CONTROL = args.control_dict[GROUPING_VAR] # control strain to use
IS_NORMAL = args.is_normal
# Perform t-tests/ANOVA if True, ranksum/kruskal if False. If None, Shapiro-Wilks tests for
# normality are perofrmed to decide between parametric/non-parametric tests
TEST_NAME = args.test # str, Choose between 'LMM' (if >1 day replicate), 'ANOVA' or 'Kruskal'
# Kruskal tests are performed instead of ANOVA if check_normal and data is not normally distributed)
# If significant features are found, pairwise t-tests are performed
#%% Compile and clean results
# Process metadata
metadata, metadata_path = process_metadata(aux_dir=AUX_DIR,
imaging_dates=args.dates,
add_well_annotations=args.add_well_annotations)
# Process feature summary results
features, metadata = process_feature_summaries(metadata_path,
RESULTS_DIR,
compile_day_summaries=args.compile_day_summaries,
imaging_dates=args.dates,
align_bluelight=args.align_bluelight)
# Clean: remove data with too many NaNs/zero std and impute remaining NaNs
features, metadata = clean_summary_results(features,
metadata,
feature_columns=None,
imputeNaN=args.impute_nans,
nan_threshold=args.nan_threshold,
description="Analyse acute response videos to investigate how \
fast the food takes to influence worm behaviour")
parser.add_argument('-j',
'--json',
help="Path to JSON parameters file",
default=JSON_PARAMETERS_PATH)
args = parser.parse_args()
args = load_json(args.json)
aux_dir = Path(args.project_dir) / 'AuxiliaryFiles'
results_dir = Path(args.project_dir) / 'Results'
# load metadata
metadata, metadata_path = process_metadata(
aux_dir,
imaging_dates=args.dates,
add_well_annotations=args.add_well_annotations,
n_wells=6)
features, metadata = process_feature_summaries(metadata_path,
results_dir,
compile_day_summaries=False,
imaging_dates=args.dates,
align_bluelight=False,
window_summaries=True,
n_wells=6)
# Subset results (rows) to remove entries for wells with unknown strain data for 'gene_name'
n = metadata.shape[0]
metadata = metadata.loc[~metadata['gene_name'].isna(), :]
features = features.reindex(metadata.index)
