comparison COBRAxy/marea.py @ 297:1402c2beb8f2 draft

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296:5c70b653539b

    parser.add_argument(
        '-pv' ,'--pValue',
        type = float, 
        default = 0.1, 
        help = 'P-Value threshold (default: %(default)s)')
    
    parser.add_argument(
        '-fc', '--fChange',
        type = float, 
        default = 1.5, 

            if np.isfinite(z_score) and max_z_score < abs(z_score): max_z_score = abs(z_score)
            comparisonResult[reactId] = [float(p_value), avg, z_score, sum(l1) / len(l1), sum(l2) / len(l2)]
        
        except (TypeError, ZeroDivisionError): continue
    
    return comparisonResult, max_z_score

def computeEnrichment(class_pat: Dict[str, List[List[float]]], ids: List[str], *, fromRAS=True) -> List[Tuple[str, str, dict, float]]:
    """
    Compares clustered data based on a given comparison mode and applies enrichment-based styling on the

297:1402c2beb8f2

    parser.add_argument(
        '-pv' ,'--pValue',
        type = float, 
        default = 0.1, 
        help = 'P-Value threshold (default: %(default)s)')

    parser.add_argument(
        '-adj' ,'--adjusted',
        type = utils.Bool("adjusted"), default = False, 
        help = 'Apply the FDR (Benjamini-Hochberg) correction (default: %(default)s)')
    
    parser.add_argument(
        '-fc', '--fChange',
        type = float, 
        default = 1.5, 

            if np.isfinite(z_score) and max_z_score < abs(z_score): max_z_score = abs(z_score)
            comparisonResult[reactId] = [float(p_value), avg, z_score, sum(l1) / len(l1), sum(l2) / len(l2)]
        
        except (TypeError, ZeroDivisionError): continue
    
    # Apply multiple testing correction if set by the user
    if ARGS.adjusted:
        
        # Retrieve the p-values from the comparisonResult dictionary
        reactIds = list(comparisonResult.keys())
        pValues = [comparisonResult[reactId][0] for reactId in reactIds]
        
        # Apply the Benjamini-Hochberg correction and update
        adjustedPValues = st.multipletests(pValues, method="fdr_bh")[1]
        for i, reactId in enumerate(reactIds):
            comparisonResult[reactId][0] = adjustedPValues[i]

    return comparisonResult, max_z_score

def computeEnrichment(class_pat: Dict[str, List[List[float]]], ids: List[str], *, fromRAS=True) -> List[Tuple[str, str, dict, float]]:
    """
    Compares clustered data based on a given comparison mode and applies enrichment-based styling on the