The predicted probability of pathogenicity was compared between MOVA and PolyPhen-2.

Create a graph corresponding to Figure 3 in the paper.

Usage

variant_predict_plot(file_name, legend_type = FALSE)

Arguments

file_name

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

legend_type

Specify "TRUE" if you want the legend, or "FALSE" if you don't want the legend. The default is "FALSE".

Details

Value

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")
variant_predict_plot("TARDBP_Target_predict.csv")

## The function is currently defined as
function (file_name, legend_type = FALSE) 
{
    df <- fread(file_name)
    df$Typevariant <- "negative"
    df[df$Type == "Target", `:=`(Typevariant, "positive")]
    f <- data.table(variant = df$Typevariant, predict = df$MOVA_predict, 
        position = df$Pos, type = "MOVA")
    f2 <- data.table(variant = df$Typevariant, predict = df$pph_prob, 
        position = df$Pos, type = "Polyphen-2")
    df2 <- rbind(f, f2)
    df2 <- transform(df2, variant = factor(variant, levels = c("positive", 
        "negative")))
    g <- ggplot(data = df2)
    g <- g + theme(axis.text = element_text(size = 12))
    g <- g + theme(legend.text = element_text(size = 12))
    g <- g + geom_point(aes(x = position, y = predict, colour = variant))
    g <- g + facet_grid(rows = vars(type), scales = "free_y")
    if (legend_type == TRUE) {
        g <- g + theme(legend.position = "bottom")
    }
    else {
        g <- g + theme(legend.position = "none")
    }
    g
  }
#> function (file_name, legend_type = FALSE) 
#> {
#>     df <- fread(file_name)
#>     df$Typevariant <- "negative"
#>     df[df$Type == "Target", `:=`(Typevariant, "positive")]
#>     f <- data.table(variant = df$Typevariant, predict = df$MOVA_predict, 
#>         position = df$Pos, type = "MOVA")
#>     f2 <- data.table(variant = df$Typevariant, predict = df$pph_prob, 
#>         position = df$Pos, type = "Polyphen-2")
#>     df2 <- rbind(f, f2)
#>     df2 <- transform(df2, variant = factor(variant, levels = c("positive", 
#>         "negative")))
#>     g <- ggplot(data = df2)
#>     g <- g + theme(axis.text = element_text(size = 12))
#>     g <- g + theme(legend.text = element_text(size = 12))
#>     g <- g + geom_point(aes(x = position, y = predict, colour = variant))
#>     g <- g + facet_grid(rows = vars(type), scales = "free_y")
#>     if (legend_type == TRUE) {
#>         g <- g + theme(legend.position = "bottom")
#>     }
#>     else {
#>         g <- g + theme(legend.position = "none")
#>     }
#>     g
#>   }
#> <environment: 0x0000011a89bd1ac8>