# Calculate of the inter-mutational distance (IMD). The inter-mutational distance (IMD) is the distance between each somatic substitution and the substitution immediately prior (Nik-Zainal et al. 2012). The `imd()` is used to graph the rainfall plot (Nik-Zainal et al. 2012). This graph localizes the regional clustering of mutations and represents the IMD on the y-axis on a log base 10 scale, with mutations ranked and ordered on the x-axis from the first variant on the short arm of chromosome 1 to the last variant on the long arm of chromosome X. ```r imd(data = NULL, chr = NULL, position, extra = NULL) ``` ## Arguments - `data`: : somatic substitution mutations of the cancer genome data set. - `chr`: : chromosome where the somatic mutation is located. - `position`: : position of somatic mutations in the DNA sequence of the cancer genome. - `extra`: : additional information to posterior graph. ## Returns `imd()` returns a data set with six variables: - **chr**: : chromosome where the somatic mutation is located. - **position**: : position of somatic mutations in the DNA sequence of the cancer genome. - **extra**: : additional information. - **number**: : ordered mutations. - **distance**: : Euclidean distance between each somatic substitution and the substitution immediately prior (IMD) (Nik-Zainal et al. 2012). - **log10distance**: : Euclidean distance between each somatic substitution and the substitution immediately prior in base 10. ## References Nik-Zainal S, Alexandrov LB, Wedge DC, et al; Breast Cancer Working Group of the International Cancer Genome Consortium. Mutational processes molding the genomes of 21 breast cancers. Cell. 2012 May 25;149(5):979-93. Wickham H. ggplot2: elegant graphics for data analysis. Springer New York, 2009. ## Author(s) David Lora ## See Also `plot` ## Examples ```r require(ggplot2) data(PD4107a) ###New variable extra <- factor(c(),levels=c("T>C","T>G","T>A","C>T","C>G","C>A")) extra[PD4107a$Ref_base=="A" & PD4107a$Mutant_base=="G"]<-"T>C" extra[PD4107a$Ref_base=="T" & PD4107a$Mutant_base=="C"]<-"T>C" extra[PD4107a$Ref_base=="A" & PD4107a$Mutant_base=="C"]<-"T>G" extra[PD4107a$Ref_base=="T" & PD4107a$Mutant_base=="G"]<-"T>G" extra[PD4107a$Ref_base=="A" & PD4107a$Mutant_base=="T"]<-"T>A" extra[PD4107a$Ref_base=="T" & PD4107a$Mutant_base=="A"]<-"T>A" extra[PD4107a$Ref_base=="G" & PD4107a$Mutant_base=="A"]<-"C>T" extra[PD4107a$Ref_base=="C" & PD4107a$Mutant_base=="T"]<-"C>T" extra[PD4107a$Ref_base=="G" & PD4107a$Mutant_base=="C"]<-"C>G" extra[PD4107a$Ref_base=="C" & PD4107a$Mutant_base=="G"]<-"C>G" extra[PD4107a$Ref_base=="G" & PD4107a$Mutant_base=="T"]<-"C>A" extra[PD4107a$Ref_base=="C" & PD4107a$Mutant_base=="A"]<-"C>A" PD4107a$extra<-extra ###Generate new data set with intermutational distance; rainfall<-imd(data=PD4107a,chr=Chr,position=Position,extra=extra) ###Rainfall plot for PD4107a cancer sample; plot(rainfall$number, rainfall$log10distance,pch=20, ylab="Intermutation distance (bp)",xlab="PD4107a",yaxt="n", col=c(rep(c("black","red"),14)[rainfall$chr])) axis(2, at=c(0,1,2,3,4,6), labels=c("1","10","100","1000","10000","1000000"), las=2, cex.axis=0.6) ###Rainfall plot for PD4107a cancer sample (Nik-Zainal et al. 2012); graph <- ggplot(data=rainfall, mapping=(aes(x=number,y=log10distance,colour=extra))) + geom_point() + scale_colour_manual(values =c("T>C"="yellow","T>G"="green", "T>A"="pink","C>T"="red","C>G"="black","C>A"="blue")) + theme(legend.title=element_blank()) + ylab("log10") + xlab("PD4107a") + scale_y_continuous(breaks = c(0, 1, 2, 3, 4, 6), labels=c("1","10","100","1000","10000","1000000")) graph ```

imd function