user_guide/cancereffectsizeR_user_guide.md
In Townsend-Lab-Yale/cancereffectsizeR: Calculate Cancer Effect Size
cancereffectsizeR user guide
Vincent Cannataro
2018-11-15
Introduction
cancereffectsizeR is an R package that may be used to calculate the effect size of single nucleotide variants (SNV) in cancer exome data[1]. It was designed for use with datasets in MAF format and works well with data from a large number of tumors, each with a large number of detected varaints. A previous version of cancereffectsizeR depended on output from MutSigCV (which runs in MATLAB) and can be found here.
This vignette is broken into several sections detailing installation, usage, and a suite of R functions provided with this package that are useful for analyzing cancer exome data. This user guide covers the version of cancereffectsizeR used in our JNCI manuscript https://doi.org/10.1093/jnci/djy168 (v0.1.0).
Installing
cancereffectsizeR utilizes a few bioinformatic R packages, and thus requires the installation of some dependencies. If you already use R to bioinformatics, you likely already have these packages installed and you can directly install cancereffectsizeR in two lines.
install.packages("devtools",repos = "https://cloud.r-project.org")
devtools::install_github("Townsend-Lab-Yale/cancereffectsizeR@0.1.0")
However, if you just downloaded R today, you will need to install the dependencies.
source("https://bioconductor.org/biocLite.R")
biocLite("rtracklayer")
biocLite("GenomicRanges")
biocLite("BSgenome")
biocLite("BSgenome.Hsapiens.UCSC.hg19")
install.packages("deconstructSigs",repos = "https://cloud.r-project.org")
install.packages("devtools",repos = "https://cloud.r-project.org")
devtools::install_github("im3sanger/dndscv@0.1.0")
devtools::install_github("Townsend-Lab-Yale/cancereffectsizeR@0.1.0")
Calculating effect size
In this example, we will calculate the effect size of SNV within LGG (low grade glioma) using the TCGA LGG dataset provided at the National Cancer Institute Genomic Data Common. Specifically using the MAF generated with mutect variant calling (NCI UUID 2c0dab06-7af9-4380-bc83-13148298da19). Download and read the MAF into memory.
# MAF files are tab delim and contain 5 rows of header to skip
LGG_MAF <- read.delim(
file = "../vignettes/TCGA.LGG.mutect.2c0dab06-7af9-4380-bc83-13148298da19.DR-7.0.somatic.maf",
header = T,skip = 5,stringsAsFactors = F)
Preprocessing
Converting hg38 to hg19
The latest TCGA data release has genomic variants in hg38 coordinates, so we need to convert these to hg19 so that the data is compatable with other packages utilized within cancereffectsizeR. hg_converter uses the hg38ToHg19.over.chain file and the rtracklayer::liftOver function to perform the conversion. Note that this function can convert between other builds with other *.over.chain files.
library(cancereffectsizeR) # load in the package
# provide the path to the over.chain file.
# I downloaded the file from
# <http://hgdownload.cse.ucsc.edu/gbdb/hg38/liftOver/hg38ToHg19.over.chain.gz>
LGG_MAF <- hg_converter(chain = "~/Downloads/hg38ToHg19.over.chain",
maf_to_convert = LGG_MAF)
#> Loading in specified MAF...
#> Number of rows in the MAF that failed to convert: 4
Adding columns with tumor sample identifier data and tumor allele data.
The TCGA does a great job documenting their data in a consistent fashion, so this step is optional if just using TCGA data. However, other sources may be less reliable, so these functions are provided in an attempt to get consistent tumor names and tumor allele nucleotides.
LGG_MAF <- unique_tumor_addition_function(MAF.file = LGG_MAF)
#> Summary statistics of the number of mutations per unique tumor:
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 1.00 26.00 36.00 69.98 49.00 14335.00
LGG_MAF <- tumor_allele_adder(MAF = LGG_MAF)
Removing DNV and TNV
We only want true single-nucleotide variant events in our data so we can get the best estimate of SNV mutation rate. Variant calling algorithms may mislabel di-nucleotide events as "SNV" and thus we need to remove these data before calculating mutation rate and effect size.
LGG_MAF <- DNP_TNP_remover(MAF = LGG_MAF)
#> Removing possible DNP
#> Total count of potential DNP removed: 168
#> DNP removal complete
Calculating cancer effect size
The effect_size_SNV() function contains the entire pipeline necessary to calculate effect sizes, assuming your data is correctly preprocessed (in hg19 coordinates, no DNP, etc.). The function utilizes deconstructSigs[2] and dndscv[3], among other freely available R packages, to first determine the intrinsic mutation rate at all sites in each gene analyzed, and then the effect size given the detected prevalence of the mutation among sequenced tumors.
LGG_selection_output <- effect_size_SNV(MAF_file = LGG_MAF,
covariate_file = "lgg_pca",
genes_for_effect_size_analysis =
c("IDH1","EGFR","TP53","BRAF"))
#> Checking if any reference alleles provided do not match reference genome...
#> Note: 4 mutations removed for exceeding the limit of mutations per gene per sample
#> No mismatched mutations! Returning original input.
#> Removing all recurrent mutations...
#> Finding the number of mutations per tumor
#> Number of tumors over specified minimum mutation number of 50: 86
#> Cleaning input to only contain tumors above the minimum...
#> Calculating trinucleotide mutation counts...
#> Calculating individual tumor mutational signatures...
#> No id variables; using all as measure variables
#> Statistical summary of the proportion of the mutational signature in each tumor sample that is 'unknown'
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 0.00000 0.02569 0.04938 0.06476 0.10100 0.19919
#> [1] Loading the environment...
#> [2] Annotating the mutations...
#> Note: 1 samples excluded for exceeding the limit of mutations per sample
#> Note: 4 mutations removed for exceeding the limit of mutations per gene per sample
#> 60 %...
#> [3] Estimating global rates...
#> [4] Running dNdSloc...
#> [5] Running dNdScv...
#> Regression model for substitutions: all covariates were used (theta = 1.97).
#> Regression model for indels: all covariates were used (theta = 0.848)
#> Calculating selection intensity...
Interpreting results
Output is grouped into four main sections.
- Selection intensity / effect size information
LGG_selection_output$selection_output
- Gene-level mutation rate data
LGG_selection_output$mutation_rates
- Trinucleotide mutational signatures (from deconstructSigs)
LGG_selection_output$trinuc_data
- And, dndscv data (from dndscv)
LGG_selection_output$dndscvout
Selection intensity summary
The main output regarding effect size calculations is found within LGG_selection_output$selection_output$all_mutations. This dataframe contains useful information for each unique molecular variant in the analysis, included selection intensity, mutation rate, frequency of occurrence within the dataset (along with proportion of tumors with the specific variant (Prop_tumors_with_specific_mut) and proportion of tumors with a mutation in this gene (Prop_of_tumors_with_this_gene_mutated).
# selection intensity data
print(head(
LGG_selection_output$selection_output$all_mutations[
order(
LGG_selection_output$selection_output$all_mutations$selection_intensity,decreasing = T),],
6),
digits = 2)
#> Gene AA_Pos Nucleotide_position Nuc_Ref Nuc_Change AA_Ref AA_Change
#> 4 IDH1 132 NA <NA> <NA> R G
#> 1 IDH1 132 NA <NA> <NA> R S
#> 3 IDH1 132 NA <NA> <NA> R H
#> 9 EGFR 252 NA <NA> <NA> R P
#> 66 TP53 175 NA <NA> <NA> R G
#> 120 TP53 273 NA <NA> <NA> R L
#> gamma selection_intensity prevalence_among_tumors mutation_rate
#> 4 5337133 2.2e+07 10 3.7e-09
#> 1 1415599 5.8e+06 9 1.3e-08
#> 3 3064357 3.6e+06 354 3.9e-07
#> 9 2668101 2.8e+06 2 1.5e-09
#> 66 749607 1.3e+06 1 2.6e-09
#> 120 491807 8.3e+05 4 1.6e-08
#> gene_AA_size dndscv_p dndscv_q Prop_tumors_with_specific_mut
#> 4 415 0e+00 0.0e+00 0.0197
#> 1 415 0e+00 0.0e+00 0.0177
#> 3 415 0e+00 0.0e+00 0.6969
#> 9 1211 1e-14 2.9e-11 0.0039
#> 66 394 0e+00 0.0e+00 0.0020
#> 120 394 0e+00 0.0e+00 0.0079
#> Prop_of_tumors_with_this_gene_mutated
#> 4 0.768
#> 1 0.768
#> 3 0.768
#> 9 0.061
#> 66 0.415
#> 120 0.415
A more detailed output is found within LGG_selection_output$selection_output$complete_mutation_data, which breaks down each molecular variants into the individual tumors it was found within.
print(head(LGG_selection_output$selection_output$complete_mutation_data),digits = 2)
#> Gene Gene_size Percent_A Percent_T Percent_G Percent_C
#> 1 IDH1 1245 0.31 0.25 0.25 0.2
#> 2 IDH1 1245 0.31 0.25 0.25 0.2
#> 3 IDH1 1245 0.31 0.25 0.25 0.2
#> 4 IDH1 1245 0.31 0.25 0.25 0.2
#> 5 IDH1 1245 0.31 0.25 0.25 0.2
#> 6 IDH1 1245 0.31 0.25 0.25 0.2
#> Nucleotide_Gene_Pos Nucleotide_chromosome_position Chromosome
#> 1 395 209113112 2
#> 2 395 209113112 2
#> 3 395 209113112 2
#> 4 395 209113112 2
#> 5 395 209113112 2
#> 6 395 209113112 2
#> Reference_Nucleotide Alternative_Nucleotide Reference_Count
#> 1 G A NA
#> 2 G A NA
#> 3 G A NA
#> 4 G A NA
#> 5 G A NA
#> 6 G A NA
#> Alternative_Count Tumor_origin Unique_patient_identifier
#> 1 NA TCGA-VM-A8CB TCGA-VM-A8CB
#> 2 NA TCGA-DB-5273 TCGA-DB-5273
#> 3 NA TCGA-HT-7687 TCGA-HT-7687
#> 4 NA TCGA-DB-5279 TCGA-DB-5279
#> 5 NA TCGA-E1-5307 TCGA-E1-5307
#> 6 NA TCGA-QH-A6X4 TCGA-QH-A6X4
#> Nucleotide_change_tally Nucleotide_mutation_rate Amino_acid_position
#> 1 354 3.9e-07 132
#> 2 354 3.9e-07 132
#> 3 354 3.9e-07 132
#> 4 354 3.9e-07 132
#> 5 354 3.9e-07 132
#> 6 354 3.9e-07 132
#> Amino_acid_codon Codon_position Amino_acid_reference
#> 1 CGT 2 R
#> 2 CGT 2 R
#> 3 CGT 2 R
#> 4 CGT 2 R
#> 5 CGT 2 R
#> 6 CGT 2 R
#> Amino_acid_alternative Amino_acid_mutation_rate Amino_acid_change_tally
#> 1 H 3.9e-07 354
#> 2 H 3.9e-07 354
#> 3 H 3.9e-07 354
#> 4 H 3.9e-07 354
#> 5 H 3.9e-07 354
#> 6 H 3.9e-07 354
#> Gamma MAF_location Nucleotide_trinuc_context
#> 1 3064357 81 CGT
#> 2 3064357 113 CGT
#> 3 3064357 138 CGT
#> 4 3064357 191 CGT
#> 5 3064357 252 CGT
#> 6 3064357 320 CGT
#> gene_level_synonymous_mutation_rate strand trinucs selection_intensity
#> 1 1.6e-07 - <NA> 3559278
#> 2 1.6e-07 - <NA> 3559278
#> 3 1.6e-07 - <NA> 3559278
#> 4 1.6e-07 - <NA> 3559278
#> 5 1.6e-07 - <NA> 3559278
#> 6 1.6e-07 - <NA> 3559278
$selection_output also contains information about the last gene analyzed, such as the nucleotide mutation rates at every position...
LGG_selection_output$selection_output$nucleotide_mutation_rates[,1:5]
#> Pos. 1 Ref: A Pos. 2 Ref: T Pos. 3 Ref: G Pos. 4 Ref: G Pos. 5 Ref: C
#> A 0.000000e+00 2.384755e-08 9.587197e-08 1.206397e-07 7.138277e-09
#> T 2.470056e-08 0.000000e+00 4.841226e-08 3.493356e-08 2.266729e-07
#> G 4.467539e-08 1.520567e-08 0.000000e+00 0.000000e+00 1.256772e-09
#> C 1.022811e-08 7.883035e-08 1.557953e-08 1.380589e-08 0.000000e+00
... and the amino acid mutations at every position.
LGG_selection_output$selection_output$amino_acid_mutation_rates[,1:5]
#> Pos. 1 Ref: Met Pos. 2 Ref: Ala Pos. 3 Ref: Ala Pos. 4 Ref: Leu
#> Phe 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> Leu 3.492867e-08 0.000000e+00 0.000000e+00 3.150253e-07
#> Ser 0.000000e+00 3.493356e-08 3.493356e-08 0.000000e+00
#> Tyr 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> Cys 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> Trp 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> Pro 0.000000e+00 1.380589e-08 1.380589e-08 5.587236e-08
#> His 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> Gln 0.000000e+00 0.000000e+00 0.000000e+00 3.289667e-08
#> Arg 1.520567e-08 0.000000e+00 0.000000e+00 1.991334e-08
#> Ile 1.598638e-07 0.000000e+00 0.000000e+00 0.000000e+00
#> Met 0.000000e+00 0.000000e+00 0.000000e+00 3.604021e-08
#> Thr 7.883035e-08 1.206397e-07 1.206397e-07 0.000000e+00
#> Asn 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> Lys 2.384755e-08 0.000000e+00 0.000000e+00 0.000000e+00
#> Val 4.467539e-08 2.266729e-07 2.266729e-07 1.342409e-08
#> Ala 0.000000e+00 2.127574e-07 2.350680e-07 0.000000e+00
#> Asp 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> Glu 0.000000e+00 7.138277e-09 7.138277e-09 0.000000e+00
#> Gly 0.000000e+00 1.256772e-09 1.256772e-09 0.000000e+00
#> STOP 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> Pos. 5 Ref: Ser
#> Phe 0.000000e+00
#> Leu 0.000000e+00
#> Ser 2.266729e-07
#> Tyr 0.000000e+00
#> Cys 2.952168e-08
#> Trp 0.000000e+00
#> Pro 0.000000e+00
#> His 0.000000e+00
#> Gln 0.000000e+00
#> Arg 2.700924e-08
#> Ile 3.604021e-08
#> Met 0.000000e+00
#> Thr 1.342409e-08
#> Asn 9.457594e-08
#> Lys 0.000000e+00
#> Val 0.000000e+00
#> Ala 0.000000e+00
#> Asp 0.000000e+00
#> Glu 0.000000e+00
#> Gly 4.801477e-08
#> STOP 0.000000e+00
[1] Cannataro, V. L., Gaffney, S. G., Townsend, J. P., “Effect sizes of somatic mutations in cancer" JNCI, (2018): https://doi.org/10.1093/jnci/djy168
[2] Rosenthal, R., McGranahan, N., Herrero, J., Taylor, B. S., & Swanton, C. (2016). deconstructSigs: delineating mutational processes in single tumors distinguishes DNA repair deficiencies and patterns of carcinoma evolution. Genome Biology, 17(1), 31. https://doi.org/10.1186/s13059-016-0893-4
[3] Martincorena, I., Raine, K. M., Gerstung, M., Dawson, K. J., Haase, K., Van Loo, P., … Campbell, P. J. (2017). Universal Patterns of Selection in Cancer and Somatic Tissues. Cell. https://doi.org/10.1016/j.cell.2017.09.042
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cancereffectsizeR user guide
Vincent Cannataro 2018-11-15
Introduction
cancereffectsizeR is an R package that may be used to calculate the effect size of single nucleotide variants (SNV) in cancer exome data[1]. It was designed for use with datasets in MAF format and works well with data from a large number of tumors, each with a large number of detected varaints. A previous version of cancereffectsizeR depended on output from MutSigCV (which runs in MATLAB) and can be found here.
This vignette is broken into several sections detailing installation, usage, and a suite of R functions provided with this package that are useful for analyzing cancer exome data. This user guide covers the version of cancereffectsizeR used in our JNCI manuscript https://doi.org/10.1093/jnci/djy168 (v0.1.0).
Installing
cancereffectsizeR utilizes a few bioinformatic R packages, and thus requires the installation of some dependencies. If you already use R to bioinformatics, you likely already have these packages installed and you can directly install cancereffectsizeR in two lines.
install.packages("devtools",repos = "https://cloud.r-project.org")
devtools::install_github("Townsend-Lab-Yale/cancereffectsizeR@0.1.0")
However, if you just downloaded R today, you will need to install the dependencies.
source("https://bioconductor.org/biocLite.R")
biocLite("rtracklayer")
biocLite("GenomicRanges")
biocLite("BSgenome")
biocLite("BSgenome.Hsapiens.UCSC.hg19")
install.packages("deconstructSigs",repos = "https://cloud.r-project.org")
install.packages("devtools",repos = "https://cloud.r-project.org")
devtools::install_github("im3sanger/dndscv@0.1.0")
devtools::install_github("Townsend-Lab-Yale/cancereffectsizeR@0.1.0")
Calculating effect size
In this example, we will calculate the effect size of SNV within LGG (low grade glioma) using the TCGA LGG dataset provided at the National Cancer Institute Genomic Data Common. Specifically using the MAF generated with mutect variant calling (NCI UUID 2c0dab06-7af9-4380-bc83-13148298da19). Download and read the MAF into memory.
# MAF files are tab delim and contain 5 rows of header to skip
LGG_MAF <- read.delim(
file = "../vignettes/TCGA.LGG.mutect.2c0dab06-7af9-4380-bc83-13148298da19.DR-7.0.somatic.maf",
header = T,skip = 5,stringsAsFactors = F)
Preprocessing
Converting hg38 to hg19
The latest TCGA data release has genomic variants in hg38 coordinates, so we need to convert these to hg19 so that the data is compatable with other packages utilized within cancereffectsizeR. hg_converter uses the hg38ToHg19.over.chain file and the rtracklayer::liftOver function to perform the conversion. Note that this function can convert between other builds with other *.over.chain files.
library(cancereffectsizeR) # load in the package
# provide the path to the over.chain file.
# I downloaded the file from
# <http://hgdownload.cse.ucsc.edu/gbdb/hg38/liftOver/hg38ToHg19.over.chain.gz>
LGG_MAF <- hg_converter(chain = "~/Downloads/hg38ToHg19.over.chain",
maf_to_convert = LGG_MAF)
#> Loading in specified MAF...
#> Number of rows in the MAF that failed to convert: 4
Adding columns with tumor sample identifier data and tumor allele data.
The TCGA does a great job documenting their data in a consistent fashion, so this step is optional if just using TCGA data. However, other sources may be less reliable, so these functions are provided in an attempt to get consistent tumor names and tumor allele nucleotides.
LGG_MAF <- unique_tumor_addition_function(MAF.file = LGG_MAF)
#> Summary statistics of the number of mutations per unique tumor:
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 1.00 26.00 36.00 69.98 49.00 14335.00
LGG_MAF <- tumor_allele_adder(MAF = LGG_MAF)
Removing DNV and TNV
We only want true single-nucleotide variant events in our data so we can get the best estimate of SNV mutation rate. Variant calling algorithms may mislabel di-nucleotide events as "SNV" and thus we need to remove these data before calculating mutation rate and effect size.
LGG_MAF <- DNP_TNP_remover(MAF = LGG_MAF)
#> Removing possible DNP
#> Total count of potential DNP removed: 168
#> DNP removal complete
Calculating cancer effect size
The effect_size_SNV() function contains the entire pipeline necessary to calculate effect sizes, assuming your data is correctly preprocessed (in hg19 coordinates, no DNP, etc.). The function utilizes deconstructSigs[2] and dndscv[3], among other freely available R packages, to first determine the intrinsic mutation rate at all sites in each gene analyzed, and then the effect size given the detected prevalence of the mutation among sequenced tumors.
LGG_selection_output <- effect_size_SNV(MAF_file = LGG_MAF,
covariate_file = "lgg_pca",
genes_for_effect_size_analysis =
c("IDH1","EGFR","TP53","BRAF"))
#> Checking if any reference alleles provided do not match reference genome...
#> Note: 4 mutations removed for exceeding the limit of mutations per gene per sample
#> No mismatched mutations! Returning original input.
#> Removing all recurrent mutations...
#> Finding the number of mutations per tumor
#> Number of tumors over specified minimum mutation number of 50: 86
#> Cleaning input to only contain tumors above the minimum...
#> Calculating trinucleotide mutation counts...
#> Calculating individual tumor mutational signatures...
#> No id variables; using all as measure variables
#> Statistical summary of the proportion of the mutational signature in each tumor sample that is 'unknown'
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 0.00000 0.02569 0.04938 0.06476 0.10100 0.19919
#> [1] Loading the environment...
#> [2] Annotating the mutations...
#> Note: 1 samples excluded for exceeding the limit of mutations per sample
#> Note: 4 mutations removed for exceeding the limit of mutations per gene per sample
#> 60 %...
#> [3] Estimating global rates...
#> [4] Running dNdSloc...
#> [5] Running dNdScv...
#> Regression model for substitutions: all covariates were used (theta = 1.97).
#> Regression model for indels: all covariates were used (theta = 0.848)
#> Calculating selection intensity...
Interpreting results
Output is grouped into four main sections.
- Selection intensity / effect size information
LGG_selection_output$selection_output - Gene-level mutation rate data
LGG_selection_output$mutation_rates - Trinucleotide mutational signatures (from deconstructSigs)
LGG_selection_output$trinuc_data - And, dndscv data (from dndscv)
LGG_selection_output$dndscvout
Selection intensity summary
The main output regarding effect size calculations is found within LGG_selection_output$selection_output$all_mutations. This dataframe contains useful information for each unique molecular variant in the analysis, included selection intensity, mutation rate, frequency of occurrence within the dataset (along with proportion of tumors with the specific variant (Prop_tumors_with_specific_mut) and proportion of tumors with a mutation in this gene (Prop_of_tumors_with_this_gene_mutated).
# selection intensity data
print(head(
LGG_selection_output$selection_output$all_mutations[
order(
LGG_selection_output$selection_output$all_mutations$selection_intensity,decreasing = T),],
6),
digits = 2)
#> Gene AA_Pos Nucleotide_position Nuc_Ref Nuc_Change AA_Ref AA_Change
#> 4 IDH1 132 NA <NA> <NA> R G
#> 1 IDH1 132 NA <NA> <NA> R S
#> 3 IDH1 132 NA <NA> <NA> R H
#> 9 EGFR 252 NA <NA> <NA> R P
#> 66 TP53 175 NA <NA> <NA> R G
#> 120 TP53 273 NA <NA> <NA> R L
#> gamma selection_intensity prevalence_among_tumors mutation_rate
#> 4 5337133 2.2e+07 10 3.7e-09
#> 1 1415599 5.8e+06 9 1.3e-08
#> 3 3064357 3.6e+06 354 3.9e-07
#> 9 2668101 2.8e+06 2 1.5e-09
#> 66 749607 1.3e+06 1 2.6e-09
#> 120 491807 8.3e+05 4 1.6e-08
#> gene_AA_size dndscv_p dndscv_q Prop_tumors_with_specific_mut
#> 4 415 0e+00 0.0e+00 0.0197
#> 1 415 0e+00 0.0e+00 0.0177
#> 3 415 0e+00 0.0e+00 0.6969
#> 9 1211 1e-14 2.9e-11 0.0039
#> 66 394 0e+00 0.0e+00 0.0020
#> 120 394 0e+00 0.0e+00 0.0079
#> Prop_of_tumors_with_this_gene_mutated
#> 4 0.768
#> 1 0.768
#> 3 0.768
#> 9 0.061
#> 66 0.415
#> 120 0.415
A more detailed output is found within LGG_selection_output$selection_output$complete_mutation_data, which breaks down each molecular variants into the individual tumors it was found within.
print(head(LGG_selection_output$selection_output$complete_mutation_data),digits = 2)
#> Gene Gene_size Percent_A Percent_T Percent_G Percent_C
#> 1 IDH1 1245 0.31 0.25 0.25 0.2
#> 2 IDH1 1245 0.31 0.25 0.25 0.2
#> 3 IDH1 1245 0.31 0.25 0.25 0.2
#> 4 IDH1 1245 0.31 0.25 0.25 0.2
#> 5 IDH1 1245 0.31 0.25 0.25 0.2
#> 6 IDH1 1245 0.31 0.25 0.25 0.2
#> Nucleotide_Gene_Pos Nucleotide_chromosome_position Chromosome
#> 1 395 209113112 2
#> 2 395 209113112 2
#> 3 395 209113112 2
#> 4 395 209113112 2
#> 5 395 209113112 2
#> 6 395 209113112 2
#> Reference_Nucleotide Alternative_Nucleotide Reference_Count
#> 1 G A NA
#> 2 G A NA
#> 3 G A NA
#> 4 G A NA
#> 5 G A NA
#> 6 G A NA
#> Alternative_Count Tumor_origin Unique_patient_identifier
#> 1 NA TCGA-VM-A8CB TCGA-VM-A8CB
#> 2 NA TCGA-DB-5273 TCGA-DB-5273
#> 3 NA TCGA-HT-7687 TCGA-HT-7687
#> 4 NA TCGA-DB-5279 TCGA-DB-5279
#> 5 NA TCGA-E1-5307 TCGA-E1-5307
#> 6 NA TCGA-QH-A6X4 TCGA-QH-A6X4
#> Nucleotide_change_tally Nucleotide_mutation_rate Amino_acid_position
#> 1 354 3.9e-07 132
#> 2 354 3.9e-07 132
#> 3 354 3.9e-07 132
#> 4 354 3.9e-07 132
#> 5 354 3.9e-07 132
#> 6 354 3.9e-07 132
#> Amino_acid_codon Codon_position Amino_acid_reference
#> 1 CGT 2 R
#> 2 CGT 2 R
#> 3 CGT 2 R
#> 4 CGT 2 R
#> 5 CGT 2 R
#> 6 CGT 2 R
#> Amino_acid_alternative Amino_acid_mutation_rate Amino_acid_change_tally
#> 1 H 3.9e-07 354
#> 2 H 3.9e-07 354
#> 3 H 3.9e-07 354
#> 4 H 3.9e-07 354
#> 5 H 3.9e-07 354
#> 6 H 3.9e-07 354
#> Gamma MAF_location Nucleotide_trinuc_context
#> 1 3064357 81 CGT
#> 2 3064357 113 CGT
#> 3 3064357 138 CGT
#> 4 3064357 191 CGT
#> 5 3064357 252 CGT
#> 6 3064357 320 CGT
#> gene_level_synonymous_mutation_rate strand trinucs selection_intensity
#> 1 1.6e-07 - <NA> 3559278
#> 2 1.6e-07 - <NA> 3559278
#> 3 1.6e-07 - <NA> 3559278
#> 4 1.6e-07 - <NA> 3559278
#> 5 1.6e-07 - <NA> 3559278
#> 6 1.6e-07 - <NA> 3559278
$selection_output also contains information about the last gene analyzed, such as the nucleotide mutation rates at every position...
LGG_selection_output$selection_output$nucleotide_mutation_rates[,1:5]
#> Pos. 1 Ref: A Pos. 2 Ref: T Pos. 3 Ref: G Pos. 4 Ref: G Pos. 5 Ref: C
#> A 0.000000e+00 2.384755e-08 9.587197e-08 1.206397e-07 7.138277e-09
#> T 2.470056e-08 0.000000e+00 4.841226e-08 3.493356e-08 2.266729e-07
#> G 4.467539e-08 1.520567e-08 0.000000e+00 0.000000e+00 1.256772e-09
#> C 1.022811e-08 7.883035e-08 1.557953e-08 1.380589e-08 0.000000e+00
... and the amino acid mutations at every position.
LGG_selection_output$selection_output$amino_acid_mutation_rates[,1:5]
#> Pos. 1 Ref: Met Pos. 2 Ref: Ala Pos. 3 Ref: Ala Pos. 4 Ref: Leu
#> Phe 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> Leu 3.492867e-08 0.000000e+00 0.000000e+00 3.150253e-07
#> Ser 0.000000e+00 3.493356e-08 3.493356e-08 0.000000e+00
#> Tyr 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> Cys 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> Trp 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> Pro 0.000000e+00 1.380589e-08 1.380589e-08 5.587236e-08
#> His 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> Gln 0.000000e+00 0.000000e+00 0.000000e+00 3.289667e-08
#> Arg 1.520567e-08 0.000000e+00 0.000000e+00 1.991334e-08
#> Ile 1.598638e-07 0.000000e+00 0.000000e+00 0.000000e+00
#> Met 0.000000e+00 0.000000e+00 0.000000e+00 3.604021e-08
#> Thr 7.883035e-08 1.206397e-07 1.206397e-07 0.000000e+00
#> Asn 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> Lys 2.384755e-08 0.000000e+00 0.000000e+00 0.000000e+00
#> Val 4.467539e-08 2.266729e-07 2.266729e-07 1.342409e-08
#> Ala 0.000000e+00 2.127574e-07 2.350680e-07 0.000000e+00
#> Asp 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> Glu 0.000000e+00 7.138277e-09 7.138277e-09 0.000000e+00
#> Gly 0.000000e+00 1.256772e-09 1.256772e-09 0.000000e+00
#> STOP 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> Pos. 5 Ref: Ser
#> Phe 0.000000e+00
#> Leu 0.000000e+00
#> Ser 2.266729e-07
#> Tyr 0.000000e+00
#> Cys 2.952168e-08
#> Trp 0.000000e+00
#> Pro 0.000000e+00
#> His 0.000000e+00
#> Gln 0.000000e+00
#> Arg 2.700924e-08
#> Ile 3.604021e-08
#> Met 0.000000e+00
#> Thr 1.342409e-08
#> Asn 9.457594e-08
#> Lys 0.000000e+00
#> Val 0.000000e+00
#> Ala 0.000000e+00
#> Asp 0.000000e+00
#> Glu 0.000000e+00
#> Gly 4.801477e-08
#> STOP 0.000000e+00
[1] Cannataro, V. L., Gaffney, S. G., Townsend, J. P., “Effect sizes of somatic mutations in cancer" JNCI, (2018): https://doi.org/10.1093/jnci/djy168
[2] Rosenthal, R., McGranahan, N., Herrero, J., Taylor, B. S., & Swanton, C. (2016). deconstructSigs: delineating mutational processes in single tumors distinguishes DNA repair deficiencies and patterns of carcinoma evolution. Genome Biology, 17(1), 31. https://doi.org/10.1186/s13059-016-0893-4
[3] Martincorena, I., Raine, K. M., Gerstung, M., Dawson, K. J., Haase, K., Van Loo, P., … Campbell, P. J. (2017). Universal Patterns of Selection in Cancer and Somatic Tissues. Cell. https://doi.org/10.1016/j.cell.2017.09.042
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