Package 'ZygosityPredictor'

calculates how many copies are affected by a germnline small variant

Description

calculates how many copies are affected by a germnline small variant

Usage

aff_germ_copies(chr, af, tcn, purity, sex, c_normal = NULL, af_normal = 0.5)

Arguments

chr	chromosome of the variant (either format 1,2,..,X,Y or chr1,..,chrX)
af	Allele-frequency of the variant (numeric value between 0 and 1)
tcn	total-copynumber at position of the variant (numeric value >0)
purity	purity of the sample (numeric value between 0 and 1 indicating the fraction of relevant sample with control/unrelevant tissue)
sex	sex of the sample (character: "male", "female", "m", "f")
c_normal	expected copy number at position of the variant in normal tissue, 1 for gonosomes in male samples, and 2 for male autosomes and all chromosomes in female samples. (The function can also assess the c_normal parameter by itself, but then the following two inputs must be provided: chr and sex)
af_normal	Allele-frequency in normal tissue (numeric value between 0 and 1) 0.5 represents heterozygous variants in diploid genome, 1 would be homozygous. Could be relevant if germline CNVs are present at the position. Then also the c_normal parameter would have to be adjusted.

Value

A numeric value indicating the affecting copies for the variant

Examples

library(dplyr)
library(purrr)
library(stringr)
aff_germ_copies(af=0.67, tcn=2, purity=0.9, chr="chrX", sex="female")

---

calculates how many copies are affected by a somatic small variant

Description

calculates how many copies are affected by a somatic small variant

Usage

aff_som_copies(chr, af, tcn, purity, sex, c_normal = NULL)

Arguments

chr	chromosome of the variant (either format 1,2,..,X,Y or chr1,..,chrX)
af	Allele-frequency of the variant (numeric value between 0 and 1)
tcn	total-copynumber at position of the variant (numeric value >0)
purity	purity of the sample (numeric value between 0 and 1 indicating the fraction of relevant sample with control/unrelevant tissue)
sex	sex of the sample (character: "male", "female", "m", "f")
c_normal	expected copy number at the position of the variant in normal tissue, 1 for gonosomes in male samples, and 2 for male autosomes and all chromosomes in female samples. (The function can also assess the c_normal parameter by itself, but then the following two inputs must be provided: chr and sex)

Value

A numeric value indicating the affecting copies for the variant

Examples

library(dplyr)
library(purrr)
library(stringr)
aff_som_copies(chr="chrX", af=0.67, tcn=2, purity=0.9, sex="female")

---

accesor for gene predictions printing detailed info about how a gene status was assigned

Description

accesor for gene predictions printing detailed info about how a gene status was assigned

Usage

gene_ov(fp, inp_gene, n = 20)

Arguments

fp	full prediction (output of predict_zygoisty())
inp_gene	name of gene that should be printed with detailed information
n	max number of rows to print, as some gene status depend on loads of phasing results#'

Value

prints overview about run from function predict_zygoisty() with specific information about provided gene

Examples

cnvs  = GenomicRanges::GRanges(
  dplyr::tibble(
    chr = "chr17",
    start = c(170060, 34520990),
    end = c(34520990, 83198614),
    tcn = c(2, 1),
    cna_type = c("neutral", "LOH")
  )
)
somatic_vars = GenomicRanges::GRanges(
  dplyr::tibble(
    chr="chr17",
    start = 7675088,
    end = 7675088,
    ref = "C",
    alt = "T",
    af = 0.65,
    gene = "TP53" 
  )
)
germline_vars = GenomicRanges::GRanges(
  dplyr::tibble(
    chr="chr17",
    start = 41771694,
    end = 41771694,
    ref = "GTGT",
    alt = "G",
    af = 0.95,
    gene = "JUP" 
  )
)
reference = GenomicRanges::GRanges(
  dplyr::tibble(
    chr = "chr17",
    start = c(7661778, 41754603),
    end = c(7687538, 41786931),
    gene = c("TP53", "JUP")
  )
)
sex = "female"
purity = 0.9
bamfile <- system.file("extdata", "ZP_example.bam", 
  package = "ZygosityPredictor")
fp <- predict_zygosity(purity = purity, sex = sex, 
  somCna = cnvs,
  somSmallVars = somatic_vars,
  germSmallVars = germline_vars,
  geneModel = reference,
  bamDna = bamfile
)
gene_ov(fp, TP53)

---

germline small variant object

Description

germline small variant object

Usage

data(GR_GENE_MODEL)

Format

## 'GR_GENE_MODEL' GRanges object

Value

Object containing gene model of hg38

---

germline small variant object

Description

germline small variant object

Usage

data(GR_SOM_SMALL_VARS)

Format

## 'GR_SOM_SMALL_VARS' GRanges object

Value

Object containing germline Indels and SNVs of SeqC2 example case

---

haploblocks

Description

haploblocks

Usage

data(GR_HAPLOBLOCKS)

Format

## 'GR_HAPLOBLOCKS' GRanges object

Value

Object containing haploblock annotations

---

copynumber object

Description

copynumber object

Usage

data(GR_SCNA)

Format

## 'GR_SCNA' GRanges object

Value

Object containing somatic copy number abberations (sCNAs) of SeqC2 example case

---

somatic small variant object

Description

somatic small variant object

Usage

data(GR_GERM_SMALL_VARS)

Format

## 'GR_GERM_SMALL_VARS' GRanges object

Value

Object containing somatic Indels and SNVs of SeqC2 example case

---

predicts zygosity of a set of variants

Description

predicts zygosity of a set of variants

Usage

predict_per_variant(
  purity,
  sex,
  somCna,
  geneModel = NULL,
  somSmallVars = NULL,
  germSmallVars = NULL,
  ploidy = NULL,
  colnameTcn = NULL,
  colnameCnaType = NULL,
  includeHomoDel = TRUE,
  includeIncompleteDel = TRUE,
  assumeSomCnaGaps = FALSE,
  byTcn = TRUE,
  ZP_env = NULL,
  verbose = FALSE
)

Arguments

purity	purity of the sample (numeric value between 0 and 1 indicating the fraction of relevant sample with control/unrelevant tissue)
sex	sex of the sample (character: "male", "female", "m", "f")
somCna	GRanges object containing all genomic regions with annotated total copynumber and cna_type as metadata columns. The total-copynumber column should be named "tcn" but also some other commonly used names. It should contain numeric values or characters that can be converted to numeric values. The cna_type column must contain the information about loss of heterozygosity (LOH). Therefore the term "LOH" must be explicitely mentioned in the column. If a genomic region is not present in the object, it will be taken as heterozygous with neutral TCN of 2.
geneModel	GRanges object containing the gene-annoattion of the used reference genome with metadata column of the gene name (gene)
somSmallVars	GRanges object containing all somatic small variants (SNV and INDEL). Required metadata columns are reference base (ref/REF), alternative base (alt/ALT), annotation of the gene name (gene/GENE) and the allele-frequency (af/AF). If the object is not provided the tool assumes there are no somatic small variants.
germSmallVars	GRanges object containing all germline small variants (SNV and INDEL). Required metadata columns are reference base (ref/REF), alternative base (alt/ALT), annotation of the gene name (gene/GENE) and the allele-frequency (af/AF) If the object is not provided the tool assumes there are no germline small variants.
ploidy	ploidy of the sample (numeric value)
colnameTcn	character indicating the name of the metadata containing the tcn information in the somCna object. If not provided the tool tries to detect the column according to default names
colnameCnaType	character indicating the name of the metadata containing cna type information in the somCna object. If not provided the tool tries to detect the column according to default names
includeHomoDel	default = TRUE; if FALSE homozygous deleteions are excluded
includeIncompleteDel	default = TRUE; if FALSE heterzygous deleteions are excluded
assumeSomCnaGaps	(logical, default=FALSE) Only required if the somCna object lacks copy number information for genomic segments on which small variants are detected. By default, variants in such regions will be excluded from the analysis as required information about the copy number is missing. These variants will be attached to the final output list in a separate tibble. To include them, this flag must be set TRUE and the ground ploidy must be given as an input. This ground ploidy will then be taken as tcn in the missing regions. If no ploidy is given the tool will assume the ground ploidy of 2 when this flag is TRUE.
byTcn	logical, default=TRUE; optional if includeHomoDel or includeIncompleteDelS is TRUE. If FALSE the tool will not use tcn as a criterion to assign large deletions. It will use the cna_type column and check for indicating strings like HOMDEL/HomoDel/DEL. Some commonly used strings are covered. It is recommended to leave this flag TRUE
ZP_env	internal variable... not recommened to be changed by user
verbose	logical, default=FALSE; prints functions that are called

Value

A list containing tibbles with all input variants

Examples

cnvs  = GenomicRanges::GRanges(
  dplyr::tibble(
    chr = "chr17",
    start = c(170060, 34520990),
    end = c(34520990, 83198614),
    tcn = c(2, 1),
    cna_type = c("neutral", "LOH")
  )
)
somatic_vars = GenomicRanges::GRanges(
  dplyr::tibble(
    chr="chr17",
    start = 7675088,
    end = 7675088,
    ref = "C",
    alt = "T",
    af = 0.65,
    gene = "TP53" 
  )
)
germline_vars = GenomicRanges::GRanges(
  dplyr::tibble(
    chr="chr17",
    start = 41771694,
    end = 41771694,
    ref = "GTGT",
    alt = "G",
    af = 0.95,
    gene = "JUP" 
  )
)
reference = GenomicRanges::GRanges(
  dplyr::tibble(
    chr = "chr17",
    start = c(7661778, 41754603),
    end = c(7687538, 41786931),
    gene = c("TP53", "JUP")
  )
)
sex = "female"
purity = 0.9
predict_per_variant(purity = purity, sex = sex, 
  somCna = cnvs,
  somSmallVars = somatic_vars,
  germSmallVars = germline_vars,
  geneModel = reference
)

---

predicts zygosity of a set of genes of a sample

Description

predicts zygosity of a set of genes of a sample

Usage

predict_zygosity(
  purity,
  sex,
  somCna,
  geneModel,
  bamDna,
  somSmallVars = NULL,
  germSmallVars = NULL,
  bamRna = NULL,
  ploidy = NULL,
  colnameTcn = NULL,
  colnameCnaType = NULL,
  includeHomoDel = TRUE,
  includeIncompleteDel = TRUE,
  showReadDetail = FALSE,
  printLog = FALSE,
  assumeSomCnaGaps = FALSE,
  byTcn = TRUE,
  vcf = NULL,
  haploBlocks = NULL,
  distCutOff = 5000,
  verbose = FALSE,
  debug = FALSE,
  logDir = NULL,
  snpQualityCutOff = 1,
  phasingMode = "fast",
  AllelicImbalancePhasing = FALSE
)

Arguments

purity	purity of the sample (numeric value between 0 and 1 indicating the fraction of relevant sample with control/unrelevant tissue)
sex	sex of the sample (character: "male", "female", "m", "f")
somCna	GRanges object containing all genomic regions with annotated total copynumber and cna_type as metadata columns. The total-copynumber column should be named "tcn" but also some other commonly used names. It should contain numeric values or characters that can be converted to numeric values. The cna_type column must contain the information about loss of heterozygosity (LOH). Therefore the term "LOH" must be explicitely mentioned in the column. If a genomic region is not present in the object, it will be taken as heterozygous with neutral TCN of 2.
geneModel	GRanges object containing the gene-annoattion of the used reference genome with metadata column of the gene name (gene)
bamDna	path to bam-file
somSmallVars	GRanges object containing all somatic small variants (SNV and INDEL). Required metadata columns are reference base (ref/REF), alternative base (alt/ALT), annotation of the gene name (gene/GENE) and the allele-frequency (af/AF). If the object is not provided the tool assumes there are no somatic small variants.
germSmallVars	GRanges object containing all germline small variants (SNV and INDEL). Required metadata columns are reference base (ref/REF), alternative base (alt/ALT), annotation of the gene name (gene/GENE) and the allele-frequency (af/AF) If the object is not provided the tool assumes there are no germline small variants.
bamRna	optional; path to rna file (bam format)
ploidy	ploidy of the sample (numeric value)
colnameTcn	character indicating the name of the metadata containing the tcn information in the somCna object. If not provided the tool tries to detect the column according to default names
colnameCnaType	character indicating the name of the metadata containing cna type information in the somCna object. If not provided the tool tries to detect the column according to default names
includeHomoDel	default = TRUE; if FALSE homozygous deleteions are excluded
includeIncompleteDel	default = TRUE; if FALSE heterzygous deleteions are excluded
showReadDetail	default = FALSE; if TRUE a table is added to the output, containing all used reads/rea-pairs with anntated read classification (mut1, mut2, both, none, skipped, dev_var)
printLog	default = FALSE; if TRUE the gene which is evaluated is printed in console, containing the query-name of each read which was used to perform haplotype-phasing and the info into which class it was assigned.
assumeSomCnaGaps	(logical, default=FALSE) Only required if the somCna object lacks copy number information for genomic segments on which small variants are detected. By default, variants in such regions will be excluded from the analysis as required information about the copy number is missing. These variants will be attached to the final output list in a separate tibble. To include them, this flag must be set TRUE and the ground ploidy must be given as an input. This ground ploidy will then be taken as tcn in the missing regions. If no ploidy is given the tool will assume the ground ploidy of 2 when this flag is TRUE.
byTcn	logical, default=TRUE; optional if includeHomoDel or includeIncompleteDelS is TRUE. If FALSE the tool will not use tcn as a criterion to assign large deletions. It will use the cna_type column and check for indicating strings like HOMDEL/HomoDel/DEL. Some commonly used strings are covered. It is recommended to leave this flag TRUE
vcf	character; path to variant call file (.vcf.gz format). Will be used (if provided) for extended SNP phasing if variants on the same gene are too far away from each other for direct haplotype phasing
haploBlocks	GRanges object containing haploblocks. Haploblocks are defined as genomic regions in which SNPs are phased to a specific allele. For example a haploblock could be chr1:1000-10000. This would mean that every genotype annotation in the format "1|0" or "0|1" of a SNP in this region will be used to phase somatic variants and define their genotype
distCutOff	numeric, default=5000; if input vcf is provided and SNP phasing is performed, this will limt the distance at which the SNP phasing should not be tried anymore. As the probability of finding overlapping reads at such a long distance is very low and the runtime will increase exponentially.
verbose	logical, default=FALSE; prints functions that are called
debug	logical, default=FALSE; prints output for debugging
logDir	character; path to directory where logfiles and detailed infos of the run can be stored, if not given, no details will be stored or printed
snpQualityCutOff	numeric, default=1; Cutoff to filter for SNPS that can be used for phasing
phasingMode	character, default="fast"; if set to full. Even if high confidence phasing result could be achieved, following phasing approaches will be carried out
AllelicImbalancePhasing	logical, default=FALSE. Enables alleleic imbalance phasing if TRUE

Value

A list of dataframes. Those are the evaluation per variant, the evaluation per gene and, if performed, the info about the haplotype-phasing.

Examples

cnvs  = GenomicRanges::GRanges(
  dplyr::tibble(
    chr = "chr17",
    start = c(170060, 34520990),
    end = c(34520990, 83198614),
    tcn = c(2, 1),
    cna_type = c("neutral", "LOH")
  )
)
somatic_vars = GenomicRanges::GRanges(
  dplyr::tibble(
    chr="chr17",
    start = 7675088,
    end = 7675088,
    ref = "C",
    alt = "T",
    af = 0.65,
    gene = "TP53" 
  )
)
germline_vars = GenomicRanges::GRanges(
  dplyr::tibble(
    chr="chr17",
    start = 41771694,
    end = 41771694,
    ref = "GTGT",
    alt = "G",
    af = 0.95,
    gene = "JUP" 
  )
)
reference = GenomicRanges::GRanges(
  dplyr::tibble(
    chr = "chr17",
    start = c(7661778, 41754603),
    end = c(7687538, 41786931),
    gene = c("TP53", "JUP")
  )
)
sex = "female"
purity = 0.9
bamfile <- system.file("extdata", "ZP_example.bam", 
  package = "ZygosityPredictor")
predict_zygosity(purity = purity, sex = sex, 
  somCna = cnvs,
  somSmallVars = somatic_vars,
  germSmallVars = germline_vars,
  geneModel = reference,
  bamDna = bamfile
)

---

accesor for ZygoistyPredictor runs. Prints an overview about the run

Description

accesor for ZygoistyPredictor runs. Prints an overview about the run

Usage

ZP_ov(fp)

Arguments

fp	full prediction (output of predict_zygoisty())

Value

prints overview about run from function predict_zygoisty()

Examples

cnvs  = GenomicRanges::GRanges(
  dplyr::tibble(
    chr = "chr17",
    start = c(170060, 34520990),
    end = c(34520990, 83198614),
    tcn = c(2, 1),
    cna_type = c("neutral", "LOH")
  )
)
somatic_vars = GenomicRanges::GRanges(
  dplyr::tibble(
    chr="chr17",
    start = 7675088,
    end = 7675088,
    ref = "C",
    alt = "T",
    af = 0.65,
    gene = "TP53" 
  )
)
germline_vars = GenomicRanges::GRanges(
  dplyr::tibble(
    chr="chr17",
    start = 41771694,
    end = 41771694,
    ref = "GTGT",
    alt = "G",
    af = 0.95,
    gene = "JUP" 
  )
)
reference = GenomicRanges::GRanges(
  dplyr::tibble(
    chr = "chr17",
    start = c(7661778, 41754603),
    end = c(7687538, 41786931),
    gene = c("TP53", "JUP")
  )
)
sex = "female"
purity = 0.9
bamfile <- system.file("extdata", "ZP_example.bam", 
  package = "ZygosityPredictor")
fp <- predict_zygosity(purity = purity, sex = sex, 
  somCna = cnvs,
  somSmallVars = somatic_vars,
  germSmallVars = germline_vars,
  geneModel = reference,
  bamDna = bamfile
)
ZP_ov(fp)

---