lint

transcriptomics_data_service/db.py

@@ -135,7 +135,9 @@ async def fetch_gene_expressions_by_experiment_id(self, experiment_result_id: st
             res = await conn.fetch(query, experiment_result_id)
         return tuple([self._deserialize_gene_expression(record) for record in res])
 
-    async def fetch_gene_expressions(self, experiments: list[str], method: str = "raw", paginate: bool = False) -> Tuple[Tuple[GeneExpression, ...], int]:
+    async def fetch_gene_expressions(
+        self, experiments: list[str], method: str = "raw", paginate: bool = False
+    ) -> Tuple[Tuple[GeneExpression, ...], int]:
         if not experiments:
             return (), 0
         # TODO: refactor this fetch_gene_expressions_by_experiment_id and implement pagination

transcriptomics_data_service/routers/normalization.py

@@ -93,9 +93,7 @@ async def _fetch_raw_counts(db, experiment_result_id: str) -> pd.DataFrame:
     Fetch raw counts from the database for the given experiment_result_id.
     Returns a DataFrame with genes as rows and samples as columns.
     """
-    expressions, _ = await db.fetch_gene_expressions(
-        experiments=[experiment_result_id], method="raw", paginate=False
-    )
+    expressions, _ = await db.fetch_gene_expressions(experiments=[experiment_result_id], method="raw", paginate=False)
     if not expressions:
         raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="Experiment result not found.")
 

transcriptomics_data_service/scripts/normalize.py

@@ -2,12 +2,14 @@
 import numpy as np
 from joblib import Parallel, delayed
 
+
 def filter_counts(counts_df):
     """Filter out genes (rows) and samples (columns) with zero total counts."""
     row_filter = counts_df.sum(axis=1) > 0
     col_filter = counts_df.sum(axis=0) > 0
     return counts_df.loc[row_filter, col_filter]
 
+
 def prepare_counts_and_lengths(counts_df, gene_lengths, scale_length=None):
     """Align counts and gene_lengths, drop zeros, and optionally scale gene lengths."""
     counts_df = counts_df.loc[gene_lengths.index]
@@ -18,11 +20,13 @@ def prepare_counts_and_lengths(counts_df, gene_lengths, scale_length=None):
         gene_lengths = gene_lengths / scale_length
     return filter_counts(counts_df), gene_lengths
 
+
 def parallel_apply(columns, func, n_jobs=-1):
     """Apply a function to each column in parallel and combine results."""
     results = Parallel(n_jobs=n_jobs)(delayed(func)(col) for col in columns)
     return pd.concat(results, axis=1)
 
+
 def trim_values(log_ratio, log_mean, w, logratio_trim, sum_trim):
     """Perform log ratio and sum trimming."""
     n = len(log_ratio)
@@ -43,6 +47,7 @@ def trim_values(log_ratio, log_mean, w, logratio_trim, sum_trim):
 
     return lr_t[final_idx], w_t[final_idx]
 
+
 def compute_TMM_normalization_factors(counts_df, logratio_trim=0.3, sum_trim=0.05, weighting=True, n_jobs=-1):
     """Compute TMM normalization factors for counts data."""
     lib_sizes = counts_df.sum(axis=0)
@@ -53,7 +58,7 @@ def compute_TMM_normalization_factors(counts_df, logratio_trim=0.3, sum_trim=0.0
     sample_names = counts_df.columns
     data_values = counts_df.values
 
-    norm_factors = pd.Series(index=sample_names, dtype='float64')
+    norm_factors = pd.Series(index=sample_names, dtype="float64")
     norm_factors[ref_sample] = 1.0
 
     def compute_norm_factor(sample):
@@ -81,7 +86,7 @@ def compute_norm_factor(sample):
         lr_final, w_final = trim_values(log_ratio, log_mean, w, logratio_trim, sum_trim)
 
         mean_M = np.sum(w_final * lr_final) / np.sum(w_final)
-        norm_factor = 2 ** mean_M
+        norm_factor = 2**mean_M
         return sample, norm_factor
 
     samples = [s for s in sample_names if s != ref_sample]
@@ -93,6 +98,7 @@ def compute_norm_factor(sample):
     norm_factors = norm_factors / np.exp(np.mean(np.log(norm_factors)))
     return norm_factors
 
+
 def tmm_normalization(counts_df, logratio_trim=0.3, sum_trim=0.05, weighting=True, n_jobs=-1):
     """Perform TMM normalization on counts data."""
     counts_df = filter_counts(counts_df)
@@ -101,21 +107,26 @@ def tmm_normalization(counts_df, logratio_trim=0.3, sum_trim=0.05, weighting=Tru
     normalized_data = counts_df.div(lib_sizes, axis=1).div(norm_factors, axis=1) * lib_sizes.mean()
     return normalized_data
 
+
 def getmm_normalization(counts_df, gene_lengths, logratio_trim=0.3, sum_trim=0.05, weighting=True, n_jobs=-1):
     """Perform GeTMM normalization on counts data."""
     counts_df, gene_lengths = prepare_counts_and_lengths(counts_df, gene_lengths)
     rpk = counts_df.mul(1e3).div(gene_lengths, axis=0)
     return tmm_normalization(rpk, logratio_trim, sum_trim, weighting, n_jobs)
 
+
 def compute_rpk(counts_df, gene_lengths_scaled, n_jobs=-1):
     """Compute RPK values in parallel."""
     columns = counts_df.columns
+
     def rpk_col(col):
         return counts_df[col] / gene_lengths_scaled
+
     rpk = parallel_apply(columns, rpk_col, n_jobs)
     rpk.columns = columns
     return rpk
 
+
 def tpm_normalization(counts_df, gene_lengths, scale_library=1e6, scale_length=1e3, n_jobs=-1):
     """Convert raw read counts to TPM in parallel."""
     counts_df, gene_lengths_scaled = prepare_counts_and_lengths(counts_df, gene_lengths, scale_length=scale_length)