Mercurial > repos > iuc > scanpy_cluster_reduce_dimension

comparison cluster_reduce_dimension.xml @ 3:ac8e59e87ce3 draft

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"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scanpy/ commit 3b41d687ff30583540d055f6995de00530cca81d"
author iuc
date Thu, 12 Dec 2019 09:25:42 -0500
parents dc2de14881ba
children 766be978777a
comparison
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2:dc2de14881ba 3:ac8e59e87ce3
5 <xml name="pca_inputs"> 5 <xml name="pca_inputs">
6 <param argument="n_comps" type="integer" min="0" value="50" label="Number of principal components to compute" help=""/> 6 <param argument="n_comps" type="integer" min="0" value="50" label="Number of principal components to compute" help=""/>
7 <param argument="dtype" type="text" value="float32" label="Numpy data type string to which to convert the result" help=""/> 7 <param argument="dtype" type="text" value="float32" label="Numpy data type string to which to convert the result" help=""/>
8 <conditional name="pca"> 8 <conditional name="pca">
9 <param argument="chunked" type="select" label="Type of PCA?"> 9 <param argument="chunked" type="select" label="Type of PCA?">
10 <option value="True">Incremental PCA on segments (incremental PCA automatically zero centers and ignores settings of `random_seed` and `svd_solver`)</option> 10 <option value="True">Incremental PCA on segments (incremental PCA automatically zero centers and ignores settings of 'random_seed' and 'svd_solver')</option>
11 <option value="False" selected="true">Full PCA</option> 11 <option value="False" selected="true">Full PCA</option>
12 </param> 12 </param>
13 <when value="True"> 13 <when value="True">
14 <param argument="chunk_size" type="integer" min="0" value="" label="chunk_size" help="Number of observations to include in each chunk"/> 14 <param argument="chunk_size" type="integer" min="0" value="" label="chunk_size" help="Number of observations to include in each chunk"/>
15 </when> 15 </when>
72 72
73 #if $method.method == 'tl.louvain' 73 #if $method.method == 'tl.louvain'
74 sc.tl.louvain( 74 sc.tl.louvain(
75 adata=adata, 75 adata=adata,
76 flavor = '$method.flavor.flavor', 76 flavor = '$method.flavor.flavor',
77 #if $method.flavor.flavor == 'vtraag' and $method.flavor.resolution 77 #if $method.flavor.flavor == 'vtraag'
78 resolution=$method.flavor.resolution, 78 resolution=$method.flavor.resolution,
79 #end if 79 #end if
80 random_state=$method.random_state, 80 random_state=$method.random_state,
81 key_added='$method.key_added', 81 key_added='$method.key_added',
82 directed=$method.directed, 82 directed=$method.directed,
183 </configfiles> 183 </configfiles>
184 <inputs> 184 <inputs>
185 <expand macro="inputs_anndata"/> 185 <expand macro="inputs_anndata"/>
186 <conditional name="method"> 186 <conditional name="method">
187 <param argument="method" type="select" label="Method used for plotting"> 187 <param argument="method" type="select" label="Method used for plotting">
188 <option value="tl.louvain">Cluster cells into subgroups, using `tl.louvain`</option> 188 <option value="tl.louvain">Cluster cells into subgroups, using 'tl.louvain'</option>
189 <option value="tl.leiden">Cluster cells into subgroups, using `tl.leiden`</option> 189 <option value="tl.leiden">Cluster cells into subgroups, using 'tl.leiden'</option>
190 <option value="pp.pca">Computes PCA (principal component analysis) coordinates, loadings and variance decomposition, using `pp.pca`</option> 190 <option value="pp.pca">Computes PCA (principal component analysis) coordinates, loadings and variance decomposition, using 'pp.pca'</option>
191 <option value="tl.pca">Computes PCA (principal component analysis) coordinates, loadings and variance decomposition, using `tl.pca`</option> 191 <option value="tl.pca">Computes PCA (principal component analysis) coordinates, loadings and variance decomposition, using 'tl.pca'</option>
192 <option value="tl.diffmap">Diffusion Maps, using `tl.diffmap`</option> 192 <option value="tl.diffmap">Diffusion Maps, using 'tl.diffmap'</option>
193 <option value="tl.tsne">t-distributed stochastic neighborhood embedding (tSNE), using `tl.tsne`</option> 193 <option value="tl.tsne">t-distributed stochastic neighborhood embedding (tSNE), using 'tl.tsne'</option>
194 <option value="tl.umap">Embed the neighborhood graph using UMAP, using `tl.umap`</option> 194 <option value="tl.umap">Embed the neighborhood graph using UMAP, using 'tl.umap'</option>
195 <option value="tl.draw_graph">Force-directed graph drawing, using `tl.draw_graph`</option> 195 <option value="tl.draw_graph">Force-directed graph drawing, using 'tl.draw_graph'</option>
196 <option value="tl.dpt">Infer progression of cells through geodesic distance along the graph, using `tl.dpt`</option> 196 <option value="tl.dpt">Infer progression of cells through geodesic distance along the graph, using 'tl.dpt'</option>
197 <option value="tl.paga">Generate cellular maps of differentiation manifolds with complex topologies, using `tl.paga`</option> 197 <option value="tl.paga">Generate cellular maps of differentiation manifolds with complex topologies, using 'tl.paga'</option>
198 </param> 198 </param>
199 <when value="tl.louvain"> 199 <when value="tl.louvain">
200 <conditional name="flavor"> 200 <conditional name="flavor">
201 <param argument="flavor" type="select" label="Flavor for the clustering" help=""> 201 <param argument="flavor" type="select" label="Flavor for the clustering" help="">
202 <option value="vtraag">vtraag (much more powerful)</option> 202 <option value="vtraag">vtraag (much more powerful)</option>
203 <option value="igraph">igraph</option> 203 <option value="igraph">igraph</option>
204 </param> 204 </param>
205 <when value="vtraag"> 205 <when value="vtraag">
206 <param argument="resolution" type="float" value="" optional="true" 206 <param argument="resolution" type="float" value="1.0"
207 label="Resolution" 207 label="Resolution"
208 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"/> 208 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"/>
209 </when> 209 </when>
210 <when value="igraph"/> 210 <when value="igraph"/>
211 </conditional> 211 </conditional>
237 <param name="learning_rate" type="float" value="1000" label="Learning rate" help="The learning rate can be a critical parameter. It should be between 100 and 1000. If the cost function increases during initial optimization, the early exaggeration factor or the learning rate might be too high. If the cost function gets stuck in a bad local minimum increasing the learning rate helps sometimes."/> 237 <param name="learning_rate" type="float" value="1000" label="Learning rate" help="The learning rate can be a critical parameter. It should be between 100 and 1000. If the cost function increases during initial optimization, the early exaggeration factor or the learning rate might be too high. If the cost function gets stuck in a bad local minimum increasing the learning rate helps sometimes."/>
238 <param name="random_state" type="integer" value="0" label="Random state" help="Change this to use different intial states for the optimization"/> 238 <param name="random_state" type="integer" value="0" label="Random state" help="Change this to use different intial states for the optimization"/>
239 <param argument="use_fast_tsne" type="boolean" truevalue="True" falsevalue="False" checked="true" label="Use the MulticoreTSNE package if possible?"/> 239 <param argument="use_fast_tsne" type="boolean" truevalue="True" falsevalue="False" checked="true" label="Use the MulticoreTSNE package if possible?"/>
240 </when> 240 </when>
241 <when value="tl.umap"> 241 <when value="tl.umap">
242 <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."/> 242 <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."/>
243 <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."/> 243 <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."/>
244 <param argument="n_components" type="integer" min="0" value="2" label="Number of dimensions of the embedding" help=""/> 244 <param argument="n_components" type="integer" min="0" value="2" label="Number of dimensions of the embedding" help=""/>
245 <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."/> 245 <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."/>
246 <param argument="alpha" type="float" value="1.0" label="Initial learning rate for the embedding optimization" help=""/> 246 <param argument="alpha" type="float" value="1.0" label="Initial learning rate for the embedding optimization" help=""/>
247 <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."/> 247 <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."/>
248 <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=""/> 248 <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=""/>
249 <param argument="init_pos" type="select" label="How to initialize the low dimensional embedding" help="Called `init` in the original UMAP"> 249 <param argument="init_pos" type="select" label="How to initialize the low dimensional embedding" help="Called 'init' in the original UMAP">
250 <option value="paga">Position from paga</option> 250 <option value="paga">Position from paga</option>
251 <option value="spectral" selected="true">Spectral embedding of the graph</option> 251 <option value="spectral" selected="true">Spectral embedding of the graph</option>
252 <option value="random">Initial embedding positions at random</option> 252 <option value="random">Initial embedding positions at random</option>
253 </param> 253 </param>
254 <expand macro="param_random_state"/> 254 <expand macro="param_random_state"/>
257 <expand macro="param_layout"/> 257 <expand macro="param_layout"/>
258 <expand macro="param_root"/> 258 <expand macro="param_root"/>
259 <expand macro="param_random_state"/> 259 <expand macro="param_random_state"/>
260 <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)"/> 260 <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)"/>
261 <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"/> 261 <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"/>
262 <param argument="key_ext" type="text" optional="true" value="" label="External key" help="If not set, it appends `layout`"/> 262 <param argument="key_ext" type="text" optional="true" value="" label="External key" help="If not set, it appends 'layout'"/>
263 </when> 263 </when>
264 <when value="tl.dpt"> 264 <when value="tl.dpt">
265 <param argument="n_dcs" type="integer" min="0" value="10" label="Number of diffusion components to use" help=""/> 265 <param argument="n_dcs" type="integer" min="0" value="10" label="Number of diffusion components to use" help=""/>
266 <param argument="n_branchings" type="integer" min="0" value="0" label="Number of branchings to detect" help=""/> 266 <param argument="n_branchings" type="integer" min="0" value="0" label="Number of branchings to detect" help=""/>
267 <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."/> 267 <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."/>
268 <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."/> 268 <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."/>
269 </when> 269 </when>
270 <when value="tl.paga"> 270 <when value="tl.paga">
271 <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`)."/> 271 <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')."/>
272 <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."/> 272 <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."/>
273 <param argument="model" type="select" label="PAGA connectivity model" help=""> 273 <param argument="model" type="select" label="PAGA connectivity model" help="">
274 <option value="v1.2">v1.2</option> 274 <option value="v1.2">v1.2</option>
275 <option value="v1.0">v1.0</option> 275 <option value="v1.0">v1.0</option>
276 </param> 276 </param>
277 </when> 277 </when>