update ans

dance/atlas/sc_similarity/anndata_similarity.py

@@ -288,7 +288,7 @@ def wasserstein_dist(self) -> float:
         b = np.ones((Y.shape[0], )) / Y.shape[0]
         M = ot.dist(X, Y, metric='euclidean')
         wasserstein_dist = ot.emd2(a, b, M)
-        return 1 / 1 + wasserstein_dist
+        return 1 / (1 + wasserstein_dist)
 
     def get_Hausdorff(self):
         X = self.X

examples/atlas/sc_similarity_examples/data_processing/merge_result_metadata.py

@@ -0,0 +1,18 @@
+tissues = ["blood", "brain", "heart", "intestine", "kidney", "lung", "pancreas"]
+import pandas as pd
+
+from dance.settings import ATLASDIR, SIMILARITYDIR
+
+if __name__ == "__main__":
+    for tissue in tissues:
+        metadata_df = pd.read_csv(ATLASDIR / f"metadatas/{tissue}_metadata.csv")
+        sweep_result_df = pd.read_csv(ATLASDIR / f"sweep_results/{tissue.capitalize()}_ans.csv")
+        sweep_result_df = sweep_result_df.rename(columns={"Dataset_id": "dataset_id"})
+        sweep_result_df["dataset_id"] = sweep_result_df["dataset_id"].str.split('(').str[0]
+        result_df = metadata_df.merge(sweep_result_df, how="outer", on="dataset_id")
+        #     result_df.to_csv(SIMILARITYDIR / f"data/results/{tissue}_result.csv")
+        # for tissue in tissues:
+        #     df=pd.read_csv(SIMILARITYDIR / f"data/results/{tissue}_result.csv")
+        with pd.ExcelWriter(SIMILARITYDIR / "data/Cell Type Annotation Atlas.xlsx", mode='a',
+                            if_sheet_exists='replace') as writer:
+            result_df.to_excel(writer, sheet_name=tissue)

examples/atlas/sc_similarity_examples/readme.md

@@ -0,0 +1,8 @@
+\[scGPT->metadatas\]+\[get_result_web->sweep_results\]+\[data_processing/merge_result_metadata.py\]->\[data/cell_type_annotation_atlas.xlsx\]
+\[data/cell_type_annotation_atlas.xlsx\]+\[similarity/analyze_atlas_accuracy.py\]->\[data/in_atlas_datas\]
+\[similarity/example_usage_anndata.py\]+\[data/in_atlas_datas\]+\[data/cell_type_annotation_atlas.xlsx\]->\[data/dataset_similarity\]
+\[data/dataset_similarity\]+\[similarity/process_tissue_similarity_matrices.py\]->\[data/new_sim\]
+
+#run_similarity_optimization.sh
+\[data/new_sim\]+\[similarity/optimize_similarity_weights.py\]+\[cache/sweep_cache.json\]->\[data/similarity_weights_results\]
+\[data/similarity_weights_results\]+\[similarity/visualize_atlas_performance.py\]+\[cache/sweep_cache.json\]->\[data/imgs\]

examples/atlas/sc_similarity_examples/run_similarity_optimization.sh

@@ -0,0 +1,23 @@
+#!/bin/bash
+
+# 定义数组
+array=("blood" "brain" "heart" "intestine" "kidney" "lung" "pancreas")
+# 循环数组并在后台运行 Python 脚本
+for tissue in "${array[@]}"
+do
+    python similarity/example_usage_anndata.py --tissue "$tissue"
+    # python similarity/optimize_similarity_weights.py --tissue "$tissue"
+    # python visualization/visualize_atlas_performance.py --tissue "$tissue"
+    # python similarity/optimize_similarity_weights.py --tissue "$tissue" --in_query
+    # python visualization/visualize_atlas_performance.py --tissue "$tissue" --in_query
+    # python similarity/optimize_similarity_weights.py --tissue "$tissue" --reduce_error
+    # python visualization/visualize_atlas_performance.py --tissue "$tissue" --reduce_error
+    # python similarity/optimize_similarity_weights.py --tissue "$tissue" --in_query --reduce_error
+    # python visualization/visualize_atlas_performance.py --tissue "$tissue" --in_query --reduce_error
+    echo "启动处理 tissue: $tissue"
+done
+
+# 等待所有后台进程完成
+wait
+
+echo "所有 Python 脚本已执行完成"

examples/atlas/sc_similarity_examples/sim_query_atlas.py → examples/atlas/sc_similarity_examples/similarity/analyze_atlas_accuracy.py

@@ -7,13 +7,17 @@
 import numpy as np
 import pandas as pd
 import yaml
+from tqdm import tqdm
 
-sys.path.append("..")
+from dance.settings import DANCEDIR, SIMILARITYDIR
+
+sys.path.append(str(DANCEDIR))
 import ast
 
 from get_result_web import get_sweep_url, spilt_web
 
 from dance import logger
+from dance.settings import entity, project
 from dance.utils import try_import
 
 file_root = str(Path(__file__).resolve().parent.parent)
@@ -70,8 +74,6 @@ def is_match(config_str):
 
 
 wandb = try_import("wandb")
-entity = "xzy11632"
-project = "dance-dev"
 
 
 def is_matching_dict(yaml_str, target_dict):
@@ -156,18 +158,20 @@ def get_ans_from_cache(query_dataset, method):
     # Get best method from step2 of atlas datasets
     # Search accuracy according to best method (all values should exist)
     ans = pd.DataFrame(index=[method], columns=[f"{atlas_dataset}_from_cache" for atlas_dataset in atlas_datasets])
-
-    sweep_url = re.search(r"step2:([^|]+)",
-                          conf_data[conf_data["dataset_id"] == query_dataset][method].iloc[0]).group(1)
+    step_str = conf_data[conf_data["dataset_id"] == query_dataset][method].iloc[0]
+    if pd.isna(step_str):
+        logger.warning(f"{query_dataset} is nan in {method}")
+        return ans
+    sweep_url = re.search(r"step2:([^|]+)", step_str).group(1)
     _, _, sweep_id = spilt_web(sweep_url)
     sweep = wandb.Api().sweep(f"{entity}/{project}/{sweep_id}")
-
-    for atlas_dataset in atlas_datasets:
-        best_yaml = conf_data[conf_data["dataset_id"] == atlas_dataset][f"{method}_best_yaml"].iloc[0]
+    runs = sweep.runs
+    for atlas_dataset in tqdm(atlas_datasets):
+        best_yaml = conf_data[conf_data["dataset_id"] == atlas_dataset][f"{method}_step2_best_yaml"].iloc[0]
         match_run = None
 
         # Find matching run configuration
-        for run in sweep.runs:
+        for run in tqdm(runs, leave=False):
             if isinstance(best_yaml, float) and np.isnan(best_yaml):
                 continue
             if is_matching_dict(best_yaml, run.config):
@@ -188,7 +192,7 @@ def get_ans_from_cache(query_dataset, method):
 parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
 parser.add_argument("--methods", default=["cta_actinn", "cta_celltypist", "cta_scdeepsort", "cta_singlecellnet"],
                     nargs="+")
-parser.add_argument("--tissue", type=str, default="blood")
+parser.add_argument("--tissue", type=str, default="pancreas")
 args = parser.parse_args()
 methods = args.methods
 tissue = args.tissue
@@ -208,7 +212,7 @@ def get_ans_from_cache(query_dataset, method):
 #     "738942eb-ac72-44ff-a64b-8943b5ecd8d9", "a5d95a42-0137-496f-8a60-101e17f263c8",
 #     "71be997d-ff75-41b9-8a9f-1288c865f921"
 # ]
-conf_data = pd.read_excel("Cell Type Annotation Atlas.xlsx", sheet_name=tissue)
+conf_data = pd.read_excel(SIMILARITYDIR / "data/Cell Type Annotation Atlas.xlsx", sheet_name=tissue)
 # conf_data = pd.read_csv(f"results/{tissue}_result.csv", index_col=0)
 atlas_datasets = list(conf_data[conf_data["queryed"] == False]["dataset_id"])
 query_datasets = list(conf_data[conf_data["queryed"] == True]["dataset_id"])
@@ -219,8 +223,9 @@ def get_ans_from_cache(query_dataset, method):
             ans.append(get_ans_from_cache(query_dataset, method))
         ans = pd.concat(ans)
         ans_all[query_dataset] = ans
-    for k, v in ans_all.items():
-        file_path = f"in_atlas_datas/{tissue}/{str(methods)}_{k}_in_atlas.csv"
+        print(query_dataset)
+        # for k, v in ans_all.items():
+        file_path = SIMILARITYDIR / f"data/in_atlas_datas/{tissue}/{str(methods)}_{query_dataset}_in_atlas.csv"
         folder_path = Path(file_path).parent
         folder_path.mkdir(parents=True, exist_ok=True)
-        v.to_csv(file_path)
+        ans.to_csv(file_path)