| Title: | Read density map construction and accession. Visualization of ChIPSeq and RNASeq data sets |
|---|---|
| Description: | This package provides efficient tools to generate, access and display read densities of sequencing based data sets such as from RNA-Seq and ChIP-Seq. |
| Authors: | Julius Muller |
| Maintainer: | Julius Muller <[email protected]> |
| License: | GPL-3 |
| Version: | 1.51.0 |
| Built: | 2024-10-31 06:36:14 UTC |
| Source: | https://github.com/bioc/TransView |
This package provides efficient tools to generate, access and display read densities of sequencing based data sets such as from RNA-Seq and ChIP-Seq.
| Package: | TransView |
| Type: | Package |
| Version: | 1.7.4 |
| URL: | http://bioconductor.org/packages/release/bioc/html/TransView.html |
| License: | GPL-3 |
| LazyLoad: | yes |
| Depends: | methods,GenomicRanges |
| Imports: | zlibbioc,gplots,IRanges |
| Suggests: | RUnit,pasillaBamSubset |
| biocViews: | Bioinformatics,DNAMethylation,GeneExpression,Transcription, Microarray,Sequencing,HighThroughputSequencing,ChIPseq,RNAseq, Methylseq,DataImport,Visualization,Clustering,MultipleComparisons |
| LinkingTo: | Rhtslib |
Index:
DensityContainer-class
Class '"DensityContainer"'
TVResults-class Class '"TVResults"'
TransView-package The TransView package: Construction and
visualisation of read density maps.
annotatePeaks Associates peaks to TSS
gtf2gr GTF file parsing
histogram-methods Histogram of the read distribution
macs2gr Convenience function for MACS output conversion
parseReads User configurable efficient assembly of read
density maps
peak2tss Changes the peak center to the TSS
plotTV Plot and cluster global read densities
plotTVData Summarize plotTV results
rmTV Free space occupied by DensityContainer
slice1 Slice read densities from a TransView dataset
slice1T Slice read densities of whole transcripts from
a TransView DensityContainer
tvStats-methods DensityContainer accessor function
Further information is available in the following vignettes:
TransView |
An introduction to TransView (source, pdf) |
Julius Muller
Maintainer: Julius Muller <[email protected]>
#see vignette#see vignette
A convenience function to associate the peak center to a TSS or gene body provided by a gtf file.
annotatePeaks(peaks, gtf, limit=c(-10e3,10e3), remove_unmatched=T, unifyBy=F, unify_fun="mean", min_genelength=0, reference="tss")annotatePeaks(peaks, gtf, limit=c(-10e3,10e3), remove_unmatched=T, unifyBy=F, unify_fun="mean", min_genelength=0, reference="tss")
peaks |
A GRanges object. |
gtf |
A GRanges object with a meta data column ‘transcript_id’ and ‘exon_id’ like e.g. from |
limit |
Maximal distance range for a peak - TSS association in base pairs. |
remove_unmatched |
If TRUE, only TSS associated peaks will be returned. |
unifyBy |
If a transcript has multiple isoforms, the peak will be associated arbitrarily to the first ID found. In order associate a peak to an isoform with specific characteristics, a |
unify_fun |
A function which will choose the isoform in case of non unique peak - TSS associations. Defaults to the isoform with the highest mean score |
min_genelength |
Genes with a total sum of all exons smaller than this value will not be associated to a peak. |
reference |
If set to ‘tss’, the transcript with the smallest distance from the TSS to the peak center will be returned. If set to ‘gene_body’ the transcript with the smallest distance from the gene body (TSS or TES) to the peak center will be returned and the distance will be zero if the peak center is located within the gene body. |
Convenience function to annotate a GRanges object having one row per peak from e.g. macs2gr. The resulting peak - TSS associations can be customized by the restricting the distance and resolving multiple matches using unify_fun.
GRanges object with row names according to the peak names provided and an added or updated meta data column ‘transcript_id’ with the associated transcript IDs and distances.
Julius Muller [email protected]
exgtf<-dir(system.file("extdata", package="TransView"),full=TRUE,patt="gtf.gz$")[2] exls<-dir(system.file("extdata", package="TransView"),full=TRUE,patt="xls$") GTF<-gtf2gr(exgtf) peaks<-macs2gr(exls,psize=500) apeaks<-annotatePeaks(peaks=peaks,gtf=GTF) apeaks.gb<-annotatePeaks(peaks=peaks,gtf=GTF,reference="gene_body")exgtf<-dir(system.file("extdata", package="TransView"),full=TRUE,patt="gtf.gz$")[2] exls<-dir(system.file("extdata", package="TransView"),full=TRUE,patt="xls$") GTF<-gtf2gr(exgtf) peaks<-macs2gr(exls,psize=500) apeaks<-annotatePeaks(peaks=peaks,gtf=GTF) apeaks.gb<-annotatePeaks(peaks=peaks,gtf=GTF,reference="gene_body")
"DensityContainer"
Container with the pointer of the actual density maps and a histogram. Inherits from internal classes storing informations about the origin and the details of the results.
Objects are created by the function parseReads() using an internal constructor.
dc represents a "DensityContainer" instance in the following
data_pointer(dc):A character string pointing to the read density map.
It points to a variable in .GlobalEnv which is essentially a list resulting from a call to parseReads.
The storage space can be freed with the rmTV function.
ex_name(dc),ex_name(dc)<-value:Get or set a string to define a name of this data set
origin(dc):Filename of the original file
histogram(dc):A histogram of read pile-ups generated across all read density maps after filtering excluding gaps.
env(dc):The environment which holds the data_pointer target.
spliced(dc),spliced(dc)<-bool:This option will mark the object to be treated like a data set with spliced reads.
readthrough_pairs(dc):If TRUE, paired reads will be connected from left to right and used as one long read.
paired(dc):Does the source file contain reads with proper pairs?
filtered(dc):Is there a range filter in place? If TRUE, slicing should be only conducted using the same filter!!
strands(dc):Which strands were parsed at all. Can be "+", "-" or "both"
filtered_reads(dc):FilteredReads class storing information about reads used for read density construction
chromosomes(dc):Character string with the chromosomes used for map construction
pos(dc):Reads used from the forward strand
neg(dc):Reads used from the reverse strand
lsize(dc):Total region covered by reads within the densities returned
gsize(dc):Equals to the sum of the length of all ranges from 0 to the last read per chromosome within the chromosome.
lcoverage(dc):Local coverage within the densities returned which is computed by local mapmass/lsize
lmaxScore(dc):Maximum read pileup within the density maps after filtering
fmapmass(dc):Total map mass after quality filtering present in the file. Equals to filtered_reads*read length
nreads(dc):Total number of reads in the file.
coverage(dc):Total coverage computed by total map mass/(chromosome end - chromosome start). Chromosome length derived from the SAM/BAM header
maxScore(dc):Maximum read pileup found in file after quality filtering
lowqual(dc):Amount of reads that did not pass the quality score set by min_quality or were not mapped
paired_reads(dc):Amount of reads having multiple segments in sequencing
proper_pairs(dc):Amount of pairs with each segment properly aligned according to the aligner
collapsed(dc):If maxDups is in place, the reads at the same position and strand exceeding this value will be counted here.
size(dc):Size in bytes occupied by the object.
signature(dc = "DensityContainer"): Fetch a slice of read densities.
signature(dc = "DensityContainer"): Recover the structure of a gene from a provided pre-processed GTF and read densities.
signature(dc = "DensityContainer", ranges = "data.frame"): Like slice1 but optimized for repeated slicing.
signature(dc = "DensityContainer", tnames = "character", gtf = "data.frame"): Like slice1T but optimized for repeated slicing.
signature(dc = "DensityContainer"): Returns a list of important metrics about the source file.
Class TransView, directly.
Class TotalReads and FilteredReads are not exported but their slots can be fully accessed by several accessors and the tvStats() method.
Julius Muller [email protected]
tvStats-methods,
slice1-methods,
sliceN-methods,
histogram-methods,
rmTV-methods
showClass("DensityContainer")showClass("DensityContainer")
Conversion of a gtf file from UCSC or ENSEMBL to a GRanges object maintaining the exon structure per transcript.
gtf2gr(gtf_file, chromosomes=NA, refseq_nm=F, gtf_feature=c("exon"),transcript_id="transcript_id",gene_id="gene_id")gtf2gr(gtf_file, chromosomes=NA, refseq_nm=F, gtf_feature=c("exon"),transcript_id="transcript_id",gene_id="gene_id")
gtf_file |
Character string with the filename of the gtf file. Fileformats from USCS and ENSEMBL are supported and gzip compression is supported. |
chromosomes |
A character vector with the chromosomes. Restricts the output to the case insensitive matching chromosomes. |
refseq_nm |
An option for GTF files based on RefSeq annotation. If TRUE only identifiers beginning with NM_ will be used. |
gtf_feature |
Defines the GTF feature types to be returned. |
transcript_id |
Defines name of the attribute within the attribute list which should be used as transcript IDs. |
gene_id |
Defines name of the attribute within the attribute list which should be used as gene IDs. |
This function parses GTF files generated by the UCSC table browser or downloaded from the ENSEMBL ftp server. It uses only rows with a 'exon' tag in the feature column (3rd column). The transcript name will be generated from the 'transcript' entry in the attribute column (9th column). The exons of each transcript are numbered using the make.unique function on the transcript name and used as row names.
GenomicRanges object with one row per exon. rownames are transcript IDs and an exon_id is provided.
Julius Muller [email protected]
exgtf<-dir(system.file("extdata", package="TransView"),full=TRUE,patt="gtf.gz$") GTF.mm9<-gtf2gr(exgtf[2]) head(GTF.mm9)exgtf<-dir(system.file("extdata", package="TransView"),full=TRUE,patt="gtf.gz$") GTF.mm9<-gtf2gr(exgtf[2]) head(GTF.mm9)
Retrieves the histogram computed by the parseReads function
## S4 method for signature 'DensityContainer' histogram(dc)## S4 method for signature 'DensityContainer' histogram(dc)
dc |
An object of class DensityContainer. |
The histogram is computed by taking the running average within a window of window size as specified by the argument hwindow to the function parseReads().
The histogram is only counting local reads within the read density maps and outside of gaps or outside of possible range filters that might be in place.
Returns a numeric vector with the histogram in 1Bp resolution starting from 0.
Julius Muller [email protected]
Parses the output of MACS Peak finding algorithm and returns a GRanges object compatible to the down stream functions of TransView
macs2gr(macs_peaks_xls, psize, amount="all", min_pileup=0, log10qval=0, log10pval=0, fenrichment=0, peak_mid="summit")macs2gr(macs_peaks_xls, psize, amount="all", min_pileup=0, log10qval=0, log10pval=0, fenrichment=0, peak_mid="summit")
macs_peaks_xls |
Full path to the file ending with ‘_peaks.xls’ located in the output folder of a MACS run. |
psize |
An integer setting the total length of the peaks. Setting psize to ‘preserve’ will keep the original peak lengths from the output file and override |
amount |
Amount of peaks returned. If an integer is provided, the returned peaks will be limited to this amount after sorting by pile up score. |
min_pileup |
Minimum pile up. |
log10qval |
Minimal log10 q-value |
log10pval |
Minimal log10 p-value |
fenrichment |
Minimal enrichment. |
peak_mid |
If set to ‘summit’, the peaks with length |
Convenience function parsing the output of a MACS file. Tested with MACS v1.4 and v.2.09
GRanges object with one row per peak and meta data score, enrichment and log10 pvalue.
Julius Muller [email protected]
exls<-dir(system.file("extdata", package="TransView"),full=TRUE,patt="xls$") peaks<-macs2gr(exls,psize=500) head(peaks)exls<-dir(system.file("extdata", package="TransView"),full=TRUE,patt="xls$") peaks<-macs2gr(exls,psize=500) head(peaks)
Returns a data frame with labels and normalized densities of the provided DensityContainer
meltPeak(..., region, control=FALSE, peak_windows = 0, bin_method="mean", rpm=TRUE, smooth=0)meltPeak(..., region, control=FALSE, peak_windows = 0, bin_method="mean", rpm=TRUE, smooth=0)
... |
DensityContainer objects |
region |
Can be one entry of the annotated output of annotatePeaks or a GRanges object with one entry and with a transcript_id and distance metadata column. |
control |
An optional vector of DensityContainer objects, that have to match the order of experiments passed as a first argument. E.g. |
peak_windows |
If set to an integer greater than 0, all binding profiles will be interpolated into this amount of windows by the method specified by |
bin_method |
Specifies the function used to summarize the bins specified by nbins. Possible methods are ‘max’, ‘mean’, ‘median’ or ‘approx’ for linear interpolation. |
rpm |
If set to |
smooth |
If greater than 0, smooth defines the smoother span as described in the function |
Convenience function which returns a data frame with one row per BP or, if peak_window greater than zero, per peak_window. The label will be taken from the ex_name slot of the DensityContainer. The slot should be set to meaningful names before using this function. All read densities will be normalized to the total map mass and if a control is provided also background subtracted.
data.frame with 3 columns: ‘NormalizedReads’, ‘Label’ and ‘Position’. Optionally a column ‘Smooth’ will be appended.
Julius Muller [email protected]
exbam<-dir(system.file("extdata", package="TransView"),full=TRUE,patt="bam$") exls<-dir(system.file("extdata", package="TransView"),full=TRUE,patt="xls$") exgtf<-dir(system.file("extdata", package="TransView"),full=TRUE,patt="gtf.gz$")[2] fn.macs<-dir(system.file("extdata", package="TransView"),full=TRUE,patt="xls$") exden.ctrl<-parseReads(exbam[1],verbose=0) exden.chip<-parseReads(exbam[2],verbose=0) peaks<-macs2gr(exls,psize=500) GTF<-gtf2gr(exgtf) peaks<-macs2gr(fn.macs,psize=500) peaks.anno<-annotatePeaks(peaks=peaks,gtf=GTF) peak1.df<-meltPeak(exden.chip,region=peaks.anno["Peak.1"],bin_method="mean",peak_windows=100,rpm=TRUE) head(peak1.df)exbam<-dir(system.file("extdata", package="TransView"),full=TRUE,patt="bam$") exls<-dir(system.file("extdata", package="TransView"),full=TRUE,patt="xls$") exgtf<-dir(system.file("extdata", package="TransView"),full=TRUE,patt="gtf.gz$")[2] fn.macs<-dir(system.file("extdata", package="TransView"),full=TRUE,patt="xls$") exden.ctrl<-parseReads(exbam[1],verbose=0) exden.chip<-parseReads(exbam[2],verbose=0) peaks<-macs2gr(exls,psize=500) GTF<-gtf2gr(exgtf) peaks<-macs2gr(fn.macs,psize=500) peaks.anno<-annotatePeaks(peaks=peaks,gtf=GTF) peak1.df<-meltPeak(exden.chip,region=peaks.anno["Peak.1"],bin_method="mean",peak_windows=100,rpm=TRUE) head(peak1.df)
Generates density maps for further downstream processing. Constructs a DensityContainer.
parseReads( filename, spliced=F, read_stranded=0, paired_only=F, readthrough_pairs=F, set_filter=NA, min_quality=0, description="NA", extendreads=0, unique_only=F, max_dups=0, hwindow=1, compression=1, verbose=1 )parseReads( filename, spliced=F, read_stranded=0, paired_only=F, readthrough_pairs=F, set_filter=NA, min_quality=0, description="NA", extendreads=0, unique_only=F, max_dups=0, hwindow=1, compression=1, verbose=1 )
filename |
Character string with the filename of the bam file. The bam file must be sorted according to genomic position. |
spliced |
This option will mark the object to be treated like a data set with spliced reads. Can be switched off also for spliced experiments for special purposes. If |
read_stranded |
0 will read tags from both strands. 1 will skip all tags from the ‘-’ strand and -1 will only utilize tags from the ‘-’ strand |
paired_only |
If |
set_filter |
Optional GRanges object or data.frame with similar structure: data.frame(chromosomes,start,end). Providing this filter will limit density maps to these regions. |
min_quality |
Phred-scaled mapping quality threshold. If 0, all reads will pass this filter. |
extendreads |
If greater 0, this amount of base pairs will be added into the strand direction of each read during density map generation. |
unique_only |
If TRUE, only unique reads with no multiple alignments will be used. This filter relies on the aligner to use the corresponding flag (0x100). |
max_dups |
If greater 0, maximally this amount of reads are allowed per start position and read direction. |
description |
An optional character string describing the experiment for labeling purposes. |
hwindow |
A numeric defining the window size used to compute the histogram. This value cannot be bigger than |
compression |
Should be left at the default value. Defines the minimal threshold in base pairs which triggers indexing and collapsing of read free regions. A smaller value leads to faster slicing at the cost of a higher memory footprint. |
readthrough_pairs |
Currently *experimental*. If |
verbose |
Verbosity level |
parseReads uses read information of one bam file and scans the entire file read wise. Every read contributes
to the density track in a user configurable manner. The resulting track will be stored in
indexed integer vectors within a list. Since each score is stored as a unsigned 16bit integer, the scores can only
be accessed with one of the slice methods slice1 or sliceN and not directly. As a consequence of the storage
format read pile ups greater than 2^16 will be capped and a warning will be issued.
If memory space is limiting, a filter can be supplied which will limit the density track to these regions.
Filtered DensityContainer should only be sliced with the same regions used for parsing, since
all other positions are set to 0 and can produce artificially low read counts.
S4 DensityContainer
Julius Muller [email protected]
exbam<-dir(system.file("extdata", package="TransView"),full=TRUE,patt="bam$") #store density maps of the whole sam/bam file in test_data exden.chip<-parseReads(exbam[2],verbose=0) #display basic information about the content of test.sam exden.chip #all data are easily accessible test_stat<-tvStats(exden.chip) test_stat$origin # histogram of hwindow sized windows ## Not run: histogram(exden.chip)exbam<-dir(system.file("extdata", package="TransView"),full=TRUE,patt="bam$") #store density maps of the whole sam/bam file in test_data exden.chip<-parseReads(exbam[2],verbose=0) #display basic information about the content of test.sam exden.chip #all data are easily accessible test_stat<-tvStats(exden.chip) test_stat$origin # histogram of hwindow sized windows ## Not run: histogram(exden.chip)
Sets the peak boundaries of an annotated GRanges object with peak locations to TSS centered ranges based on the transcript_id column.
peak2tss(peaks, gtf, peak_len=500)peak2tss(peaks, gtf, peak_len=500)
peaks |
An annotated GRanges object with a meta data column ‘transcript_id’ and ‘exon_id’ like e.g. from |
gtf |
A GRanges object with a meta data column ‘transcript_id’ like e.g. from |
peak_len |
The desired total size of the region with the TSS located in the middle. |
Convenience function to change the peak centers to TSS for e.g. plotting with plotTV.
A GRanges object
Julius Muller [email protected]
exgtf<-dir(system.file("extdata", package="TransView"),full=TRUE,patt="gtf.gz$")[2] fn.macs<-dir(system.file("extdata", package="TransView"),full=TRUE,patt="xls$") GTF<-gtf2gr(exgtf) peaks<-macs2gr(fn.macs,psize=500) peaks.anno<-annotatePeaks(peaks=peaks,gtf=GTF) peak2tss(peaks.anno, GTF, peak_len=500)exgtf<-dir(system.file("extdata", package="TransView"),full=TRUE,patt="gtf.gz$")[2] fn.macs<-dir(system.file("extdata", package="TransView"),full=TRUE,patt="xls$") GTF<-gtf2gr(exgtf) peaks<-macs2gr(fn.macs,psize=500) peaks.anno<-annotatePeaks(peaks=peaks,gtf=GTF) peak2tss(peaks.anno, GTF, peak_len=500)
Plotting facility for DensityContainer.
plotTV( ..., regions, gtf=NA, scale="global", cluster="none", control = F, peak_windows = 0, ex_windows=100, bin_method="mean", show_names=T, label_size=1, zero_alpha=0.5, colr=c("white","blue", "red"), colr_df="redgreen", colour_spread=c(0.05,0.05), key_limit="auto", key_limit_rna="auto", set_zero="center", rowv=NA, gclust="peaks", norm_readc=T, no_key=F, stranded_peak=T, ck_size=c(2,1), remove_lowex=0, verbose=1, showPlot=T, name_width=2, pre_mRNA=F)plotTV( ..., regions, gtf=NA, scale="global", cluster="none", control = F, peak_windows = 0, ex_windows=100, bin_method="mean", show_names=T, label_size=1, zero_alpha=0.5, colr=c("white","blue", "red"), colr_df="redgreen", colour_spread=c(0.05,0.05), key_limit="auto", key_limit_rna="auto", set_zero="center", rowv=NA, gclust="peaks", norm_readc=T, no_key=F, stranded_peak=T, ck_size=c(2,1), remove_lowex=0, verbose=1, showPlot=T, name_width=2, pre_mRNA=F)
... |
Depending on the combination of arguments and limited by the layout up to 20 DensityContainer and maximally one |
regions |
GRanges object with uniformly sized regions used for plotting or character vector with IDs matching column ‘transcript_id’ in the GTF. |
gtf |
A GRanges object with a meta data column ‘transcript_id’ and ‘exon_id’ like e.g. from |
scale |
A character string that determines the row scaling of the colors. Defaults to ‘global’ which results in a global maximum and minimum read value to be plotted across experiments. Alternative is ‘individual’ for individual scaling. |
cluster |
Sets the clustering method of the read densities. Defaults to ‘none’. If an integer is passed, kmeans clustering will be performed with |
control |
A vector of DensityContainer objects, matching the order of experiments passed as a first argument. E.g. |
show_names |
If |
label_size |
Font size of the row and axis labels. |
zero_alpha |
Determines the alpha level of the line indicating the zero point within the peaks. |
colr |
A vector containing the 3 colors used for the lowest, middle and highest values respectively. |
colr_df |
Determines the color in case a |
colour_spread |
sets the distance of the maximum and minimum value to the saturation levels of the plot. The first value for the left side (Peak profiles) and the right for the expression plots. Can be used to adjust the contrast. |
key_limit |
If left at the default, the upper and lower saturation levels the peak profile colour keys will be automatically determined based on colour_spread. Can be manually overridden by a numeric vector with upper and lower levels. |
key_limit_rna |
If left at the default, the upper and lower saturation levels the transcript profile colour keys will be automatically determined based on colour_spread. Can be manually overridden by a numeric vector with upper and lower levels. |
set_zero |
if set to an integer, it determines the zero point of the x axis below the plot. E.g. a value of 250 will scale the x-axis of a 500bp peak from -250 to +250. |
rowv |
If a numeric vector is provided, no clustering will be performed and all rows will be ordered based on the values of this vector. Alternatively a TVResults object can be provided to reproduce previous k-means clustering. |
peak_windows |
If set to an integer greater than 0, all binding profiles will be interpolated into this amount of windows by the method specified by |
ex_windows |
An integer that determines the amount of points at which the read densities of an expression experiment will get interpolated by the method specified by |
bin_method |
Specifies the function used to summarize the bins specified by nbins. Possible methods are ‘max’, ‘mean’, ‘median’ or ‘approx’ for linear interpolation. |
gclust |
If |
norm_readc |
If set to |
no_key |
If |
stranded_peak |
If |
ck_size |
Determines the size of the colour key in the form |
remove_lowex |
Numeric that sets the threshold for the average read density per base pair for expression data sets. Transcripts not passing will be filtered out and a message will be displayed. |
verbose |
Verbosity level |
showPlot |
If |
name_width |
Determines the width of the space for the peak and gene names. |
pre_mRNA |
All expression data will be plotted from the start of the first exon to the end of the last exon including all introns. |
Plots a false color image using the image function similar to heatmap.2 of gplots but based on read densities.
There are 2 different kind of plots, that can be combined or plotted individually: expression profiles and peak profiles.
"Peak profile plots": Peak profiles are plotted if a DensityContainer instance is supplied with the spliced slot set to FALSE. The image consists of color coded, optionally total read normalized read pileups as a stacked false color image with one peak per row. The size of the peaks is soleley relying on the genomic range passed with peaks. If strand information is available through peaks, all peaks on the reverse strand will be reversed.
"Transcript profile plots": If the spliced slot of the respective DensityContainer is set to TRUE, an expression profile will be plotted. First, each expression profile will be normalized to the total amount of reads of the source BAM/SAM file and reduced to ex_windows as calculated by the approx function. The optional clustering will then be performed and subsequently all expression profiles will be scaled across rows so that each row has a mean of zero and standard deviation of one.
"Heatmap": Instead of a DensityContainer with spliced set to TRUE, one matrix can be provided. The data will be scaled analogous to ‘Expression profile plots’ and plotted as a heatmap using the image command.
"Mixed plots": If DensityContainer instances with spliced slot set to TRUE or a matrix are combined with DensityContainer with the spliced slot set to FALSE, the peak profiles will be plotted on the left and the expression plots will be plotted on the right. The gclust argument determines the clustered groups.
Returns a TVResults class object with the results of the clustering.
Julius Muller [email protected]
exbam<-dir(system.file("extdata", package="TransView"),full=TRUE,patt="bam$") exls<-dir(system.file("extdata", package="TransView"),full=TRUE,patt="xls$") exden.ctrl<-parseReads(exbam[1],verbose=0) exden.chip<-parseReads(exbam[2],verbose=0) peaks<-macs2gr(exls,psize=500) cluster_res<-plotTV(exden.chip,exden.ctrl,regions=peaks,cluster=5,norm_readc=FALSE,showPlot=FALSE) summary(cluster_res)exbam<-dir(system.file("extdata", package="TransView"),full=TRUE,patt="bam$") exls<-dir(system.file("extdata", package="TransView"),full=TRUE,patt="xls$") exden.ctrl<-parseReads(exbam[1],verbose=0) exden.chip<-parseReads(exbam[2],verbose=0) peaks<-macs2gr(exls,psize=500) cluster_res<-plotTV(exden.chip,exden.ctrl,regions=peaks,cluster=5,norm_readc=FALSE,showPlot=FALSE) summary(cluster_res)
plotTVData returns the ordering and clustering results as internally calculated by plotTV.
## S4 method for signature 'TVResults' plotTVData(tvr)## S4 method for signature 'TVResults' plotTVData(tvr)
tvr |
A TVResults object as returned by plotTV |
If k-means or manual clustering was performed, row means per cluster will be returned in a data.frame. Otherwise row means over the whole data will be returned.
Returns a data.frame of the clustering results with five columns: Position, Cluster, Sample, Average_scores and Plot
Julius Muller [email protected]
exbam<-dir(system.file("extdata", package="TransView"),full=TRUE,patt="bam$") exls<-dir(system.file("extdata", package="TransView"),full=TRUE,patt="xls$") exden.ctrl<-parseReads(exbam[1],verbose=0) exden.chip<-parseReads(exbam[2],verbose=0) peaks<-macs2gr(exls,psize=500) cluster_res<-plotTV(exden.chip,exden.ctrl,regions=peaks,cluster=5,norm_readc=FALSE,showPlot=FALSE) summaryTV(cluster_res) tvdata<-plotTVData(cluster_res)exbam<-dir(system.file("extdata", package="TransView"),full=TRUE,patt="bam$") exls<-dir(system.file("extdata", package="TransView"),full=TRUE,patt="xls$") exden.ctrl<-parseReads(exbam[1],verbose=0) exden.chip<-parseReads(exbam[2],verbose=0) peaks<-macs2gr(exls,psize=500) cluster_res<-plotTV(exden.chip,exden.ctrl,regions=peaks,cluster=5,norm_readc=FALSE,showPlot=FALSE) summaryTV(cluster_res) tvdata<-plotTVData(cluster_res)
Free space occupied by DensityContainer
## S4 method for signature 'DensityContainer' rmTV(dc)## S4 method for signature 'DensityContainer' rmTV(dc)
dc |
An object of class DensityContainer. |
None
Julius Muller [email protected]
exbam<-dir(system.file("extdata", package="TransView"),full=TRUE,patt="bam$") #store density maps of the whole sam/bam file in test_data exden.chip<-parseReads(exbam[2]) rmTV(exden.chip)exbam<-dir(system.file("extdata", package="TransView"),full=TRUE,patt="bam$") #store density maps of the whole sam/bam file in test_data exden.chip<-parseReads(exbam[2]) rmTV(exden.chip)
slice1 returns read densities of a genomic interval. sliceN takes a GRanges object or a data.frame with genomic coordinates and returns a list of read densities.
## S4 method for signature 'DensityContainer,character,numeric,numeric' slice1(dc, chrom, start, end, control=FALSE, input_method="-",treads_norm=TRUE, nbins=0, bin_method="mean") ## S4 method for signature 'DensityContainer' sliceN(dc, ranges, toRle=FALSE, control=FALSE, input_method="-",treads_norm=TRUE, nbins=0, bin_method="mean")## S4 method for signature 'DensityContainer,character,numeric,numeric' slice1(dc, chrom, start, end, control=FALSE, input_method="-",treads_norm=TRUE, nbins=0, bin_method="mean") ## S4 method for signature 'DensityContainer' sliceN(dc, ranges, toRle=FALSE, control=FALSE, input_method="-",treads_norm=TRUE, nbins=0, bin_method="mean")
dc |
Source DensityContainer object |
chrom |
A case sensitive string of the chromosome |
start, end
|
Genomic start and end of the slice |
ranges |
A GRanges object or a data.frame. |
toRle |
The return values will be converted to a |
control |
An optional DensityContainer which will used as control and by default subtracted from |
input_method |
Defines the handling of the optional control DensityContainer. ‘-’ will subtract the control from the actual data and ‘/’ will return log2 fold change ratios with an added pseudo count of 1 read. |
treads_norm |
If |
nbins |
If all input regions have equal length and nbins greater than 0, all densities will be summarized using the method specified by bin_method into nbins windows of approximately equal size. |
bin_method |
Character string that specifies the function used to summarize or expand the bins specified by nbins. Valid methods are ‘max’, ‘mean’ or ‘median’. |
slice1 is a fast method to slice a vector of read densities from a DensityContainer object. The vector can be optionally background subtracted. If the query region exceeds chromosome boundaries or if an non matching chromosome name will be passed, a warning will be issued and a NULL vector will be returned.
sliceN returns a list with N regions corresponding to N rows in the GRanges object or the data.frame. A list with the
corresponding read densities will be returned and row names will be conserved. Optionally
the return values can be converted to a RleList for seamless integration into the
IRanges package.
slice1 returns a numeric vector of read densities
sliceN returns a list of read densities and optionally an RleList
Julius Muller [email protected]
exbam<-dir(system.file("extdata", package="TransView"),full=TRUE,patt="bam$") exls<-dir(system.file("extdata", package="TransView"),full=TRUE,patt="xls$") #store density maps of the whole sam/bam file in test_data exden.ctrl<-parseReads(exbam[1],verbose=0) exden.chip<-parseReads(exbam[2],verbose=0) peaks<-macs2gr(exls,psize=500) #returns vector of read counts per base pair slice1(exden.chip,"chr2",30663080,30663580)[300:310] slice1(exden.ctrl,"chr2",30663080,30663580)[300:310] slice1(exden.chip,"chr2",30663080,30663580,control=exden.ctrl,treads_norm=FALSE)[300:310] xout<-sliceN(exden.chip,ranges=peaks) lapply(xout,function(x)sum(x)/length(x)) xout<-sliceN(exden.ctrl,ranges=peaks) lapply(xout,function(x)sum(x)/length(x)) xout<-sliceN(exden.chip,ranges=peaks,control=exden.ctrl,treads_norm=FALSE) lapply(xout,function(x)sum(x)/length(x))exbam<-dir(system.file("extdata", package="TransView"),full=TRUE,patt="bam$") exls<-dir(system.file("extdata", package="TransView"),full=TRUE,patt="xls$") #store density maps of the whole sam/bam file in test_data exden.ctrl<-parseReads(exbam[1],verbose=0) exden.chip<-parseReads(exbam[2],verbose=0) peaks<-macs2gr(exls,psize=500) #returns vector of read counts per base pair slice1(exden.chip,"chr2",30663080,30663580)[300:310] slice1(exden.ctrl,"chr2",30663080,30663580)[300:310] slice1(exden.chip,"chr2",30663080,30663580,control=exden.ctrl,treads_norm=FALSE)[300:310] xout<-sliceN(exden.chip,ranges=peaks) lapply(xout,function(x)sum(x)/length(x)) xout<-sliceN(exden.ctrl,ranges=peaks) lapply(xout,function(x)sum(x)/length(x)) xout<-sliceN(exden.chip,ranges=peaks,control=exden.ctrl,treads_norm=FALSE) lapply(xout,function(x)sum(x)/length(x))
slice1T returns read densities of a transcript. sliceNT takes the output of with genomic coordinates and returns a list of read densities.
## S4 method for signature 'DensityContainer,character' slice1T(dc, tname, gtf, control=FALSE, input_method="-", concatenate=T, stranded=T, treads_norm=T, nbins=0, bin_method="mean") ## S4 method for signature 'DensityContainer,character' sliceNT(dc, tnames, gtf, toRle=FALSE, control=FALSE, input_method="-", concatenate=T, stranded=T, treads_norm=T, nbins=0, bin_method="mean")## S4 method for signature 'DensityContainer,character' slice1T(dc, tname, gtf, control=FALSE, input_method="-", concatenate=T, stranded=T, treads_norm=T, nbins=0, bin_method="mean") ## S4 method for signature 'DensityContainer,character' sliceNT(dc, tnames, gtf, toRle=FALSE, control=FALSE, input_method="-", concatenate=T, stranded=T, treads_norm=T, nbins=0, bin_method="mean")
dc |
Source DensityContainer object |
tname, tnames
|
A character string or a character vector with matching identifiers of the provided gtf |
gtf |
A GRanges object with a meta data column ‘transcript_id’ and ‘exon_id’ like e.g. from |
toRle |
The return values will be converted to a |
control |
An optional DensityContainer which will used as control and by default subtracted from |
input_method |
Defines the handling of the optional control DensityContainer. ‘-’ will subtract the control from the actual data and ‘/’ will return log2 fold change ratios with an added pseudo count of 1 read. |
concatenate |
Logical that determines whether exons will be concatenated to one numeric vector (default) or returned as a list of vectors per exon. |
stranded |
If TRUE, the resulting vector will be reversed for reads on the reverse strand. |
treads_norm |
If |
nbins |
If all input regions have equal length and nbins greater than 0, all densities will be summarized using the method specified by bin_method into nbins windows of approximately equal size. |
bin_method |
Character string that specifies the function used to summarize or expand the bins specified by nbins. Valid methods are ‘max’, ‘mean’ or ‘median’. |
slice1T and sliceNT provide a convenient method to access the read densities from a DensityContainer
of spliced reads. The transcript structure will be constructed based on the provided gtf information.
slice1T is a fast alternative to sliceNT to slice one vector of read densities corresponding to the structure of one transcript and reads can be optionally background subtracted. If the query region exceeds chromosome boundaries or if an non matching chromosome name will be passed, a warning will be issued and a NULL vector will be returned.
sliceN slices N regions corresponding to N rows in the range GRanges object. A list with the
corresponding read densities will be returned and row names will be conserved. Optionally
the return values can be converted to a RleList for seamless integration into the
IRanges package.
slice1T returns a numeric vector of read densities
sliceNT returns a list of read densities and optionally an RleList
Julius Muller [email protected]
library("pasillaBamSubset") exgtf<-dir(system.file("extdata", package="TransView"),full=TRUE,patt="gtf.gz$")[1] fn.pas_paired<-untreated1_chr4() exden.exprs<-parseReads(fn.pas_paired,spliced=TRUE,verbose=0) GTF.dm3<-gtf2gr(exgtf) slice1T(exden.exprs,tname="NM_001014688",gtf=GTF.dm3,concatenate=FALSE) my_genes<-sliceNT(exden.exprs,unique(mcols(GTF.dm3)$transcript_id[101:150]),gtf=GTF.dm3) lapply(my_genes,function(x)sum(x)/length(x))library("pasillaBamSubset") exgtf<-dir(system.file("extdata", package="TransView"),full=TRUE,patt="gtf.gz$")[1] fn.pas_paired<-untreated1_chr4() exden.exprs<-parseReads(fn.pas_paired,spliced=TRUE,verbose=0) GTF.dm3<-gtf2gr(exgtf) slice1T(exden.exprs,tname="NM_001014688",gtf=GTF.dm3,concatenate=FALSE) my_genes<-sliceNT(exden.exprs,unique(mcols(GTF.dm3)$transcript_id[101:150]),gtf=GTF.dm3) lapply(my_genes,function(x)sum(x)/length(x))
"TVResults"
Container holding the results of a call to plotTV().
Objects are created by the function plotTV() using an internal constructor.
tvr represents a "TVResults" instance in the following
parameters(tvr):Holds all parameters used to call plotTV
clusters(tvr):Returns numeric vector with the clsuter of each cluster.
cluster_order(tvr):Ordering of the rows within the original regions passed to plotTV with regard to the clusters.
scores_peaks(tvr):Scores of the peaks. Corresponds to the values within the plot after interpolation and normalization.
scores_rna(tvr):Scores of the transcripts. Corresponds to the values within the plot after interpolation and normalization.
summaryTV(tvr):Returns a data frame with the clustering results of the internal data.
signature(tvr = "TVResults"): Returns a data frame with summarized clustering results.
Not all slots are currently being exported.
Julius Muller [email protected]
showClass("TVResults")showClass("TVResults")
Retrieve important metrics from the outcome of parseReads() stored in class DensityContainer and its super classes.
## S4 method for signature 'DensityContainer' tvStats(dc)## S4 method for signature 'DensityContainer' tvStats(dc)
dc |
An object of class DensityContainer. |
Returns a list with the slots of the DensityContainer and its super classes.
In detail:
"ex_name": A user provided string to define a name of this dataset
"origin": Filename of the original file
"spliced": Should the class be treated like an RNA-Seq experiment for e.g. plotTV?
"paired": Does the source file contain reads with proper pairs?
"readthrough_pairs": If TRUE, paired reads will be connected from left to right as one long read.
"filtered": Is there a range filter in place? If yes, slicing should be only conducted using the same filter!!
"strands": Which strands were parsed at all. Can be "+", "-" or "both"
"nreads": Total number of reads
"coverage": Total coverage computed by total map mass/(chromosome end - chromosome start). Chromosome length derived from the SAM/BAM header
"maxScore": Maximum read pileup found in file
"lowqual": Amount of reads that did not pass the quality score set by min_quality or were not mapped
"paired_reads": Amount of reads having multiple segments in sequencing
"proper_pairs": Amount of pairs with each segment properly aligned according to the aligner
"collapsed": If maxDups is in place, the reads at the same position and strand exceeding this value will be counted here.
"compression": Size of a gap triggering an index event
"chromosomes": Character string with the chromosomes with reads used for map construction
"filtered":_reads Amount of reads
"pos": Reads used from the forward strand
"neg": Reads used from the reverse strand
"lcoverage": Local coverage which is computed by filtered map mass/covered region
"lmaxScore": Maximum score of the density maps
"size": Size in bytes occupied by the object
Julius Muller [email protected]