Merge pull request #76 from nf-core/gearyc

Add Geary's C autocorrelation method for SVG report
nf-core · Apr 16, 2024 · d898c8f · d898c8f
2 parents b882ba4 + d832412
commit d898c8f
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Showing 6 changed files with 24 additions and 5 deletions.
diff --git a/bin/spatially_variable_genes.qmd b/bin/spatially_variable_genes.qmd
@@ -10,6 +10,7 @@ jupyter: python3
 #| tags: [parameters]
 #| echo: false
 input_sdata = "sdata_processed.zarr"  # Input: SpatialData file
+svg_autocorr_method = "moran"  # Parameter: SVG autocorrelation method
 n_top_svgs = 14  # Number of spatially variable genes to plot
 artifact_dir = "artifacts"  # Output directory
 output_csv = "spatially_variable_genes.csv"  # Output: gene list
@@ -117,15 +118,18 @@ patterns. Here we use [Moran's I](https://en.wikipedia.org/wiki/Moran%27s_I) aut
 
 ```{python}
 adata.var_names_make_unique()
-sq.gr.spatial_autocorr(adata, mode="moran")
-adata.uns["moranI"].head(n_top_svgs)
-#[TODO] add gearyC as optional mode
+sq.gr.spatial_autocorr(adata, mode=svg_autocorr_method)
+if svg_autocorr_method == "moran":
+    svg_autocorr_method_string = "moranI"
+else:
+    svg_autocorr_method_string = "gearyC"
+adata.uns[svg_autocorr_method_string].head(n_top_svgs)
 ```
 
 ```{python}
 #| echo: false
 # Save the spatially variable genes to a CSV file:
-adata.uns["moranI"].to_csv(os.path.join(artifact_dir, output_csv))
+adata.uns[svg_autocorr_method_string].to_csv(os.path.join(artifact_dir, output_csv))
 ```
 
 ```{python}

diff --git a/modules.json b/modules.json
@@ -7,7 +7,7 @@
                 "nf-core": {
                     "fastqc": {
                         "branch": "master",
-                        "git_sha": "f4ae1d942bd50c5c0b9bd2de1393ce38315ba57c",
+                        "git_sha": "285a50500f9e02578d90b3ce6382ea3c30216acd",
                         "installed_by": ["modules"]
                     },
                     "multiqc": {

diff --git a/modules/nf-core/fastqc/main.nf b/modules/nf-core/fastqc/main.nf
diff --git a/nextflow.config b/nextflow.config
@@ -30,6 +30,7 @@ params {
     cluster_resolution        = 1.0
 
     // Spatial differential expression
+    svg_autocorr_method       = "moran"
     n_top_svgs                = 14
 
     // MultiQC options

diff --git a/nextflow_schema.json b/nextflow_schema.json
@@ -148,6 +148,13 @@
                     "help_text": "The resolution controls the coarseness of the clustering, where a higher resolution leads to more clusters.",
                     "fa_icon": "fas fa-circle-nodes"
                 },
+                "svg_autocorr_method": {
+                    "type": "string",
+                    "default": "moran",
+                    "description": "The method to use for spatially variable gene autocorrelation.",
+                    "enum": ["moran", "geary"],
+                    "fa_icon": "fas fa-circle-nodes"
+                },
                 "n_top_svgs": {
                     "type": "integer",
                     "default": 14,

diff --git a/subworkflows/local/downstream.nf b/subworkflows/local/downstream.nf
@@ -82,6 +82,7 @@ workflow DOWNSTREAM {
         .map { tuple(it[0], spatially_variable_genes_notebook) }
     spatially_variable_genes_params = [
         input_sdata: "sdata_processed.zarr",
+        svg_autocorr_method: params.svg_autocorr_method,
         n_top_svgs: params.n_top_svgs,
         artifact_dir: "artifacts",
         output_csv: "spatially_variable_genes.csv",