formated snakeameke files

SpatialHackathon · Feb 23, 2024 · 350b8ba · 350b8ba
1 parent 1da98d2
commit 350b8ba
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Showing 4 changed files with 111 additions and 55 deletions.
diff --git a/workflows/download.smk b/workflows/download.smk
@@ -1,11 +1,13 @@
 import os
 from shared.functions import get_git_directory
 
+
 # workflow specific setting
 configfile: "example_configs/download_config.yaml"
 # listed all the available datasets here
 configfile: "path_configs/datasets.yaml"
 
+
 print("Run Download Workflow")
 
 # Attach the specific github directory here
@@ -14,15 +16,18 @@ GIT_DIR = get_git_directory(config)
 # Leave only datasets
 datasets = config.pop("datasets")
 
+
 # Get all the dataset folder
 def get_all_input(wildcards):
     all_folder = []
     for dataset in config["use_datasets"]:
         all_folder.append(config["results_dir"] + "/" + dataset)
     return all_folder
 
+
 ############## starting snakemake pipelines ##################
 
+
 # Defining all output wanted from this snakemake
 rule all:
     input:

diff --git a/workflows/example_configs/methods_config.yaml b/workflows/example_configs/methods_config.yaml
@@ -8,19 +8,19 @@ seed: 2023
 # Methods to run for the pipeline, modify based on your need
 use_methods:
   - "bass"
-  - "BayesSpace"
-  - "DRSC"
-  - "GraphST"
-  - "SEDR"
-  - "SOTIP"
-  - "SpiceMix"
-  - "maple"
-  - "meringue"
-  - "precast"
-  - "SC.MEB"
-  - "spaGCN"
-  - "stardust"
-  - "DeepST"
+#  - "BayesSpace"
+#  - "DRSC"
+#  - "GraphST"
+#  - "SEDR"
+#  - "SOTIP"
+#  - "SpiceMix"
+#  - "maple"
+#  - "meringue"
+#  - "precast"
+#  - "SC_MEB"
+#  - "spaGCN"
+#  - "stardust"
+#  - "DeepST"
 #  - "STAGATE"       # res not n_clust
 #  - "scanpy"
 #  - "SpaceFlow"

diff --git a/workflows/methods.smk b/workflows/methods.smk
@@ -3,6 +3,7 @@ import json
 
 from shared.functions import get_git_directory, get_ncluster, get_sample_dirs
 
+
 # script specific setting
 configfile: "example_configs/methods_config.yaml"
 # All methods available
@@ -14,6 +15,7 @@ SEED = config["seed"]
 
 methods = config.pop("methods")
 
+
 # Find the technology of the datasets from their experiments.json
 def get_technology(path):
     import json
@@ -50,6 +52,7 @@ def create_input_all(wildcards):
         files += create_input(method)
     return files
 
+
 rule all:
     input:
         create_input_all,
@@ -61,13 +64,13 @@ def get_sample_image(wildcards):
         opt = json.load(file)
 
     if opt["image"]:
-      files = ["H_E.tiff", "H_E.png"]
-      for file in files:
-          image = config["data_dir"] + "/" + wildcards.sample + "/" + file
-          if os.path.isfile(image):
-              return "--image " + image
+        files = ["H_E.tiff", "H_E.png"]
+        for file in files:
+            image = config["data_dir"] + "/" + wildcards.sample + "/" + file
+            if os.path.isfile(image):
+                return "--image " + image
     else:
-      return ""
+        return ""
 
 
 def get_config_file(wildcards):
@@ -84,17 +87,20 @@ def get_config_file(wildcards):
 ##########################################################
 # requirements
 
+
 # Find if the method has an additional shell scripts for installation
 def get_requirements(wildcards):
     if methods[wildcards.method].get("env_additional") is not None:
         return f"{wildcards.method}_requirements.info"
     else:
         return []
 
+
 # if additional scripts are found, go through this process before generating the results
 rule installation_requirements:
     params:
-        install_script=lambda wildcards: GIT_DIR + methods[wildcards.method]["env_additional"],
+        install_script=lambda wildcards: GIT_DIR
+        + methods[wildcards.method]["env_additional"],
     output:
         temp("{method}_requirements.info"),
     conda:
@@ -108,11 +114,13 @@ rule installation_requirements:
 ##########################################################
 # methods
 
+
 # Get optargs options based on optargs files
 def get_optargs(wildcards):
     with open(GIT_DIR + methods[wildcards.method]["optargs"], "r") as file:
         opt = json.load(file)
-    return(opt)
+    return opt
+
 
 # Get matrix in the input session
 def get_matrix_input(wildcards):
@@ -122,40 +130,51 @@ def get_matrix_input(wildcards):
     # Find preprocessing steps
     match opt["matrix"]:
         case "counts":
-            matrix_input=config["data_dir"] + f"/{wildcards.sample}/counts.mtx"
+            matrix_input = config["data_dir"] + f"/{wildcards.sample}/counts.mtx"
         case "transform":
-            matrix_input=config["data_dir"] + f"/{wildcards.sample}/log1p/counts.mtx"
+            matrix_input = config["data_dir"] + f"/{wildcards.sample}/log1p/counts.mtx"
         case "dimensionality_reduction":
-            matrix_input=config["data_dir"] + f"/{wildcards.sample}/log1p/hvg/pca_20/dimensionality_reduction.tsv"
+            matrix_input = (
+                config["data_dir"]
+                + f"/{wildcards.sample}/log1p/hvg/pca_20/dimensionality_reduction.tsv"
+            )
 
     if matrix_input == []:
-        raise(ValueError("no valid matrix option! Check your optargs.json file!"))
+        raise (ValueError("no valid matrix option! Check your optargs.json file!"))
 
     return matrix_input
 
+
 # Get features
 def get_feature_input(wildcards):
     opt = get_optargs(wildcards)
 
     # feature input option
     if opt["integrated_feature_selection"]:
-        feature_input=config["data_dir"] + f"/{wildcards.sample}/log1p/hvg/features.tsv"
+        feature_input = (
+            config["data_dir"] + f"/{wildcards.sample}/log1p/hvg/features.tsv"
+        )
     else:
-        feature_input=config["data_dir"] + f"/{wildcards.sample}/features.tsv"
+        feature_input = config["data_dir"] + f"/{wildcards.sample}/features.tsv"
 
     return feature_input
 
+
 # Get neighbors
 def get_neighbor_input(wildcards):
     opt = get_optargs(wildcards)
 
     neighbor_input = []
     # feature input option
     if opt["neighbors"]:
-        neighbor_input=config["data_dir"] + f"/{wildcards.sample}/delaunay_triangulation/spatial_connectivities.mtx"
+        neighbor_input = (
+            config["data_dir"]
+            + f"/{wildcards.sample}/delaunay_triangulation/spatial_connectivities.mtx"
+        )
 
     return neighbor_input
 
+
 rule method_with_config:
     input:
         coordinates=config["data_dir"] + "/{sample}/coordinates.tsv",
@@ -168,16 +187,20 @@ rule method_with_config:
         dir=directory(config["data_dir"] + "/{sample}/{method}/{config_file_name}"),
         file=config["data_dir"] + "/{sample}/{method}/{config_file_name}/domains.tsv",
     params:
-        matrix=lambda wildcards: "-m " if get_optargs(wildcards)["matrix"]!="dimensionality_reduction" else "--dim_red ",
+        matrix=lambda wildcards: (
+            "-m "
+            if get_optargs(wildcards)["matrix"] != "dimensionality_reduction"
+            else "--dim_red "
+        ),
         neighbors=lambda wildcards: "-n " if get_optargs(wildcards)["neighbors"] else "",
         n_clusters=lambda wildcards: get_ncluster(
             config["data_dir"] + "/samples.tsv", wildcards.sample
-            ),
+        ),
         technology=TECHNOLOGY,
         seed=SEED,
         configfile=get_config_file,
         image=get_sample_image,
-        script=lambda wildcards: GIT_DIR + methods[wildcards.method]["script"]
+        script=lambda wildcards: GIT_DIR + methods[wildcards.method]["script"],
     conda:
         lambda wildcards: GIT_DIR + methods[wildcards.method]["env"]
     wildcard_constraints:
@@ -198,6 +221,7 @@ rule method_with_config:
             --config {params.configfile}
         """
 
+
 rule method_without_config:
     input:
         coordinates=config["data_dir"] + "/{sample}/coordinates.tsv",
@@ -210,14 +234,17 @@ rule method_without_config:
         dir=directory(config["data_dir"] + "/{sample}/{method}"),
         file=config["data_dir"] + "/{sample}/{method}/domains.tsv",
     params:
-        matrix=lambda wildcards: "-m " if get_optargs(wildcards)["matrix"]!="dimensionality_reduction" else "--dim_red ",
+        matrix=lambda wildcards: (
+            "-m "
+            if get_optargs(wildcards)["matrix"] != "dimensionality_reduction"
+            else "--dim_red "
+        ),
         neighbors=lambda wildcards: "-n " if get_optargs(wildcards)["neighbors"] else "",
         n_clusters=lambda wildcards: get_ncluster(
             config["data_dir"] + "/samples.tsv", wildcards.sample
-            ),
+        ),
         technology=TECHNOLOGY,
         seed=SEED,
-        configfile=get_config_file,
         image=get_sample_image,
         script=lambda wildcards: GIT_DIR + methods[wildcards.method]["script"],
     conda:
@@ -237,4 +264,4 @@ rule method_without_config:
             --n_clusters {params.n_clusters} \
             --technology {params.technology} \
             --seed {params.seed}
-        """
+        """