Function to compare MOVA and PolyPhen-2.

This function plots the ROC curve in PolyPhen-2 for each gene and returns the AUC.

Usage

PolyPhen_MOVA(file_name, phenotype = "Target", col = "orange")

Arguments

file_name

The "gene name_Target or Pathogenic_predict.csv" file output by the MOVA function

phenotype

Specify "Target" if you want the positive variant to be the Target variant only, or "Pathogenic" if you want to include the Pathogenic variant as well. The default is "Target".

col

Specifies the color of the ROC curve. Default is orange.

Details

Value

Return the AUC for each gene in PolyPhen-2.

References

Author

Yuya Hatano

Note

See also

Examples

Hgmd_divide("./source/TARDBP.csv")
Edit_gnomAD_file("./source/gnomAD_v3.1.2_ENST00000240185_2023_02_14_13_23_48.csv", "./source/TARDBP_gnomAD.csv")
Edit_variant_data("../CADD_REVEL/AlphScore_final.tsv", "./source/TARDBP.csv", "./source/TARDBP_gnomAD.csv", "Q13148", "TARDBP", "./source/TARDBPvariantdata.csv")
Edit_polyphen_data("../dbNSFP/dbNSFP4.3a/dbNSFP4.3a_variant.chr1","./source/TARDBPvariantdata.csv", "TARDBP_alph.csv",  "Q13148",  11012654, 11025492 ,"./source/TARDBPvariantdatapol.csv", "./source/TARDBP_alphpol.csv")
Edit_final_variant_file("./source/TARDBP_alphpol.csv","./source/TARDBP_alphpol2.csv")
MOVA("./source/Q13148.fa", "TARDBP", "./source/AF-Q13148-F1-model_v2.pdb","./source/TARDBP_alphpol2.csv", "./source/TARDBPvariantdatapol.csv")
PolyPhen_MOVA("TARDBP_Target_predict.csv")
AlphScore_MOVA("TARDBP_Target_predict.csv")
CADD_MOVA("TARDBP_Target_predict.csv")
REVEL_MOVA("TARDBP_Target_predict.csv")
EVE_MOVA("TARDBP_Target_predict.csv", "../dementia/EVE_all_data/variant_files/TADBP_HUMAN.csv")
##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--  or do  help(data=index)  for the standard data sets.

## The function is currently defined as
function (file_name, phenotype = "Target", col = "orange") 
{
    df <- fread(file_name)
    if (phenotype == "Target") {
        df <- df[(df$Type == "Target") | (df$Type == "Ctrl"), 
            ]
    }
    df$predict <- df$pph_prob
    if (phenotype == "Target") {
        df$result <- df$Type2
    }
    else {
        df$result <- df$Type3
    }
    pred <- prediction(df$predict, df$result)
    perf <- performance(pred, "tpr", "fpr")
    plot(perf, col = col, add = TRUE)
    auc.tmp <- performance(pred, "auc")
    auc <- as.numeric(auc.tmp@y.values)
    return(auc)
  }
#> function (file_name, phenotype = "Target", col = "orange") 
#> {
#>     df <- fread(file_name)
#>     if (phenotype == "Target") {
#>         df <- df[(df$Type == "Target") | (df$Type == "Ctrl"), 
#>             ]
#>     }
#>     df$predict <- df$pph_prob
#>     if (phenotype == "Target") {
#>         df$result <- df$Type2
#>     }
#>     else {
#>         df$result <- df$Type3
#>     }
#>     pred <- prediction(df$predict, df$result)
#>     perf <- performance(pred, "tpr", "fpr")
#>     plot(perf, col = col, add = TRUE)
#>     auc.tmp <- performance(pred, "auc")
#>     auc <- as.numeric(auc.tmp@y.values)
#>     return(auc)
#>   }
#> <environment: 0x0000011a8f2dda78>