Using supersigs

library(supersigs)

Introduction

The supersigs package implements the supervised method proposed by Afsari, et al. to find signatures (“SuperSigs”). In this vignette, we cover how to preprocess your data and run the method in supersigs.

Preprocessing your data

VCF file

If you have a VCF file, you can use readVcf from the VariantAnnotation package to read in your VCF file as a VCF object. The age of each patient should be stored as age in the colData of your VCF object. Then use process_vcf to transform the VCF object into a simplified data frame format, which will be explained further in Example data.

If you do not have a VCF file, skip to Example data.

Example data

The method uses single-base mutations in exomic data from cancer samples. Specifically, it requires data on every sample’s mutations, the positions of those mutations, and the age of all patients. This data can be represented as a list of mutations. Below is an example dataset (stored and accessible from the supersigs R package). If you have a VCF file, read the VCF file section to see how to process your data into the following format.

Transform data

Once you’ve read in your data, you will need to transform it into a data frame of features before running the core functions. This involves 2 steps:

  1. First, we assume that mutations are the same regardless of the strand on which it occurred. For example, this means that C>A mutations are considered the same as G>T mutations and we will convert all G>T mutations to be denoted as C>A mutations.

  2. Because the features used are built upon trinucleotide features (e.g. A[C>A]T), this will require matching your mutations to a reference genome to identify what the flanking bases of every mutation are. In our example below, we will use the hg19 reference genome.

Both of these steps are done by the make_matrix function. Note that using the make_matrix function requires installing and loading a reference genome (BSgenome.Hsapiens.UCSC.hg19 and BSgenome.Hsapiens.UCSC.hg38 are supported).

We apply make_matrix to transform our example dataset (example_dt) into a data frame of trinucleotide mutations (input_dt), which is the format required by the supersigs R package. Each row in input_dt corresponds to a different patient and the values in the columns are the number of mutations for each trinucleotide mutation.

Getting your signature

To apply the supervised method on your data, run the get_signature function. The function has two parameters: an input data frame data and the factor (e.g. factor = "Smoking"). data is a data frame with the following columns:

The process of converting a VCF file to this format is covered in Preprocessing your data. An example for data is printed below.

suppressPackageStartupMessages({
  library(dplyr)
})

# Add IndVar column
input_dt <- input_dt %>%
  mutate(IndVar = c(1, 1, 1, 0, 0)) %>%
  relocate(IndVar)

head(input_dt)
#> # A tibble: 5 × 99
#>   IndVar sample_id   age `A[T>G]T` `C[T>A]A` `G[C>A]A` `G[C>G]G` `G[C>G]T`
#>    <dbl>     <dbl> <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>
#> 1      1         1    50         1         1         1         1         1
#> 2      1         2    55         0         0         0         0         0
#> 3      1         3    72         1         1         0         0         0
#> 4      0         4    53         0         0         0         0         0
#> 5      0         5    48         0         1         1         0         0
#> # … with 91 more variables: A[C>G]T <dbl>, C[C>A]T <dbl>, C[T>C]G <dbl>,
#> #   T[C>A]C <dbl>, T[C>A]T <dbl>, A[T>C]C <dbl>, C[T>C]C <dbl>, T[T>A]C <dbl>,
#> #   C[C>G]G <dbl>, G[C>T]A <dbl>, A[C>A]T <dbl>, C[C>A]C <dbl>, G[T>G]T <dbl>,
#> #   C[C>T]C <dbl>, T[C>T]C <dbl>, A[C>T]C <dbl>, G[C>T]C <dbl>, C[C>T]T <dbl>,
#> #   T[C>T]T <dbl>, A[C>T]T <dbl>, G[C>T]T <dbl>, C[C>T]A <dbl>, T[C>T]A <dbl>,
#> #   A[C>T]A <dbl>, C[C>T]G <dbl>, T[C>T]G <dbl>, A[C>T]G <dbl>, G[C>T]G <dbl>,
#> #   A[C>A]C <dbl>, G[C>A]C <dbl>, G[C>A]T <dbl>, C[C>A]A <dbl>, …

Once you have the correct data format, apply get_signature to the dataset to get your SuperSig, which is an S4 object containing four slots:

set.seed(1)
supersig <- get_signature(data = input_dt, factor = "Smoking")
#> Begin feature engineering...
#> Begin cross-validated selection over 4 features and 15 inner folds...
#> ...testing inner fold 1
#> ...testing inner fold 2
#> ...testing inner fold 3
#> ...testing inner fold 4
#> ...testing inner fold 5
#> ...testing inner fold 6
#> ...testing inner fold 7
#> ...testing inner fold 8
#> ...testing inner fold 9
#> ...testing inner fold 10
#> ...testing inner fold 11
#> ...testing inner fold 12
#> ...testing inner fold 13
#> ...testing inner fold 14
#> ...testing inner fold 15
supersig
#> Signature:
#>              X1
#> 1 -0.0007475199
#> Features:
#> $X1
#>       F11       F12       F13       F14      F117      F118      F119      F120 
#> "A[C>A]A" "A[C>A]C" "A[C>A]G" "A[C>A]T" "A[C>G]A" "A[C>G]C" "A[C>G]G" "A[C>G]T" 
#>      F132      F133      F134      F135      F148      F149      F150      F151 
#> "A[C>T]A" "A[C>T]C" "A[C>T]G" "A[C>T]T" "A[T>A]A" "A[T>A]C" "A[T>A]G" "A[T>A]T" 
#>      F163      F164      F165      F166      F179      F180      F181       F15 
#> "A[T>C]A" "A[T>C]C" "A[T>C]G" "A[T>C]T" "A[T>G]A" "A[T>G]C" "A[T>G]G" "C[C>A]A" 
#>       F16       F17       F18      F121      F122      F123      F124      F136 
#> "C[C>A]C" "C[C>A]G" "C[C>A]T" "C[C>G]A" "C[C>G]C" "C[C>G]G" "C[C>G]T" "C[C>T]A" 
#>      F137      F138      F139      F152      F153      F154      F167      F168 
#> "C[C>T]C" "C[C>T]G" "C[C>T]T" "C[T>A]C" "C[T>A]G" "C[T>A]T" "C[T>C]A" "C[T>C]C" 
#>      F169      F170      F182      F183      F184      F185       F19      F110 
#> "C[T>C]G" "C[T>C]T" "C[T>G]A" "C[T>G]C" "C[T>G]G" "C[T>G]T" "G[C>A]A" "G[C>A]C" 
#>      F111      F112      F125      F126      F127      F140      F141      F142 
#> "G[C>A]G" "G[C>A]T" "G[C>G]A" "G[C>G]C" "G[C>G]T" "G[C>T]A" "G[C>T]C" "G[C>T]G" 
#>      F143      F155      F156      F157      F158      F171      F172      F173 
#> "G[C>T]T" "G[T>A]A" "G[T>A]C" "G[T>A]G" "G[T>A]T" "G[T>C]A" "G[T>C]C" "G[T>C]G" 
#>      F174      F186      F187      F188      F189      F113      F114      F115 
#> "G[T>C]T" "G[T>G]A" "G[T>G]C" "G[T>G]G" "G[T>G]T" "T[C>A]A" "T[C>A]C" "T[C>A]G" 
#>      F116      F128      F129      F130      F131      F144      F145      F146 
#> "T[C>A]T" "T[C>G]A" "T[C>G]C" "T[C>G]G" "T[C>G]T" "T[C>T]A" "T[C>T]C" "T[C>T]G" 
#>      F147      F159      F160      F161      F162      F175      F176      F177 
#> "T[C>T]T" "T[T>A]A" "T[T>A]C" "T[T>A]G" "T[T>A]T" "T[T>C]A" "T[T>C]C" "T[T>C]G" 
#>      F178      F190      F191      F192      F193 
#> "T[T>C]T" "T[T>G]A" "T[T>G]C" "T[T>G]G" "T[T>G]T" 
#> 
#> Model:
#> $Logit
#> 
#> Call:  glm(formula = IndVar ~ ., family = binomial(), data = x)
#> 
#> Coefficients:
#> (Intercept)           X1  
#>       7.118      -86.601  
#> 
#> Degrees of Freedom: 4 Total (i.e. Null);  3 Residual
#> Null Deviance:       6.73 
#> Residual Deviance: 4.279     AIC: 8.279

To obtain a signature representation that is more interpretable, you can group the trinucleotide features within each feature using the simplify_signature function (with an option to use IUPAC labels). This is useful for making plots of signatures.

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()

Using a signature

To apply the SuperSig to a new dataset, use the predict_signature function. This function returns the new dataset with columns for feature counts for the signature and a score column for the predicted classification score.

Below is an example for the SuperSig we trained in the previous section. We reuse input_dt as our “new data” for illustrative purposes, but in practice, you would use a different dataset from the one that was used to train the signature (e.g. a test set).

newdata = predict_signature(supersig, newdata = input_dt, factor = "Smoking")

newdata %>%
  select(X1, score)
#> # A tibble: 5 × 2
#>       X1 score
#>    <dbl> <dbl>
#> 1 0.04   0.975
#> 2 0.0909 0.320
#> 3 0.0417 0.971
#> 4 0.0943 0.259
#> 5 0.0833 0.475

In addition, you may wish to use a SuperSig pre-trained on TCGA data. These are accessible from the package in supersig_ls, where each element of the list is a SuperSig. There are 67 SuperSigs that have been trained on various tissues and factors. The names are printed below (formatted as “factor (tissue)”). Details regarding the training of these signatures are discussed in Afsari, et al. (2021, ELife).

names(supersig_ls)
#>  [1] "AGE (LAML)"      "AGE (BLCA)"      "AGE (LUAD)"      "AGE (LGG)"      
#>  [5] "AGE (HNSCC)"     "AGE (KIRC)"      "AGE (KIRP)"      "AGE (KICH)"     
#>  [9] "AGE (LIHC)"      "AGE (STAD)"      "AGE (THCA)"      "AGE (UVM)"      
#> [13] "AGE (SKCM)"      "AGE (ACC)"       "AGE (CHOL)"      "AGE (GBM)"      
#> [17] "AGE (CESC)"      "AGE (COAD)"      "AGE (PCPG)"      "AGE (PAAD)"     
#> [21] "AGE (PRAD)"      "AGE (ESCSQ)"     "AGE (ESCAD)"     "AGE (UCEC)"     
#> [25] "AGE (UCS)"       "AGE (BRCA)"      "AGE (SARC)"      "AGE (TGCT)"     
#> [29] "AGE (THYM)"      "AGE (OV)"        "SMOKING (BLCA)"  "SMOKING (LUAD)" 
#> [33] "SMOKING (HNSCC)" "SMOKING (KIRP)"  "SMOKING (PAAD)"  "SMOKING (ESCSQ)"
#> [37] "SMOKING (ESCAD)" "SMOKING (CESC)"  "POLE (UCEC)"     "POLE (STAD)"    
#> [41] "POLE (COAD)"     "POLE (BRCA)"     "MSI (UCEC)"      "MSI (STAD)"     
#> [45] "MSI (COAD)"      "BRCA (BRCA)"     "BRCA (OV)"       "UV* (SKCM)"     
#> [49] "POLD (UCEC)"     "POLD (STAD)"     "MGMT (GBM)"      "MGMT (LGG)"     
#> [53] "IDH (LGG)"       "IDH (GBM)"       "BMI (UCEC)"      "BMI (KIRP)"     
#> [57] "BMI (ESCA)"      "BMI (COAD)"      "ALCOHOL (HNSCC)" "ALCOHOL (ESCA)" 
#> [61] "ALCOHOL (LIHC)"  "HepB (LIHC)"     "HepC (LIHC)"     "AAcid (BLCA)"   
#> [65] "Asb* (MESO)"     "APOPEC (CESC)"   "APOPEC (KIRC)"
# Use pre-trained signature
newdata = predict_signature(supersig_ls[["SMOKING (LUAD)"]], 
                            newdata = input_dt, factor = "Smoking")
newdata %>%
  select(IndVar, X1, X2, X3, score)
#> # A tibble: 5 × 5
#>   IndVar     X1    X2    X3 score
#>    <dbl>  <dbl> <dbl> <dbl> <dbl>
#> 1      1 0.02       0     0 0.305
#> 2      1 0.0182     0     0 0.303
#> 3      1 0          0     0 0.280
#> 4      0 0.0189     0     0 0.304
#> 5      0 0.0417     0     0 0.333

Partially supervised signatures

In some cases, you may be interested in removing the contribution of a supervised signature from your data frame of mutations as a way to adjust for a particular factor. For example, suppose that we are interested in the deciphering a signature for smoking in lung cancer. We can first remove the contribution of the aging signature in lung cancer, before learning the smoking signature with a supervised or unsupervised method. We discuss in Afsari, et al. (2021, ELife) how doing so can lead to better performance.

adjusted_dt <- partial_signature(data = input_dt, object = supersig)
head(adjusted_dt)
#> # A tibble: 5 × 99
#>   IndVar sample_id   AGE `A[T>G]T` `C[T>A]A` `G[C>A]A` `G[C>G]G` `G[C>G]T`
#>    <dbl>     <dbl> <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>
#> 1      1         1    50         1         1    1.04           1    1.04  
#> 2      1         2    55         0         0    0.0411         0    0.0411
#> 3      1         3    72         1         1    0.0538         0    0.0538
#> 4      0         4    53         0         0    0.0396         0    0.0396
#> 5      0         5    48         0         1    1.04           0    0.0359
#> # … with 91 more variables: A[C>G]T <dbl>, C[C>A]T <dbl>, C[T>C]G <dbl>,
#> #   T[C>A]C <dbl>, T[C>A]T <dbl>, A[T>C]C <dbl>, C[T>C]C <dbl>, T[T>A]C <dbl>,
#> #   C[C>G]G <dbl>, G[C>T]A <dbl>, A[C>A]T <dbl>, C[C>A]C <dbl>, G[T>G]T <dbl>,
#> #   C[C>T]C <dbl>, T[C>T]C <dbl>, A[C>T]C <dbl>, G[C>T]C <dbl>, C[C>T]T <dbl>,
#> #   T[C>T]T <dbl>, A[C>T]T <dbl>, G[C>T]T <dbl>, C[C>T]A <dbl>, T[C>T]A <dbl>,
#> #   A[C>T]A <dbl>, C[C>T]G <dbl>, T[C>T]G <dbl>, A[C>T]G <dbl>, G[C>T]G <dbl>,
#> #   A[C>A]C <dbl>, G[C>A]C <dbl>, G[C>A]T <dbl>, C[C>A]A <dbl>, …

Session info

sessionInfo()
#> R Under development (unstable) (2021-10-19 r81077)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 20.04.3 LTS
#> 
#> Matrix products: default
#> BLAS:   /home/biocbuild/bbs-3.15-bioc/R/lib/libRblas.so
#> LAPACK: /home/biocbuild/bbs-3.15-bioc/R/lib/libRlapack.so
#> 
#> locale:
#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
#>  [3] LC_TIME=en_GB              LC_COLLATE=C              
#>  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
#>  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
#>  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
#> 
#> attached base packages:
#> [1] stats4    stats     graphics  grDevices utils     datasets  methods  
#> [8] base     
#> 
#> other attached packages:
#>  [1] ggplot2_3.3.5                     dplyr_1.0.7                      
#>  [3] BSgenome.Hsapiens.UCSC.hg19_1.4.3 BSgenome_1.63.0                  
#>  [5] rtracklayer_1.55.0                VariantAnnotation_1.41.0         
#>  [7] Rsamtools_2.11.0                  Biostrings_2.63.0                
#>  [9] XVector_0.35.0                    SummarizedExperiment_1.25.0      
#> [11] Biobase_2.55.0                    GenomicRanges_1.47.1             
#> [13] GenomeInfoDb_1.31.0               IRanges_2.29.0                   
#> [15] S4Vectors_0.33.0                  MatrixGenerics_1.7.0             
#> [17] matrixStats_0.61.0                BiocGenerics_0.41.0              
#> [19] supersigs_1.3.0                  
#> 
#> loaded via a namespace (and not attached):
#>   [1] colorspace_2.0-2         rjson_0.2.20             ellipsis_0.3.2          
#>   [4] class_7.3-19             farver_2.1.0             listenv_0.8.0           
#>   [7] furrr_0.2.3              bit64_4.0.5              AnnotationDbi_1.57.0    
#>  [10] prodlim_2019.11.13       fansi_0.5.0              lubridate_1.8.0         
#>  [13] xml2_1.3.2               codetools_0.2-18         splines_4.2.0           
#>  [16] cachem_1.0.6             knitr_1.36               jsonlite_1.7.2          
#>  [19] pROC_1.18.0              caret_6.0-90             dbplyr_2.1.1            
#>  [22] png_0.1-7                compiler_4.2.0           httr_1.4.2              
#>  [25] assertthat_0.2.1         Matrix_1.3-4             fastmap_1.1.0           
#>  [28] cli_3.1.0                htmltools_0.5.2          prettyunits_1.1.1       
#>  [31] tools_4.2.0              gtable_0.3.0             glue_1.4.2              
#>  [34] GenomeInfoDbData_1.2.7   reshape2_1.4.4           rappdirs_0.3.3          
#>  [37] Rcpp_1.0.7               jquerylib_0.1.4          vctrs_0.3.8             
#>  [40] nlme_3.1-153             iterators_1.0.13         timeDate_3043.102       
#>  [43] gower_0.2.2              xfun_0.27                stringr_1.4.0           
#>  [46] globals_0.14.0           lifecycle_1.0.1          restfulr_0.0.13         
#>  [49] XML_3.99-0.8             future_1.22.1            zlibbioc_1.41.0         
#>  [52] MASS_7.3-54              scales_1.1.1             ipred_0.9-12            
#>  [55] hms_1.1.1                parallel_4.2.0           curl_4.3.2              
#>  [58] yaml_2.2.1               memoise_2.0.0            sass_0.4.0              
#>  [61] biomaRt_2.51.0           rpart_4.1-15             stringi_1.7.5           
#>  [64] RSQLite_2.2.8            highr_0.9                BiocIO_1.5.0            
#>  [67] foreach_1.5.1            filelock_1.0.2           GenomicFeatures_1.47.1  
#>  [70] BiocParallel_1.29.0      lava_1.6.10              rlang_0.4.12            
#>  [73] pkgconfig_2.0.3          bitops_1.0-7             rsample_0.1.0           
#>  [76] evaluate_0.14            lattice_0.20-45          purrr_0.3.4             
#>  [79] labeling_0.4.2           GenomicAlignments_1.31.0 recipes_0.1.17          
#>  [82] bit_4.0.4                tidyselect_1.1.1         parallelly_1.28.1       
#>  [85] plyr_1.8.6               magrittr_2.0.1           R6_2.5.1                
#>  [88] generics_0.1.1           DelayedArray_0.21.0      DBI_1.1.1               
#>  [91] pillar_1.6.4             withr_2.4.2              survival_3.2-13         
#>  [94] KEGGREST_1.35.0          RCurl_1.98-1.5           nnet_7.3-16             
#>  [97] tibble_3.1.5             future.apply_1.8.1       crayon_1.4.1            
#> [100] utf8_1.2.2               BiocFileCache_2.3.0      rmarkdown_2.11          
#> [103] progress_1.2.2           grid_4.2.0               data.table_1.14.2       
#> [106] blob_1.2.2               ModelMetrics_1.2.2.2     digest_0.6.28           
#> [109] tidyr_1.1.4              munsell_0.5.0            bslib_0.3.1