Author: Shixiang Wang ( wangsx1@sysucc.org.cn )
Date: 2024-05-11

# Analyze Copy Number Signatures with sigminer

#### Shixiang Wang ( wangsx1@sysucc.org.cn )

#### 2024-05-11

> Exploring copy number signatures with recently developed approach have been described at The repertoire of copy number alteration signatures in human cancer.

> A more general introduction please read Extract, Analyze and Visualize Mutational Signatures with Sigminer.

    library(sigminer)
    #> Registered S3 method overwritten by 'sigminer':
    #>   method      from
    #>   print.bytes Rcpp
    #> sigminer version 2.3.1
    #> - Star me at https://github.com/ShixiangWang/sigminer
    #> - Run hello() to see usage and citation.

For this analysis, data with six columns are required.

- Chromosome
- Start.bp
- End.bp
- modal_cn (i.e. total copy number, integer)
- minor_cn (i.e. copy number for minor allele, integer)
- sample

## Generate allele-specific copy number profile

    load(system.file("extdata", "toy_segTab.RData",
      package = "sigminer", mustWork = TRUE
    ))

    set.seed(1234)
    segTabs$minor_cn <- sample(c(0, 1), size = nrow(segTabs), replace = TRUE)
    cn <- read_copynumber(segTabs,
      seg_cols = c("chromosome", "start", "end", "segVal"),
      genome_measure = "wg", complement = TRUE, add_loh = TRUE
    )
    #> ℹ [2024-05-11 12:07:22.722145]: Started.
    #> ℹ [2024-05-11 12:07:22.739754]: Genome build  : hg19.
    #> ℹ [2024-05-11 12:07:22.741917]: Genome measure: wg.
    #> ℹ [2024-05-11 12:07:22.743996]: When add_loh is TRUE, use_all is forced to TRUE.
    #> Please drop columns you don't want to keep before reading.
    #> ✔ [2024-05-11 12:07:22.76519]: Chromosome size database for build obtained.
    #> ℹ [2024-05-11 12:07:22.767823]: Reading input.
    #> ✔ [2024-05-11 12:07:22.770067]: A data frame as input detected.
    #> ✔ [2024-05-11 12:07:22.772699]: Column names checked.
    #> ✔ [2024-05-11 12:07:22.777028]: Column order set.
    #> ✔ [2024-05-11 12:07:22.796121]: Chromosomes unified.
    #> ✔ [2024-05-11 12:07:22.824277]: Value 2 (normal copy) filled to uncalled chromosomes.
    #> ✔ [2024-05-11 12:07:22.833025]: Data imported.
    #> ℹ [2024-05-11 12:07:22.835415]: Segments info:
    #> ℹ [2024-05-11 12:07:22.8376]:     Keep - 477
    #> ℹ [2024-05-11 12:07:22.839727]:     Drop - 0
    #> ✔ [2024-05-11 12:07:22.842663]: Segments sorted.
    #> ℹ [2024-05-11 12:07:22.844732]: Adding LOH labels...
    #> ℹ [2024-05-11 12:07:22.848268]: Joining adjacent segments with same copy number value. Be patient...
    #> ✔ [2024-05-11 12:07:23.026732]: 410 segments left after joining.
    #> ✔ [2024-05-11 12:07:23.029618]: Segmental table cleaned.
    #> ℹ [2024-05-11 12:07:23.031965]: Annotating.
    #> ✔ [2024-05-11 12:07:23.056107]: Annotation done.
    #> ℹ [2024-05-11 12:07:23.058526]: Summarizing per sample.
    #> ✔ [2024-05-11 12:07:23.094252]: Summarized.
    #> ℹ [2024-05-11 12:07:23.09669]: Generating CopyNumber object.
    #> ✔ [2024-05-11 12:07:23.099685]: Generated.
    #> ℹ [2024-05-11 12:07:23.101863]: Validating object.
    #> ✔ [2024-05-11 12:07:23.104097]: Done.
    #> ℹ [2024-05-11 12:07:23.106758]: 0.385 secs elapsed.

    cn
    #> An object of class CopyNumber
    #> =============================
    #>                           sample n_of_seg n_of_cnv n_of_amp n_of_del n_of_vchr
    #>                           <char>    <int>    <int>    <int>    <int>     <int>
    #>  1: TCGA-DF-A2KN-01A-11D-A17U-01       34        6        5        1         4
    #>  2: TCGA-19-2621-01B-01D-0911-01       34        8        5        3         5
    #>  3: TCGA-B6-A0X5-01A-21D-A107-01       29        8        4        4         2
    #>  4: TCGA-A8-A07S-01A-11D-A036-01       39       11        2        9         4
    #>  5: TCGA-26-6174-01A-21D-1842-01       44       13        8        5         8
    #>  6: TCGA-CV-7432-01A-11D-2128-01       41       16        7        9         9
    #>  7: TCGA-06-0644-01A-02D-0310-01       47       19        5       14         8
    #>  8: TCGA-A5-A0G2-01A-11D-A042-01       40       21        5       16        10
    #>  9: TCGA-99-7458-01A-11D-2035-01       49       26       10       16        13
    #> 10: TCGA-05-4417-01A-22D-1854-01       53       37       33        4        17
    #>     n_loh cna_burden
    #>     <int>      <num>
    #>  1:    15      0.000
    #>  2:    20      0.095
    #>  3:    18      0.083
    #>  4:    21      0.106
    #>  5:    24      0.113
    #>  6:    24      0.188
    #>  7:    33      0.158
    #>  8:    23      0.375
    #>  9:    33      0.304
    #> 10:    29      0.617
    cn@data
    #>      chromosome     start       end segVal                       sample
    #>          <char>     <num>     <num>  <int>                       <char>
    #>   1:       chr1   3218923 116319008      2 TCGA-05-4417-01A-22D-1854-01
    #>   2:       chr1 116324707 120523902      1 TCGA-05-4417-01A-22D-1854-01
    #>   3:       chr1 149879545 247812431      4 TCGA-05-4417-01A-22D-1854-01
    #>   4:      chr10    423671 135224372      3 TCGA-05-4417-01A-22D-1854-01
    #>   5:      chr11    458784  19461653      3 TCGA-05-4417-01A-22D-1854-01
    #>  ---
    #> 406:       chr6   1016984 170898549      2 TCGA-DF-A2KN-01A-11D-A17U-01
    #> 407:       chr7    746917 158385118      2 TCGA-DF-A2KN-01A-11D-A17U-01
    #> 408:       chr8    617885 145225107      2 TCGA-DF-A2KN-01A-11D-A17U-01
    #> 409:       chr9    790234 140938075      2 TCGA-DF-A2KN-01A-11D-A17U-01
    #> 410:       chrX         1 155270560      2 TCGA-DF-A2KN-01A-11D-A17U-01
    #>       minor_cn    loh .loh_frac
    #>          <num> <lgcl>     <num>
    #>   1: 1.0000000  FALSE        NA
    #>   2: 0.0000000   TRUE        NA
    #>   3: 0.5000000   TRUE 0.1175943
    #>   4: 1.0000000  FALSE        NA
    #>   5: 1.0000000  FALSE        NA
    #>  ---
    #> 406: 0.3333333   TRUE 0.9979494
    #> 407: 1.0000000  FALSE        NA
    #> 408: 1.0000000  FALSE        NA
    #> 409: 0.5000000   TRUE 0.8328715
    #> 410:        NA  FALSE        NA

## Classify the segments with Steele et al method

> If you want to try other type of copy number signatures, change the method argument.

    tally_s <- sig_tally(cn, method = "S")
    #> ℹ [2024-05-11 12:07:23.165562]: Started.
    #> ℹ [2024-05-11 12:07:23.171528]: When you use method 'S', please make sure you have set 'join_adj_seg' to FALSE and 'add_loh' to TRUE in 'read_copynumber() in the previous step!
    #> ✔ [2024-05-11 12:07:23.197549]: Matrix generated.
    #> ℹ [2024-05-11 12:07:23.200068]: 0.034 secs elapsed.

    str(tally_s$all_matrices, max.level = 1)
    #> List of 2
    #>  $ CN_40: int [1:10, 1:40] 0 0 0 0 0 0 0 0 0 0 ...
    #>   ..- attr(*, "dimnames")=List of 2
    #>  $ CN_48: int [1:10, 1:48] 0 0 0 0 0 0 0 0 0 0 ...
    #>   ..- attr(*, "dimnames")=List of 2

## Find de novo signatures

    sig_denovo = sig_auto_extract(tally_s$all_matrices$CN_48)
    #> Select Run 3, which K = 2 as best solution.
    head(sig_denovo$Signature)
    #>                         Sig1          Sig2
    #> 0:homdel:0-100Kb    0.000000  0.000000e+00
    #> 0:homdel:100Kb-1Mb  0.000000  0.000000e+00
    #> 0:homdel:>1Mb       0.000000  0.000000e+00
    #> 1:LOH:0-100Kb       3.609460 3.819129e-242
    #> 1:LOH:100Kb-1Mb     6.316554 2.814800e-127
    #> 1:LOH:1Mb-10Mb     13.535473 2.784288e-190

## Refit (19) reference signatures

This directly calculates the contribution of 19 reference signatures.

    act_refit = sig_fit(t(tally_s$all_matrices$CN_48), sig_index = "ALL", sig_db = "CNS_TCGA")
    #> ℹ [2024-05-11 12:07:24.377693]: Started.
    #> ✔ [2024-05-11 12:07:24.379994]: Signature index detected.
    #> ℹ [2024-05-11 12:07:24.382046]: Checking signature database in package.
    #> ℹ [2024-05-11 12:07:24.386141]: Checking signature index.
    #> ℹ [2024-05-11 12:07:24.388193]: Valid index for db 'CNS_TCGA':
    #> CN1 CN2 CN3 CN4 CN5 CN6 CN7 CN8 CN9 CN10 CN11 CN12 CN13 CN14 CN15 CN16 CN17 CN18 CN19
    #> ✔ [2024-05-11 12:07:24.390339]: Database and index checked.
    #> ✔ [2024-05-11 12:07:24.392602]: Signature normalized.
    #> ℹ [2024-05-11 12:07:24.394607]: Checking row number for catalog matrix and signature matrix.
    #> ✔ [2024-05-11 12:07:24.396599]: Checked.
    #> ℹ [2024-05-11 12:07:24.398572]: Checking rownames for catalog matrix and signature matrix.
    #> ✔ [2024-05-11 12:07:24.400536]: Checked.
    #> ✔ [2024-05-11 12:07:24.402494]: Method 'QP' detected.
    #> ✔ [2024-05-11 12:07:24.414918]: Corresponding function generated.
    #> ℹ [2024-05-11 12:07:24.417316]: Calling function.
    #> ℹ [2024-05-11 12:07:24.419917]: Fitting sample: TCGA-05-4417-01A-22D-1854-01
    #> ℹ [2024-05-11 12:07:24.42325]: Fitting sample: TCGA-06-0644-01A-02D-0310-01
    #> ℹ [2024-05-11 12:07:24.425641]: Fitting sample: TCGA-19-2621-01B-01D-0911-01
    #> ℹ [2024-05-11 12:07:24.427867]: Fitting sample: TCGA-26-6174-01A-21D-1842-01
    #> ℹ [2024-05-11 12:07:24.430052]: Fitting sample: TCGA-99-7458-01A-11D-2035-01
    #> ℹ [2024-05-11 12:07:24.432224]: Fitting sample: TCGA-A5-A0G2-01A-11D-A042-01
    #> ℹ [2024-05-11 12:07:24.434394]: Fitting sample: TCGA-A8-A07S-01A-11D-A036-01
    #> ℹ [2024-05-11 12:07:24.436542]: Fitting sample: TCGA-B6-A0X5-01A-21D-A107-01
    #> ℹ [2024-05-11 12:07:24.438711]: Fitting sample: TCGA-CV-7432-01A-11D-2128-01
    #> ℹ [2024-05-11 12:07:24.440867]: Fitting sample: TCGA-DF-A2KN-01A-11D-A17U-01
    #> ✔ [2024-05-11 12:07:24.443045]: Done.
    #> ℹ [2024-05-11 12:07:24.445076]: Generating output signature exposures.
    #> ✔ [2024-05-11 12:07:24.44793]: Done.
    #> ℹ [2024-05-11 12:07:24.450022]: 0.072 secs elapsed.

We can use some threshold to keep really contributed signautres.

    act_refit2 = act_refit[apply(act_refit, 1, function(x) sum(x) > 0.1),]

    rownames(act_refit2)
    #>  [1] "CN1"  "CN2"  "CN3"  "CN4"  "CN9"  "CN11" "CN12" "CN13" "CN14" "CN19"

## Plot signatures

For de novo signatures:

    show_sig_profile(sig_denovo, mode = "copynumber", method = "S", style = "cosmic")

Show the activity/exposure.

    show_sig_exposure(sig_denovo)

For reference signatures, you can just select what you want:

    show_sig_profile(
      get_sig_db("CNS_TCGA")$db[, rownames(act_refit2)],
      style = "cosmic",
      mode = "copynumber", method = "S", check_sig_names = FALSE)

Similarly for showing activity.

    show_sig_exposure(act_refit2)

NOTE that this case shows relatively large difference with different approaches, so you need to pick based on your data size/quality and double-check the results. In general, for small-size data set, the refitting approach is recommended.

## Signature assignment

To assign the de-novo signatures to reference signatures, we use cosine similarity.

    get_sig_similarity(sig_denovo, sig_db = "CNS_TCGA")
    #> -Comparing against COSMIC signatures
    #> ------------------------------------
    #> --Found Sig1 most similar to CN1
    #>    Aetiology: See https://cancer.sanger.ac.uk/signatures/cn/ [similarity: 0.706]
    #> --Found Sig2 most similar to CN2
    #>    Aetiology: See https://cancer.sanger.ac.uk/signatures/cn/ [similarity: 0.771]
    #> ------------------------------------
    #> Return result invisiblely.