comparison plot.xml @ 1:fee4b4eb0ef5 draft default tip

planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/seurat_v5 commit 566984b588e88225f0b3f2dae88c6fd084315e7c

0:2622dd85416f

<tool id="seurat_plot" name="Seurat Visualize" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="@PROFILE@">
    <description>- Plot cells, features and dimensions</description>
    <macros>
        <import>macros.xml</import>
    </macros>
    <expand macro="requirements"/>
    <expand macro="version_command"/>
    <command detect_errors="exit_code"><![CDATA[
@CMD@
    ]]></command>

<https://satijalab.org/seurat/reference/vlnplot>`__

FeatureScatter
==============

Create a scatter plot of two features (typically feature expression), across a set of single cells. Cells are colored by their identity class. 
Pearson correlation between the two features is displayed above the plot.

More details on the `seurat documentation
<https://satijalab.org/seurat/reference/featurescatter>`__

<https://satijalab.org/seurat/reference/vizdimloadings>`__

DimPlot
=======

Graph the output of a dimensional reduction technique on a 2D scatter plot where each point is a cell and it's positioned based on the cell embeddings determined by the reduction technique. 
By default, cells are colored by their identity class (can be changed with the group.by parameter).

More details on the `seurat documentation
<https://satijalab.org/seurat/reference/dimplot>`__

DimHeatmap
==========

Draw a heatmap focusing on a principal component. Both cells and genes are sorted by their principal component scores. 
Allows for nice visualization of sources of heterogeneity in the dataset.

More details on the `seurat documentation
<https://satijalab.org/seurat/reference/dimheatmap>`__

ElbowPlot
=========

Plot the standard deviations of the principal components for easy identification of an elbow in the graph - plots PCA as default reduction. 

More details on the `seurat documentation
<https://satijalab.org/seurat/reference/elbowplot>`__

FeaturePlot

<https://satijalab.org/seurat/reference/doheatmap>`__

DotPlot
=======

Visualize how feature expression changes across different identity classes (e.g. clusters). 
The size of the dot encodes the percentage of cells within a class that express the gene, while the color encodes the AverageExpression level across all cells within a class.

More details on the `seurat documentation
<https://satijalab.org/seurat/reference/dotplot>`__

1:fee4b4eb0ef5

<tool id="seurat_plot" name="Seurat Visualize" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="@PROFILE@">
    <description>- Plot cells, features and dimensions</description>
    <macros>
        <import>macros.xml</import>
    </macros>
    <expand macro="bio_tools"/>
    <expand macro="requirements"/>
    <expand macro="version_command"/>
    <command detect_errors="exit_code"><![CDATA[
@CMD@
    ]]></command>

<https://satijalab.org/seurat/reference/vlnplot>`__

FeatureScatter
==============

Create a scatter plot of two features (typically feature expression), across a set of single cells. Cells are colored by their identity class.
Pearson correlation between the two features is displayed above the plot.

More details on the `seurat documentation
<https://satijalab.org/seurat/reference/featurescatter>`__

<https://satijalab.org/seurat/reference/vizdimloadings>`__

DimPlot
=======

Graph the output of a dimensional reduction technique on a 2D scatter plot where each point is a cell and it's positioned based on the cell embeddings determined by the reduction technique.
By default, cells are colored by their identity class (can be changed with the group.by parameter).

More details on the `seurat documentation
<https://satijalab.org/seurat/reference/dimplot>`__

DimHeatmap
==========

Draw a heatmap focusing on a principal component. Both cells and genes are sorted by their principal component scores.
Allows for nice visualization of sources of heterogeneity in the dataset.

More details on the `seurat documentation
<https://satijalab.org/seurat/reference/dimheatmap>`__

ElbowPlot
=========

Plot the standard deviations of the principal components for easy identification of an elbow in the graph - plots PCA as default reduction.

More details on the `seurat documentation
<https://satijalab.org/seurat/reference/elbowplot>`__

FeaturePlot

<https://satijalab.org/seurat/reference/doheatmap>`__

DotPlot
=======

Visualize how feature expression changes across different identity classes (e.g. clusters).
The size of the dot encodes the percentage of cells within a class that express the gene, while the color encodes the AverageExpression level across all cells within a class.

More details on the `seurat documentation
<https://satijalab.org/seurat/reference/dotplot>`__