Main concepts
1. maf data filtering
When we download the maf data from the tcga, we need to do some pre-filtering to the maf data.
the function filter_MAF will do the maf filtering. This function includes a list of functions:
(1)hg_converter: whether converte the mutations coordinate, hg19 -> hg38 or hg38 -> hg19.
(2)unique_tumor_addition_function: Adds a column called “Unique_patient_identifier” to your MAF file
(3)tumor_allele_adder: add tumor allele
(4)DNP_TNP_remover: removing dinucleotide
(5)removing_patients: removing patients according to their mutation numbers or patients IDs.
(6)retaining MC3 mutations.
(7)getAltFreq : calculate the alternative allele frequecy of mutations.
library(MafData)
library(dndscv)
library(maftools)
# this maf is hg38
maf <- read.delim(file = gzfile(system.file("extdata", "TCGA.KIRC.maf.gz", package = "MafData")),
header = T, stringsAsFactors = F)
# load patientsID: These patients are removed hyper-mutations ones.
patientsID = read.delim(system.file("extdata", "panCancer.patients.Sort.ID", package = "MafData"),
header = F)
colnames(patientsID) = c("Cancer", "MutNum", "PatientsID")
chain = system.file("extdata", "hg38ToHg19.over.chain", package = "MafData")
# select the first 10 patients
maf_hg19 = filter_MAF(MAF = maf, convert_coordinate = T, chain = chain, patientsID = patientsID$ID[1:10])
#> 1) hg_converter; d:/Program Files/R/R-3.6.1/library/MafData/extdata/hg38ToHg19.over.chain
#> Loading in specified MAF...
#> Number of rows in the MAF that failed to convert: 2
#> 2) unique_tumor_addition_function ;summary for tumor data
#> Summary statistics of the number of mutations per unique tumor:
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 8.00 44.00 58.00 62.86 76.00 678.00
#> 3) tumor_allele_adder; check out tumor column names.
#> [1] "3) nrow of MAF: 21121 "
#> 4) DNP_TNP_remover; Removing DNV and TNV
#> Removing possible DNP
#> Total count of potential DNP removed: 615
#> DNP removal complete
#> [1] "4) nrow of MAF: 20516 "
#> 5)removing_patients; removing patients.
#> # 1)removing patients. 2) removing patients more than N mutations.
#> Summary for Patients
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 8.00 42.75 56.50 61.06 74.00 674.00
#> [1] the Total Patients are 336
#> [2] Patients remain 336 after mutations cutoff between 0 - 3000
#> [4] 0 patients are removing out
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 8.00 42.75 56.50 61.06 74.00 674.00
#> [5] In summary, 0 mutations are removing out
# convert from hg38 to hg19
maf_hg19 = filter_MAF(MAF = maf, convert_coordinate = T, chain = chain)
#> 1) hg_converter; d:/Program Files/R/R-3.6.1/library/MafData/extdata/hg38ToHg19.over.chain
#> Loading in specified MAF...
#> Number of rows in the MAF that failed to convert: 2
#> 2) unique_tumor_addition_function ;summary for tumor data
#> Summary statistics of the number of mutations per unique tumor:
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 8.00 44.00 58.00 62.86 76.00 678.00
#> 3) tumor_allele_adder; check out tumor column names.
#> [1] "3) nrow of MAF: 21121 "
#> 4) DNP_TNP_remover; Removing DNV and TNV
#> Removing possible DNP
#> Total count of potential DNP removed: 615
#> DNP removal complete
#> [1] "4) nrow of MAF: 20516 "
#> 5)removing_patients; removing patients.
#> # 1)removing patients. 2) removing patients more than N mutations.
#> Summary for Patients
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 8.00 42.75 56.50 61.06 74.00 674.00
#> [1] the Total Patients are 336
#> [2] Patients remain 336 after mutations cutoff between 0 - 3000
#> [4] 0 patients are removing out
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 8.00 42.75 56.50 61.06 74.00 674.00
#> [5] In summary, 0 mutations are removing out
# set the mutation number cutoff is 10-500
maf_hg19 = filter_MAF(MAF = maf, maxNum = 500, minNum = 10, convert_coordinate = T,
chain = chain)
#> 1) hg_converter; d:/Program Files/R/R-3.6.1/library/MafData/extdata/hg38ToHg19.over.chain
#> Loading in specified MAF...
#> Number of rows in the MAF that failed to convert: 2
#> 2) unique_tumor_addition_function ;summary for tumor data
#> Summary statistics of the number of mutations per unique tumor:
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 8.00 44.00 58.00 62.86 76.00 678.00
#> 3) tumor_allele_adder; check out tumor column names.
#> [1] "3) nrow of MAF: 21121 "
#> 4) DNP_TNP_remover; Removing DNV and TNV
#> Removing possible DNP
#> Total count of potential DNP removed: 615
#> DNP removal complete
#> [1] "4) nrow of MAF: 20516 "
#> 5)removing_patients; removing patients.
#> # 1)removing patients. 2) removing patients more than N mutations.
#> Summary for Patients
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 8.00 42.75 56.50 61.06 74.00 674.00
#> [1] the Total Patients are 336
#> [2] Patients remain 334 after mutations cutoff between 10 - 500
#> [4] 2 patients are removing out
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 10.00 43.00 56.50 59.38 73.75 182.00
#> [5] In summary, 682 mutations are removing out
# filtering MC3 mutations.
maf_hg19 = filter_MAF(MAF = maf, filterMC3 = T, addAltFreq = T, maxNum = 500, minNum = 10,
convert_coordinate = T, chain = chain)
#> 1) hg_converter; d:/Program Files/R/R-3.6.1/library/MafData/extdata/hg38ToHg19.over.chain
#> Loading in specified MAF...
#> Number of rows in the MAF that failed to convert: 2
#> 2) unique_tumor_addition_function ;summary for tumor data
#> Summary statistics of the number of mutations per unique tumor:
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 8.00 44.00 58.00 62.86 76.00 678.00
#> 3) tumor_allele_adder; check out tumor column names.
#> [1] "3) nrow of MAF: 21121 "
#> 4) DNP_TNP_remover; Removing DNV and TNV
#> Removing possible DNP
#> Total count of potential DNP removed: 615
#> DNP removal complete
#> [1] "4) nrow of MAF: 20516 "
#> 5)removing_patients; removing patients.
#> # 1)removing patients. 2) removing patients more than N mutations.
#> Summary for Patients
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 8.00 42.75 56.50 61.06 74.00 674.00
#> [1] the Total Patients are 336
#> [2] Patients remain 334 after mutations cutoff between 10 - 500
#> [4] 2 patients are removing out
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 10.00 43.00 56.50 59.38 73.75 182.00
#> [5] In summary, 682 mutations are removing out
#> 6) filterMC3
#> 7) getAltFreq
# get the alternative allele frequency.
maf_hg19 = filter_MAF(MAF = maf, filterMC3 = F, addAltFreq = T, maxNum = 500, minNum = 10,
convert_coordinate = T, chain = chain)
#> 1) hg_converter; d:/Program Files/R/R-3.6.1/library/MafData/extdata/hg38ToHg19.over.chain
#> Loading in specified MAF...
#> Number of rows in the MAF that failed to convert: 2
#> 2) unique_tumor_addition_function ;summary for tumor data
#> Summary statistics of the number of mutations per unique tumor:
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 8.00 44.00 58.00 62.86 76.00 678.00
#> 3) tumor_allele_adder; check out tumor column names.
#> [1] "3) nrow of MAF: 21121 "
#> 4) DNP_TNP_remover; Removing DNV and TNV
#> Removing possible DNP
#> Total count of potential DNP removed: 615
#> DNP removal complete
#> [1] "4) nrow of MAF: 20516 "
#> 5)removing_patients; removing patients.
#> # 1)removing patients. 2) removing patients more than N mutations.
#> Summary for Patients
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 8.00 42.75 56.50 61.06 74.00 674.00
#> [1] the Total Patients are 336
#> [2] Patients remain 334 after mutations cutoff between 10 - 500
#> [4] 2 patients are removing out
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 10.00 43.00 56.50 59.38 73.75 182.00
#> [5] In summary, 682 mutations are removing out
#> 7) getAltFreq
# Summary patients mutations
patients_col_name = "Tumor_Sample_Barcode"
patients = data.frame(ID = rownames(table(maf[, patients_col_name])), MutNum = as.numeric(table(maf[,
patients_col_name])))
summary(patients$MutNum)
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 8.00 44.00 58.00 62.87 76.00 678.002. filter mutations by individual functions.
message("1) hg_converter; ", chain)
#> 1) hg_converter; d:/Program Files/R/R-3.6.1/library/MafData/extdata/hg38ToHg19.over.chain
maf <- hg_converter(chain = chain, maf_to_convert = maf)
#> Loading in specified MAF...
#> Number of rows in the MAF that failed to convert: 2
message("2) unique_tumor_addition_function ;summary for tumor data")
#> 2) unique_tumor_addition_function ;summary for tumor data
maf <- unique_tumor_addition_function(MAF.file = maf)
#> Summary statistics of the number of mutations per unique tumor:
message("3) tumor_allele_adder; check out tumor column names.")
#> 3) tumor_allele_adder; check out tumor column names.
maf <- tumor_allele_adder(MAF = maf)
message("4) DNP_TNP_remover; Removing DNV and TNV")
#> 4) DNP_TNP_remover; Removing DNV and TNV
maf <- DNP_TNP_remover(MAF = maf)
#> Removing possible DNP
#> Total count of potential DNP removed: 615
#> DNP removal complete
message("5)removing_patients; removing patients.
# 1)removing patients. 2) removing patients more than N mutations.")
#> 5)removing_patients; removing patients.
#> # 1)removing patients. 2) removing patients more than N mutations.
maf <- removing_patients(MAF = maf, patientsID = patientsID$ID, maxNum = 3000, minNum = 0)
#> Summary for Patients
#> [1] the Total Patients are 336
#> [2] Patients remain 336 after mutations cutoff between 0 - 3000
#> [4] 0 patients are removing out
#> [5] In summary, 0 mutations are removing out
message("6) filterMC3")
#> 6) filterMC3
if (is.character(maf[, "MC3_Overlap"])) {
maf[, "MC3_Overlap"] = ifelse(toupper(maf[, "MC3_Overlap"]) == "TRUE", TRUE,
FALSE)
}
if (is.logical(maf[, "MC3_Overlap"])) {
maf <- maf[maf[, "MC3_Overlap"] == TRUE, ]
} else {
message(sprintf("The column %s is not TRUE or FALSE", "MC3_Overlap"))
}
message("7) getAltFreq")
#> 7) getAltFreq
maf <- getAltFreq(MAF = maf, t_depth = "t_depth", t_alt_count = "t_alt_count")
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 8.00 44.00 58.00 62.86 76.00 678.00
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 8.00 42.75 56.50 61.06 74.00 674.00
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 8.00 42.75 56.50 61.06 74.00 674.003. calculating cancer genes dN/dS
# out_formate: convert the MAF formate for the dndscv.
KIRC.mut = out_formate(MAF = maf_hg19)
KIRC.dndscv = dndscv(mutations = KIRC.mut, max_muts_per_gene_per_sample = 1000, outp = 3)
#> [1] Loading the environment...
#> [2] Annotating the mutations...
#> 59% ...
#> [3] Estimating global rates...
#> [4] Running dNdSloc...
#> [5] Running dNdScv...
#> Regression model for substitutions (theta = 8.41).
KIRC.driver = KIRC.dndscv$sel_cv
head(KIRC.driver)
#> gene_name n_syn n_mis n_non n_spl wmis_cv wnon_cv wspl_cv
#> 18924 VHL 0 59 25 13 299.085682 2122.34086 2122.34086
#> 12546 PBRM1 2 19 37 11 10.873851 227.24694 227.24694
#> 1885 BAP1 1 11 8 3 15.742797 149.78385 149.78385
#> 15589 SETD2 0 10 16 2 4.969256 84.21034 84.21034
#> 10868 MTOR 2 22 0 0 8.811754 0.00000 0.00000
#> 18057 TP53 1 4 1 2 10.316483 67.52147 67.52147
#> pmis_cv ptrunc_cv pallsubs_cv qmis_cv qtrunc_cv
#> 18924 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.00000000
#> 12546 8.797880e-10 0.000000e+00 0.000000e+00 8.837911e-06 0.00000000
#> 1885 2.244412e-08 0.000000e+00 0.000000e+00 1.127312e-04 0.00000000
#> 15589 1.078775e-03 0.000000e+00 0.000000e+00 7.589485e-01 0.00000000
#> 10868 3.634676e-09 4.433266e-01 1.268618e-08 2.434143e-05 0.93421475
#> 18057 1.457493e-03 1.759641e-05 1.496756e-06 7.589485e-01 0.05050422
#> qallsubs_cv
#> 18924 0.000000e+00
#> 12546 0.000000e+00
#> 1885 0.000000e+00
#> 15589 0.000000e+00
#> 10868 5.097563e-05
#> 18057 5.011886e-033. load data into maftools
library(maftools)
KIRC.maf <- read.maf(maf = maf_hg19, isTCGA = T)-Validating –Removed 12 duplicated variants -Silent variants: 7054 -Summarizing –Mutiple centers found BCM;BI–Possible FLAGS among top ten genes: TTN MUC16 HMCN1 -Processing clinical data –Missing clinical data -Finished in 3.550s elapsed (1.090s cpu)
# calculate diversity
KIRC.ab.het <- inferHeterogeneity(maf = KIRC.maf, vafCol = "t_alt_freq", top = nrow(getSampleSummary(KIRC.maf)))
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#> Processing TCGA-CW-5591..
#> Processing TCGA-EU-5904..
#> Processing TCGA-B0-5693..
#> Processing TCGA-BP-5174..
#> Processing TCGA-BP-5177..
#> Processing TCGA-BP-5180..
#> Processing TCGA-BP-5183..
#> Processing TCGA-CJ-5678..
#> Processing TCGA-CZ-5455..
#> Processing TCGA-CZ-5988..
#> Processing TCGA-B8-5551..
#> Processing TCGA-BP-4971..
#> Processing TCGA-BP-4998..
#> Processing TCGA-BP-5186..
#> Processing TCGA-DV-5573..
#> Processing TCGA-MW-A4EC..
#> Processing TCGA-B0-5707..
#> Processing TCGA-B0-5710..
#> Processing TCGA-B0-5711..
#> Processing TCGA-BP-4962..
#> Processing TCGA-BP-4972..
#> Processing TCGA-BP-4982..
#> Processing TCGA-BP-5170..
#> Processing TCGA-CZ-5460..
#> Processing TCGA-CZ-5989..
#> Processing TCGA-T7-A92I..
#> Processing TCGA-B0-5100..
#> Processing TCGA-BP-5008..
#> Processing TCGA-A3-3380..
#> Processing TCGA-B0-5113..
#> Processing TCGA-B8-4148..
#> Processing TCGA-B8-5552..
#> Processing TCGA-BP-5000..
#> Processing TCGA-BP-5001..
#> Processing TCGA-B0-5400..
#> Processing TCGA-B8-4146..
#> Processing TCGA-CJ-4899..
#> Processing TCGA-CZ-4863..
#> Processing TCGA-A3-A8CQ..
#> Processing TCGA-BP-4970..
#> Processing TCGA-CZ-5458..
#> Processing TCGA-AK-3447..
#> Processing TCGA-AK-3465..
#> Processing TCGA-B0-5081..
#> Processing TCGA-BP-4961..
#> Processing TCGA-BP-4973..
#> Processing TCGA-B4-5834..
#> Processing TCGA-B8-A54F..
#> Processing TCGA-CJ-4908..
#> Processing TCGA-BP-5007..
#> Processing TCGA-CZ-5452..
#> Processing TCGA-BP-4974..
#> Processing TCGA-CW-5583..
#> Processing TCGA-BP-5184..
#> Processing TCGA-BP-5194..
#> Processing TCGA-A3-A8OW..
#> Processing TCGA-A3-A8OX..
#> Processing TCGA-BP-4987..
#> Processing TCGA-CJ-5681..
#> Processing TCGA-6D-AA2E..
#> Processing TCGA-CZ-5454..
#> Processing TCGA-DV-5574..
#> Processing TCGA-DV-5575..
#> Processing TCGA-AK-3440..
#> Processing TCGA-AS-3777..
#> Processing TCGA-B2-5636..
#> Processing TCGA-BP-4795..
#> Processing TCGA-DV-5567..
#> Processing TCGA-B0-4700..
#> Processing TCGA-B8-5165..
#> Processing TCGA-B8-A54K..
#> Processing TCGA-DV-5568..
#> Processing TCGA-BP-4975..
#> Processing TCGA-B0-5083..
#> Processing TCGA-B8-5545..
#> Processing TCGA-CW-6097..
#> Processing TCGA-A3-3374..
#> Processing TCGA-BP-4177..
#> Processing TCGA-AK-3427..
#> Processing TCGA-BP-4760..
#> Processing TCGA-DV-A4VZ..
#> Processing TCGA-AK-3453..
#> Processing TCGA-DV-5569..
#> Processing TCGA-DV-5576..
#> Processing TCGA-B8-5546..
#> Processing TCGA-AK-3443..
#> Processing TCGA-B0-5117..
KIRC.ab.het.math = unique.data.frame(KIRC.ab.het$clusterData[, c("Tumor_Sample_Barcode",
"MATH")])
plot(density(KIRC.ab.het.math$MATH))