluster
 * An output PDF that provides heatmaps for:
    * The initial and final clustering (as determined using random forest)
    * Heatmaps for each of the most differentially expressed genes in each cluster

The tool requires the RData input from the previous filtering / normalisation / confounder removal step to work.



]]></help>
    <expand macro="citations" />

8:2b0d3e2f402a

<tool id="raceid_inspectclusters" name="Cluster Inspection using RaceID" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="@PROFILE@">
    <description>examines gene expression within clusters</description>
    <macros>
        <import>macros.xml</import>
        <import>macros_cheetah.xml</import>
        <macro name="select_cells" token_sectionname="@SECTIONNAME@" token_sectiontitle="@SECTIONTITLE@" >

        </macro>
    </macros>
    <expand macro="requirements" />
    <expand macro="version_command_config" prog="clusterinspect.R" cheetah="INSPECTCLUSTERS_CHEETAH" out="&#38;&#62; '$outlog'" />
    <inputs>
        <param name="inputrds" type="data" format="rds" label="Input RaceID RDS" help="Requires the RDS output from the cluster analysis" />
        <conditional name="plotgen">
            <param name="do_opt" type="select" label="Plot Clusters?" help="Generates tSNE and F-R plots" >
                <option value="yes" selected="true" >Yes</option>
                <option value="no" >No</option>
            </param>

RaceID3
=========

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.

This module inspects the clusters generated from the previous clustering step (and requires the output RDS file from it as input).

The tool offers three modes of inspection which can all be activated at the same time, resulting in a single PDF report:

 * Plot All Clusters:
   * Produces a tSNE of all clusters, as well as a force-directed Fruchterman-Reingold (F-R) layout which may have tidier plots.

 * A list of the most differentially expressed genes in each cluster
 * An output PDF that provides heatmaps for:
    * The initial and final clustering (as determined using random forest)
    * Heatmaps for each of the most differentially expressed genes in each cluster

The tool requires the RDS input from the previous filtering / normalisation / confounder removal step to work.



]]></help>
    <expand macro="citations" />