| Title: | Calculate strandness information of a bam file |
|---|---|
| Description: | This package aims to quantify and remove putative double strand DNA from a strand-specific RNA sample. There are also options and methods to plot the positive/negative proportions of all sliding windows, which allow users to have an idea of how much the sample was contaminated and the appropriate threshold to be used for filtering. |
| Authors: | Thu-Hien To [aut, cre], Steve Pederson [aut] |
| Maintainer: | Thu-Hien To <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.25.0 |
| Built: | 2024-10-31 05:35:54 UTC |
| Source: | https://github.com/bioc/strandCheckR |
This package aims to quantify and remove putative double strand DNA from a strand-specific RNA sample. There are also options and methods to plot the positive/negative proportions of all sliding windows, which allow users to have an idea of how much the sample was contaminated and the appropriate threshold to be used for filtering.
The package has some following main functions:
- getStrandFromBamFile: calculate positive/negative proprortion and
sum of reads over all sliding windows from a bam file
- plotHist: plot histogram of positive proportion of windows
calculated from getStrandFromBamFile method
- plotWin: plot positive proportion vs number of reads of
windows calculated from getStrandFromBamFile method
- filterDNA: filter a bam file
Thu-Hien To & Steve Pederson
Maintainer: Thu-Hien To <[email protected]>
bamfilein <- system.file("extdata","s1.sorted.bam",package = "strandCheckR") windows <- getStrandFromBamFile(bamfilein) plotWin(windows) plotHist(windows) filterDNA(file = bamfilein,destination = "filter.bam")bamfilein <- system.file("extdata","s1.sorted.bam",package = "strandCheckR") windows <- getStrandFromBamFile(bamfilein) plotWin(windows) plotHist(windows) filterDNA(file = bamfilein,destination = "filter.bam")
Check the first 100000 first reads of the bam file to see whether it is single-end or paired-end
checkPairedEnd(file, yieldSize = 1e+05)checkPairedEnd(file, yieldSize = 1e+05)
file |
the input bam file. Your bamfile should be sorted and have an index file located at the same path as well. |
yieldSize |
the number of reads to be checked, 100000 by default. |
return TRUE if the input file is paired end, and FALSE if it is single end
file <- system.file('extdata','s1.sorted.bam',package = 'strandCheckR') checkPairedEnd(file)file <- system.file('extdata','s1.sorted.bam',package = 'strandCheckR') checkPairedEnd(file)
Filter putative double strand DNA from a strand specific RNA-seq using a window sliding across the genome.
filterDNA(file, destination, statFile, sequences, mapqFilter = 0, paired, yieldSize = 1e+06, winWidth = 1000L, winStep = 100L, readProp = 0.5, threshold = 0.7, pvalueThreshold = 0.05, useCoverage = FALSE, mustKeepRanges, getWin = FALSE, minCov = 0, maxCov = 0, errorRate = 0.01)filterDNA(file, destination, statFile, sequences, mapqFilter = 0, paired, yieldSize = 1e+06, winWidth = 1000L, winStep = 100L, readProp = 0.5, threshold = 0.7, pvalueThreshold = 0.05, useCoverage = FALSE, mustKeepRanges, getWin = FALSE, minCov = 0, maxCov = 0, errorRate = 0.01)
file |
the input bam file to be filterd. Your bamfile should be sorted and have an index file located at the same path. |
destination |
the file path where the filtered output will be written |
statFile |
the file to write the summary of the results |
sequences |
the list of sequences to be filtered |
mapqFilter |
every read that has mapping quality below |
paired |
if TRUE then the input bamfile will be considered as paired-end reads. If missing, 100 thousands first reads will be inspected to test if the input bam file in paired-end or single-end. |
yieldSize |
by default is 1e6, i.e. the bam file is read by block of reads whose size is defined by this parameter. It is used to pass to same parameter of the scanBam function. |
winWidth |
the length of the sliding window, 1000 by default. |
winStep |
the step length to sliding the window, 100 by default. |
readProp |
a read is considered to be included in a window if at least
|
threshold |
the strand proportion threshold to test whether to keep a window or not. 0.7 by default |
pvalueThreshold |
the threshold for the p-value in the test of keeping windows. 0.05 by default |
useCoverage |
if TRUE, then the strand information in each window corresponds to the sum of coverage coming from positive/negative reads; and not the number of positive/negative reads as default. |
mustKeepRanges |
a GRanges object; all reads that map to those ranges will be kept regardless the strand proportion of the windows containing them. |
getWin |
if TRUE, the function will not only filter the bam file but also return a data frame containing the information of all windows of the original and filtered bam file. |
minCov |
if |
maxCov |
if |
errorRate |
the probability that an RNA read takes the false strand. 0.01 by default. |
filterDNA reads a bam file containing strand specific RNA reads, and
filter reads coming from putative double strand DNA.
Using a window sliding across the genome, we calculate the positive/negative
proportion of reads in each window.
We then use logistic regression to estimate the strand proportion of reads in
each window, and calculate the p-value when comparing that to a given
threshold.
Let be the strand proportion of reads in a window.
Null hypothesis for positive window: .
Null hypothesis for negative window: .
Only windows with p-value <= pvalueThreshold are kept. For a kept
positive window, each positive read in this window is kept with the
probability (P-M)/P where P be the number of positive reads, and M be the
number of negative reads. That is because those M negative reads are
supposed to come from double-strand DNA, then there should be also M
postive reads among the P positive reads come from double-strand DNA. In
other words, there are only (P-M) positive reads come from RNA. Each
negative read is kept with the probability equalling the rate that an RNA
read of your sample has wrong strand, which is errorRate.
Similar for kept negative windows.
Since each alignment can be belonged to several windows, then the probability of keeping an alignment is the maximum probability defined by all windows that contain it.
if getWin is TRUE: a DataFrame object which could also be
obtained by the function getStrandFromBamFile
getStrandFromBamFile, plotHist,
plotWin
file <- system.file('extdata','s2.sorted.bam',package = 'strandCheckR') filterDNA(file,sequences='10',destination='out.bam')file <- system.file('extdata','s2.sorted.bam',package = 'strandCheckR') filterDNA(file,sequences='10',destination='out.bam')
Get the number of positive/negative reads/coverage of all slding windows from the bam input files
getStrandFromBamFile(files, sequences, mapqFilter = 0, yieldSize = 1e+06, winWidth = 1000L, winStep = 100L, readProp = 0.5, paired)getStrandFromBamFile(files, sequences, mapqFilter = 0, yieldSize = 1e+06, winWidth = 1000L, winStep = 100L, readProp = 0.5, paired)
files |
the input bam files. Your bamfiles should be sorted and have their index files located at the same path. |
sequences |
character vector used to restrict analysed alignments to a subset of chromosomes (i.e. sequences) within the provided bam file. These correspond to chromosomes/scaffolds of the reference genome to which the reads were mapped. If absent, the whole bam file will be read. NB: This must match the chromosomes as defined in your reference genome. If the reference chromosomes were specified using the 'chr' prefix, ensure the supplied vector matches this specification. |
mapqFilter |
every read that has mapping quality below
|
yieldSize |
by default is 1e6, i.e. the bam file is read by block of reads whose size is defined by this parameter. It is used to pass to same parameter of the scanBam function. |
winWidth |
the width of the sliding window, 1000 by default. |
winStep |
the step length to sliding the window, 100 by default. |
readProp |
A read is considered to be included in a window if at least
|
paired |
if TRUE then the input bamfile will be considered as paired-end reads. If missing, 100 thousands first reads will be inspected to test if the input bam file in paired-end or single-end. |
This function moves along the specified chromosomes (i.e. sequences) using a sliding window approach, and counts the number of reads in each window which align to the +/- strands of the reference genome. As well as the number of reads, the total coverage for each strand is also returned for each window, representing the total number of bases covered.
Average coverage for the entire window can be simply calculated by dividing the total coverage by the window size.
a DataFrame object containing the number of positive/negative reads and coverage of each window sliding across the bam file. The returned DataFrame has 10 columns:
Type: can be either SE if the input file contains single-end reads, or R1/R2 if the input file contains paired-end reads.
Seq: the reference sequence (chromosome/scaffold) that the reads were mapped to.
Start: the start position of the sliding window.
End: the end position of the sliding window.
NbPos/NbNeg: number of positive/negative reads that overlap the sliding window.
CovPos/CovNeg: number of bases coming from positive/negative reads that were mapped in the sliding window.
MaxCoverage: the maximum coverage within the sliding window.
File: the name of the input file.
file <- system.file('extdata','s1.sorted.bam',package = 'strandCheckR') win <- getStrandFromBamFile(file,sequences='10') winfile <- system.file('extdata','s1.sorted.bam',package = 'strandCheckR') win <- getStrandFromBamFile(file,sequences='10') win
Get the number of positive/negative reads of all windows from
read information obtained from scanBam function
getStrandFromReadInfo(readInfo, winWidth = 1000L, winStep = 100L, readProp = 0.5, subset = NULL)getStrandFromReadInfo(readInfo, winWidth = 1000L, winStep = 100L, readProp = 0.5, subset = NULL)
readInfo |
a list contains read information returned by
|
winWidth |
the length of the sliding window, 1000 by default. |
winStep |
the step length to sliding the window, 100 by default. |
readProp |
A read is considered to be included in a window if at least
|
subset |
an integer vector specifying the subset of reads to consider |
a DataFrame object containing the number of positive/negative reads and coverage of each window sliding .
filterDNA, getStrandFromBamFile
library(Rsamtools) file <- system.file('extdata','s2.sorted.bam',package = 'strandCheckR') readInfo <- scanBam(file, param = ScanBamParam(what = c("pos","cigar","strand"))) getStrandFromReadInfo(readInfo[[1]],1000,100,0.5)library(Rsamtools) file <- system.file('extdata','s2.sorted.bam',package = 'strandCheckR') readInfo <- scanBam(file, param = ScanBamParam(what = c("pos","cigar","strand"))) getStrandFromReadInfo(readInfo[[1]],1000,100,0.5)
Get the ranges of sliding windows that overlap each range of an IRanges object.
getWinOverlapEachIRange(x, winWidth = 1000L, winStep = 100L, readProp = 0.5, maxWin = Inf)getWinOverlapEachIRange(x, winWidth = 1000L, winStep = 100L, readProp = 0.5, maxWin = Inf)
x |
an IRanges object containing the start and end position of each read fragment. |
winWidth |
the width of the sliding window, 1000 by default. |
winStep |
the step length to sliding the window, 100 by default. |
readProp |
A read is considered to be included in a window if at least
|
maxWin |
The maximum window ID |
This finds the windows that overlap each range of the input IRanges object. Each range corresponds to a read fragment. This allows the total number of read fragments within a window to be calculated simply using coverage.
An IRanges object containing the index of the windows overlapping each read fragment
library(IRanges) x <- IRanges(start=round(runif(100,1000,10000)),width=100) getWinOverlapEachIRange(x)library(IRanges) x <- IRanges(start=round(runif(100,1000,10000)),width=100) getWinOverlapEachIRange(x)
Calculate the window ranges that overlap each read fragment
getWinOverlapEachReadFragment(readInfo, strand, winWidth, winStep, readProp, useCoverage = FALSE, subset = NULL)getWinOverlapEachReadFragment(readInfo, strand, winWidth, winStep, readProp, useCoverage = FALSE, subset = NULL)
readInfo |
a list contains the read information |
strand |
the considering strand |
winWidth |
the width of the sliding window, 1000 by default. |
winStep |
the step length to sliding the window, 100 by default. |
readProp |
a read fragment is considered to be included in a window if
and only if at least |
useCoverage |
either base on coverage or number of reads |
subset |
if we consider only a subset of the input reads |
If useCoverage=FALSE: an IRanges object which contains the
range of sliding windows that overlap each read fragment.
If useCoverage=TRUE: a list of two objects, the first one is the
later IRanges object, the second one is an integer-Rle object which contains
the coverage of the input readInfo
library(Rsamtools) file <- system.file('extdata','s2.sorted.bam',package = 'strandCheckR') readInfo <- scanBam(file, param = ScanBamParam(what = c("pos","cigar","strand"))) getWinOverlapEachReadFragment(readInfo[[1]],"+",1000,100,0.5)library(Rsamtools) file <- system.file('extdata','s2.sorted.bam',package = 'strandCheckR') readInfo <- scanBam(file, param = ScanBamParam(what = c("pos","cigar","strand"))) getWinOverlapEachReadFragment(readInfo[[1]],"+",1000,100,0.5)
Get the sliding windows that overlap a GRanges object.
getWinOverlapGRanges(x, seqInfo, winWidth = 1000L, winStep = 100L, nbOverlapBases = 1)getWinOverlapGRanges(x, seqInfo, winWidth = 1000L, winStep = 100L, nbOverlapBases = 1)
x |
a GRanges object, which defines the coordinates of
the ranges in the reference genome that all reads mapped to those ranges
must be kept by the filtering method |
seqInfo |
a data frame that contains some key information of the sequences |
winWidth |
the length of the sliding window, 1000 by default. |
winStep |
the step length to sliding the window, 100 by default. |
nbOverlapBases |
a window is considered to overlap with a range of
|
This finds the windows that overlaps the positive/negative strand of a
GRanges object. The GRanges object, which is mustKeepRanges in the
filterDNA method, defines the coordinates of the ranges in the
reference genome that all reads mapped to those ranges must be kept by the
filtering method filterDNA.
This method makes use of the method getWinOverlapEachIRange by
pretending each given range as the range of a read. Since the widths of
x are not necessarily the same (as normal read lengths), we
use nbOverlapBases to specify the minimum number of bases that a
window should overlap with a range of x, instead of using proprotion
as readProp in getWinOverlapEachIRange.
A list of two logical vectors (for positive and negative strand) defining which windows that overlap with the given GRanges object.
library(GenomicRanges) x <- GRanges(seqnames = "10",ranges = IRanges(start = c(10000,15000), end=c(20000,30000)),strand = c("+","-")) seqInfo <- data.frame("Sequence"=10,"FirstBaseInPart"=1) getWinOverlapGRanges(x,seqInfo) seqInfo <- data.frame("Sequence"=10,"FirstBaseInPart"=10000000) getWinOverlapGRanges(x,seqInfo)library(GenomicRanges) x <- GRanges(seqnames = "10",ranges = IRanges(start = c(10000,15000), end=c(20000,30000)),strand = c("+","-")) seqInfo <- data.frame("Sequence"=10,"FirstBaseInPart"=1) getWinOverlapGRanges(x,seqInfo) seqInfo <- data.frame("Sequence"=10,"FirstBaseInPart"=10000000) getWinOverlapGRanges(x,seqInfo)
Intersect the windows with an annotation data frame to get features that overlap with each window
intersectWithFeature(windows, annotation, getFeatureInfo = FALSE, overlapCol = "OverlapFeature", mcolsAnnot, collapse, ...)intersectWithFeature(windows, annotation, getFeatureInfo = FALSE, overlapCol = "OverlapFeature", mcolsAnnot, collapse, ...)
windows |
data frame containing the strand information of the sliding
windows. Windows can be obtained using the function |
annotation |
a Grange object that you want to intersect with your windows. It can have mcols which contains the information or features that could be able to integrate to the input windows |
getFeatureInfo |
whether to get the information of features in the mcols of annotation data or not. If FALSE the return windows will have an additional column indicating whether a window overlaps with any range of the annotion data. If TRUE the return windows will contain the information of features that overlap each window |
overlapCol |
the columnn name of the return windows indicating whether a window overlaps with any range of the annotion data. |
mcolsAnnot |
the column names of the mcols of the annotation data that you want to get information |
collapse |
character which is used collapse multiple features that overlap with a same window into a string. If missing then we don't collapse them. |
... |
used to pass parameters to GenomicRanges::findOverlaps |
the input windows DataFrame with some additional columns
getStrandFromBamFile, plotHist,
plotWin
bamfilein = system.file('extdata','s2.sorted.bam',package = 'strandCheckR') windows <- getStrandFromBamFile(file = bamfilein) #add chr before chromosome names to be consistent with the annotation windows$Seq <- paste0('chr',windows$Seq) library(TxDb.Hsapiens.UCSC.hg38.knownGene) annot <- transcripts(TxDb.Hsapiens.UCSC.hg38.knownGene) # get the transcript names that overlap with each window windows <- intersectWithFeature(windows,annot,mcolsAnnot='tx_name') # just want to know whether there's any transcript that # overlaps with each window windows <- intersectWithFeature(windows,annot,overlapCol='OverlapTranscript') plotHist(windows,facets = 'OverlapTranscript') plotWin(windows,facets = 'OverlapTranscript')bamfilein = system.file('extdata','s2.sorted.bam',package = 'strandCheckR') windows <- getStrandFromBamFile(file = bamfilein) #add chr before chromosome names to be consistent with the annotation windows$Seq <- paste0('chr',windows$Seq) library(TxDb.Hsapiens.UCSC.hg38.knownGene) annot <- transcripts(TxDb.Hsapiens.UCSC.hg38.knownGene) # get the transcript names that overlap with each window windows <- intersectWithFeature(windows,annot,mcolsAnnot='tx_name') # just want to know whether there's any transcript that # overlaps with each window windows <- intersectWithFeature(windows,annot,overlapCol='OverlapTranscript') plotHist(windows,facets = 'OverlapTranscript') plotWin(windows,facets = 'OverlapTranscript')
Plot the histogram of positive proportions of the input
data frame coming from getStrandFromBamFile
plotHist(windows, save = FALSE, file = "hist.pdf", groupBy = NULL, normalizeBy = NULL, split = c(10, 100, 1000), breaks = 100, useCoverage = FALSE, heatmap = FALSE, ...)plotHist(windows, save = FALSE, file = "hist.pdf", groupBy = NULL, normalizeBy = NULL, split = c(10, 100, 1000), breaks = 100, useCoverage = FALSE, heatmap = FALSE, ...)
windows |
data frame containing the strand information of the sliding
windows. Windows can be obtained using the function
|
save |
if TRUE, then the plot will be save into the file given by
|
file |
the file name to save to plot |
groupBy |
the columns that will be used to split the data. |
normalizeBy |
instead of using the raw read count/coverage, we will
normalize it to a proportion by dividing it to the total number of read
count/coverage of windows that have the same value in the |
split |
an integer vector that specifies how you want to partition the
windows based on the coverage. By default |
breaks |
an integer giving the number of bins for the histogram |
useCoverage |
if TRUE then plot the coverage strand information, otherwise plot the number of reads strand information. FALSE by default |
heatmap |
if TRUE, then use heat map to plot the histogram, otherwise use barplot. FALSE by default. |
... |
used to pass parameters to facet_wrap |
If heatmap=FALSE: a ggplot object
bamfilein = system.file('extdata','s1.sorted.bam',package = 'strandCheckR') win <- getStrandFromBamFile(file = bamfilein,sequences='10') plotHist(win)bamfilein = system.file('extdata','s1.sorted.bam',package = 'strandCheckR') win <- getStrandFromBamFile(file = bamfilein,sequences='10') plotHist(win)
Plot the number of reads vs the proportion of '+' stranded reads of all windows from the input data frame.
plotWin(windows, split = c(10, 100, 1000), threshold = c(0.6, 0.7, 0.8, 0.9), save = FALSE, file = "win.pdf", groupBy = NULL, useCoverage = FALSE, ...)plotWin(windows, split = c(10, 100, 1000), threshold = c(0.6, 0.7, 0.8, 0.9), save = FALSE, file = "win.pdf", groupBy = NULL, useCoverage = FALSE, ...)
windows |
data frame containing the strand information of the sliding
windows. Windows should be obtained using the function
|
split |
an integer vector that specifies how you want to partition the
windows based on coverage.
By default |
threshold |
a |
save |
if TRUE, then the plot will be save into the file given by
|
file |
the file name to save to plot |
groupBy |
the column that will be used to split the data (which will be used in the facets method of ggplot2). |
useCoverage |
if TRUE then plot the coverage strand information, otherwise plot the number of reads strand information. FALSE by default |
... |
used to pass parameters to facet_wrap during plotting |
This function will plot the proportion of '+' stranded reads for
each window, against the number of reads in each window.
The threshold lines indicate the hypothetical boundary where windows will
contain reads to kept or discarded using the filtering methods of
filterDNA.
Any plot can be easily modified using standard ggplot2 syntax (see Examples)
The plot will be returned as a standard ggplot2 object
getStrandFromBamFile, plotHist
bamfilein = system.file('extdata','s2.sorted.bam',package = 'strandCheckR') windows <- getStrandFromBamFile(file = bamfilein,sequences = '10') plotWin(windows) # Change point colour using ggplot2 library(ggplot2) plotWin(windows) + scale_colour_manual(values = rgb(seq(0, 1, length.out = 4), 0, 0))bamfilein = system.file('extdata','s2.sorted.bam',package = 'strandCheckR') windows <- getStrandFromBamFile(file = bamfilein,sequences = '10') plotWin(windows) # Change point colour using ggplot2 library(ggplot2) plotWin(windows) + scale_colour_manual(values = rgb(seq(0, 1, length.out = 4), 0, 0))