comparison COBRAxy/flux_to_map.py @ 295:626b6d1de075 draft

Uploaded

294:274223ff4fb0

    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, 
        help = 'Fold-Change threshold (default: %(default)s)')
    

    parser.add_argument(
        '-op', '--option',
        type = str, 
        choices = ['datasets', 'dataset_class'],
        help='dataset or dataset and class')

    
    return p_value, z_score

def compareDatasetPair(dataset1Data :List[List[float]], dataset2Data :List[List[float]], ids :List[str]) -> Tuple[Dict[str, List[Union[float, FoldChange]]], float]:
    #TODO: the following code still suffers from "dumbvarnames-osis"
    tmp :Dict[str, List[Union[float, FoldChange]]] = {}
    count   = 0
    max_z_score = 0
    for l1, l2 in zip(dataset1Data, dataset2Data):
        reactId = ids[count]
        count += 1

            avg1 = sum(l1) / len(l1)
            avg2 = sum(l2) / len(l2)
            f_c = fold_change(avg1, avg2)
            if np.isfinite(z_score) and max_z_score < abs(z_score): max_z_score = abs(z_score)
            
            tmp[reactId] = [float(p_value), f_c, z_score, avg1, avg2]
        except (TypeError, ZeroDivisionError): continue
    
    return tmp, max_z_score

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

295:626b6d1de075

    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, 
        help = 'Fold-Change threshold (default: %(default)s)')
    
    parser.add_argument(
        '-op', '--option',
        type = str, 
        choices = ['datasets', 'dataset_class'],
        help='dataset or dataset and class')

    
    return p_value, z_score

def compareDatasetPair(dataset1Data :List[List[float]], dataset2Data :List[List[float]], ids :List[str]) -> Tuple[Dict[str, List[Union[float, FoldChange]]], float]:
    #TODO: the following code still suffers from "dumbvarnames-osis"
    comparisonResult :Dict[str, List[Union[float, FoldChange]]] = {}
    count   = 0
    max_z_score = 0
    for l1, l2 in zip(dataset1Data, dataset2Data):
        reactId = ids[count]
        count += 1

            avg1 = sum(l1) / len(l1)
            avg2 = sum(l2) / len(l2)
            f_c = fold_change(avg1, avg2)
            if np.isfinite(z_score) and max_z_score < abs(z_score): max_z_score = abs(z_score)
            
            comparisonResult[reactId] = [float(p_value), f_c, z_score, avg1, avg2]
        except (TypeError, ZeroDivisionError): continue

    # Apply multiple testing correction if set by the user
    if ARGS.adjusted:
        
        # Retrive 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]) -> List[Tuple[str, str, dict, float]]:
    """
    Compares clustered data based on a given comparison mode and applies enrichment-based styling on the
    provided metabolic map.