--- a/cluster_reduce_dimension.xml	Thu Dec 05 07:11:19 2019 -0500
+++ b/cluster_reduce_dimension.xml	Thu Dec 12 09:25:42 2019 -0500
@@ -7,7 +7,7 @@
             <param argument="dtype" type="text" value="float32" label="Numpy data type string to which to convert the result" help=""/>
             <conditional name="pca">
                 <param argument="chunked" type="select" label="Type of PCA?">
-                    <option value="True">Incremental PCA on segments (incremental PCA automatically zero centers and ignores settings of `random_seed` and `svd_solver`)</option>
+                    <option value="True">Incremental PCA on segments (incremental PCA automatically zero centers and ignores settings of 'random_seed' and 'svd_solver')</option>
                     <option value="False" selected="true">Full PCA</option>
                 </param>
                 <when value="True">
@@ -74,7 +74,7 @@
 sc.tl.louvain(
     adata=adata,
     flavor = '$method.flavor.flavor',
-    #if $method.flavor.flavor == 'vtraag' and $method.flavor.resolution
+    #if $method.flavor.flavor == 'vtraag'
     resolution=$method.flavor.resolution,
     #end if
     random_state=$method.random_state,
@@ -185,16 +185,16 @@
         <expand macro="inputs_anndata"/>
         <conditional name="method">
             <param argument="method" type="select" label="Method used for plotting">
-                <option value="tl.louvain">Cluster cells into subgroups, using `tl.louvain`</option>
-                <option value="tl.leiden">Cluster cells into subgroups, using `tl.leiden`</option>
-                <option value="pp.pca">Computes PCA (principal component analysis) coordinates, loadings and variance decomposition, using `pp.pca`</option>
-                <option value="tl.pca">Computes PCA (principal component analysis) coordinates, loadings and variance decomposition, using `tl.pca`</option>
-                <option value="tl.diffmap">Diffusion Maps, using `tl.diffmap`</option>
-                <option value="tl.tsne">t-distributed stochastic neighborhood embedding (tSNE), using `tl.tsne`</option>
-                <option value="tl.umap">Embed the neighborhood graph using UMAP, using `tl.umap`</option>
-                <option value="tl.draw_graph">Force-directed graph drawing, using `tl.draw_graph`</option>
-                <option value="tl.dpt">Infer progression of cells through geodesic distance along the graph, using `tl.dpt`</option>
-                <option value="tl.paga">Generate cellular maps of differentiation manifolds with complex topologies, using `tl.paga`</option>
+                <option value="tl.louvain">Cluster cells into subgroups, using 'tl.louvain'</option>
+                <option value="tl.leiden">Cluster cells into subgroups, using 'tl.leiden'</option>
+                <option value="pp.pca">Computes PCA (principal component analysis) coordinates, loadings and variance decomposition, using 'pp.pca'</option>
+                <option value="tl.pca">Computes PCA (principal component analysis) coordinates, loadings and variance decomposition, using 'tl.pca'</option>
+                <option value="tl.diffmap">Diffusion Maps, using 'tl.diffmap'</option>
+                <option value="tl.tsne">t-distributed stochastic neighborhood embedding (tSNE), using 'tl.tsne'</option>
+                <option value="tl.umap">Embed the neighborhood graph using UMAP, using 'tl.umap'</option>
+                <option value="tl.draw_graph">Force-directed graph drawing, using 'tl.draw_graph'</option>
+                <option value="tl.dpt">Infer progression of cells through geodesic distance along the graph, using 'tl.dpt'</option>
+                <option value="tl.paga">Generate cellular maps of differentiation manifolds with complex topologies, using 'tl.paga'</option>
             </param>
             <when value="tl.louvain">
                 <conditional name="flavor">
@@ -203,7 +203,7 @@
                         <option value="igraph">igraph</option>
                     </param>
                     <when value="vtraag">
-                        <param argument="resolution" type="float" value="" optional="true"
+                        <param argument="resolution" type="float" value="1.0"
                             label="Resolution"
                             help="Higher resolution means finding more and smaller clusters, which defaults to 1.0. See “Time as a resolution parameter” in Lambiotte et al, 2009"/>
                     </when>
@@ -239,14 +239,14 @@
                 <param argument="use_fast_tsne" type="boolean" truevalue="True" falsevalue="False" checked="true" label="Use the MulticoreTSNE package if possible?"/>
             </when>
             <when value="tl.umap">
-                <param argument="min_dist" type="float" value="0.5" label="Effective minimum distance between embedded points" help="Smaller values will result in a more clustered/clumped embedding where nearby points on the manifold are drawn closer together, while larger values will result on a more even dispersal of points. The value should be set relative to the `spread` value, which determines the scale at which embedded points will be spread out. The default of in the `umap-learn` package is 0.1."/>
-                <param argument="spread" type="float" value="1.0" label="Effective scale of embedded points" help="In combination with `min_dist` this determines how clustered/clumped the embedded points are."/>
+                <param argument="min_dist" type="float" value="0.5" label="Effective minimum distance between embedded points" help="Smaller values will result in a more clustered/clumped embedding where nearby points on the manifold are drawn closer together, while larger values will result on a more even dispersal of points. The value should be set relative to the 'spread' value, which determines the scale at which embedded points will be spread out. The default of in the 'umap-learn' package is 0.1."/>
+                <param argument="spread" type="float" value="1.0" label="Effective scale of embedded points" help="In combination with 'min_dist' this determines how clustered/clumped the embedded points are."/>
                 <param argument="n_components" type="integer" min="0" value="2" label="Number of dimensions of the embedding" help=""/>
-                <param argument="maxiter" type="integer" min="0" value="" optional="true" label="Number of iterations (epochs) of the optimization" help="Called `n_epochs` in the original UMAP."/>
+                <param argument="maxiter" type="integer" min="0" value="" optional="true" label="Number of iterations (epochs) of the optimization" help="Called 'n_epochs' in the original UMAP."/>
                 <param argument="alpha" type="float" value="1.0" label="Initial learning rate for the embedding optimization" help=""/>
                 <param argument="gamma" type="float" value="1.0" label="Weighting applied to negative samples in low dimensional embedding optimization" help="Values higher than one will result in greater weight being given to negative samples."/>
                 <param argument="negative_sample_rate" type="integer" min="0" value="5" label="The number of negative edge/1-simplex samples to use per positive edge/1-simplex sample in optimizing the low dimensional embedding" help=""/>
-                <param argument="init_pos" type="select" label="How to initialize the low dimensional embedding" help="Called `init` in the original UMAP">
+                <param argument="init_pos" type="select" label="How to initialize the low dimensional embedding" help="Called 'init' in the original UMAP">
                     <option value="paga">Position from paga</option>
                     <option value="spectral" selected="true">Spectral embedding of the graph</option>
                     <option value="random">Initial embedding positions at random</option>
@@ -259,17 +259,17 @@
                 <expand macro="param_random_state"/>
                 <param argument="init_pos" type="text" optional="true" value="" label="Precomputed coordinates for initialization" help="It should be a valid 2d observation (e.g. paga)"/>
                 <param argument="adjacency" type="data" format="mtx" optional="true" label="Sparse adjacency matrix of the graph" help="If not set, it uses the unstructured annotation (uns) / neighbors / connectivities"/>
-                <param argument="key_ext" type="text" optional="true" value="" label="External key" help="If not set, it appends `layout`"/>
+                <param argument="key_ext" type="text" optional="true" value="" label="External key" help="If not set, it appends 'layout'"/>
             </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="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>
             <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="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>