The expected number of mutations (or copy number segment records) with each signature was determined after a scaling transformation V ~ WH = W'H' where W' = WU' and H' = UH. The scaling matrix U is a KxK diagnal matrix (K is signature number, U' is the inverse of U) with the element corresponding to the L1-norm of column vectors of W (ie. the sum of the elements of the vector). As a result, the k-th row vector of the final matrix H' represents the absolute exposure (activity) of the k-th process across samples (e.g., for SBS, the estimated (or expected) number of mutations generated by the k-th process). Of note, for copy number signatures, only components of feature CN was used for calculating H'.
get_sig_exposure(
Signature,
type = c("absolute", "relative"),
rel_threshold = 0.01
)a Signature object obtained either from sig_extract or sig_auto_extract,
or just a raw exposure matrix with column representing samples (patients) and row
representing signatures.
'absolute' for signature exposure and 'relative' for signature relative exposure.
only used when type is 'relative', relative exposure less
than (<=) this value will be set to 0 and thus all signature exposures
may not sum to 1. This is similar to this argument in sig_fit.
a data.table
Kim, Jaegil, et al. "Somatic ERCC2 mutations are associated with a distinct genomic signature in urothelial tumors." Nature genetics 48.6 (2016): 600.
# Load mutational signature
load(system.file("extdata", "toy_mutational_signature.RData",
package = "sigminer", mustWork = TRUE
))
# Get signature exposure
expo1 <- get_sig_exposure(sig2)
expo1
#> sample Sig1 Sig2 Sig3
#> <char> <num> <num> <num>
#> 1: TCGA-AB-2802 0.0000000 7.602352 1.2566069
#> 2: TCGA-AB-2803 0.0000000 12.007936 0.7412961
#> 3: TCGA-AB-2804 0.0000000 4.838900 0.0000000
#> 4: TCGA-AB-2805 0.2000784 12.007936 0.0000000
#> 5: TCGA-AB-2806 0.2923331 10.273787 0.0000000
#> ---
#> 184: TCGA-AB-3007 0.0000000 6.691893 0.0000000
#> 185: TCGA-AB-3008 0.2000784 2.940509 0.0000000
#> 186: TCGA-AB-3009 0.0000000 25.388449 2.4198934
#> 187: TCGA-AB-3011 0.0000000 4.838900 0.0000000
#> 188: TCGA-AB-3012 0.0000000 5.770868 0.0000000
expo2 <- get_sig_exposure(sig2, type = "relative")
expo2
#> sample Sig1 Sig2 Sig3
#> <char> <num> <num> <num>
#> 1: TCGA-AB-2802 0.00000000 0.8581541 0.14184589
#> 2: TCGA-AB-2803 0.00000000 0.9418556 0.05814437
#> 3: TCGA-AB-2804 0.00000000 1.0000000 0.00000000
#> 4: TCGA-AB-2805 0.01638910 0.9836109 0.00000000
#> 5: TCGA-AB-2806 0.02766703 0.9723330 0.00000000
#> ---
#> 182: TCGA-AB-3007 0.00000000 1.0000000 0.00000000
#> 183: TCGA-AB-3008 0.06370731 0.9362927 0.00000000
#> 184: TCGA-AB-3009 0.00000000 0.9129796 0.08702041
#> 185: TCGA-AB-3011 0.00000000 1.0000000 0.00000000
#> 186: TCGA-AB-3012 0.00000000 1.0000000 0.00000000