hecking that we have data from the same person concord <- variantConcordance(tumor, normal) concord ##now that we know we have tumor and normal data from the same sample we need one ##more bit of data before we get the tumor-specific variants ##Actually calling the sample-specific variants TS<- tumorNormalCompare(tumor_gr=tumor, normal_gr=normal, normal_raw=raw, normal_cov=cov) ##Writing a vcf file ##Next we want to write out our tumor-specific varints out to a vcf file cgpGr2vcf(TS, file = "~/temp_ts.vcf", sample_id="Test_sam", project = "SNVsOmuC_Vignette") ##annotaing variants and loading VCF files library(VariantAnnotation) fl <- "/home/jeremiah/temp_ts.vcf.gz" vcf <- readVcf(fl, "hg19") vcf rowData(vcf) ####################################################################### ## We can also use VariantAnnotation to identify potentially functional ## using the GRanges objects generated by gmapR and SNVsOmuC directly ####################################################################### seqlevels(TS) <- paste("chr", seqlevels(TS), sep = "") genome(TS) <- "hg19" seqlevels(TS)<-seqlevels(TS)[1:24] strand(TS)<-"*" library(TxDb.Hsapiens.UCSC.hg19.knownGene) txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene coding <- predictCoding(TS, varAllele=DNAStringSet(values(TS)$alt), txdb, seqSource=Hsapiens) coding ##Making some plots ##Finally we want make some plots to check out our data. ##The first plot shows us the mutation transition/transversion rate matrix plotTitv(variantsGR, main = "Single-sample SNVs") plotTitv(TS, main = "tumor specific mutations") ##and finally we want to plot our variants on the genome seqlevels(tumor) <- paste("chr", seqlevels(tumor), sep = "") genome(tumor) <- "hg19" seqlevels(tumor)<-seqlevels(tumor)[1:24] plotTumor(tumor,TS) \end{document}