v1.0.0: Added parallel assignment and cpu parameter for configurable core usage

Author: mdbarnesUCSD

CHANGELOG.md

@@ -6,6 +6,15 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 
 ## [Unreleased]
 
+## [1.0.0] - 2025-10-07
+
+### Added
+- Parallel execution of SPA runs for improved performance on multi-core systems.
+- `cpu` parameter to control the number of processor cores used during assignment. Defaults to `-1` to use all available cores.
+
+### Documentation
+- Updated README to document the new `cpu` parameter.
+
 ## [0.2.6] - 2025-09-17
 
 ### Added

README.md

@@ -74,10 +74,12 @@ Analyze.cosmic_fit(samples, output, input_type="matrix", context_type="96",
 | make_plots | Boolean | Toggle on and off for making and saving plots. The default value is True. |
 | sample_reconstruction_plots | String | Select the output format for sample reconstruction plots. Valid inputs are {'pdf', 'png', 'both', 'none'}. The default value is 'none'. If set to 'png' or 'both', the external binary `poppler` must be installed. Install via `conda install -c conda-forge poppler` or `brew install poppler` on macOS. |
 | verbose | Boolean | Prints detailed statements. The default value is False. |
+| cpu | Integer | Number of processor cores to use during assignment. The default value is -1, which uses all available cores. |
 | volume | String | Path to SigProfilerAssignment volumes. Used for Docker/Singularity. Environmental variable "SIGPROFILERASSIGNMENT_VOLUME" takes precedence. Default value is None. |
 
 
 
+
 ### <a name="subgroups"></a> Signature Subgroups
 
 When using COSMIC reference signatures, some subgroups of signatures can be removed to improve the refitting analysis. To use this feature, the `exclude_signature_subgroups` parameter should be added, following the sintax below:

SigProfilerAssignment/Analyzer.py

@@ -160,5 +160,5 @@ def cosmic_fit(
         export_probabilities_per_mutation=export_probabilities_per_mutation,
         sample_reconstruction_plots=sample_reconstruction_plots,
         volume=volume,
-        cpu=cpu
+        cpu=cpu,
     )

SigProfilerAssignment/DecompositionPlots/PlotDecomposition.py

@@ -281,16 +281,22 @@ def calculate_similarities(denovo, denovo_name, est_denovo):
     if denovo.shape[0] == 1536:
         denovo["IndexKey"] = denovo.iloc[:, 0].astype(str)
         denovo["GroupKey"] = denovo["IndexKey"].str[1:8]
-        denovo_grouped = denovo.groupby("GroupKey", as_index=False).sum(numeric_only=True)
+        denovo_grouped = denovo.groupby("GroupKey", as_index=False).sum(
+            numeric_only=True
+        )
         denovo_grouped.rename(columns={"GroupKey": "MutationType"}, inplace=True)
         denovo = denovo_grouped.reset_index(drop=True)
 
     elif denovo.shape[0] == 288:
         denovo["MutationType"] = denovo["MutationType"].astype(str)  # Ensure strings
-        denovo["GroupKey"] = denovo["MutationType"].str[2:9]  # Extract substring for grouping
-        denovo_grouped = denovo.groupby("GroupKey", as_index=False).sum(numeric_only=True)
+        denovo["GroupKey"] = denovo["MutationType"].str[
+            2:9
+        ]  # Extract substring for grouping
+        denovo_grouped = denovo.groupby("GroupKey", as_index=False).sum(
+            numeric_only=True
+        )
         denovo_grouped.rename(columns={"GroupKey": "MutationType"}, inplace=True)
-        denovo = denovo_grouped.reset_index(drop=True) # updated pandas 2.0.0
+        denovo = denovo_grouped.reset_index(drop=True)  # updated pandas 2.0.0
     sample_names = [denovo_name]
 
     if sample_names is False:

SigProfilerAssignment/DecompositionPlots/PlotDecomposition_SV32.py

@@ -128,6 +128,7 @@
 PLOT_NAME = 0
 CONTRIBUTION = 1
 
+
 # Helper functions for plotting the layout of a graph with 1-5 basis signatures
 # Parameters:
 #   bases 		- (List of Strings) The list of basis names

SigProfilerAssignment/controllers/cli_controller.py

@@ -146,7 +146,7 @@ def parse_arguments_common(args: List[str], description: str) -> argparse.Namesp
             "'pdf' (generate only PDF), "
             "'both' (PDF + PNG), or "
             "'png' (PNG only, PDF removed)."
-        )
+        ),
     )
 
     return parser.parse_args(args)