bioconductor-supersigs

name	bioconductor-supersigs
description	Generate SuperSigs (supervised mutational signatures) from single nucleotide variants in the cancer genome. Functions included in the package allow the user to learn supervised mutational signatures from their data and apply them to new data. The methodology is based on the one described in Afsari (2021, ELife).

supersigs

Workflows

Standard Workflow

Preprocess mutation data, build a trinucleotide feature matrix, and train a supervised mutational signature (SuperSig).

library(supersigs)
library(VariantAnnotation)
library(BSgenome.Hsapiens.UCSC.hg19)
library(dplyr)

# 1. Read VCF and add patient age to colData
fl <- system.file("extdata", "chr22.vcf.gz", package="VariantAnnotation")
vcf <- VariantAnnotation::readVcf(fl, "hg19")
vcf <- vcf[, 1]
positions <- geno(vcf)$GT != "0|0"
vcf <- vcf[positions[, 1],]
colData(vcf)$age <- 50

# 2. Process VCF to a simplified data frame
dt <- process_vcf(vcf)

# 3. Transform mutations into a trinucleotide matrix
input_dt <- make_matrix(dt)

# 4. Add the indicator variable (IndVar)
input_dt <- input_dt %>%
  mutate(IndVar = c(1, 1, 1, 0, 0)) %>%
  relocate(IndVar)

# 5. Train the supervised signature
set.seed(1)
supersig <- get_signature(data = input_dt, factor = "Smoking")

# 6. Simplify the signature for visualization
features <- simplify_signature(object = supersig, iupac = FALSE)

Note: Input is a VCF object or a data frame of mutations with sample IDs, ages, and genomic coordinates; output is a trained SuperSig S4 object and simplified signature features.

Partial Signature Adjustment

Remove the contribution of a supervised signature from a mutation data frame to adjust for a confounding factor.

library(supersigs)
# Adjust the mutation matrix by removing the signature's contribution
adjusted_dt <- partial_signature(data = input_dt, object = supersig)

Note: Input is a feature count data frame and a trained SuperSig object; output is an adjusted mutation feature data frame.

Predict Supersig

Apply a trained or pre-trained SuperSig to a new dataset to predict classification scores.

library(supersigs)
library(dplyr)
# Apply a trained SuperSig to a new dataset
newdata <- predict_signature(supersig, newdata = input_dt, factor = "Smoking")
# Or use a pre-trained signature from supersig_ls
newdata_pretrained <- predict_signature(supersig_ls[["SMOKING (LUAD)"]], newdata = input_dt, factor = "Smoking")

Note: Input is a trained SuperSig object (or pre-trained from supersig_ls) and a new feature count data frame; output is a data frame containing predicted classification scores and feature counts.

When to Use

• Supervised Signature Generation: Generating supervised mutational signatures ("SuperSigs") from single nucleotide variants (SNVs) in cancer genomes (get_signature).
• Exposure Classification: Predicting exposure classification scores on new datasets using custom or pre-trained TCGA signatures (predict_signature, supersig_ls).
• Confounder Adjustment: Adjusting mutation datasets to remove confounding factors (e.g., aging) before downstream signature analysis (partial_signature).

When NOT to Use

• Unsupervised Deconvolution: For unsupervised mutational signature deconvolution (e.g., standard NMF-based signature extraction).
• Missing Clinical Metadata: When patient age or clinical exposure indicators are completely unavailable.

Data Requirements

• Input mutation data containing sample_id, age, chromosome, position, ref, and alt columns.
• A supported reference genome package installed (e.g., BSgenome.Hsapiens.UCSC.hg19 or BSgenome.Hsapiens.UCSC.hg38) for trinucleotide context extraction.

Key Parameters

• data: Input data frame containing IndVar, sample_id, age, and 96 trinucleotide mutation count columns in get_signature.
• factor: The clinical factor or exposure of interest (e.g., "Smoking", "Age") in get_signature and predict_signature.
• object: A trained SuperSig S4 object in simplify_signature and partial_signature.
• iupac (FALSE): Logical indicating whether to use IUPAC labels when simplifying signatures in simplify_signature.
• newdata: New dataset to predict on in predict_signature.

Best Practices

• Ensure the VCF object contains patient age in colData(vcf)$age before calling process_vcf.
• Set a random seed (set.seed) before running get_signature to ensure reproducibility of cross-validation folds.
• Use simplify_signature(..., iupac = TRUE) to group trinucleotide features into more interpretable IUPAC representations for plotting.
• Adjust for confounding factors like aging using partial_signature before learning other signatures.

Common Pitfalls

• Missing IndVar column: get_signature requires a logical/numeric indicator variable named IndVar in the input data frame. Fix: Add and relocate the IndVar column using dplyr::mutate and dplyr::relocate.
• Reference genome mismatch: Using the wrong reference genome version (e.g., hg19 vs hg38) during make_matrix. Fix: Ensure the correct BSgenome package is loaded and matches the coordinates in your mutation data.

Alternatives

• SomaticSignatures: For unsupervised mutational signature analysis.
• MutationalPatterns: For comprehensive mutational signature extraction and visualization.

Citations

• Afsari, B. et al. (2021). Supervised mutational signatures for cancer genomics. eLife.

References

• Homepage: bioconductor.org/packages/supersigs
• Vignette: https://bioconductor.org/packages/release/bioc/vignettes/supersigs/inst/doc/supersigs.html