突变特征可视化--sigminer

BiocManager::install("sigminer")
library(sigminer)

# 加载数据集
load(system.file("extdata", "toy_segTab.RData",
                 package = "sigminer", mustWork = TRUE
))
cn <- read_copynumber(segTabs,
                      seg_cols = c("chromosome", "start", "end", "segVal"),
                      genome_build = "hg19", complement = FALSE, verbose = TRUE
)

show_cn_profile(cn, nrow = 2, ncol = 1)

show_cn_circos(cn, samples = 1)

show_cn_distribution(cn, mode = "ld")
show_cn_distribution(cn, mode = "cd")

library(maftools)
tcgaAvailable()
set.seed(1234)
 #brca <- readRDS("data/BRCA.RDs")
brca <- tcga_load("BRCA")
brca <- subsetMaf(brca,
                  tsb = as.character(sample(brca@variants.per.sample$Tumor_Sample_Barcode, 100))
)
saveRDS(brca, file = "data/brca.rds")
brca <- readRDS("data/brca.rds")
mt_tally <- sig_tally(
  brca,
  ref_genome = "BSgenome.Hsapiens.UCSC.hg19",
  useSyn = TRUE
)
mt_sig <- sig_unify_extract(mt_tally$nmf_matrix, range = 10, nrun = 10)
sig_signature(mt_sig2)[1:5, ]
show_sig_profile(mt_sig, mode = "SBS", paint_axis_text = FALSE, x_label_angle = 90)

options(sigminer.copynumber.max = 20)
# Load copy number object
load(system.file("extdata", "toy_copynumber.RData",
                 package = "sigminer", mustWork = TRUE
))
# Use method designed by Wang, Shixiang et al.
cn_tally_W <- sig_tally(cn, method = "W")
sig_w <- sig_extract(cn_tally_W$nmf_matrix, n_sig = 2)
show_sig_profile(sig_w,
  mode = "copynumber",
  normalize = "feature",
  method = "W",
  style = "cosmic"
)

show_sig_consensusmap(mt_sig)

library(sigminer)
data("simulated_catalogs")
mat <- t(simulated_catalogs$set1)
mat[1:5, 1:5]

e1 <- bp_extract_signatures(mat, range = 8:12, n_bootstrap = 5, n_nmf_run = 10)

bp_show_survey2(e1, highlight = 10)

obj <- bp_get_sig_obj(e1, 10)

show_sig_profile(obj, mode = "SBS", style = "cosmic")

show_sig_exposure(obj, rm_space = TRUE)

sim <- get_sig_similarity(obj, sig_db = "SBS")
if (require(pheatmap)) {
    pheatmap::pheatmap(sim$similarity)
}