updated docs for run_umap API change

parashardhapola · Jul 30, 2021 · d414eb1 · d414eb1
1 parent a8fb23f
commit d414eb1
Show file tree

Hide file tree

Showing 5 changed files with 7 additions and 7 deletions.
diff --git a/docs/source/vignettes/basic_tutorial_scATACseq.mdnb b/docs/source/vignettes/basic_tutorial_scATACseq.mdnb
@@ -75,7 +75,7 @@ Non-linear dimension reduction using UMAP and tSNE are performed in the same way
 <!-- #endregion -->
 
 ```python
-ds.run_umap(fit_n_epochs=250, min_dist=0.5, parallel=True)
+ds.run_umap(n_epochs=250, min_dist=0.5, parallel=True)
 ```
 
 ```python

diff --git a/docs/source/vignettes/basic_tutorial_scRNAseq.mdnb b/docs/source/vignettes/basic_tutorial_scRNAseq.mdnb
@@ -159,7 +159,7 @@ ds.make_graph(feat_key='hvgs', k=11, dims=15, n_centroids=100)
 Next we run UMAP on the graph calculated above. Here we will not provide which cell key or feature key to be used, because we want the UMAP to run on all the cells that were not filtered out and with the feature key used to calculate the latest graph. We can provide the parameter values for the UMAP algorithm here.
 
 ```python
-ds.run_umap(fit_n_epochs=250, spread=5, min_dist=1, parallel=True)
+ds.run_umap(n_epochs=250, spread=5, min_dist=1, parallel=True)
 ```
 
 The UMAP results are saved in the cell metadata table as seen below in columns: **RNA_UMAP1** and **RNA_UMAP2**

diff --git a/docs/source/vignettes/data_projection.mdnb b/docs/source/vignettes/data_projection.mdnb
@@ -135,7 +135,7 @@ Scarf introduces Unified UMAPs, a strategy to embed target cells onto the refere
 
 ```python
 ds_ctrl.run_unified_umap(target_names=['stim'], ini_embed_with='RNA_UMAP', target_weight=1,
-                         use_k=5, fit_n_epochs=100, tx_n_epochs=10)
+                         use_k=5, n_epochs=100)
 ```
 
 Since the results of unified embedding contain 'foreign' cells, `plot_layout` function cannot be used to visualize all the cells. A specialized method, `plot_unified_layout` takes care of this issue. The following example shows co-embedded control (reference) and stimulated  (target) PBMCs.

diff --git a/docs/source/vignettes/merging_datasets.mdnb b/docs/source/vignettes/merging_datasets.mdnb
@@ -141,7 +141,7 @@ ds.make_graph(feat_key='hvgs', k=21, dims=25, n_centroids=100)
 Calculating UMAP embedding of cells:
 
 ```python
-ds.run_umap(fit_n_epochs=250, spread=5, min_dist=1, parallel=True)
+ds.run_umap(n_epochs=250, spread=5, min_dist=1, parallel=True)
 ```
 
 ```python
@@ -185,7 +185,7 @@ ds.make_graph(feat_key='hvgs', k=21, dims=25, n_centroids=100, pca_cell_key='is_
 We run UMAP as usual, but the UMAP embeddings are saved in a new cell attribute column so as to not overwrite the previous UMAP values. The new column will be called `RNA_pUMAP`; 'RNA' is automatically prepend because the assay name is `RNA`
 
 ```python
-ds.run_umap(fit_n_epochs=250, spread=5, min_dist=1, parallel=True, label='pUMAP')
+ds.run_umap(n_epochs=250, spread=5, min_dist=1, parallel=True, label='pUMAP')
 ```
 
 Visualize the new UMAP

diff --git a/docs/source/vignettes/multiple_modalities.mdnb b/docs/source/vignettes/multiple_modalities.mdnb
@@ -98,7 +98,7 @@ Now we process the RNA assay to perform feature selection, create KNN graph, run
 ```python
 ds.mark_hvgs(min_cells=20, top_n=500, min_mean=-3, max_mean=2, max_var=6)
 ds.make_graph(feat_key='hvgs', k=11, dims=15, n_centroids=100)
-ds.run_umap(fit_n_epochs=250, spread=5, min_dist=1, parallel=True)
+ds.run_umap(n_epochs=250, spread=5, min_dist=1, parallel=True)
 ds.run_leiden_clustering(resolution=1)
 ```
 
@@ -153,7 +153,7 @@ ds.make_graph(from_assay='ADT', feat_key='I', k=11, dims=11, n_centroids=100)
 UMAP and clustering can be run on ADT assay by simply setting `from_assay` parameter value to 'ADT':
 
 ```python
-ds.run_umap(from_assay='ADT', fit_n_epochs=250, spread=5, min_dist=1, parallel=True)
+ds.run_umap(from_assay='ADT', n_epochs=250, spread=5, min_dist=1, parallel=True)
 ds.run_leiden_clustering(from_assay='ADT', resolution=1)
 ```