## ----include = FALSE---------------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, warning = FALSE, comment = "#>" ) ## ----setup-------------------------------------------------------------------- library(supersigs) ## ----------------------------------------------------------------------------- # Load packages for make_matrix function suppressPackageStartupMessages({ library(VariantAnnotation) }) fl <- system.file("extdata", "chr22.vcf.gz", package="VariantAnnotation") vcf <- VariantAnnotation::readVcf(fl, "hg19") # Subset to first sample vcf <- vcf[, 1] # Subset to row positions with homozygous or heterozygous alt positions <- geno(vcf)$GT != "0|0" vcf <- vcf[positions[, 1],] colData(vcf)$age <- 50 # Add patient age to colData dt <- process_vcf(vcf) head(dt) ## ----------------------------------------------------------------------------- head(example_dt) ## ----------------------------------------------------------------------------- # Load packages for make_matrix function suppressPackageStartupMessages({ library(BSgenome.Hsapiens.UCSC.hg19) }) ## ----------------------------------------------------------------------------- input_dt <- make_matrix(example_dt) head(input_dt) ## ----------------------------------------------------------------------------- suppressPackageStartupMessages({ library(dplyr) }) # Add IndVar column input_dt <- input_dt %>% mutate(IndVar = c(1, 1, 1, 0, 0)) %>% relocate(IndVar) head(input_dt) ## ----------------------------------------------------------------------------- set.seed(1) supersig <- get_signature(data = input_dt, factor = "Smoking") supersig ## ----------------------------------------------------------------------------- features <- simplify_signature(object = supersig, iupac = FALSE) features_iupac <- simplify_signature(object = supersig, iupac = TRUE) ## ----------------------------------------------------------------------------- library(ggplot2) data.frame(features = names(features_iupac), differences = features_iupac) %>% ggplot(aes(x = features, y = differences)) + geom_col() + theme_minimal() ## ----------------------------------------------------------------------------- newdata = predict_signature(supersig, newdata = input_dt, factor = "Smoking") newdata %>% select(X1, score) ## ----------------------------------------------------------------------------- names(supersig_ls) ## ----------------------------------------------------------------------------- # Use pre-trained signature newdata = predict_signature(supersig_ls[["SMOKING (LUAD)"]], newdata = input_dt, factor = "Smoking") newdata %>% select(IndVar, X1, X2, X3, score) ## ----------------------------------------------------------------------------- adjusted_dt <- partial_signature(data = input_dt, object = supersig) head(adjusted_dt) ## ----sessionInfo-------------------------------------------------------------- sessionInfo() ## ----eval = F, include=F------------------------------------------------------ # build_vignettes()