diff inspect.xml @ 0:5d2e17328afe draft

planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scanpy/ commit 92f85afaed0097d1879317a9f513093fce5481d6

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/inspect.xml	Mon Mar 04 10:15:38 2019 -0500
@@ -0,0 +1,179 @@
+<tool id="scanpy_inspect" name="Inspect with scanpy" version="@galaxy_version@">
+    <description></description>
+    <macros>
+        <import>macros.xml</import>
+    </macros>
+    <expand macro="requirements"/>
+    <expand macro="version_command"/>
+    <command detect_errors="exit_code"><![CDATA[
+@CMD@
+      ]]></command>
+    <configfiles>
+        <configfile name="script_file"><![CDATA[
+@CMD_imports@
+@CMD_read_inputs@
+
+#if $method.method == "tl.paga"
+sc.tl.paga(
+    adata=adata,
+    groups='$method.groups',
+    use_rna_velocity =$method.use_rna_velocity,
+    model='$method.model',
+    copy=False)
+#elif $method.method == "tl.dpt"
+sc.tl.dpt(
+    adata=adata,
+    n_dcs=$method.n_dcs,
+    n_branchings=$method.n_branchings,
+    min_group_size=$method.min_group_size,
+    allow_kendall_tau_shift=$method.allow_kendall_tau_shift,
+    copy=False)
+adata.obs.to_csv('$obs', sep='\t')
+#end if
+
+@CMD_anndata_write_outputs@
+]]></configfile>
+    </configfiles>
+    <inputs>
+        <expand macro="inputs_anndata"/>
+        <conditional name="method">
+            <param argument="method" type="select" label="Method used for plotting">
+                <!--<option value="tl.paga_compare_paths">, using `tl.paga_compare_paths`</option>!-->
+                <!--<option value="tl.paga_degrees">, using `tl.paga_degrees`</option>!-->
+                <!--<option value="tl.paga_expression_entropies">, using `tl.paga_expression_entropies`</option>!-->
+                <option value="tl.paga">Generate cellular maps of differentiation manifolds with complex topologies, using `tl.paga`</option>
+                <option value="tl.dpt">Infer progression of cells through geodesic distance along the graph, using `tl.dpt`</option>
+            </param>
+            <when value="tl.paga">
+                <param argument="groups" type="text" value="louvain" label="Key for categorical in the input" help="You can pass your predefined groups by choosing any categorical annotation of observations (`adata.obs`)."/>
+                <param argument="use_rna_velocity" type="boolean" truevalue="False" falsevalue="False" checked="false" label="Use RNA velocity to orient edges in the abstracted graph and estimate transitions?" help="Requires that `adata.uns` contains a directed single-cell graph with key `['velocyto_transitions']`. This feature might be subject to change in the future."/>
+                <param argument="model" type="select" label="PAGA connectivity model" help="">
+                    <option value="v1.2">v1.2</option>
+                    <option value="v1.0">v1.0</option>
+                </param>
+            </when>
+            <when value="tl.dpt">
+                <param argument="n_dcs" type="integer" min="0" value="10" label="Number of diffusion components to use" help=""/>
+                <param argument="n_branchings" type="integer" min="0" value="0" label="Number of branchings to detect" help=""/>
+                <param argument="min_group_size" type="float" min="0" value="0.01" label="Min group size" help="During recursive splitting of branches ('dpt groups') for `n_branchings` &gt; 1, do not consider groups that contain less than `min_group_size` data points. If a float, `min_group_size` refers to a fraction of the total number of data points."/>
+                <param argument="allow_kendall_tau_shift" type="boolean" truevalue="True" falsevalue="False" checked="true" label="Allow Kendal tau shift?" help="If a very small branch is detected upon splitting, shift away from maximum correlation in Kendall tau criterion of Haghverdi et al (2016) to stabilize the splitting."/>
+            </when>
+        </conditional>
+        <expand macro="anndata_output_format"/>
+    </inputs>
+    <outputs>
+        <expand macro="anndata_outputs"/>
+        <data name="obs" format="tabular" label="${tool.name} on ${on_string}: Observations annotation">
+            <filter>method['method'] == 'tl.dpt'</filter>
+        </data>
+    </outputs>
+    <tests>
+        <test>
+            <conditional name="input">
+                <param name="format" value="h5ad" />
+                <param name="adata" value="pp.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad" />
+            </conditional>
+            <conditional name="method">
+                <param name="method" value="tl.paga"/>
+                <param name="groups" value="paul15_clusters"/>
+                <param name="use_rna_velocity" value="False"/>
+                <param name="model" value="v1.2"/>
+            </conditional>
+            <param name="anndata_output_format" value="h5ad" />
+            <assert_stdout>
+                <has_text_matching expression="sc.tl.paga"/>
+                <has_text_matching expression="groups='paul15_clusters'"/>
+                <has_text_matching expression="use_rna_velocity =False"/>
+                <has_text_matching expression="model='v1.2'"/>
+            </assert_stdout>
+            <output name="anndata_out_h5ad" file="tl.paga.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad" ftype="h5" compare="sim_size">
+                <assert_contents>
+                    <has_h5_keys keys="X, obs, obsm, uns, var" />
+                </assert_contents>
+            </output>
+        </test>
+        <test>
+            <conditional name="input">
+                <param name="format" value="h5ad" />
+                <param name="adata" value="tl.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad" />
+            </conditional>
+            <conditional name="method">
+                <param name="method" value="tl.dpt"/>
+                <param name="n_dcs" value="15"/>
+                <param name="n_branchings" value="1"/>
+                <param name="min_group_size" value="0.01"/>
+                <param name="allow_kendall_tau_shift" value="True"/>
+            </conditional>
+            <param name="anndata_output_format" value="h5ad" />
+            <assert_stdout>
+                <has_text_matching expression="sc.tl.dpt"/>
+                <has_text_matching expression="n_dcs=15"/>
+                <has_text_matching expression="n_branchings=1"/>
+                <has_text_matching expression="min_group_size=0.01"/>
+                <has_text_matching expression="allow_kendall_tau_shift=True"/>
+            </assert_stdout>
+            <output name="anndata_out_h5ad" file="tl.dpt.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad" ftype="h5" compare="sim_size">
+                <assert_contents>
+                    <has_h5_keys keys="X, obs, obsm, uns, var" />
+                </assert_contents>
+            </output>
+            <output name="obs" file="tl.dpt.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.obs.tabular" compare="sim_size"/>
+        </test>
+    </tests>
+    <help><![CDATA[
+Generate cellular maps of differentiation manifolds with complex topologies (`tl.paga`)
+=======================================================================================
+
+By quantifying the connectivity of partitions (groups, clusters) of the
+single-cell graph, partition-based graph abstraction (PAGA) generates a much
+simpler abstracted graph (*PAGA graph*) of partitions, in which edge weights
+represent confidence in the presence of connections. By tresholding this
+confidence in `paga`, a much simpler representation of data
+can be obtained.
+
+The confidence can be interpreted as the ratio of the actual versus the
+expected value of connetions under the null model of randomly connecting
+partitions. We do not provide a p-value as this null model does not
+precisely capture what one would consider "connected" in real data, hence it
+strongly overestimates the expected value. See an extensive discussion of
+this in Wolf et al (2017).
+
+Together with a random walk-based distance measure, this generates a partial
+coordinatization of data useful for exploring and explaining its variation.
+
+More details on the `tl.paga scanpy documentation
+<https://scanpy.readthedocs.io/en/latest/api/scanpy.api.tl.paga.html#scanpy.api.tl.paga>`_
+
+
+Infer progression of cells through geodesic distance along the graph (`tl.dpt`)
+===============================================================================
+
+Reconstruct the progression of a biological process from snapshot
+data. `Diffusion Pseudotime` has been introduced by Haghverdi et al (2016) and
+implemented within Scanpy (Wolf et al, 2017). Here, we use a further developed
+version, which is able to deal with disconnected graphs (Wolf et al, 2017) and can
+be run in a `hierarchical` mode by setting the parameter
+`n_branchings>1`. We recommend, however, to only use
+`tl.dpt` for computing pseudotime (`n_branchings=0`) and
+to detect branchings via `paga`. For pseudotime, you need
+to annotate your data with a root cell. 
+
+This requires to run `pp.neighbors`, first. In order to
+reproduce the original implementation of DPT, use `method=='gauss'` in
+this. Using the default `method=='umap'` only leads to minor quantitative
+differences, though.
+
+
+If `n_branchings==0`, no field `dpt_groups` will be written.
+
+- dpt_pseudotime : Array of dim (number of samples) that stores the pseudotime of each cell, that is, the DPT distance with respect to the root cell.
+- dpt_groups : Array of dim (number of samples) that stores the subgroup id ('0','1', ...) for each cell. The groups  typically correspond to 'progenitor cells', 'undecided cells' or 'branches' of a process.
+
+The tool is similar to the R package `destiny` of Angerer et al (2016).
+
+More details on the `tl.dpt scanpy documentation
+<https://scanpy.readthedocs.io/en/latest/api/scanpy.api.tl.dpt.html#scanpy.api.tl.dpt>`_
+
+    ]]></help>
+    <expand macro="citations"/>
+</tool>
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