Merge pull request #143 from DendrouLab/fc_sparsedocsfix

Fc sparsedocsfix
DendrouLab · Nov 27, 2023 · 482082f · 482082f
2 parents 2fb974d + 73c85a4
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diff --git a/docs/tutorials/index.md b/docs/tutorials/index.md
@@ -4,12 +4,12 @@ Tutorials
 Check out the following tutorials which take you through some common analysis steps with Panpipes:
 
 
-- [Ingest pipeline](https://panpipes-tutorials.readthedocs.io/en/latest/ingesting_data/Ingesting_data_with_panpipes.html)
-- [Preprocessing pipeline](https://panpipes-tutorials.readthedocs.io/en/latest/filtering_data/filtering_data_with_panpipes.html)
-- [Integration pipeline](https://panpipes-tutorials.readthedocs.io/en/latest/uni_multi_integration/Integrating_data_with_panpipes.html)
-- [Clustering pipeline](https://panpipes-tutorials.readthedocs.io/en/latest/clustering/clustering_tutorial.html)
-- [Reference mapping](https://panpipes-tutorials.readthedocs.io/en/latest/refmap_pancreas/Reference_mapping.html)
-- [Visualization pipeline](https://panpipes-tutorials.readthedocs.io/en/latest/visualization/vis_with_panpipes.html)
+- [Ingest workflow](https://panpipes-tutorials.readthedocs.io/en/latest/ingesting_data/Ingesting_data_with_panpipes.html)
+- [Preprocessing workflow](https://panpipes-tutorials.readthedocs.io/en/latest/filtering_data/filtering_data_with_panpipes.html)
+- [Integration workflow](https://panpipes-tutorials.readthedocs.io/en/latest/uni_multi_integration/Integrating_data_with_panpipes.html)
+- [Clustering workflow](https://panpipes-tutorials.readthedocs.io/en/latest/clustering/clustering_tutorial.html)
+- [Reference mapping workflow](https://panpipes-tutorials.readthedocs.io/en/latest/refmap_pancreas/Reference_mapping.html)
+- [Visualization workflow](https://panpipes-tutorials.readthedocs.io/en/latest/visualization/vis_with_panpipes.html)
 
 Spatial analysis:
 

diff --git a/docs/usage/normalization_methods.md b/docs/usage/normalization_methods.md
@@ -31,17 +31,18 @@ Additionally, the normalized data can be scaled using scanpy's [sc.pp.scale](htt
 
 
 
-Layers nomenclature
+Layers nomenclature within each modality
 ============================
 
-Normalised data is saved in each modality slot in their specific layers:
+Raw, Normalised and Scaled data are saved for each modality in their specific layers:
 
     atac.layers["logTF_norm"] = atac.X.copy()
-
+
+Using the following nomenclature:
 
 | method                 | layer           | modality      |
 | ---------------------- | --------------- | ------------- |
-| raw counts             | "raw_counts     | RNA/ATAC/PROT |
+| raw counts             | "raw_counts"    | RNA/ATAC/PROT |
 | standard log1p         | "logged_counts" | RNA or ATAC   |
 | scaled counts          | "scaled_counts" | RNA or ATAC   |
 | clr                    | "clr"           | PROT          |
@@ -52,3 +53,4 @@ Normalised data is saved in each modality slot in their specific layers:
 
 
 
+
diff --git a/docs/workflows/clustering.md b/docs/workflows/clustering.md
@@ -6,9 +6,10 @@ The panpipes clustering pipeline runs the following steps:
 ```
 neighbors:
   rna:
-    use_existing: False
-    # number of Principal Components to calculate for neighbours and umap:
-    dim_red: X_harmony (or any reduced dimensional represenation in your MuData object)
+    use_existing: True
+    # which representation in .obsm to use for nearest neighbors
+    # if dim_red=X_pca and X_pca not in .obsm, will be computed with default parameters
+    dim_red: X_pca
     #how many components to use for clustering
     n_dim_red: 30
     # number of neighbours
@@ -43,4 +44,6 @@ neighbors:
 ## Expected structure of MuData object
 The ideal way to run `panpipes clustering` is to use the output mudata file from `panpipes preprocess` or `panpipes integration`, as this will make sure the MuData object has correctly names layers and slots. 
 
-The bare minimum MuData object required is normalised data in the X slot of each modality,  a sample_id column in each slot of the obs and the outer obs, and the layers indicated in the find_markers section of the yml.
+The bare minimum MuData object required is normalised data in the X slot of each modality,  a sample_id column in each slot of the obs and the outer obs, and the layers indicated in the find_markers section of the yml.
+
+Check the [clustering tutorial](https://panpipes-tutorials.readthedocs.io/en/latest/clustering/clustering_tutorial.html) for an example of how to run the workflow.
diff --git a/docs/workflows/index.rst b/docs/workflows/index.rst
@@ -7,8 +7,8 @@ Workflows
     qc
     preprocess
     integration
-    refmap
     clustering
+    refmap
     vis
     ingest_spatial
     preprocess_spatial