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Function to compare MOVA and CADD + AlphScore.

CADD_AlphScore.Rd

This function plots the ROC curve in CADD + AlphScore for each gene and returns the AUC.

Usage

CADD_AlphScore(file_name, phenotype = "Target", col = "gray")

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 gray.

Details

Value

Return the AUC for each gene in CADD + AlphScore.

References

Author

Yuya Hatano

Note

See also

Examples

Hgmd_divide("./source/OPTN_hgmd.csv")
Edit_gnomAD_file("./source/gnomAD_v3.1.2_ENST00000378748_2023_02_15_13_19_36.csv", "./source/OPTN_gnomAD.csv")
Edit_variant_data("../CADD_REVEL/AlphScore_final.tsv", "./source/OPTN_hgmd.csv", "./source/OPTN_gnomAD.csv", "Q96CV9", "OPTN", "./source/OPTNvariantdata.csv")
Edit_polyphen_data("../dbNSFP/dbNSFP4.3a/dbNSFP4.3a_variant.chr10","./source/OPTNvariantdata.csv", "OPTN_alph.csv", "Q96CV9",13100173,13138291,  "./source/OPTNvariantdatapol.csv", "./source/OPTN_alphpol.csv")
Edit_final_variant_file("./source/OPTN_alphpol.csv","./source/OPTN_alphpol2.csv")
MOVA("./source/Q96CV9.fa", "OPTN", "./source/AF-Q96CV9-F1-model_v2.pdb","./source/OPTN_alphpol2.csv", "./source/OPTNvariantdatapol.csv")
Com_CADD_MOVA("OPTN", "OPTN_Target_finalpredict.csv", "OPTN_Target_predict_orig.csv", "OPTN_Target_predict.csv", alphscore = 1)
MOVA_redraw("OPTN_Target_predict_orig.csv",col="blue")
CADD_MOVA("OPTN_Target_predict.csv", col="black")
CADD_AlphScore("OPTN_Target_predict.csv", col="gray")
MOVA_redraw("OPTN_Target_predict_orig_CADDMOVA.csv",col="red", add=TRUE)


## The function is currently defined as
function (file_name, phenotype = "Target", col = "gray") 
{
    df <- fread(file_name)
    if (phenotype == "Target") {
        df <- df[(df$Type == "Target") | (df$Type == "Ctrl"), 
            ]
    }
    df$predict <- df$glm_AlphCadd
    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 = "gray") 
#> {
#>     df <- fread(file_name)
#>     if (phenotype == "Target") {
#>         df <- df[(df$Type == "Target") | (df$Type == "Ctrl"), 
#>             ]
#>     }
#>     df$predict <- df$glm_AlphCadd
#>     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: 0x0000011a8436f818>