CRC

Description

Genomic sequencing of colorectal adenocarcinomas identifies a recurrent VTI1A-TCF7L2 fusion sample data set

Usage

data(CRC)

Details

The data used in this study is from this a paper http://www.nature.com/ng/journal/v43/n10/full/ng.936.html.

Examples

data(CRC)

GC content computation for BSgenome

Description

Compute GC content in a certain region of a BSgenome object

Usage

GCcontent(obj, ..., view.width, as.prob = TRUE)

Arguments

Details

GC content is an interesting variable may be related to various biological questions. So we need a way to compute GC content in a certain region of a reference genome. GCcontent function is a wrapper around getSeq function in BSgenome package and letterFrequency , letterFrequencyInSlidingView in Biostrings package.

if the view.width is specified, the GC content will be computed in the sliding view

Value

Numeric value indicate count or percentage

Author

Tengfei Yin

Examples

library(BSgenome.Hsapiens.UCSC.hg19)
GCcontent(Hsapiens, GRanges("chr1", IRanges(1e6, 1e6 + 1000)))

Adding disjoint levels to a GenomicRanges object

Description

Adding disjoint levels to a GenomicRanges object

Usage

list(list("addStepping"), list("GenomicRanges"))(obj, group.name, extend.size = 0,
                       fix = "center",
                       group.selfish = TRUE)

Arguments

Details

This is a tricky question, for example, pair-end RNA-seq data could be represented as two set of GenomicRanges object, one indicates the read, one indicates the junction. At the same time, we need to make sure pair-ended read are shown on the same level, and nothing falls in between. For better visualization of the data, we may hope to add invisible extended buffer to the reads, so closely neighbored reads will be on the different levels.

Value

A modified GenmicRanges object with stepping as one column.

Author

Tengfei Yin

Examples

library(GenomicRanges)
set.seed(1)
N <- 500
## sample GRanges
gr <- GRanges(seqnames =
sample(c("chr1", "chr2", "chr3", "chrX", "chrY"),
size = N, replace = TRUE),
IRanges(
start = sample(1:300, size = N, replace = TRUE),
width = sample(70:75, size = N,replace = TRUE)),
strand = sample(c("+", "-", "*"), size = N,
replace = TRUE),
value = rnorm(N, 10, 3), score = rnorm(N, 100, 30),
group = sample(c("Normal", "Tumor"),
size = N, replace = TRUE),
pair = sample(letters, size = N,
replace = TRUE))

## grouping and extending
head(addStepping(gr))
head(addStepping(gr, group.name = "pair"))
gr.close <- GRanges(c("chr1", "chr1"), IRanges(c(10, 20), width = 9))
addStepping(gr.close)
addStepping(gr.close, extend.size = 5)

Utils for parsing (un)evaluated arguments

Description

Utilities for parsing (un)evaluated arguments

Usage

parseArgsForAes(args)
parseArgsForNonAes(args)

Arguments

Value

For parseArgsForAes return a unevaluated arguments.

For parseArgsNonForAes return a evaluated/quoted arguments.

Author

Tengfei Yin

Examples

args <- alist(a = color, b = "b")
# parseArgsForAes(args)

biovizBase is a package which provides utilities and color scheme...

Description

biovizBase is a package which provides utilities and color scheme for higher level graphic package which aim to visualize biological data especially genetic data.

Details

This package provides default color scheme for nucleotide, strand, amino acid, try to pass colorblind checking as possible as we can. And also provide giemsa stain result color scheme used to show cytoband. This package also provides utilites to manipulate and summarize raw data to get them ready to be visualized.

Color blind safe palette generator

Description

This function help users create a function based on specified color blind safe palette. And the returned function could be used for color generation.

Usage

genDichromatPalInfo()
genBrewerBlindPalInfo()
genBlindPalInfo()
colorBlindSafePal(palette)
blind.pal.info
brewer.pal.blind.info
dichromat.pal.blind.info

Arguments

Details

We get those color-blind safe palette based on http://colorbrewer2.org/ and http://geography.uoregon.edu/datagraphics/color_scales.htm those color are implemented in two packages, RColorBrewer and dichromat. But RColorBrewer doesn't provide subset of color-blind safe palette info. And dichromat doesn't group color based on "quality", "sequential" and "divergent", so we pick those color manually and create a combined palette, blind.pal.info.

colorBlindSafePal will return a function, this function will take two arguments, n and repeatable . if n is smaller than 3(n >= 3 is required by RColorBrewer), we use 3 instead and return a color vector. If n is over the maxcolors column in blind.pal.info, we use maxcolors instead when repeatable set to FALSE , if repeatable set to TRUE we repeat the color of all the allowed colors(length equals maxcolors ) in the same order. This has special case in certain graphics which is always displayed side by side and don't worry about the repeated colors being neighbors.

genBrewerBlindPalInfo return brewer.pal.blind.info data frame containing all color-blind safe palettes from brewer.pal.info defined in RColorBrewer, but it's not only just subset of it, it also changes some maxcolors info.

genDichromatPalInfo return dichromat.pal.blind.info data frame.

genBlindPalInfo return blind.pal.info data frame.

Value

A color generating function with arguments n and repeatable . n specifying how many different discrete colors you want to get from them palette, and if repeatable turned on and set to TRUE, you can specify n even larger than maximum color. The color will be repeated following the same order.

Author

Tengfei Yin

Examples

library(scales)
## brewer subse of only color blind palette
brewer.pal.blind.info
genBrewerBlindPalInfo()
## dichromat info
dichromat.pal.blind.info
genDichromatPalInfo()
## all color blind palette, adding id/pkg.
blind.pal.info
## with no parameters, just return blind.pal.info
colorBlindSafePal()
mypal <- colorBlindSafePal(20)
## or pass character name
mypal <- colorBlindSafePal("Set2")
mypal12 <- colorBlindSafePal(22)
show_col(mypal(12, repeatable = FALSE)) # warning
show_col(mypal(11, repeatable = TRUE))  # no warning, and repeat
show_col(mypal12(12))

Checking if string contains letters or not

Description

Test if a string contain any letters

Usage

containLetters(obj, all=FALSE)

Arguments

Details

Useful when processing/sorting seqnames

Value

Logical value

Author

tengfei

Examples

containLetters("XYZ123")
containLetters("XYZ123", TRUE)

crc1.GeRL

Description

CRC-1 mutation and structural rearrangment

Usage

data(crc1.GeRL)

Details

GenomicRangesList contains somatic mutation, reaarangment etc for a tumor sample, please check the reference for detials.

References

Genomic sequencing of colorectal adenocarcinomas identifies a recurrent VTI1A-TCF7L2 fusion

Examples

data(crc1.GeRL)
crc1.GeRL

Fetching GRanges from various data source

Description

Fetching Granges from various data source, currently supported objects are TxDb, EnsDb, GAlignments and BamFile.

Usage

list(list("crunch"), list("TxDb"))(obj, which, columns = c("tx_id", "tx_name","gene_id"),
       type = c("all", "reduce"), truncate.gaps = FALSE,
       truncate.fun = NULL, ratio = 0.0025)
list(list("crunch"), list("EnsDb"))(obj, which, columns = c("tx_id", "gene_name","gene_id"),
       type = c("all", "reduce"), truncate.gaps = FALSE,
       truncate.fun = NULL, ratio = 0.0025)
list(list("crunch"), list("GAlignments"))(obj, which, truncate.gaps = FALSE,
       truncate.fun = NULL, ratio = 0.0025)
list(list("crunch"), list("BamFile"))(obj, which, ..., type = c("gapped.pair", "raw", "all"),
       truncate.gaps = FALSE, truncate.fun = NULL, ratio = 0.0025)

Arguments

Value

GRanges object.

Author

Tengfei Yin

Examples

library(biovizBase)
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
data(genesymbol, package = "biovizBase")
txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene
obj <- txdb
temp <- crunch(txdb, which = genesymbol["BRCA1"], type = "all")
temp <- crunch(txdb, which = genesymbol["BRCA1"], type = "reduce")

## Fetching data from a EnsDb object.
library(EnsDb.Hsapiens.v75)
edb <- EnsDb.Hsapiens.v75
gr <- genesymbol["BRCA1"]
seqlevels(gr) <- sub(seqlevels(gr), pattern="chr", replacement="")
temp <- crunch(edb, which = gr)

## Alternatively, use the GenenameFilter from the AnnotationFilter package:
temp <- crunch(edb, which = GenenameFilter("BRCA1"))

## Or a filter expression
temp <- crunch(edb, which = ~ genename == "BRCA1")

Subset of RNA editing sites in hg19...

Description

Subset of RNA editing sites in hg19

Usage

data(darned_hg19_subset500)

Details

This data set provides a subset(500 sites only) of hg19 RNA editing sites, and originally from DARNED http://darned.ucc.ie/ for the hg19 assembly.

Examples

data(darned_hg19_subset500)
darned_hg19_subset500

Estimation of Coverage

Description

Estimation of Coverage

Usage

list(list("estimateCoverage"), list("BamFile"))(x, maxBinSize = 2^14)

Arguments

Value

A GRanges object.

Author

Michael Lawrence

Transform GRangesList to GRanges

Description

Transform GRangesList to GRanges.

Usage

flatGrl(object, indName = "grl_name")

Arguments

Details

This method is different from default stack , it integrate elementMetadata of GRangesList to the final coerced GRanges .

Value

A GRanges object.

Author

Tengfei Yin

Examples

library(GenomicRanges)
gr1 <- GRanges("chr1", IRanges(1:10, width = 5))
gr2 <- GRanges("chr2", IRanges(1:10, width = 5))
obj <- GRangesList(gr1, gr2)
values(obj) <- data.frame(a = 1:2, b = letters[1:2])
stack(obj)
flatGrl(obj)

Gene symbols with position...

Description

Gene symbols with position

Usage

data(genesymbol)

Details

This data set provides genen symbols in human with position and starnd information, stored as GRanges object.

Examples

data(genesymbol)
head(genesymbol)
genesymbol["RBM17"]

Color scheme getter for biological data

Description

This function tries to get default color scheme either from fixed palette or options for specified data set, usually just biological data.

Usage

getBioColor(type = c("DNA_BASES_N", "DNA_BASES", "DNA_ALPHABET",
                     "RNA_BASES_N", "RNA_BASES", "RNA_ALPHABET",
                     "IUPAC_CODE_MAP", "AMINO_ACID_CODE", "AA_ALPHABET",
                     "STRAND", "CYTOBAND"),
            source = c("option", "default"))

Arguments

Details

Most data set specified in the type argument are defined in Biostrings package, such as "DNA_BASES", "DNA_ALPHABET", "RNA_BASES", "RNA_ALPHABET", "IUPAC_CODE_MAP", "AMINO_ACID_CODE", "AA_ALPHABET", please check the manual for more details.

"DNA_BASES_N" is just "DNA_BASES" with extra "N" used in certain cases, like the result from applyPileup in Rsamtools package. We start with the five most used nucleotides, A,T,C,G,N , In genetics, GC-content usually has special biological significance because GC pair is bound by three hydrogen bonds instead of two like AT pairs. So it has higher thermostability which could result in different significance, like higher annealing temperature in PCR. So we hope to choose warm color for G,C and cold color for A,T , and a color in between to represent N . They are chosen from diverging color set of color brewer . So we should be able to easily tell the GC enriched region. This set of color also passed vischeck for colorblind people.

In GRanges object, we have strand which contains three levels, +, -, *. We are using a qualitative color set from Color . This color set pass the colorblind test. It should be a safe color set to use to color strand.

For most default color scheme if they are under 18, we are trying to use package dichromat to set color for color blind people. But for data set that contains more than 18 objects, it's not possible to assign colorblind-safe color to them anymore. So we need to repeat some color. It should not matter too much, because even normal people cannot tell the difference anymore.

Here are the definition for the data sets.

list(" ", " ", list(list("DNA_BASES"), list(" ", " Contains ", list("A,C,T,G"), ". ", " ")), " ", " ", list(list("DNA_ALPHABET"), list(" ", " This alphabet contains all letters from the IUPAC Extended Genetic ", " Alphabet (see "?IUPAC_CODE_MAP") + the gap ("-") and the hard ", " masking ("+") letters.
", " ")), " ", " ", list(list("DNA_BASES_N"), list(" ", " Contains ", list("A,C,T,G,N"), " ", " ")), " ", " ", list(list("RNA_BASES_N"),

list("

", " Contains ", list("A,C,U,G,N"), " ", " ")), " ", " ", list(list("RNA_BASES"), list(" ", " Contains ", list("A,C,T,G"), " ", " ")), " ", " ", list(list("RNA_ALPHABET"), list(" ", " This alphabet contains all letters from the IUPAC Extended Genetic ", " Alphabet (see ?IUPAC_CODE_MAP) where "T" is replaced by "U" ", " + the gap ("-") and the hard masking ("+") letters. ", " ")), " ", " ", list(list("IUPAC_CODE_MAP"), list(" ",

"      The "IUPAC_CODE_MAP" named character vector contains the mapping

", " from the IUPAC nucleotide ambiguity codes to their meaning. ", " ")), " ", " ", list(list("AMINO_ACID_CODE"), list(" ", " Single-Letter Amino Acid Code (see "?AMINO_ACID_CODE"). ", " ")), " ", " ", list(list("AA_ALPHABET"), list(" ", " This alphabet contains all letters from the Single-Letter Amino ", " Acid Code (see "?AMINO_ACID_CODE") + the stop ("*"), the gap ", " ("-") and the hard masking ("+") letters
",

"    ")), "

", " ", list(list("STRAND"), list(" ", " Contains ", list(""+", "-", "*""), " ", " ")), " ", " ", list(list("CYTOBAND"), list(" ", " Contains giemsa stain results:", list("gneg, gpos25, gpos50, gpos75, ", " gpos100, gvar, stalk, acen"), ". Color defined in package geneplotter. ", " ")), " ", " ")

Value

A character of vector contains color(rgb), and the names of the vector is originally from the name of different data set. e.g. for DNA_BASES, it's just A,C,T,G . This allow users to get color for a vector of specified names. Please see the examples below.

Author

Tengfei Yin

Examples

opts <- getOption("biovizBase")
opts$DNABasesNColor[1] <- "red"
options(biovizBase = opts)
## get from option(default)
getBioColor("DNA_BASES_N")
## get default fixed color
getBioColor("DNA_BASES_N", source = "default")
seqs <- c("A", "C", "T", "G", "G", "G", "C")
## get colors for a sequence.
getBioColor("DNA_BASES_N")[seqs]

Get formals from functions

Description

Get formals from functions, used for dispatching arguments inside.

Usage

getFormalNames(..., remove.dots = TRUE)

Arguments

Value

A character vector for formal arguments.

Author

Tengfei Yin

Examples

getFormalNames(plot, sapply)

get gaps for a stepping transformed GRanges object

Description

Get gaps for a stepping transformed GRanges object, for visualization purpose. So a extra "stepping" column is required. Please see details below for motivation.

Usage

list(list("getGaps"), list("GRanges"))(obj, group.name = NULL, facets = NULL)

Arguments

Details

Since faceting is a subset and group process in visualization stage, some statistical computation need to be taken place after that. This leaves some computation like computing gaps hard based on solely GRanges object. Extra information like facets formula and group method would help to generate gaps which make sure they are aligned on the same level and within the same panel for grouped intervals. facets variables will be added to gaps GRanges along with group.name.

Value

A GRanges object representing gaps and for each row, the "stepping" column help later visualization and make sure gaps and intervals they are generated from are showed on the expected place.

Author

Tengfei Yin

Examples

set.seed(1)
N <- 100
library(GenomicRanges)
gr <- GRanges(seqnames =
sample(c("chr1", "chr2", "chr3"),
size = N, replace = TRUE),
IRanges(
start = sample(1:300, size = N, replace = TRUE),
width = sample(70:75, size = N,replace = TRUE)),
strand = sample(c("+", "-", "*"), size = N,
replace = TRUE),
value = rnorm(N, 10, 3), score = rnorm(N, 100, 30),
sample = sample(c("Normal", "Tumor"),
size = N, replace = TRUE),
pair = sample(letters, size = N,
replace = TRUE))

grl <- splitByFacets(gr, sample ~ seqnames)
gr <- unlist(endoapply(grl, addStepping))
gr.gaps <- getGaps(gr, group.name = "stepping", facets = sample ~ seqnames)

Get ideogram information

Description

This function tries to parse ideogram information from seqlengths of a GRanges object. This information is usually used to plot chromosome background for kaytogram or esitmate proper lengths of chromosomes from data space for showing overview.

Usage

getIdeoGR(gr)

Arguments

Value

A ideogram GRanges object, each row indicate one single chromosome, and start with 1 and end with real chromosome length or estimated lengths.

Author

Tengfei Yin

Examples

library(GenomicRanges)
data("hg19IdeogramCyto", package = "biovizBase")
hg19IdeogramCyto
## without seqlengths, simply reduce
getIdeoGR(hg19IdeogramCyto)
## with seqlengths
gr <- GRanges("chr1",  IRanges(1,3))
seqlevels(gr) <- c("chr1", "chr2", "chr3")
nms <- c(100, 200, 300)
names(nms) <- c("chr1", "chr2", "chr3")
seqlengths(gr) <- nms
gr
getIdeoGR(gr)

Get ideogram.

Description

Get ideogram w/o cytoband for certain genome

Usage

getIdeogram(genome, subchr, cytobands=TRUE)

Arguments

Details

This function require a network connection, it will parse the data on the fly, function is a wrapper of some functionality from rtracklayer package to get certain table like cytoBand, a full table schema could be found http://genome.ucsc.edu/cgi-bin/hgTables in UCSC genome browser.This is useful for visualization of the whole genome or single chromosome, you can see some examples in ggbio package.

Value

A GRanges object.

Author

Tengfei Yin

Examples

hg19IdeogramCyto <- getIdeogram("hg19", cytoband = TRUE)

Get scale information from a GRanges

Description

Trying to get scale information from a GRanges object, used for circular view for geom "scale".

Usage

getScale(gr, unit = NULL, n = 100, type = c("M", "B", "sci"))

Arguments

Value

A GRanges object, with extra column: "type" indicate it's longer major ticks or shorter minor ticks. "scale.y" indicates y height for major and minor ticks. Default ratio is 3:1.

Author

Tengfei Yin

Hg19 ideogram without cytoband information...

Description

Hg19 ideogram without cytoband information

Usage

data(hg19Ideogram)

Details

This data set provides hg19 genome information wihout cytoband information.

Examples

data(hg19Ideogram)
hg19Ideogram

Hg19 ideogram with cytoband information...

Description

Hg19 ideogram with cytoband information

Usage

data(hg19IdeogramCyto)

Details

This data set provides hg19 genome information with cytoband information.

Examples

data(hg19IdeogramCyto)
hg19IdeogramCyto

ideogram without cytoband information

Description

ideogram without cytoband information

Usage

data(ideo)

Details

This data set provides hg19, hg18, mm10, mm9 genome information wihout cytoband information as a lit.

Examples

data(ideo)
ideo

ideogram with cytoband information

Description

ideogram with cytoband information

Usage

data(ideoCyto)

Details

This data set provides hg19, hg18, mm10, mm9 genome information with cytoband information as a lit.

Examples

data(ideoCyto)
ideoCyto

Ideogram checking

Description

Check if an object is ideogram or not

Usage

isIdeogram(obj)

Arguments

Details

Simply test if it's the result coming from getIdeogram function or not, make sure it's a GRanges and with extra column

Value

A logical value to indicate it's a ideogram or not.

Author

Tengfei Yin

Examples

data(hg19IdeogramCyto)
data(hg19Ideogram)
isIdeogram(hg19IdeogramCyto)
isIdeogram(hg19Ideogram)

Utils for Splicing Summary

Description

Utilities used for summarizing isoforms

Usage

isJunctionRead(cigar)
isMatchedWithModel(model, gr)

Arguments

Details

isJunctionRead simply parsing the CIGAR string to see if there is "N" in between and return a logical vector of the same length as cigar parameters, indicate it's junction read or not.

isMatchedWithModel mapping gr to model , and counting overlapped cases for each row of model, If gr contains all the read, this will return a logical vector of the same length as gr , and indicate if each read is the support for this model. NOTICE: we only assume it's a full model, so each model here is simply one isoform. So we only treat the gaped reads which only overlapped with two consecutive exons in model as one support for it.

Value

Logical vectors.

Author

Tengfei Yin

Examples

library(GenomicAlignments)
bamfile <- system.file("extdata", "SRR027894subRBM17.bam",
package="biovizBase")
## get index of junction read
which(isJunctionRead(cigar(readGAlignments(bamfile))))
##
model <- GRanges("chr1", IRanges(c(10, 20, 30, 40), width  = 5))
gr <- GRanges("chr1", IRanges(c(10, 10, 12, 22, 33), c(31, 40, 22, 32,
44)))
isMatchedWithModel(model, gr)

Simple ideogram checking

Description

Check if an object is a simple ideogram or not

Usage

isSimpleIdeogram(obj)

Arguments

Details

test if it's GRanges or not, doesn't require cytoband information. But it double check to see if there is only one entry per chromosome

Value

A logical value to indicate it's a simple ideogram or not.

Author

tengfei

Examples

data(hg19IdeogramCyto)
data(hg19Ideogram)
isSimpleIdeogram(hg19IdeogramCyto)
isSimpleIdeogram(hg19Ideogram)

parse x and y label information from a specific object

Description

parse y label information, object specific.

Usage

list(list("getYLab"), list("TxDb"))(obj)
list(list("getXLab"), list("GRanges"))(obj)
list(list("getXLab"), list("GRangesList"))(obj)
list(list("getXLab"), list("GAlignments"))(obj)

Arguments

Value

a string.

Author

Tengfei Yin

Estimated max gaps

Description

Compute an estimated max gap information, which could be used as max.gap argument in shringkageFun .

Usage

list(list("maxGap"), list("GenomicRanges"))(obj, ratio = 0.0025)

Arguments

Details

This function tries to estimate an appropriate max gap to be used for creating a shrinkage function.

Value

A numeric value

Seealso

shrinkageFun

Author

Tengfei Yin

Examples

require(GenomicRanges)
gr1 <- GRanges("chr1", IRanges(start = c(100, 300, 600),
end = c(200, 400, 800)))
gr2 <- GRanges("chr1", IRanges(start = c(100, 350, 550),
end = c(220, 500, 900)))
gaps.gr <- intersect(gaps(gr1, start = min(start(gr1))),
gaps(gr2, start = min(start(gr2))))
shrink.fun <- shrinkageFun(gaps.gr, max.gap = maxGap(gaps.gr))
shrink.fun(gr1)
shrink.fun(gr2)

mold data into data.frame

Description

mold data into data.frame usued for visualization, so it's sometimes not as simple as coersion, for example, we add midpoint varialbe for mapping.

Usage

list(list("mold"), list("IRanges"))(data)
list(list("mold"), list("GRanges"))(data)
list(list("mold"), list("GRangesList"))(data,indName = "grl_name")
list(list("mold"), list("Seqinfo"))(data)
list(list("mold"), list("matrix"))(data)
list(list("mold"), list("eSet"))(data)
list(list("mold"), list("ExpressionSet"))(data)
list(list("mold"), list("RangedSummarizedExperiment"))(data, assay.id = 1)
list(list("mold"), list("Views"))(data)
list(list("mold"), list("Rle"))(data)
list(list("mold"), list("RleList"))(data)
list(list("mold"), list("VRanges"))(data)

Arguments

Value

a data.frame object.

Author

Tengfei Yin

Summarize reads for certain region

Description

This function summarize reads from bam files for nucleotides on single base unit in a given region, this allows the downstream mismatch summary analysis.

Usage

pileupAsGRanges(bams, regions, DNABases=c("A", "C", "G", "T", "N"), ...)

Arguments

Details

It's a wrapper around applyPileup function in Rsamtools package, more detailed control could be found under manual of ApplyPileupsParam function in Rsamtools. pileupAsGRanges function return a GRanges object which including summary of nucleotides, depth, bam file path. This object could be read directly into pileupGRangesAsVariantTable function for mismatch summary.

Value

A GRanges object, each row is one single base unit. and elementMetadata contains summary about this position about all nucleotides specified by DNABases. and depth for total reads, bam for file path.

Author

Michael Lawrence, Tengfei Yin

Examples

library(Rsamtools)
data(genesymbol)
library(BSgenome.Hsapiens.UCSC.hg19)
bamfile <- system.file("extdata", "SRR027894subRBM17.bam", package="biovizBase")
test <- pileupAsGRanges(bamfile, region = genesymbol["RBM17"])
test.match <- pileupGRangesAsVariantTable(test, Hsapiens)
head(test[,-7])
head(test.match[,-5])

Mismatch summary

Description

Compare to reference genome and compute mismatch summary for certain region of reads.

Usage

pileupGRangesAsVariantTable(gr, genome, DNABases=c("A", "C", "G", "T", "N"))

Arguments

Details

User need to make sure to pass the right reference genome to this function to get the right summary. This function drop the position has no reads and only keep the region with coverage in the summary. The result could be used to show stacked barchart for mismatch summary.

Value

A GRanges object. Containing the following elementMetadata

• list("ref") list("Nucleotide in reference genome.")

• list("read") list("Nucleotide contained in the reads at particular position, ", "if multiple nucleotide, either matched or unmatched are found, they will be ", "summarized in different rows.")

• list("count") list("Count for read column.")

• list("match") list("Logical value, whether matched to reference genome or not")

• list("bam") list("Character indicate bam file path.")

Author

Michael Lawrence, Tengfei Yin

Examples

library(Rsamtools)
data(genesymbol)
library(BSgenome.Hsapiens.UCSC.hg19)
bamfile <- system.file("extdata", "SRR027894subRBM17.bam", package="biovizBase")
test <- pileupAsGRanges(bamfile, region = genesymbol["RBM17"])
test.match <- pileupGRangesAsVariantTable(test, Hsapiens)
head(test[,-7])
head(test.match[,-5])

Show colors

Description

Plot color legend, simple way to check your default color scheme

Usage

plotColorLegend(colors, labels, title)

Arguments

Details

Show color sheme as a legend style, labels

Value

A graphic device showing color legend.

Author

Tengfei Yin

Examples

cols <- getOption("biovizBase")$baseColor
plotColorLegend(cols, title = "strand legend")

get x scale breaks and labels

Description

get x scale breaks and labels for GRanges with different coordintes(currently only "truncate_gaps" and "genome" supported).

Usage

list(list("getXScale"), list("GRanges"))(obj, type = c("default", "all", "left", "right"))

Arguments

Value

list of breaks and labels.

Author

Tengfei Yin

Examples

library(GenomicRanges)
gr1 <- GRanges("chr1", IRanges(start = c(100, 300, 600),
end = c(200, 400, 800)))
shrink.fun1 <- shrinkageFun(gaps(gr1), max.gap = maxGap(gaps(gr1), 0.15))
shrink.fun2 <- shrinkageFun(gaps(gr1), max.gap = 0)
s1 <- shrink.fun1(gr1)
getXScale(s1)
#  coord:genome
set.seed(1)
gr1 <- GRanges("chr1", IRanges(start = as.integer(runif(20, 1, 100)),
width = 5))
gr2 <- GRanges("chr2", IRanges(start = as.integer(runif(20, 1, 100)),
width = 5))
gr <- c(gr1, gr2)
gr.t <- transformToGenome(gr, space.skip = 1)
getXScale(gr.t)

Show colors

Description

Show colors with color string or names of color vectors.

Usage

showColor(colors, label = c("color", "name"))

Arguments

Value

A plot.

Author

Tengfei Yin

Examples

showColor(getBioColor("CYTOBAND"))

Shrinkage function

Description

Create a shrinkage function based on specified gaps and shrinkage rate.

Usage

## For IRanges
list(list("shrinkageFun"), list("IRanges"))(obj, max.gap = 1L)
## For GenomicRanges
list(list("shrinkageFun"), list("GenomicRanges"))(obj, max.gap = 1L)
is_coord_truncate_gaps(obj)

Arguments

Details

shrinkageFun function will read in a GenomicRanges object which represent the gaps, and return a function which works for another GenomicRanges object, to shrink that object based on previously specified gaps shrinking information. You could use this function to treat multiple tracks(e.g. GRanges) to make sure they shrunk based on the common gaps and the same ratio.

is_coord_truncate_gaps is used to check if a GRanges object is in "truncate_gaps" coordiantes or not.

Value

A shrinkage function which could shrink a GenomicRanges object, this function will add coord "truncate_gaps" and max.gap to metadata, ".ori" for oringal data as extra column

Author

Michael Lawrence, Tengfei Yin

Examples

library(GenomicRanges)
gr1 <- GRanges("chr1", IRanges(start = c(100, 300, 600),
end = c(200, 400, 800)))

shrink.fun1 <- shrinkageFun(gaps(gr1), max.gap = maxGap(gaps(gr1), 0.1))
shrink.fun2 <- shrinkageFun(gaps(gr1, start(gr1), end(gr1)), max.gap = maxGap(gaps(gr1), 0.1))
shrink.fun3 <- shrinkageFun(gaps(gr1), max.gap = 0)
s1 <- shrink.fun1(gr1)
s2 <- shrink.fun2(gr1)
s3 <- shrink.fun3(gr1)
metadata(s1)$coord
values(s1)$.ori
is_coord_truncate_gaps(s1)

split a GRanges by formula

Description

Split a GRanges by formula into GRangesList. Parse variables in formula and form a interaction factors then split the GRanges by the factos.

Usage

list(list("splitByFacets"), list("GRanges,formula"))(object, facets)
list(list("splitByFacets"), list("GRanges,GRanges"))(object, facets)
list(list("splitByFacets"), list("GRanges,NULL"))(object, facets)
list(list("splitByFacets"), list("GRanges,missing"))(object, facets)

Arguments

Details

if facets is formula, factors are crreated based on interaction of variables in formula, then split it with this factor. If facets is GRanges object, it first subset the original data by facets GRanges , then split by each region in the facets. If facets is NULL , split just by seqnames. This function is used to perform computation in conjunction with facets argments in higher level graphic function.

Value

A GRangesList .

Author

Tengfei Yin

Examples

library(GenomicRanges)
N <- 1000
gr <- GRanges(seqnames =
sample(c("chr1", "chr2", "chr3"),
size = N, replace = TRUE),
IRanges(
start = sample(1:300, size = N, replace = TRUE),
width = sample(70:75, size = N,replace = TRUE)),
strand = sample(c("+", "-", "*"), size = N,
replace = TRUE),
value = rnorm(N, 10, 3), score = rnorm(N, 100, 30),
sample = sample(c("Normal", "Tumor"),
size = N, replace = TRUE),
pair = sample(letters, size = N,
replace = TRUE))
facets <- sample ~ seqnames
splitByFacets(gr, facets)
splitByFacets(gr)
gr.sub <- GRanges("chr1", IRanges(c(1, 200, 250), width = c(50, 10,
30)))
splitByFacets(gr, gr.sub)

strip dots around a formula variables

Description

strip dots around variables in a formula.

Usage

strip_formula_dots(formula)

Arguments

Value

A formula wihout ".." around for all variables.

Author

Tengfei Yin

Examples

obj <- ..coverage.. ~ seqnames
strip_formula_dots(obj)

Subset list of arguments by functions

Description

find arguments matched by formals of passed functions,

Usage

subsetArgsByFormals(args, ..., remove.dots = TRUE)

Arguments

Value

argumnets that matched with passed function.

Author

Tengfei Yin

Examples

args <- list(x = 1:3, simplify = TRUE, b = "b")
subsetArgsByFormals(args, plot, sapply)

Transform GRanges to different coordinates and layout

Description

Used for coordiante genome transformation, other transformation in circular view.

Usage

list(list("transformToGenome"), list("GRanges"))(data, space.skip = 0.1, chr.weight
= NULL)
list(list("transformToGenome"), list("GRangesList"))(data, space.skip = 0.1,
chr.weight = NULL)
list(list("transformToArch"), list("GRanges"))(data, width = 1)
transformToCircle(data, x = NULL, y = NULL, ylim = NULL, 
                  radius = 10, trackWidth =10,
                  direction = c("clockwise", "anticlockwise"),
                   mul = 0.05)
transformToRectInCircle(data, y = NULL, space.skip = 0.1, trackWidth = 10, radius = 10,
                      direction = c("clockwise", "anticlockwise"),
                      n = 100, mul = 0.05, chr.weight = NULL)
transformToBarInCircle(data, y = NULL, space.skip = 0.1, trackWidth = 10, radius = 10,
                     direction = c("clockwise", "anticlockwise"),
                      n = 100, mul = 0.05, chr.weight = NULL)
transformToSegInCircle(data, y = NULL, space.skip = 0.1, trackWidth = 10, radius = 10,
                      direction = c("clockwise", "anticlockwise"), n =
                      100, chr.weight = NULL)
transformToLinkInCircle(data, linked.to, space.skip = 0.1, trackWidth = 10, radius = 10,
                      link.fun = function(x, y, n = 100) bezier(x, y, evaluation = n),
                      direction = c("clockwise", "anticlockwise"), chr.weight = NULL)
transformDfToGr(data, seqnames = NULL, start = NULL, end = NULL,
                            width = NULL, strand = NULL,
                            to.seqnames = NULL, to.start = NULL, to.end = NULL,
                            to.width = NULL, to.strand = NULL, linked.to
                            = to.gr)
list(list("transformToDf"), list("GRanges"))(data)
is_coord_genome(data)

Arguments

Value

A GRanges object, with calculated new variables, including ".circle.x" for transformed x position, ".circle.y" for transformed y position, ".circle.angle" for transformed angle.

Author

Tengfei Yin

Examples

library(biovizBase)
library(GenomicRanges)
set.seed(1)
gr1 <- GRanges("chr1", IRanges(start = as.integer(runif(20, 1, 2e9)),
width = 5))
gr2 <- GRanges("chr2", IRanges(start = as.integer(runif(20, 1, 2e9)),
width = 5))
gr <- c(gr1, gr2)
gr.t <- transformToGenome(gr, space.skip = 0.1)
is_coord_genome(gr.t)
transformToCircle(gr.t)
transformToRectInCircle(gr)
transformToSegInCircle(gr)
values(gr1)$to.gr <- gr2
transformToLinkInCircle(gr1, linked.to = "to.gr")

Transform GRanges with New Coordinates

Description

For graphics, like linked plot, e.g. generated by qplotRangesLinkedToData function in package ggbio. we need to generate a new set of coordinates which is used for even spaced statistics track.

Usage

transformGRangesForEvenSpace(gr)

Arguments

Details

Most used internally for special graphics, like qplotRangesLinkedToData function in package ggbio.

Value

A GRanges object as passed in, with new column x.new which indicate the static track coordinates, in this way, we could map the new coordinates with the old one.

Author

Tengfei Yin

Examples

library(GenomicRanges)
gr <- GRanges("chr1", IRanges(seq(1,100, length.out = 10), width = 5))
library(biovizBase)
transformGRangesForEvenSpace(gr)