Merge pull request #531 from aertslab/v1.0a2

V1.0a2

.gitignore

@@ -3,3 +3,5 @@ __pycahe__/
 *.py[cod]
 scplus_rtd
 .python-version
+build/*
+*.egg*

pyproject.toml

@@ -10,8 +10,8 @@ authors = [
 ]
 description = "SCENIC+ is a python package to build gene regulatory networks (GRNs) using combined or separate single-cell gene expression (scRNA-seq) and single-cell chromatin accessibility (scATAC-seq) data."
 readme = "README.md"
-version = "1.0a1"
-requires-python = ">=3.8"
+version = "1.0a2"
+requires-python = ">=3.8,<=3.11.8"
 keywords = ["scATAC", "GRN inference", "eGRN inference"]
 license = { file = "LICENCE.txt" }
 classifiers = [

src/scenicplus/cli/commands.py

@@ -817,7 +817,8 @@ def infer_grn(
         keep_only_activating: bool,
         rho_threshold: float,
         min_target_genes: int,
-        n_cpu: int):
+        n_cpu: int,
+        seed: int):
     """
     Infer gene regulatory network.
 
@@ -869,6 +870,8 @@ def infer_grn(
         Minimum number of target genes.
     n_cpu : int
         Number of parallel processes to run.
+    seed: int
+        Random seed to use.
 
     """
     from scenicplus.grn_builder.gsea_approach import build_grn
@@ -906,7 +909,8 @@ def infer_grn(
         min_target_genes=min_target_genes,
         n_cpu=n_cpu,
         merge_eRegulons=True,
-        disable_tqdm=False)
+        disable_tqdm=False,
+        seed=seed)
 
     log.info("Formatting eGRN as table.")
     eRegulon_metadata = _format_egrns(

src/scenicplus/cli/scenicplus.py

@@ -880,7 +880,8 @@ def eGRN(arg):
             keep_only_activating=arg.keep_only_activating_eRegulons,
             rho_threshold=arg.rho_threshold,
             min_target_genes=arg.min_target_genes,
-            n_cpu=arg.n_cpu)
+            n_cpu=arg.n_cpu,
+            seed=arg.seed)
 
     parser.set_defaults(func=eGRN)
     # Required arguments
@@ -984,6 +985,11 @@ def eGRN(arg):
         action="store", type=int, required=False,
         default=1,
         help="Number of cores to use. Default is 1.")
+    parser.add_argument(
+        "--seed", dest="seed",
+        action="store", type=int, required=False,
+        default=666,
+        help="Seed to use. Default is 666.")
 
 def add_parser_for_aucell(subparser:argparse._SubParsersAction):
     parser:argparse.ArgumentParser = subparser.add_parser(

src/scenicplus/data_wrangling/adata_cistopic_wrangling.py

@@ -91,8 +91,10 @@ def process_multiome_data(
     GEX_gene_metadata = GEX_anndata.var.copy(deep=True)
     GEX_cell_metadata = GEX_anndata.obs.copy(deep=True).loc[
         common_cells]
-    ACC_region_metadata = cisTopic_obj.region_data.copy(deep=True).loc[
-        imputed_acc_obj.feature_names]
+    ACC_region_metadata = pd.DataFrame(index = imputed_acc_obj.feature_names)
+    ACC_region_metadata = ACC_region_metadata.merge(
+        cisTopic_obj.region_data, left_index = True, right_index = True, how = "left")
+    ACC_region_metadata = ACC_region_metadata.loc[imputed_acc_obj.feature_names]
     ACC_cell_metadata = cisTopic_obj.cell_data.copy(deep=True).loc[
         common_cells]
 
@@ -331,7 +333,9 @@ def process_non_multiome_data(
     # generate cell metadata
     metadata_cell = pd.DataFrame(index=meta_cell_names_ACC,
                                     data={key_to_group_by: [x.split(meta_cell_split)[0] for x in meta_cell_names_ACC]})
-    ACC_region_metadata = cisTopic_obj.region_data.copy(deep=True)
+    ACC_region_metadata = pd.DataFrame(index = imputed_acc_obj.feature_names)
+    ACC_region_metadata = ACC_region_metadata.merge(
+        cisTopic_obj.region_data, left_index = True, right_index = True, how = "left")
     ACC_region_metadata_subset = ACC_region_metadata.loc[imputed_acc_obj.feature_names]
 
     mudata = MuData(

src/scenicplus/grn_builder/gsea.py

@@ -1,18 +1,14 @@
 from gseapy.algorithm import enrichment_score
 from gseapy.algorithm import gsea_compute
-from gseapy.plot import GSEAPlot
 import numpy as np
 import pandas as pd
 
-seed = 666
-
-
 def run_gsea(ranked_gene_list: pd.Series,
              gene_set: list,
              n_perm: int = 1000,
              ascending: bool = False,
-             return_res: bool = False,
-             name: str = 'gene_set'):
+             name: str = 'gene_set',
+             seed: int = 555):
     """
     Calculates gene set enrichment score (and estimates its significance) and leading edge for the gene set in the ranked gene list using gseapy prerank.
 
@@ -39,7 +35,7 @@ def run_gsea(ranked_gene_list: pd.Series,
     gmt = {name: list(gene_set)}
     gsea_results, ind, rank_ES, gs = gsea_compute(data=ranked_gene_list, n=n_perm, gmt=gmt,
                                                   weighted_score_type=1, permutation_type='gene_set', method=None,
-                                                  pheno_pos='Pos', pheno_neg='Neg', classes=None, ascending=ascending)
+                                                  pheno_pos='Pos', pheno_neg='Neg', classes=None, ascending=ascending, seed = seed)
 
     # extract enrichment scores
     gseale = list(gsea_results)[0]
@@ -62,13 +58,7 @@ def run_gsea(ranked_gene_list: pd.Series,
     pval = gseale[2]
     fdr = gseale[3]
     LE = list(map(str, ranked_gene_list.iloc[ldg_pos].index))
-    if return_res:
-        res = GSEAPlot(ranked_gene_list, name, ind, nes, pval, fdr, RES,
-                       pheno_pos='', pheno_neg='', figsize=(6, 5.5),
-                       cmap='seismic', ofname=None)
-        return nes, pval, LE, res
-    else:
-        return nes, pval, LE
+    return nes, pval, LE
 
 
 def run_enrichr(ranked_gene_list: np.array,

src/scenicplus/grn_builder/gsea_approach.py

@@ -62,11 +62,13 @@
 logging.basicConfig(level=level, format=format, handlers=handlers)
 log = logging.getLogger('GSEA')
 
+
 def _run_gsea_for_e_module(
         e_module:eRegulon, 
         rnk:pd.Series, 
         gsea_n_perm:int, 
-        context: frozenset):
+        context: frozenset,
+        seed: int):
     """
     Helper function to run gsea for single e_module
 
@@ -92,11 +94,12 @@ def _run_gsea_for_e_module(
             NES, pval, LE_genes = run_gsea(
                 ranked_gene_list=rnk,
                 gene_set=gene_set,
-                n_perm=gsea_n_perm)
+                n_perm=gsea_n_perm,
+                seed=seed)
         except:
             NES = np.nan
             pval = np.nan
-            LE_genes = np.nan
+            LE_genes = [np.nan]
         return eRegulon(
             transcription_factor=TF,
             cistrome_name=e_module.cistrome_name,
@@ -134,6 +137,7 @@ def build_grn(
         n_cpu=1,
         merge_eRegulons=True,
         disable_tqdm=False,
+        seed=555,
         **kwargs) -> List[eRegulon]:
     log.info('Thresholding region to gene relationships')
     # some tfs are missing from tf_to_gene because they are not 
@@ -185,9 +189,10 @@ def build_grn(
                 e_module=e_module,
                 rnk=TF_to_ranking_pos[e_module.transcription_factor],
                 gsea_n_perm=gsea_n_perm,
-                context=frozenset(['positive tf2g']))
+                context=frozenset(['positive tf2g']),
+                seed=seed)
             for e_module in tqdm(
-                e_modules, 
+                e_modules,
                 total = len(e_modules),
                 desc="Running for Positive TF to gene")
             if e_module.transcription_factor in pos_TFs)
@@ -198,7 +203,8 @@ def build_grn(
                 e_module=e_module,
                 rnk=TF_to_ranking_neg[e_module.transcription_factor],
                 gsea_n_perm=gsea_n_perm,
-                context=frozenset(['negative tf2g']))
+                context=frozenset(['negative tf2g']),
+                seed=seed)
             for e_module in tqdm(
                 e_modules, 
                 total = len(e_modules),