sigminer (version 2.3.1)

get_sig_similarity: Calculate Similarity between Identified Signatures and Reference Signatures

Description

The reference signatures can be either a Signature object specified by Ref argument or known COSMIC signatures specified by sig_db argument. Two COSMIC databases are used for comparisons - "legacy" which includes 30 signaures, and "SBS" - which includes updated/refined 65 signatures. This function is modified from compareSignatures() in maftools package. NOTE: all reference signatures are generated from gold standard tool: SigProfiler.

Usage

get_sig_similarity(
  Signature,
  Ref = NULL,
  sig_db = c("SBS", "legacy", "DBS", "ID", "TSB", "SBS_Nik_lab", "RS_Nik_lab",
    "RS_BRCA560", "RS_USARC", "CNS_USARC", "CNS_TCGA", "CNS_TCGA176", "CNS_PCAWG176",
    "SBS_hg19", "SBS_hg38", "SBS_mm9", "SBS_mm10", "DBS_hg19", "DBS_hg38", "DBS_mm9",
    "DBS_mm10", "SBS_Nik_lab_Organ", "RS_Nik_lab_Organ", "latest_SBS_GRCh37",
    "latest_DBS_GRCh37", "latest_ID_GRCh37", "latest_SBS_GRCh38", "latest_DBS_GRCh38",
    "latest_SBS_mm9", "latest_DBS_mm9", "latest_SBS_mm10", "latest_DBS_mm10",
    "latest_SBS_rn6", "latest_DBS_rn6", "latest_CN_GRCh37", 
    
    "latest_RNA-SBS_GRCh37", "latest_SV_GRCh38"),
  db_type = c("", "human-exome", "human-genome"),
  method = "cosine",
  normalize = c("row", "feature"),
  feature_setting = sigminer::CN.features,
  set_order = TRUE,
  pattern_to_rm = NULL,
  verbose = TRUE
)

Value

a list containing smilarities, aetiologies if available, best match and RSS.

Arguments

Signature

a Signature object or a component-by-signature matrix/data.frame (sum of each column is 1) or a normalized component-by-sample matrix/data.frame (sum of each column is 1). More please see examples.

Ref

default is NULL, can be a same object as Signature.

sig_db

default 'legacy', it can be 'legacy' (for COSMIC v2 'SBS'), 'SBS', 'DBS', 'ID' and 'TSB' (for COSMIV v3.1 signatures) for small scale mutations. For more specific details, it can also be 'SBS_hg19', 'SBS_hg38', 'SBS_mm9', 'SBS_mm10', 'DBS_hg19', 'DBS_hg38', 'DBS_mm9', 'DBS_mm10' to use COSMIC v3 reference signatures from Alexandrov, Ludmil B., et al. (2020) (reference #1). In addition, it can be one of "SBS_Nik_lab_Organ", "RS_Nik_lab_Organ", "SBS_Nik_lab", "RS_Nik_lab" to refer reference signatures from Degasperi, Andrea, et al. (2020) (reference #2); "RS_BRCA560", "RS_USARC" to reference signatures from BRCA560 and USARC cohorts; "CNS_USARC" (40 categories), "CNS_TCGA" (48 categories) to reference copy number signatures from USARC cohort and TCGA; "CNS_TCGA176" (176 categories) and "CNS_PCAWG176" (176 categories) to reference copy number signatures from PCAWG and TCGA separately. UPDATE, the latest version of reference version can be automatically downloaded and loaded from https://cancer.sanger.ac.uk/signatures/downloads/ when a option with latest_ prefix is specified (e.g. "latest_SBS_GRCh37"). Note: the signature profile for different genome builds are basically same. And specific database (e.g. 'SBS_mm10') contains less signatures than all COSMIC signatures (because some signatures are not detected from Alexandrov, Ludmil B., et al. (2020)). For all available options, check the parameter setting.

db_type

only used when sig_db is enabled. "" for keeping default, "human-exome" for transforming to exome frequency of component, and "human-genome" for transforming to whole genome frequency of component. Currently only works for 'SBS'.

method

default is 'cosine' for cosine similarity.

normalize

one of "row" and "feature". "row" is typically used for common mutational signatures. "feature" is designed by me to use when input are copy number signatures.

feature_setting

a data.frame used for classification. Only used when method is "Wang" ("W"). Default is CN.features. Users can also set custom input with "feature", "min" and "max" columns available. Valid features can be printed by unique(CN.features$feature).

set_order

if TRUE, order the return similarity matrix.

pattern_to_rm

patterns for removing some features/components in similarity calculation. A vector of component name is also accepted. The remove operation will be done after normalization. Default is NULL.

verbose

if TRUE, print extra info.

Author

Shixiang Wang

References

Alexandrov, Ludmil B., et al. "The repertoire of mutational signatures in human cancer." Nature 578.7793 (2020): 94-101.

Degasperi, Andrea, et al. "A practical framework and online tool for mutational signature analyses show intertissue variation and driver dependencies." Nature cancer 1.2 (2020): 249-263.

Steele, Christopher D., et al. "Undifferentiated sarcomas develop through distinct evolutionary pathways." Cancer Cell 35.3 (2019): 441-456.

Nik-Zainal, Serena, et al. "Landscape of somatic mutations in 560 breast cancer whole-genome sequences." Nature 534.7605 (2016): 47-54.

Steele, Christopher D., et al. "Signatures of copy number alterations in human cancer." Nature 606.7916 (2022): 984-991.

Examples

# Load mutational signature
load(system.file("extdata", "toy_mutational_signature.RData",
  package = "sigminer", mustWork = TRUE
))

s1 <- get_sig_similarity(sig2, Ref = sig2)
s1

s2 <- get_sig_similarity(sig2)
s2
s3 <- get_sig_similarity(sig2, sig_db = "SBS")
s3

# Set order for result similarity matrix
s4 <- get_sig_similarity(sig2, sig_db = "SBS", set_order = TRUE)
s4

## Remove some components
## in similarity calculation
s5 <- get_sig_similarity(sig2,
  Ref = sig2,
  pattern_to_rm = c("T[T>G]C", "T[T>G]G", "T[T>G]T")
)
s5

## Same to DBS and ID signatures
x1 <- get_sig_db("DBS_hg19")
x2 <- get_sig_db("DBS_hg38")
s6 <- get_sig_similarity(x1$db, x2$db)
s6