Package 'tadar' reference manual

Title:	Transcriptome Analysis of Differential Allelic Representation
Description:	This package provides functions to standardise the analysis of Differential Allelic Representation (DAR). DAR compromises the integrity of Differential Expression analysis results as it can bias expression, influencing the classification of genes (or transcripts) as being differentially expressed. DAR analysis results in an easy-to-interpret value between 0 and 1 for each genetic feature of interest, where 0 represents identical allelic representation and 1 represents complete diversity. This metric can be used to identify features prone to false-positive calls in Differential Expression analysis, and can be leveraged with statistical methods to alleviate the impact of such artefacts on RNA-seq data.
Authors:	Lachlan Baer [aut, cre] , Stevie Pederson [aut]
Maintainer:	Lachlan Baer <[email protected]>
License:	GPL-3
Version:	1.3.2
Built:	2024-09-24 21:18:42 UTC
Source:	https://github.com/bioc/tadar

Assign DAR values to genomic features

Description

Assign DAR values to genomic features of interest by averaging the DAR values of ranges that overlap the feature range.

Usage

assignFeatureDar(
  dar,
  features,
  dar_val = c("origin", "region"),
  fill_missing = NA
)

## S4 method for signature 'GRangesList,GRanges'
assignFeatureDar(
  dar,
  features,
  dar_val = c("origin", "region"),
  fill_missing = NA
)
assignFeatureDar(
  dar,
  features,
  dar_val = c("origin", "region"),
  fill_missing = NA
)

## S4 method for signature 'GRangesList,GRanges'
assignFeatureDar(
  dar,
  features,
  dar_val = c("origin", "region"),
  fill_missing = NA
)

Arguments

`dar`	`GRangesList` with DAR values of the associated ranges contained in metadata columns. Ranges that represent DAR regions are recommended to assign the greatest number of features with DAR values. This results in an assigned estimate of DAR in the region surrounding the feature. Alternatively, the use of DAR origin ranges results in an assigned average of DAR solely within the feature. Ranges can be converted between origins and regions with flipRanges.
`features`	`GRanges` object specifying the features of interest.
`dar_val`	`character(1)` specifying the whether to use origin or region DAR values for the chosen ranges. Options are "origin" and "region". A warning will be produced if the chosen `dar_val` does not match the ranges detected in the object provided to the `dar` argument, as this is likely unintended by the user.
`fill_missing`	The DAR value to assign features with no overlaps. Defaults to `NA`.

Value

GRangesList with ranges representing features of interest that overlap at least one DAR range. Feature metadata columns are retained and an additional column is added for the assigned DAR value.

Examples

data("chr1_genes")
fl <- system.file("extdata", "chr1.vcf.bgz", package="tadar")
genotypes <- readGenotypes(fl)
groups <- list(
    group1 = paste0("sample", 1:6),
    group2 = paste0("sample", 7:13)
)
counts <- countAlleles(genotypes, groups)
counts_filt <- filterLoci(counts)
props <- countsToProps(counts_filt)
contrasts <- matrix(
    data = c(1, -1),
    dimnames = list(
        Levels = c("group1", "group2"),
        Contrasts = c("group1v2")
    )
)
dar <- dar(props, contrasts, region_loci = 5)
assignFeatureDar(dar, chr1_genes, dar_val = "origin")

dar_regions <- flipRanges(dar, extend_edges = TRUE)
assignFeatureDar(dar_regions, chr1_genes, dar_val = "region")

data("chr1_genes")
fl <- system.file("extdata", "chr1.vcf.bgz", package="tadar")
genotypes <- readGenotypes(fl)
groups <- list(
    group1 = paste0("sample", 1:6),
    group2 = paste0("sample", 7:13)
)
counts <- countAlleles(genotypes, groups)
counts_filt <- filterLoci(counts)
props <- countsToProps(counts_filt)
contrasts <- matrix(
    data = c(1, -1),
    dimnames = list(
        Levels = c("group1", "group2"),
        Contrasts = c("group1v2")
    )
)
dar <- dar(props, contrasts, region_loci = 5)
assignFeatureDar(dar, chr1_genes, dar_val = "origin")

dar_regions <- flipRanges(dar, extend_edges = TRUE)
assignFeatureDar(dar_regions, chr1_genes, dar_val = "region")

Genomic feature example data

Description

Gene features for example usage. Generation of this data is documented in system.file("data-raw/chr1_genes.R", package = "tadar").

Usage

data(chr1_genes)
data(chr1_genes)

Format

An object of class GRanges of length 1456.

Value

GRanges object with 1456 ranges and 2 metadata columns.

chr1_genes: Ranges represent gene features for chromosome 1 of zebrafish GRCz11 genome.

Source

https://www.ensembl.org

Differential expression example data

Description

p-values from differential expressiong testing for example usage.

Usage

data(chr1_tt)
data(chr1_tt)

Format

An object of class tbl_df (inherits from tbl, data.frame) with 716 rows and 5 columns.

Value

A 716 x 5 tibble object.

Count alleles within each experimental group

Description

Summarise the alleles from genotype calls at each single nucleotide locus within each sample group.

Usage

countAlleles(genotypes, groups)

## S4 method for signature 'GRanges,list'
countAlleles(genotypes, groups)
countAlleles(genotypes, groups)

## S4 method for signature 'GRanges,list'
countAlleles(genotypes, groups)

Arguments

`genotypes`	`GRanges` object with metadata columns containing genotype information for all samples.
`groups`	Named `list` specifying the sample grouping structure, where each element contains a character vector of sample names.

Value

GRangesList containing a summary of allele counts at each range. Each element of the list represents a distinct sample group.

Examples

fl <- system.file("extdata", "chr1.vcf.bgz", package="tadar")
genotypes <- readGenotypes(fl)
groups <- list(
    group1 = paste0("sample", 1:6),
    group2 = paste0("sample", 7:13)
)
countAlleles(genotypes, groups)

fl <- system.file("extdata", "chr1.vcf.bgz", package="tadar")
genotypes <- readGenotypes(fl)
groups <- list(
    group1 = paste0("sample", 1:6),
    group2 = paste0("sample", 7:13)
)
countAlleles(genotypes, groups)

Convert allele counts to proportions

Description

Normalise allele-level counts across samples by converting to a proportion of total alleles in all samples.

Usage

countsToProps(counts)

## S4 method for signature 'GRangesList'
countsToProps(counts)
countsToProps(counts)

## S4 method for signature 'GRangesList'
countsToProps(counts)

Arguments

counts

GRangesList containing a summary of allele counts at each range. Each element of the list represents a distinct sample group.

Value

GRangesList containing a summary of normalised allele counts (i.e. as proportions) at each range. Each element of the list represents a distinct sample group.

Examples

fl <- system.file("extdata", "chr1.vcf.bgz", package="tadar")
genotypes <- readGenotypes(fl)
groups <- list(
    group1 = paste0("sample", 1:6),
    group2 = paste0("sample", 7:13)
)
counts <- countAlleles(genotypes, groups)
counts_filt <- filterLoci(counts)
countsToProps(counts_filt)

fl <- system.file("extdata", "chr1.vcf.bgz", package="tadar")
genotypes <- readGenotypes(fl)
groups <- list(
    group1 = paste0("sample", 1:6),
    group2 = paste0("sample", 7:13)
)
counts <- countAlleles(genotypes, groups)
counts_filt <- filterLoci(counts)
countsToProps(counts_filt)

Calculate Differential Allelic Representation (DAR)

Description

Calculate DAR between two sample groups.

Usage

dar(props, contrasts, region_fixed = NULL, region_loci = NULL)

## S4 method for signature 'GRangesList,matrix'
dar(props, contrasts, region_fixed = NULL, region_loci = NULL)
dar(props, contrasts, region_fixed = NULL, region_loci = NULL)

## S4 method for signature 'GRangesList,matrix'
dar(props, contrasts, region_fixed = NULL, region_loci = NULL)

Arguments

`props`	`GRangesList` containing a summary of normalised allele counts (i.e. as proportions) at each range. Each element of the list represents a distinct sample group.
`contrasts`	Contrast `matrix` specifying which sample groups to to calculate DAR between. Each column must represent a single contrast, and rows represent the levels (i.e. sample groups) to be contrasted. The two levels involved with each contrast should be specified with `1` and `-1`.
`region_fixed`	`integer(1)` specifying the width (in base pairs) of a fixed sliding window used for averaging DAR values within a region, which is centralised around the origin. Must be an integer greater than 1. This argument takes precedence over `region_loci`.
`region_loci`	`integer(1)` specifying the number of loci to include in an elastic sliding window used for averaging DAR values within a region. Must be an odd integer in order to incorporate the origin locus and an equal number of loci either side. Only used when `region_fixed` is NULL.

Details

DAR is calculated as the Euclidean distance between the allelic proportions (i.e. proportion of As, Cs, Gs and Ts) of two sample groups at a single nucleotide locus, scaled such that all values range inclusively between 0 and 1. A DAR value of 0 represents identical allelic representation between the two sample groups, while a DAR value of 1 represents complete diversity.

Value

GRangesList containing DAR values at each overlapping range between the contrasted sample groups. Two types of DAR values are reported in the metadata columns of each GRanges object:

dar_origin: The raw DAR values calculated at single nucleotide positions (the origin) between sample groups. These values represent DAR estimates at a precise locus.
dar_region: The mean of raw DAR values in a specified region surrounding the origin. This is optionally returned using either of the region_fixed or region_loci arguments, which control the strategy and size for establishing regions (more information below). This option exists because eQTLs don't necessarily confer their effects on genes in close proximity. Therefore, DAR estimates that are representative of regions may be more suitable for downstream assignment DAR values to genomic features.

Each element of the list represents a single contrast defined in the input contrast matrix.

Examples

fl <- system.file("extdata", "chr1.vcf.bgz", package="tadar")
genotypes <- readGenotypes(fl)
groups <- list(
    group1 = paste0("sample", 1:6),
    group2 = paste0("sample", 7:13)
)
counts <- countAlleles(genotypes, groups)
counts_filt <- filterLoci(counts)
props <- countsToProps(counts_filt)
contrasts <- matrix(
    data = c(1, -1),
    dimnames = list(
        Levels = c("group1", "group2"),
        Contrasts = c("group1v2")
    )
)
dar(props, contrasts, region_loci = 5)

fl <- system.file("extdata", "chr1.vcf.bgz", package="tadar")
genotypes <- readGenotypes(fl)
groups <- list(
    group1 = paste0("sample", 1:6),
    group2 = paste0("sample", 7:13)
)
counts <- countAlleles(genotypes, groups)
counts_filt <- filterLoci(counts)
props <- countsToProps(counts_filt)
contrasts <- matrix(
    data = c(1, -1),
    dimnames = list(
        Levels = c("group1", "group2"),
        Contrasts = c("group1v2")
    )
)
dar(props, contrasts, region_loci = 5)

Filter loci

Description

Filter loci based on allele count criteria.

Usage

filterLoci(counts, filter = n_called > n_missing)

## S4 method for signature 'GRangesList'
filterLoci(counts, filter = n_called > n_missing)
filterLoci(counts, filter = n_called > n_missing)

## S4 method for signature 'GRangesList'
filterLoci(counts, filter = n_called > n_missing)

Arguments

counts

GRangesList containing a summary of allele counts at each range. Each element of the list represents a distinct sample group.

filter

A logical expression indicating which rows to keep. Possible values include:

n_called The number of total alleles called.
n_missing The number of total alleles not reported.
n_0, n_1, n_2, n_3 The number of ref, alt1, alt2 and alt3 alleles respectively.

All values represent the sum of counts across all samples within the group. Defaults to return loci where the number of samples containing allele information is greater than number samples with missing information.

Value

GRangesList containing a summary of allele counts at each range passing the filter criteria. Each element of the list represents a distinct sample group.

Examples

fl <- system.file("extdata", "chr1.vcf.bgz", package="tadar")
genotypes <- readGenotypes(fl)
groups <- list(
    group1 = paste0("sample", 1:6),
    group2 = paste0("sample", 7:13)
)
counts <- countAlleles(genotypes, groups)
filterLoci(counts)

fl <- system.file("extdata", "chr1.vcf.bgz", package="tadar")
genotypes <- readGenotypes(fl)
groups <- list(
    group1 = paste0("sample", 1:6),
    group2 = paste0("sample", 7:13)
)
counts <- countAlleles(genotypes, groups)
filterLoci(counts)

Convert DAR origin ranges to DAR region ranges, or vice versa

Description

Convert the ranges element associated with origin DAR values to ranges associated with the region DAR values. This function can also be used to revert back to the original object containing origin ranges if desired.

Usage

flipRanges(dar, extend_edges = FALSE)

## S4 method for signature 'GRangesList'
flipRanges(dar, extend_edges = FALSE)
flipRanges(dar, extend_edges = FALSE)

## S4 method for signature 'GRangesList'
flipRanges(dar, extend_edges = FALSE)

Arguments

dar

GRangesList with ranges representing single nucleotide (origin) positions.

extend_edges

logical(1) specifying if region DAR ranges at the edges of each chromosome should be extended to cover the entire chromosome when converting from origin ranges to region ranges. This argument is only considered if region_loci was used to construct regions in the dar() function. Useful for downstream assignment of DAR values to genomic features that exist at the 5' or 3' edges of the chromosome, which would have otherwise been missed.

Value

GRangesList with ranges that represent either DAR regions or DAR origins, depending on the ranges of the input object.

Examples

fl <- system.file("extdata", "chr1.vcf.bgz", package="tadar")
genotypes <- readGenotypes(fl)
groups <- list(
    group1 = paste0("sample", 1:6),
    group2 = paste0("sample", 7:13)
)
counts <- countAlleles(genotypes, groups)
counts_filt <- filterLoci(counts)
props <- countsToProps(counts_filt)
contrasts <- matrix(
    data = c(1, -1),
    dimnames = list(
        Levels = c("group1", "group2"),
        Contrasts = c("group1v2")
    )
)

## Establish regions using an elastic sliding window
dar <- dar(props, contrasts, region_loci = 5)
## Convert ranges to regions associated with dar_region values
dar_regions <- flipRanges(dar)
## Optionally extend the outer regions to completely cover chromosomes
dar_regions <- flipRanges(dar, extend_edges = TRUE)
## Convert back to origin ranges associated with dar_origin values
flipRanges(dar_regions)

## Establish regions using a fixed sliding window
dar <- dar(props, contrasts, region_fixed = 1001)
## Convert ranges to regions associated with dar_region values
dar_regions <- flipRanges(dar)
## Convert back to origin ranges associated with dar_origin values
flipRanges(dar_regions)

fl <- system.file("extdata", "chr1.vcf.bgz", package="tadar")
genotypes <- readGenotypes(fl)
groups <- list(
    group1 = paste0("sample", 1:6),
    group2 = paste0("sample", 7:13)
)
counts <- countAlleles(genotypes, groups)
counts_filt <- filterLoci(counts)
props <- countsToProps(counts_filt)
contrasts <- matrix(
    data = c(1, -1),
    dimnames = list(
        Levels = c("group1", "group2"),
        Contrasts = c("group1v2")
    )
)

## Establish regions using an elastic sliding window
dar <- dar(props, contrasts, region_loci = 5)
## Convert ranges to regions associated with dar_region values
dar_regions <- flipRanges(dar)
## Optionally extend the outer regions to completely cover chromosomes
dar_regions <- flipRanges(dar, extend_edges = TRUE)
## Convert back to origin ranges associated with dar_origin values
flipRanges(dar_regions)

## Establish regions using a fixed sliding window
dar <- dar(props, contrasts, region_fixed = 1001)
## Convert ranges to regions associated with dar_region values
dar_regions <- flipRanges(dar)
## Convert back to origin ranges associated with dar_origin values
flipRanges(dar_regions)

DAR-moderated p-values

Description

Moderate p-values from DE testing using assigned DAR values.

Usage

modP(pvals, dar, slope = -1.8)

## S4 method for signature 'numeric,numeric'
modP(pvals, dar, slope = -1.8)
modP(pvals, dar, slope = -1.8)

## S4 method for signature 'numeric,numeric'
modP(pvals, dar, slope = -1.8)

Arguments

`pvals`	`numeric` of feature-level p-values from differential expression testing.
`dar`	`numeric` of DAR values assigned to corresponding features tested for differential expression.
`slope`	`numeric(1)` specifying the slope of alpha fit.

Value

numeric of DAR-moderated p-values of same length as input p-values.

Examples

data("chr1_genes")
data("chr1_tt")
fl <- system.file("extdata", "chr1.vcf.bgz", package="tadar")
genotypes <- readGenotypes(fl)
groups <- list(
    group1 = paste0("sample", 1:6),
    group2 = paste0("sample", 7:13)
)
counts <- countAlleles(genotypes, groups)
counts_filt <- filterLoci(counts)
props <- countsToProps(counts_filt)
contrasts <- matrix(
    data = c(1, -1),
    dimnames = list(
        Levels = c("group1", "group2"),
        Contrasts = c("group1v2")
    )
)
dar <- dar(props, contrasts, region_loci = 5)
dar_regions <- flipRanges(dar, extend_edges = TRUE)
gene_dar <- assignFeatureDar(dar_regions, chr1_genes, dar_val = "region")
chr1_tt <- merge(chr1_tt, mcols(gene_dar$group1v2), sort = FALSE)
chr1_tt$darP <- modP(chr1_tt$PValue, chr1_tt$dar)

data("chr1_genes")
data("chr1_tt")
fl <- system.file("extdata", "chr1.vcf.bgz", package="tadar")
genotypes <- readGenotypes(fl)
groups <- list(
    group1 = paste0("sample", 1:6),
    group2 = paste0("sample", 7:13)
)
counts <- countAlleles(genotypes, groups)
counts_filt <- filterLoci(counts)
props <- countsToProps(counts_filt)
contrasts <- matrix(
    data = c(1, -1),
    dimnames = list(
        Levels = c("group1", "group2"),
        Contrasts = c("group1v2")
    )
)
dar <- dar(props, contrasts, region_loci = 5)
dar_regions <- flipRanges(dar, extend_edges = TRUE)
gene_dar <- assignFeatureDar(dar_regions, chr1_genes, dar_val = "region")
chr1_tt <- merge(chr1_tt, mcols(gene_dar$group1v2), sort = FALSE)
chr1_tt$darP <- modP(chr1_tt$PValue, chr1_tt$dar)

Plot DAR across a chromosome

Description

Use Gviz to plot the trend in DAR across a chromosomal region with the option to add features of interest as separate tracks.

Usage

plotChrDar(
  dar,
  dar_val = c("origin", "region"),
  chr,
  foi,
  foi_anno,
  foi_highlight = TRUE,
  features,
  features_anno,
  features_highlight = TRUE,
  title = ""
)

## S4 method for signature 'GRanges'
plotChrDar(
  dar,
  dar_val = c("origin", "region"),
  chr,
  foi,
  foi_anno,
  foi_highlight = TRUE,
  features,
  features_anno,
  features_highlight = TRUE,
  title = ""
)
plotChrDar(
  dar,
  dar_val = c("origin", "region"),
  chr,
  foi,
  foi_anno,
  foi_highlight = TRUE,
  features,
  features_anno,
  features_highlight = TRUE,
  title = ""
)

## S4 method for signature 'GRanges'
plotChrDar(
  dar,
  dar_val = c("origin", "region"),
  chr,
  foi,
  foi_anno,
  foi_highlight = TRUE,
  features,
  features_anno,
  features_highlight = TRUE,
  title = ""
)

Arguments

`dar`	`GRanges` with DAR values of associated ranges contained in metadata columns. Used to build the DataTrack showing the trend in DAR across the chromosome. If ranges of the input object span regions (i.e. post application of `flipRanges()`), data points are plotted at the midpoint of the region.
`dar_val`	`character(1)` specifying the whether to use origin or region DAR values for the chosen ranges. Options are "origin" and "region". The default ("region") represents averaged DAR values across a region and produces a smoother graph.
`chr`	Optional. `character(1)` specifying the chromosome to subset all `GRanges` objects. Plotting is only possible across a single chromosome and is therefore required if supplying `GRanges` objects spanning multiple chromosomes. Also controls the track title for the GenomeAxisTrack.
`foi`	Optional. `GRanges` object of features of interest (foi) to be highlighted and labelled along the GenomeAxisTrack.
`foi_anno`	Optional. `character(1)` specifying the name of the `mcol` containing labels associated with feature ranges in `foi`.
`foi_highlight`	`logical(1)` specifying if the positions of `foi` should be overlayed on the DataTrack showing the trend in DAR. Useful for visually inspecting DAR at the location of chosen features. Default is `TRUE`.
`features`	Optional. `GRanges` object of features to be plotted on a separate AnnotationTrack.
`features_anno`	Optional. `character(1)` specifying the name of the `mcol` containing labels associated with feature ranges in `features`.
`features_highlight`	`logical(1)` specifying if the positions of `features` should be overlayed on the DataTrack showing the trend in DAR. Useful for visually inspecting DAR at the location of chosen features. Default is `TRUE`.
`title`	`character(1)`. The main plot title.

Value

A Gviz object

Examples

set.seed(230822)
data("chr1_genes")
foi <- sample(chr1_genes, 1)
features <- sample(chr1_genes, 20)
fl <- system.file("extdata", "chr1.vcf.bgz", package="tadar")
genotypes <- readGenotypes(fl)
groups <- list(
    group1 = paste0("sample", 1:6),
    group2 = paste0("sample", 7:13)
)
counts <- countAlleles(genotypes, groups)
counts_filt <- filterLoci(counts)
props <- countsToProps(counts_filt)
contrasts <- matrix(
    data = c(1, -1),
    dimnames = list(
        Levels = c("group1", "group2"),
        Contrasts = c("group1v2")
    )
)
dar <- dar(props, contrasts, region_loci = 5)
plotChrDar(
    dar = dar$group1v2, dar_val = "region", chr = "1",
    foi = foi, foi_anno = "gene_name", foi_highlight = TRUE,
    features = features, features_anno = "gene_name",
    features_highlight = TRUE,
    title = "Example plot of DAR along Chromosome 1"
)

set.seed(230822)
data("chr1_genes")
foi <- sample(chr1_genes, 1)
features <- sample(chr1_genes, 20)
fl <- system.file("extdata", "chr1.vcf.bgz", package="tadar")
genotypes <- readGenotypes(fl)
groups <- list(
    group1 = paste0("sample", 1:6),
    group2 = paste0("sample", 7:13)
)
counts <- countAlleles(genotypes, groups)
counts_filt <- filterLoci(counts)
props <- countsToProps(counts_filt)
contrasts <- matrix(
    data = c(1, -1),
    dimnames = list(
        Levels = c("group1", "group2"),
        Contrasts = c("group1v2")
    )
)
dar <- dar(props, contrasts, region_loci = 5)
plotChrDar(
    dar = dar$group1v2, dar_val = "region", chr = "1",
    foi = foi, foi_anno = "gene_name", foi_highlight = TRUE,
    features = features, features_anno = "gene_name",
    features_highlight = TRUE,
    title = "Example plot of DAR along Chromosome 1"
)

Plot the Empirical Cumulative Distribution Function of DAR

Description

Plot the ECDF of DAR for each chromosome.

Usage

plotDarECDF(dar, dar_val = c("origin", "region"), highlight = NULL)

## S4 method for signature 'GRanges'
plotDarECDF(dar, dar_val = c("origin", "region"), highlight = NULL)
plotDarECDF(dar, dar_val = c("origin", "region"), highlight = NULL)

## S4 method for signature 'GRanges'
plotDarECDF(dar, dar_val = c("origin", "region"), highlight = NULL)

Arguments

`dar`	`GRanges` object with metadata columns containing the desired DAR values to plot.
`dar_val`	`character(1)` specifying the whether to plot dar_origin or dar_region values. Options are "origin" and "region".
`highlight`	`character(1)` specifying the chromosome to highlight with a different colour.

Value

A ggplot2 object.

Examples

set.seed(230704)
## Use simulated data that illustrates a commonly encountered scenario
simulate_dar <- function(n, mean) {
    vapply(
        rnorm(n = n, mean = mean),
        function(x) exp(x) / (1 + exp(x)),
        numeric(1)
    )
}
gr <- GRanges(
    paste0(rep(seq(1,25), each = 100), ":", seq(1,100)),
    dar_origin = c(simulate_dar(2400, -2), simulate_dar(100, 0.5))
)
## No highlighting, all chromosomes will be given individual colours
plotDarECDF(gr, dar_val = "origin") +
    theme_bw()

## With highlighting
plotDarECDF(gr, dar_val = "origin", highlight = "25") +
    scale_colour_manual(values = c("TRUE" = "red", "FALSE" = "grey")) +
    theme_bw()

set.seed(230704)
## Use simulated data that illustrates a commonly encountered scenario
simulate_dar <- function(n, mean) {
    vapply(
        rnorm(n = n, mean = mean),
        function(x) exp(x) / (1 + exp(x)),
        numeric(1)
    )
}
gr <- GRanges(
    paste0(rep(seq(1,25), each = 100), ":", seq(1,100)),
    dar_origin = c(simulate_dar(2400, -2), simulate_dar(100, 0.5))
)
## No highlighting, all chromosomes will be given individual colours
plotDarECDF(gr, dar_val = "origin") +
    theme_bw()

## With highlighting
plotDarECDF(gr, dar_val = "origin", highlight = "25") +
    scale_colour_manual(values = c("TRUE" = "red", "FALSE" = "grey")) +
    theme_bw()

Read genotypes from a VCF file

Description

Extract genotypes from a VCF file into a GRanges object for downstream DAR analysis.

Usage

readGenotypes(file, unphase = TRUE, ...)

## S4 method for signature 'character'
readGenotypes(file, unphase = TRUE, ...)

## S4 method for signature 'TabixFile'
readGenotypes(file, unphase = TRUE, ...)
readGenotypes(file, unphase = TRUE, ...)

## S4 method for signature 'character'
readGenotypes(file, unphase = TRUE, ...)

## S4 method for signature 'TabixFile'
readGenotypes(file, unphase = TRUE, ...)

Arguments

`file`	The file path of a VCF file containing genotype data. Alternatively, a `TabixFile` as described in readVcf.
`unphase`	A `logical` specifying if phasing information should be removed from genotypes. This is required if proceeding with DAR analysis.
`...`	Passed to readVcf.

Details

Extract genotypes from a VCF file with the option to remove phasing information for DAR analysis.

Value

A GRanges object constructed from the CHROM, POS, ID and REF fields of the supplied VCF file. Genotype data for each sample present in the VCF file is added to the metadata columns.

Examples

fl <- system.file("extdata", "chr1.vcf.bgz", package="tadar")
readGenotypes(fl)

fl <- system.file("extdata", "chr1.vcf.bgz", package="tadar")
readGenotypes(fl)

Unphase genotypes

Description

Remove phasing information from genotype calls.

Usage

unphaseGT(gt)

## S4 method for signature 'matrix'
unphaseGT(gt)

## S4 method for signature 'data.frame'
unphaseGT(gt)
unphaseGT(gt)

## S4 method for signature 'matrix'
unphaseGT(gt)

## S4 method for signature 'data.frame'
unphaseGT(gt)

Arguments

`gt`	`matrix` or `data.frame` containing sample genotype information.

Details

Phasing information is not required for a simple DAR analysis. Removing this enables easy counting of alleles from genotype calls.

Value

matrix containing unphased genotype calls.

Examples

library(VariantAnnotation)
fl <- system.file("extdata", "chr1.vcf.bgz", package="tadar")
vcf <- readVcf(fl)
gt <- geno(vcf)$GT
unphaseGT(gt)

library(VariantAnnotation)
fl <- system.file("extdata", "chr1.vcf.bgz", package="tadar")
vcf <- readVcf(fl)
gt <- geno(vcf)$GT
unphaseGT(gt)

Package 'tadar'

Help Index

Assign DAR values to genomic features

Description

Usage

Arguments

Value

Examples

Genomic feature example data

Description

Usage

Format

Value

Source

Differential expression example data

Description

Usage

Format

Value

Count alleles within each experimental group

Description

Usage

Arguments

Value

Examples

Convert allele counts to proportions

Description

Usage

Arguments

Value

Examples

Calculate Differential Allelic Representation (DAR)

Description

Usage

Arguments

Details

Value

Examples

Filter loci

Description

Usage

Arguments

Value

Examples

Convert DAR origin ranges to DAR region ranges, or vice versa

Description

Usage

Arguments

Value

Examples

DAR-moderated p-values

Description

Usage

Arguments

Value

Examples

Plot DAR across a chromosome

Description

Usage

Arguments

Value

Examples

Plot the Empirical Cumulative Distribution Function of DAR

Description

Usage

Arguments

Value

Examples

Read genotypes from a VCF file

Description

Usage

Arguments

Details

Value

Examples

Unphase genotypes

Description

Usage

Arguments

Details