Process input data into an object of class 'INCOMMON', ready for
downstream analyses (e.g. classify).
Usage
init(genomic_data, clinical_data, gene_roles = INCOMMON::cancer_gene_census)Arguments
- genomic_data
a data table of annotated mutations with columns sample name
sample, mutant chromosomechr, mutation start positionfrom, mutation end positionto, reference alleleref, alternative allelealt, integer sequencing depthDP, integer number of reads with variantNV, variant allele frequencyVAFand gene namegeneas Hugo Symbol, protein sequence of the variant in HGVS recommended format, preferably 1-letter amino-acid codeHGVSp_Short.- clinical_data
a data table of clinical data with compulsory matching sample names
sampleand sample puritypurity, and optional clinical features like tumor type ONCOTREE codetumor_type(required for tumor specific priors), overall survival statusOS_STATUSand timeOS_MONTHS(required for survival analysis),SAMPLE_TYPE(Primary or Metastasis) and number of metastasesMET_COUNT(required for metastatic propensity analysis), metastatic siteMETASTATIC_SITE(required for metastatic tropism analysis), plus any other useful covariate.- gene_roles
A data table reporting
genenames and associatedgene_role("oncogene" or "TSG"). The default is taken from COSMIC Cancer Gene Census v98.
Examples
# Example input data from the MSK-MetTropism cohort, released with the package
data(MSK_genomic_data)
print(MSK_genomic_data)
#> # A tibble: 224,939 × 10
#> sample chr from to ref alt DP NV VAF gene
#> <chr> <chr> <dbl> <dbl> <chr> <chr> <int> <int> <dbl> <chr>
#> 1 P-0028912 chr17 7577121 7577121 G A 837 133 0.159 TP53
#> 2 P-0028912 chr6 111983080 111983081 - A 698 141 0.202 FYN
#> 3 P-0028912 chrX 53246994 53246994 G A 832 85 0.102 KDM5C
#> 4 P-0003698 chr17 7576852 7576852 C A 437 109 0.249 TP53
#> 5 P-0003698 chr3 49933259 49933259 C A 591 86 0.146 MST1R
#> 6 P-0003698 chr5 149435631 149435631 C T 360 36 0.1 CSF1R
#> 7 P-0003698 chr13 32913797 32913797 G C 1027 162 0.158 BRCA2
#> 8 P-0003698 chr13 32914259 32914259 G C 1021 182 0.178 BRCA2
#> 9 P-0003698 chr19 11136104 11136104 G T 573 98 0.171 SMARCA4
#> 10 P-0003698 chr22 41543840 41543840 G A 416 45 0.108 EP300
#> # ℹ 224,929 more rows
data(MSK_clinical_data)
print(MSK_clinical_data)
#> # A tibble: 25,368 × 17
#> # Rowwise:
#> sample tumor_type purity OS_MONTHS OS_STATUS SAMPLE_TYPE MET_COUNT
#> <chr> <chr> <dbl> <dbl> <dbl> <chr> <dbl>
#> 1 P-0000004 BRCA 0.5 3.78 1 Primary 2
#> 2 P-0000015 BRCA 0.4 13.9 1 Metastasis 8
#> 3 P-0000024 UCEC 0.4 35.1 1 Metastasis 8
#> 4 P-0000025 UCEC 0.3 46 1 Metastasis 13
#> 5 P-0000026 UCEC 0.1 80.6 0 Metastasis 11
#> 6 P-0000034 BLCA 0.4 0.79 1 Primary 4
#> 7 P-0000037 HCC 0.9 80.9 0 Metastasis 1
#> 8 P-0000039 PLEMESO 0.4 5.62 1 Primary 5
#> 9 P-0000041 BRCA 0.3 13.6 1 Primary 9
#> 10 P-0000042 PLEMESO 0.4 56.9 1 Primary 0
#> # ℹ 25,358 more rows
#> # ℹ 10 more variables: METASTATIC_SITE <chr>, MET_SITE_COUNT <dbl>,
#> # PRIMARY_SITE <chr>, SUBTYPE_ABBREVIATION <chr>, GENE_PANEL <chr>,
#> # SEX <chr>, TMB_NONSYNONYMOUS <dbl>, FGA <dbl>, AGE_AT_SEQUENCING <dbl>,
#> # RACE <chr>
# Initialize the INCOMMON object (note the outputs to screen)
x = init(genomic_data = MSK_genomic_data, clinical_data = MSK_clinical_data)
#> ── INCOMMON - Inference of copy number and mutation multiplicity in oncology ───
#>
#> ── Genomic data ──
#>
#> ✔ Found 25659 samples, with 224939 mutations in 491 genes
#> ! No read counts found for 1393 mutations in 1393 samples
#> ! Gene name not provided for 1393 mutations
#> ! 201 genes could not be assigned a role (TSG or oncogene)
#>
#> ── Clinical data ──
#>
#> ℹ Provided clinical features:
#>
#> ✔ sample (required for classification)
#> ✔ purity (required for classification)
#> ✔ tumor_type
#> ✔ OS_MONTHS
#> ✔ OS_STATUS
#> ✔ SAMPLE_TYPE
#> ✔ MET_COUNT
#> ✔ METASTATIC_SITE
#> ✔ MET_SITE_COUNT
#> ✔ PRIMARY_SITE
#> ✔ SUBTYPE_ABBREVIATION
#> ✔ GENE_PANEL
#> ✔ SEX
#> ✔ TMB_NONSYNONYMOUS
#> ✔ FGA
#> ✔ AGE_AT_SEQUENCING
#> ✔ RACE
#>
#> ✔ Found 25257 matching samples
#> ✖ Found 513 unmatched samples
# An S3 method can be used to report to screen what is in the object
print(x)
#> ── [ INCOMMON ] 175054 PASS mutations across 24018 samples, with 290 mutant gen
#> ℹ Average sample purity: 0.4
#> ℹ Average sequencing depth: 649
#> # A tibble: 175,054 × 27
#> sample tumor_type purity chr from to ref alt DP NV VAF
#> <chr> <chr> <dbl> <chr> <dbl> <dbl> <chr> <chr> <int> <int> <dbl>
#> 1 P-0028912 CHOL 0.3 chr17 7.58e6 7.58e6 G A 837 133 0.159
#> 2 P-0028912 CHOL 0.3 chrX 5.32e7 5.32e7 G A 832 85 0.102
#> 3 P-0003698 BLCA 0.2 chr17 7.58e6 7.58e6 C A 437 109 0.249
#> 4 P-0003698 BLCA 0.2 chr5 1.49e8 1.49e8 C T 360 36 0.1
#> 5 P-0003698 BLCA 0.2 chr13 3.29e7 3.29e7 G C 1027 162 0.158
#> 6 P-0003698 BLCA 0.2 chr13 3.29e7 3.29e7 G C 1021 182 0.178
#> 7 P-0003698 BLCA 0.2 chr19 1.11e7 1.11e7 G T 573 98 0.171
#> 8 P-0003698 BLCA 0.2 chr22 4.15e7 4.15e7 G A 416 45 0.108
#> 9 P-0003698 BLCA 0.2 chrX 4.49e7 4.49e7 C T 730 194 0.266
#> 10 P-0003823 BLCA 0.6 chr5 1.30e6 1.30e6 G A 218 138 0.633
#> # ℹ 175,044 more rows
#> # ℹ 16 more variables: gene <chr>, gene_role <chr>, OS_MONTHS <dbl>,
#> # OS_STATUS <dbl>, SAMPLE_TYPE <chr>, MET_COUNT <dbl>, METASTATIC_SITE <chr>,
#> # MET_SITE_COUNT <dbl>, PRIMARY_SITE <chr>, SUBTYPE_ABBREVIATION <chr>,
#> # GENE_PANEL <chr>, SEX <chr>, TMB_NONSYNONYMOUS <dbl>, FGA <dbl>,
#> # AGE_AT_SEQUENCING <dbl>, RACE <chr>