CAGEr: an R package for CAGE (Cap Analysis of Gene Expression) data analysis and promoterome mining
5 July 2023
Package
CAGEr 2.6.1
Contents
- 1 Introduction
- 2 Input data for CAGEr
- 3 The CAGEr workflow
- 3.1 Getting started
- 3.2 Creating a CAGEexp object
- 3.3 Reading in the data
- 3.4 Quality controls and preliminary analyses
- 3.5 Merging of replicates
- 3.6 Normalization
- 3.7 CTSS clustering
- 3.8 Promoter width
- 3.9 Creating consensus promoters across samples
- 3.10 Track export for genome browsers
- 3.11 Expression profiling
- 3.12 Differential expression analysis
- 3.13 Shifting promoters
- 3.14 Enhancers
- 4 Appendix
- Session info
- References
1 Introduction
This document describes how to use CAGEr CAGEr, a Bioconductor package designed to process, analyse and visualise Cap Analysis of Gene Expression (CAGE) sequencing data. CAGE (Kodzius et al. 2006) is a high-throughput method for transcriptome analysis that utilizes cap trapping (Carninci et al. 1996), a technique based on the biotinylation of the 7-methylguanosine cap of Pol II transcripts, to pulldown the 5′-complete cDNAs reversely transcribed from the captured transcripts. A linker sequence is ligated to the 5′ end of the cDNA and a specific restriction enzyme is used to cleave off a short fragment from the 5′ end. Resulting fragments are then amplified and sequenced using massive parallel high-throughput sequencing technology, which results in a large number of short sequenced tags that can be mapped back to the referent genome to infer the exact position of the transcription start sites (TSSs) used for transcription of captured RNAs (Figure 1). The number of CAGE tags supporting each TSS gives the information on the relative frequency of its usage and can be used as a measure of expression from that specific TSS. Thus, CAGE provides information on two aspects of capped transcriptome: genome-wide 1bp-resolution map of TSSs and transcript expression levels. This information can be used for various analyses, from 5′ centered expression profiling (Takahashi et al. 2012) to studying promoter architecture (Carninci et al. 2006).
Figure 1: Overview of CAGE experiment
CAGE samples derived from various organisms (genomes) can be analysed by CAGEr and the only limitation is the availability of the referent genome as a BSgenome package in case when raw mapped CAGE tags are processed. CAGEr provides a comprehensive workflow that starts from mapped CAGE tags and includes reconstruction of TSSs and promoters and their visualisation, as well as more specialized downstream analyses like promoter width, expression profiling and differential TSS usage. It can use both Binary Sequence Alignment Map (BAM) files of aligned CAGE tags or files with genomic locations of TSSs and number of supporting CAGE tags as input. If BAM files are provided CAGEr constructs TSSs from aligned CAGE tags and counts the number of tags supporting each TSS, while allowing filtering out low-quality tags and removing technology-specific bias. It further performs normalization of raw CAGE tag count, clustering of TSSs into tag clusters (TC) and their aggregation across multiple CAGE experiments into promoters to construct the promoterome. Various methods for normalization and clustering of TSSs are supported. Exporting data into different types of track objects allows export and various visualisations of TSSs and clusters (promoters) in the UCSC Genome Browser, which facilitate generation of hypotheses. CAGEr manipulates multiple CAGE experiments at once and performs analyses across datasets, including expression profiling and detection of differential TSS usage (promoter shifting). Multicore option for parallel processing is supported on Unix-like platforms, which significantly reduces computing time.
Here are some of the functionalities provided in this package:
Reading in multiple CAGE datasets from various sources; user provided BAM or TSS input files, public CAGE datasets from accompanying data package.
Correcting systematic G nucleotide addition bias at the 5′ end of CAGE tags.
Plotting pairwise scatter plots, calculating correlation between datasets and merging datasets.
Normalizing raw CAGE tag count: simple tag per million (tpm) or power-law based normalization (Balwierz et al. 2009).
Clustering individual TSSs into tag clusters (TCs) and aggregating clusters across multiple CAGE datasets to create a set of consensus promoters.
Making bedGraph or BED files of individual TSSs or clusters for visualisation in the genome browser.
Expression clustering of individual TSSs or consensus promoters into distinct expression profiles using common clustering algorithms.
Calculating promoter width based on the cumulative distribution of CAGE signal along the promoter.
Scoring and statistically testing differential TSS usage (promoter shifting) and detecting promoters that shift between two samples.
Several data packages are accompanying CAGEr package. They contain majority of the up-to-date publicly available CAGE data produced by major consortia including FANTOM and ENCODE. These include FANTOM3and4CAGE package available from Bioconductor, as well as ENCODEprojectCAGE and ZebrafishDevelopmentalCAGE packages available from http://promshift.genereg.net/CAGEr/. In addition, direct fetching of TSS data from FANTOM5 web resource (the largest collection of TSS data for human and mouse) from within CAGEr is also available. These are all valuable resources of genome-wide TSSs in various tissue/cell types for various model organisms that can be used directly in R. A separate vignette describes how these public datasets can be included into a workflow provided by CAGEr. For further information on the content of the data packages and the list of available CAGE datasets please refer to the vignette of the corresponding data package.
For further details on the implemented methods and for citing the CAGEr package in your work please refer to (Haberle et al. 2015).
2 Input data for CAGEr
CAGEr package supports three types of CAGE data input:
Sequenced CAGE tags mapped to the genome: either BAM (Binary Sequence Alignment Map) files of sequenced CAGE tags aligned to the referent genome (including the paired-end data such as CAGEscan) or BED files of CAGE tags (fragments).
CAGE detected TSSs (CTSSs): tab separated files with genomic coordinates of TSSs and number of tags supporting each TSS. The file should not contain a header and the data must be organized in four columns:
- name of the chromosome: names must match the names of chromosomes in the corresponding BSgenome package.
- 1-based coordinate of the TSS on the chromosome
- genomic strand: should be either + or -
- number of CAGE tags supporting that TSS
Publicly available CAGE datasets from R data package: Several data packages containing CAGE data for various organisms produced by major consortia are accompanying this package. Selected subset of these data can be used as input for .
The type and the format of the input files is specified at the beginning of the workflow, when the
CAGEset object is created (section 3.2). This is done by setting the inputFilesType argument,
which accepts the following self-explanatory options referring to formats mentioned above:
"bam", "bamPairedEnd", "bed", "ctss", "CTSStable".
In addition, the package provides a method for coercing a data.frame object containing single
base-pair TSS information into a CAGEset object (as described in section 4.1), which can be
further used in the workflow described below.
3 The CAGEr workflow
3.1 Getting started
We start the workflow by creating a CAGEexp object, which is a container for storing CAGE datasets and all the results that will be generated by applying specific functions. The CAGEexp objects are an extension of the MultiAssayExperiment class, and therefore can use all their methods. The expression data is stored in CAGEexp using SummarizedExperiment objects, and can also access their methods.
To load the CAGEr package and the other libraries into your R environment type:
library(CAGEr)3.2 Creating a CAGEexp object
3.2.1 Specifying a genome assembly
In this tutorial we will be using data from zebrafish Danio rerio that was
mapped to the danRer7 assembly of the genome. Therefore, the corresponding
genome package BSgenome.Drerio.UCSC.danRer7 has to be installed.
It will be automatically loaded by CAGEr commands when needed.
In case the data is mapped to a genome that is not readily available through
BSgenome package (not in the list returned by BSgenome::available.genomes()
function), a custom BSgenome package can be build and installed first.
(See the vignette within the BSgenome package for instructions on how to build
a custom genome package). The genomeName argument can then be set to the name
of the build genome package when creating a CAGEexp object (see the section
Creating CAGEexp object below). It can also be set to NULL as a last
resort when no BSgenome package is available.
The BSgenome package is required by the CAGEr functions that need access to
the genome sequence, for instance for G-correction. It is also used provide
seqinfo information to the various Bioconductor objects produced by CAGEr.
For this reason, CAGEr will discard alignments that are not on chromosomes
named in the BSgenome package. If this is not desirable, set genomeName
to NULL.
3.2.2 Specifying input files
The subset of zebrafish (Danio rerio) developmental time-series CAGE data generated by (Nepal et al. 2013) will be used in the following demonstration of the CAGEr workflow.
Files with genomic coordinates of TSSs detected by CAGE in 4 zebrafish
developmental stages are included in this package in the extdata subdirectory.
The files contain TSSs from a part of chromosome 17 (26,000,000-46,000,000), and
there are two files for one of the developmental stages (two independent
replicas). The data in files is organized in four tab-separated columns as
described above in section 2.
inputFiles <- list.files( system.file("extdata", package = "CAGEr")
, "ctss$"
, full.names = TRUE)3.2.3 Creating the object
The CAGEexp object is crated with the CAGEexp constructor, that requires
information on file path and type, sample names and reference genome name.
ce <- CAGEexp( genomeName = "BSgenome.Drerio.UCSC.danRer7"
, inputFiles = inputFiles
, inputFilesType = "ctss"
, sampleLabels = sub( ".chr17.ctss", "", basename(inputFiles))
)To display the created object type:
ce## A CAGEexp object of 0 listed
## experiments with no user-defined names and respective classes.
## Containing an ExperimentList class object of length 0:
## Functionality:
## experiments() - obtain the ExperimentList instance
## colData() - the primary/phenotype DataFrame
## sampleMap() - the sample coordination DataFrame
## `$`, `[`, `[[` - extract colData columns, subset, or experiment
## *Format() - convert into a long or wide DataFrame
## assays() - convert ExperimentList to a SimpleList of matrices
## exportClass() - save data to flat filesThe supplied information can be seen with the colData accessor, whereas all other
slots are still empty, since no data has been read yet and no analysis conducted.
colData(ce)## DataFrame with 5 rows and 3 columns
## inputFiles inputFilesType sampleLabels
## <character> <character> <character>
## Zf.30p.dome /tmp/RtmpYc836s/Rins.. ctss Zf.30p.dome
## Zf.high /tmp/RtmpYc836s/Rins.. ctss Zf.high
## Zf.prim6.rep1 /tmp/RtmpYc836s/Rins.. ctss Zf.prim6.rep1
## Zf.prim6.rep2 /tmp/RtmpYc836s/Rins.. ctss Zf.prim6.rep2
## Zf.unfertilized.egg /tmp/RtmpYc836s/Rins.. ctss Zf.unfertilized.egg3.3 Reading in the data
In case when the CAGE / TSS data is to be read from input files, an empty CAGEexp object with
information about the files is first created as described above in section 3.2.
To actually read in the data into the object we use getCTSS() function, that will add
an experiment called tagCountMatrix to the CAGEexp object.
ce <- getCTSS(ce)
ce## A CAGEexp object of 1 listed
## experiment with a user-defined name and respective class.
## Containing an ExperimentList class object of length 1:
## [1] tagCountMatrix: RangedSummarizedExperiment with 23343 rows and 5 columns
## Functionality:
## experiments() - obtain the ExperimentList instance
## colData() - the primary/phenotype DataFrame
## sampleMap() - the sample coordination DataFrame
## `$`, `[`, `[[` - extract colData columns, subset, or experiment
## *Format() - convert into a long or wide DataFrame
## assays() - convert ExperimentList to a SimpleList of matrices
## exportClass() - save data to flat filesThis function reads the provided files in the order they were specified in the
inputFiles argument. It creates a single set of all TSSs detected across all
input datasets (union of TSSs) and a table with counts of CAGE tags supporting
each TSS in every dataset. (Note that in case when a CAGEr object is
created by coercion from an existing expression table there is no need to call
getCTSS()).
Genomic coordinates of all TSSs and numbers of supporting CAGE tags in every input
sample can be retrieved using the CTSStagCountSE() function. CTSScoordinatesGR() accesses
the CTSS coordinates and CTSStagCountDF() accesses the CTSS expression values.1 Data can also
be accessed directly using the native methods of the MultiAssayExperiment and
SummarizedExperiment classes, for example ce[["tagCountMatrix"]],
rowRanges(ce[["tagCountMatrix"]]) and assay(ce[["tagCountMatrix"]]).
CTSStagCountSE(ce)## class: RangedSummarizedExperiment
## dim: 23343 5
## metadata(0):
## assays(1): counts
## rownames: NULL
## rowData names(0):
## colnames(5): Zf.30p.dome Zf.high Zf.prim6.rep1 Zf.prim6.rep2
## Zf.unfertilized.egg
## colData names(0):CTSScoordinatesGR(ce)## CTSS object with 23343 positions and 0 metadata columns:
## seqnames pos strand
## <Rle> <integer> <Rle>
## [1] chr17 26027430 +
## [2] chr17 26050540 +
## [3] chr17 26118088 +
## [4] chr17 26142853 +
## [5] chr17 26166954 +
## ... ... ... ...
## [23339] chr17 45975041 -
## [23340] chr17 45975540 -
## [23341] chr17 45975544 -
## [23342] chr17 45982697 -
## [23343] chr17 45999921 -
## -------
## seqinfo: 26 sequences (1 circular) from danRer7 genome
## BSgenome name: BSgenome.Drerio.UCSC.danRer7CTSStagCountDF(ce)## DataFrame with 23343 rows and 5 columns
## Zf.30p.dome Zf.high Zf.prim6.rep1 Zf.prim6.rep2 Zf.unfertilized.egg
## <Rle> <Rle> <Rle> <Rle> <Rle>
## 1 0 0 1 0 0
## 2 0 0 0 0 1
## 3 0 0 1 0 0
## 4 0 0 0 1 0
## 5 0 0 1 0 0
## ... ... ... ... ... ...
## 23339 1 0 0 0 0
## 23340 0 2 0 0 0
## 23341 0 1 0 0 0
## 23342 0 0 1 0 0
## 23343 1 0 0 0 0CTSStagCountGR(ce, 1) # GRanges for one sample with expression count.## CTSS object with 7277 positions and 1 metadata column:
## seqnames pos strand | score
## <Rle> <integer> <Rle> | <Rle>
## [1] chr17 26222417 + | 1
## [2] chr17 26323229 + | 1
## [3] chr17 26453603 + | 2
## [4] chr17 26453615 + | 1
## [5] chr17 26453632 + | 3
## ... ... ... ... . ...
## [7273] chr17 45901810 - | 1
## [7274] chr17 45901814 - | 1
## [7275] chr17 45901816 - | 1
## [7276] chr17 45975041 - | 1
## [7277] chr17 45999921 - | 1
## -------
## seqinfo: 26 sequences (1 circular) from danRer7 genome
## BSgenome name: BSgenome.Drerio.UCSC.danRer7Note that the samples are ordered in the way they were supplied when creating the CAGEexp object and will be presented in that order in all the results and plots. To check sample labels and their ordering type:
sampleLabels(ce)## #FF0000 #CCFF00 #00FF66
## "Zf.30p.dome" "Zf.high" "Zf.prim6.rep1"
## #0066FF #CC00FF
## "Zf.prim6.rep2" "Zf.unfertilized.egg"In addition, a colour is assigned to each sample, which is consistently used to depict that sample
in all the plots. By default a rainbow palette of colours is used and the hexadecimal format of
the assigned colours can be seen as names attribute of sample labels shown above. The colours can
be changed to taste at any point in the workflow using the setColors() function.
3.4 Quality controls and preliminary analyses
3.4.1 Genome annotations
By design, CAGE tags map transcription start sites and therefore detect promoters. Quantitatively, the proportion of tags that map to promoter regions will depend both on the quality of the libraries and the quality of the genome annotation, which may be incomplete. Nevertheless, strong variations between libraries prepared in the same experiment may be used for quality controls.
CAGEr can intersect CTSSes with reference transcript models and annotate them with
the name(s) of the models, and the region categories promoter, exon, intron and
unknown, by using the annotateCTSS function. The reference models can be GENCODE
loaded with the import.gff function of the rtracklayer package,
or any other input that has the same structure, see help("annotateCTSS") for details.
In this example, we will use a sample annotation for zebrafish (see help("exampleZv9_annot")).
ce <- annotateCTSS(ce, exampleZv9_annot)The annotation results are stored as tag counts in the sample metadata, and as new columns in the CTSS genomic ranges
colData(ce)[,c("librarySizes", "promoter", "exon", "intron", "unknown")]## DataFrame with 5 rows and 5 columns
## librarySizes promoter exon intron unknown
## <integer> <integer> <integer> <integer> <integer>
## Zf.30p.dome 41814 37843 2352 594 1025
## Zf.high 45910 41671 2848 419 972
## Zf.prim6.rep1 34053 29531 2714 937 871
## Zf.prim6.rep2 34947 30799 2320 834 994
## Zf.unfertilized.egg 56140 51114 2860 400 1766CTSScoordinatesGR(ce)## CTSS object with 23343 positions and 2 metadata columns:
## seqnames pos strand | genes annotation
## <Rle> <integer> <Rle> | <Rle> <Rle>
## [1] chr17 26027430 + | unknown
## [2] chr17 26050540 + | grid1a promoter
## [3] chr17 26118088 + | grid1a exon
## [4] chr17 26142853 + | grid1a intron
## [5] chr17 26166954 + | grid1a exon
## ... ... ... ... . ... ...
## [23339] chr17 45975041 - | unknown
## [23340] chr17 45975540 - | unknown
## [23341] chr17 45975544 - | unknown
## [23342] chr17 45982697 - | unknown
## [23343] chr17 45999921 - | unknown
## -------
## seqinfo: 26 sequences (1 circular) from danRer7 genome
## BSgenome name: BSgenome.Drerio.UCSC.danRer7A function plotAnnot is provided to plot the annotations as stacked bar plots.
Here, all the CAGE libraries look very promoter-specific.
plotAnnot(ce, "counts")## Warning: Removed 20 rows containing missing values (`geom_segment()`).## Warning: Removed 20 rows containing missing values (`geom_point()`).
3.4.2 Correlation between samples
As part of the basic sanity checks, we can explore the data by looking at the
correlation between the samples. The plotCorrelation2() function will plot
pairwise scatter plots of expression scores per TSS or consensus cluster and
calculate correlation coefficients between all possible pairs of
samples2 Alternatively, the plotCorrelation() function does the same and
colors the scatterplots according to point density, but is much slower.. A
threshold can be set, so that only regions with an expression score (raw or
normalized) above the threshold (either in one or both samples) are
considered when calculating correlation. Three different correlation measures
are supported: Pearson’s, Spearman’s and Kendall’s correlation coefficients.
Note that while the scatterplots are on a logarithmic scale with pseudocount
added to the zero values, the correlation coefficients are calculated on
untransformed (but thresholded) data.
corr.m <- plotCorrelation2( ce, samples = "all"
, tagCountThreshold = 1, applyThresholdBoth = FALSE
, method = "pearson")
Figure 2: Correlation of raw CAGE tag counts per TSS
3.5 Merging of replicates
Based on calculated correlation we might want to merge and/or rearrange some of the datasets. To
rearrange the samples in the temporal order of the zebrafish development (unfertilized egg -> high
-> 30 percent dome -> prim6) and to merge the two replicas for the prim6 developmental stage we use
the mergeSamples() function:
ce <- mergeSamples(ce, mergeIndex = c(3,2,4,4,1),
mergedSampleLabels = c("Zf.unfertilized.egg", "Zf.high", "Zf.30p.dome", "Zf.prim6"))
ce <- annotateCTSS(ce, exampleZv9_annot)The mergeIndex argument controls which samples will be merged and how the final dataset will be
ordered. Samples labeled by the same number (in our case samples three and four) will be merged
together by summing number of CAGE tags per TSS. The final set of samples will be ordered in the
ascending order of values provided in mergeIndex and will be labeled by the labels provided in
the mergedSampleLabels argument. Note that mergeSamples function resets all slots with results
of downstream analyses, so in case there were any results in the CAGEexp object prior to merging,
they will be removed. Thus, annotation has to be redone.
3.6 Normalization
Library sizes (number of total sequenced tags) of individual experiments differ, thus
normalization is required to make them comparable. The librarySizes function returns the total
number of CAGE tags in each sample:
librarySizes(ce)## [1] 56140 45910 41814 69000The CAGEr package supports both simple tags per million normalization and power-law based normalization. It has been shown that many CAGE datasets follow a power-law distribution (Balwierz et al. 2009). Plotting the number of CAGE tags (X-axis) against the number of TSSs that are supported by <= of that number of tags (Y-axis) results in a distribution that can be approximated by a power-law. On a log-log scale this reverse cumulative distribution will manifest as a monotonically decreasing linear function, which can be defined as
\[y = -1 * \alpha * x + \beta\]
and is fully determined by the slope \(\alpha\) and total number of tags T (which together with \(\alpha\) determines the value of \(\beta\)).
To check whether our CAGE datasets follow power-law distribution and in which range of values, we
can use the plotReverseCumulatives function:
plotReverseCumulatives(ce, fitInRange = c(5, 1000), onePlot = TRUE)
Figure 3: Reverse cumulative distribution of CAGE tags
In addition to the reverse cumulative plots (Figure 3), a power-law distribution will be fitted to each reverse cumulative using values in the specified range (denoted with dashed lines in Figure 3) and the value of \(\alpha\) will be reported for each sample (shown in the brackets in the Figure 3 legend). The plots can help in choosing the optimal parameters for power-law based normalization. We can see that the reverse cumulative distributions look similar and follow the power-law in the central part of the CAGE tag counts values with a slope between -1.1 and -1.3. Thus, we choose a range from 5 to 1000 tags to fit a power-law, and we normalize all samples to a referent power-law distribution with a total of 50,000 tags and slope of -1.2 (\(\alpha = 1.2\)).3 Note that since this example dataset contains only data from one part of chromosome 17 and the total number of tags is very small, we normalize to a referent distribution with a similarly small number of tags. When analyzing full datasets it is reasonable to set total number of tags for referent distribution to one million to get normalized tags per million values.
To perform normalization we pass these parameters to the normalizeTagCount function.
ce <- normalizeTagCount(ce, method = "powerLaw", fitInRange = c(5, 1000), alpha = 1.2, T = 5*10^4)## Warning: The dim() method for DataFrameList objects is deprecated. Please use
## dims() on these objects instead.## Warning: The nrow() method for DataFrameList objects is deprecated. Please use
## nrows() on these objects instead.## Warning: The ncol() method for DataFrameList objects is deprecated. Please use
## ncols() on these objects instead.## Warning: The dim() method for DataFrameList objects is deprecated. Please use
## dims() on these objects instead.## Warning: The nrow() method for DataFrameList objects is deprecated. Please use
## nrows() on these objects instead.## Warning: The ncol() method for DataFrameList objects is deprecated. Please use
## ncols() on these objects instead.ce[["tagCountMatrix"]]## class: RangedSummarizedExperiment
## dim: 23343 4
## metadata(0):
## assays(2): counts normalizedTpmMatrix
## rownames: NULL
## rowData names(2): genes annotation
## colnames(4): Zf.unfertilized.egg Zf.high Zf.30p.dome Zf.prim6
## colData names(0):The normalization is performed as described in (Balwierz et al. 2009):
- Power-law is fitted to the reverse cumulative distribution in the specified range of CAGE tags values to each sample separately.
- A referent power-law distribution is defined based on the provided
alpha(slope in the log-log representation) andT(total number of tags) parameters. SettingTto 1 million results in normalized tags per million (tpm) values. - Every sample is normalized to the defined referent distribution, i.e. given the parameters that approximate its own power-law distribution it is calculated how many tags would each TSS have in the referent power-law distribution.
In addition to the two provided normalization methods, a pass-through option none can be set as
method parameter to keep using raw tag counts in all downstream steps. Note that
normalizeTagCount() has to be applied to CAGEr object before moving to next steps. Thus, in
order to keep using raw tag counts run the function with method="none". In that case, all
results and parameters in the further steps that would normally refer to normalized CAGE signal
(denoted as tpm), will actually be raw tag counts.
3.7 CTSS clustering
Transcription start sites are found in the promoter region of a gene and reflect the transcriptional activity of that promoter (Figure 5). TSSs in the close proximity of each other give rise to a functionally equivalent set of transcripts and are likely regulated by the same promoter elements. Thus, TSSs can be spatially clustered into larger transcriptional units, called tag clusters (TCs) that correspond to individual promoters. CAGEr supports three methods for spatial clustering of TSSs along the genome, two ab initio methods driven by the data itself, as well as assigning TSSs to predefined genomic regions:
Simple distance-based clustering in which two neighbouring TSSs are joined together if they are closer than some specified distance (greedy algorithm);
Parametric clustering of data attached to sequences based on the density of the signal (Frith et al. 2007), http://www.cbrc.jp/paraclu/;
Counting TSSs and their signal in a set of user supplied genomic regions (e.g. annotation derived promoter regions or other regions of interest).
These functionalities are provided in the function clusterCTSS(), which accepts additional
arguments for controlling which CTSSs will be included in the clustering as well as for
refining the final set of tag clusters.
We will perform a simple distance-based clustering using 20 bp as a maximal allowed distance between two neighbouring TSSs. Prior to clustering we will filter out low-fidelity TSSs - the ones supported by less than 1 normalized tag counts in all of the samples.
ce <- clusterCTSS( ce
, threshold = 1
, thresholdIsTpm = TRUE
, nrPassThreshold = 1
, method = "distclu"
, maxDist = 20
, removeSingletons = TRUE
, keepSingletonsAbove = 5)Our final set of tag clusters will not include singletons (clusters with only one TSS), unless the
normalized signal is above 5, it is a reasonably supported TSS. The clusterCTSS
function creates a set of clusters for each sample separately; for each cluster it returns the
genomic coordinates, counts the number of TSSs within the cluster, determines the (1-based) position of the
most frequently used (dominant) TSS, calculates the total CAGE signal within the cluster and CAGE
signal supporting the dominant TSS only. We can extract tag clusters for a desired sample from
CAGEexp object by calling the tagClustersGR function:
tagClustersGR(ce, sample = "Zf.unfertilized.egg")## TagClusters object with 517 ranges and 4 metadata columns:
## seqnames ranges strand | score nr_ctss
## <Rle> <IRanges> <Rle> | <Rle> <integer>
## 1 chr17 26453632-26453708 + | 26.9709371501973 12
## 2 chr17 26564508-26564610 + | 128.637202208017 24
## 3 chr17 26595637-26595793 + | 216.999442534332 35
## 4 chr17 26596033-26596091 + | 10.4200035230486 9
## 5 chr17 26596118-26596127 + | 12.1994648486481 4
## ... ... ... ... . ... ...
## 513 chr17 45700182-45700187 - | 9.61820033171689 3
## 514 chr17 45901329-45901330 - | 1.96212698267798 2
## 515 chr17 45901698-45901710 - | 27.6544648890639 4
## 516 chr17 45901732-45901784 - | 119.96944736195 15
## 517 chr17 45901814-45901816 - | 3.24804276430515 2
## dominant_ctss tpm.dominant_ctss
## <integer> <Rle>
## 1 26453667 8.25013739501972
## 2 26564585 29.2832717171977
## 3 26595750 100.970036599366
## 4 26596070 3.21686412708414
## 5 26596118 5.74180188433636
## ... ... ...
## 513 45700182 6.37015756741174
## 514 45901329 1.30374775871268
## 515 45901698 23.7480429007934
## 516 45901749 83.4545595936441
## 517 45901816 1.94429500559247
## -------
## seqinfo: 26 sequences (1 circular) from danRer7 genome3.8 Promoter width
Genome-wide mapping of TSSs using CAGE has initially revealed two major classes of promoters in mammals (Carninci et al. 2006), with respect to the number and distribution of TSSs within the promoter. They have been further correlated with differences in the underlying sequence and the functional classes of the genes they regulate, as well as the organization of the chromatin around them.
- “broad” promoters with multiple TSSs characterized by a high GC content and overlap with a CpG island, which are associated with widely expressed or developmentally regulated genes;
- “sharp” promoters with one dominant TSS often associated with a TATA-box at a fixed upstream distance, which often regulate tissue-specific transcription.
Thus, the width of the promoter is an important characteristic that distinguishes different
functional classes of promoters. CAGEr analyzes promoter width across all samples present
in the CAGEexp object. It defines promoter width by taking into account both the positions
and the CAGE signal at TSSs along the tag cluster, thus making it more robust with respect
to total expression and local level of noise at the promoter. Width of every tag cluster is
calculated as following:
- Cumulative distribution of CAGE signal along the cluster is calculated.
- A “lower” (
qLow) and an “upper” (qUp) quantile are selected by the user. - From the 5′ end the position, the position of a quantile \(q\) is determined as the first base in which of the cumulative expression is higher or equal to \(q\%\) of the total CAGE signal of that cluster.
- Promoter interquantile width is defined as the distance (in base pairs) between the two quantile positions.
The procedure is schematically shown in Figure 4.
Figure 4: Cumulative distribution of CAGE signal and definition of interquantile width
Required computations are done using cumulativeCTSSdistribution and quantilePositions
functions, which calculate cumulative distribution for every tag cluster in each of the
samples and determine the positions of selected quantiles, respectively:
ce <- cumulativeCTSSdistribution(ce, clusters = "tagClusters", useMulticore = T)
ce <- quantilePositions(ce, clusters = "tagClusters", qLow = 0.1, qUp = 0.9)Tag clusters and their interquantile width can be retrieved by calling tagClusters function:
tagClustersGR( ce, "Zf.unfertilized.egg"
, returnInterquantileWidth = TRUE, qLow = 0.1, qUp = 0.9)## TagClusters object with 517 ranges and 7 metadata columns:
## seqnames ranges strand | score nr_ctss
## <Rle> <IRanges> <Rle> | <Rle> <integer>
## 1 chr17 26453632-26453708 + | 26.9709371501973 12
## 2 chr17 26564508-26564610 + | 128.637202208017 24
## 3 chr17 26595637-26595793 + | 216.999442534332 35
## 4 chr17 26596033-26596091 + | 10.4200035230486 9
## 5 chr17 26596118-26596127 + | 12.1994648486481 4
## ... ... ... ... . ... ...
## 513 chr17 45700182-45700187 - | 9.61820033171689 3
## 514 chr17 45901329-45901330 - | 1.96212698267798 2
## 515 chr17 45901698-45901710 - | 27.6544648890639 4
## 516 chr17 45901732-45901784 - | 119.96944736195 15
## 517 chr17 45901814-45901816 - | 3.24804276430515 2
## dominant_ctss tpm.dominant_ctss q_0.1 q_0.9 interquantile_width
## <integer> <Rle> <Rle> <Rle> <Rle>
## 1 26453667 8.25013739501972 36 72 37
## 2 26564585 29.2832717171977 17 81 65
## 3 26595750 100.970036599366 37 114 78
## 4 26596070 3.21686412708414 1 50 50
## 5 26596118 5.74180188433636 1 10 10
## ... ... ... ... ... ...
## 513 45700182 6.37015756741174 1 6 6
## 514 45901329 1.30374775871268 1 2 2
## 515 45901698 23.7480429007934 1 2 2
## 516 45901749 83.4545595936441 2 21 20
## 517 45901816 1.94429500559247 1 3 3
## -------
## seqinfo: 26 sequences (1 circular) from danRer7 genomeOnce the cumulative distributions and the positions of quantiles have been calculated, the histograms of interquantile width can be plotted to globally compare the promoter width across different samples (Figure ??:
plotInterquantileWidth(ce, clusters = "tagClusters", tpmThreshold = 3, qLow = 0.1, qUp = 0.9)
Significant difference in the promoter width might indicate global differences in the modes of gene regulation between the two samples. The histograms can also help in choosing an appropriate width threshold for separating sharp and broad promoters.
3.9 Creating consensus promoters across samples
Tag clusters are created for each sample individually and they are often sample-specific, thus can
be present in one sample but absent in another. In addition, in many cases tag clusters do not
coincide perfectly within the same promoter region, or there might be two clusters in one sample
and only one larger in the other. To be able to compare genome-wide transcriptional activity
across samples and to perform expression profiling, a single set of consensus clusters needs to
be created. This is done using the aggregateTagClusters function, which aggregates tag clusters
from all samples into a single set of non-overlapping consensus clusters:
ce <- aggregateTagClusters(ce, tpmThreshold = 5, qLow = 0.1, qUp = 0.9, maxDist = 100)
ce$outOfClusters / ce$librarySizes *100## Zf.unfertilized.egg Zf.high Zf.30p.dome Zf.prim6
## 23.0 23.3 23.9 26.6Tag clusters can be aggregated using their full span (from start to end) or using positions of
previously calculated quantiles as their boundaries. Only tag clusters above given tag count
threshold will be considered and two clusters will be aggregated together if their boundaries
(i.e. either starts and ends or positions of quantiles) are <= maxDist apart. Final set
of consensus clusters can be retrieved by:
consensusClustersGR(ce)## ConsensusClusters object with 285 ranges and 3 metadata columns:
## seqnames ranges strand | score
## <Rle> <IRanges> <Rle> | <numeric>
## chr17:26453647-26453719:+ chr17 26453647-26453719 + | 129.734
## chr17:26564524-26564591:+ chr17 26564524-26564591 + | 231.065
## chr17:26595673-26595750:+ chr17 26595673-26595750 + | 835.858
## chr17:26596033-26596339:+ chr17 26596033-26596339 + | 141.826
## chr17:26645157-26645514:+ chr17 26645157-26645514 + | 2582.780
## ... ... ... ... . ...
## chr17:45534727-45534729:- chr17 45534727-45534729 - | 176.50299
## chr17:45545922-45545996:- chr17 45545922-45545996 - | 866.28004
## chr17:45554314-45554345:- chr17 45554314-45554345 - | 37.79642
## chr17:45700092-45700187:- chr17 45700092-45700187 - | 9.21467
## chr17:45901695-45901752:- chr17 45901695-45901752 - | 789.81397
## dominant_ctss tpm.dominant_ctss
## <GRanges> <numeric>
## chr17:26453647-26453719:+ chr17:26453701:+ 37.9264
## chr17:26564524-26564591:+ chr17:26564585:+ 65.7220
## chr17:26595673-26595750:+ chr17:26595750:+ 368.6590
## chr17:26596033-26596339:+ chr17:26596198:+ 55.5861
## chr17:26645157-26645514:+ chr17:26645160:+ 317.3201
## ... ... ...
## chr17:45534727-45534729:- chr17:45534727:- 146.95219
## chr17:45545922-45545996:- chr17:45545991:- 277.01814
## chr17:45554314-45554345:- chr17:45554339:- 17.51376
## chr17:45700092-45700187:- chr17:45700182:- 9.21467
## chr17:45901695-45901752:- chr17:45901749:- 283.80412
## -------
## seqinfo: 26 sequences (1 circular) from danRer7 genomewhich will return genomic coordinates and sum of CAGE signal across all samples for each consensus
cluster (the tpm column).
Analogously to tag clusters, analysis of promoter width can be performed for consensus clusters
as well, using the same cumulativeCTSSdistribution, quantilePositions
and plotInterquantileWidth functions as described above, but by setting
the clusters parameter to "consensusClusters". Like the CTSS, the consensus clusters can
also be annotated:
ce <- annotateConsensusClusters(ce, exampleZv9_annot)
ce <- cumulativeCTSSdistribution(ce, clusters = "consensusClusters", useMulticore = TRUE)
ce <- quantilePositions(ce, clusters = "consensusClusters", qLow = 0.1, qUp = 0.9, useMulticore = TRUE)Although consensus clusters are created to represent consensus across all samples, they obviously have different CAGE signal and can have different width or position of the dominant TSS in the different samples. Sample-specific information on consensus clusters can be retrieved with the function, by specifying desired sample name (analogous to retrieving tag clusters):
consensusClustersGR( ce, sample = "Zf.unfertilized.egg"
, returnInterquantileWidth = TRUE, qLow = 0.1, qUp = 0.9)## ConsensusClusters object with 285 ranges and 9 metadata columns:
## seqnames ranges strand | score
## <Rle> <IRanges> <Rle> | <numeric>
## chr17:26453647-26453719:+ chr17 26453647-26453719 + | 19.1354
## chr17:26564524-26564591:+ chr17 26564524-26564591 + | 96.9145
## chr17:26595673-26595750:+ chr17 26595673-26595750 + | 177.6584
## chr17:26596033-26596339:+ chr17 26596033-26596339 + | 30.6125
## chr17:26645157-26645514:+ chr17 26645157-26645514 + | 585.4393
## ... ... ... ... . ...
## chr17:45534727-45534729:- chr17 45534727-45534729 - | 0.00000
## chr17:45545922-45545996:- chr17 45545922-45545996 - | 208.58390
## chr17:45554314-45554345:- chr17 45554314-45554345 - | 8.31445
## chr17:45700092-45700187:- chr17 45700092-45700187 - | 6.37016
## chr17:45901695-45901752:- chr17 45901695-45901752 - | 132.65464
## dominant_ctss tpm.dominant_ctss annotation
## <GRanges> <numeric> <Rle>
## chr17:26453647-26453719:+ chr17:26453701:+ 37.9264 promoter
## chr17:26564524-26564591:+ chr17:26564585:+ 65.7220 promoter
## chr17:26595673-26595750:+ chr17:26595750:+ 368.6590 promoter
## chr17:26596033-26596339:+ chr17:26596198:+ 55.5861 promoter
## chr17:26645157-26645514:+ chr17:26645160:+ 317.3201 promoter
## ... ... ... ...
## chr17:45534727-45534729:- chr17:45534727:- 146.95219 intron
## chr17:45545922-45545996:- chr17:45545991:- 277.01814 promoter
## chr17:45554314-45554345:- chr17:45554339:- 17.51376 promoter
## chr17:45700092-45700187:- chr17:45700182:- 9.21467 exon
## chr17:45901695-45901752:- chr17:45901749:- 283.80412 promoter
## genes q_0.1 q_0.9 interquantile_width tpm
## <Rle> <Rle> <Rle> <Rle> <numeric>
## chr17:26453647-26453719:+ ttc7b 21 57 37 19.1354
## chr17:26564524-26564591:+ nrde2 1 65 65 96.9145
## chr17:26595673-26595750:+ larp1b 12 78 67 177.6584
## chr17:26596033-26596339:+ larp1b 38 272 235 30.6125
## chr17:26645157-26645514:+ pgrmc2 1 109 109 585.4393
## ... ... ... ... ... ...
## chr17:45534727-45534729:- znf106a 1 1 1 0.00000
## chr17:45545922-45545996:- znf106a 24 70 47 208.58390
## chr17:45554314-45554345:- tmem206 26 32 7 8.31445
## chr17:45700092-45700187:- susd4 4 93 90 6.37016
## chr17:45901695-45901752:- arf6b 4 55 52 132.65464
## -------
## seqinfo: 26 sequences (1 circular) from danRer7 genomeThis will, in addition to genomic coordinates of the consensus clusters, which are constant across all samples, also return the position of the dominant TSS, the CAGE signal (tpm) and the interquantile width specific for a given sample. Note that when specifying individual sample, only the consensus clusters that have some CAGE signal in that sample will be returned (which will be a subset of all consensus clusters).
When setting sample = NULL sample-agnostic information per consensus cluster
is provided.
This includes the interquantile width and dominant TSS information for each
consensus cluster independent of the samples.
Again, specifying interquantile boundaries, qLow and qUp, has a similar
behaviour for returnInterquantileWidth = TRUE.
3.10 Track export for genome browsers
CAGE data can be visualized in the genomic context by converting raw or normalized CAGE tag counts to a track object and exporting it to a file format such as BED, bedGraph or BigWig for uploading (or linking) to a genome browser`4 Note that the ZENBU genome browser can also display natively data from BAM or BED files as coverage tracks.. The (exportToTrack) function can take a variety of inputs representing CTSS, Tag Clusters or Consensus Clusters, with raw or normalised expression scores. When asked to export multiple samples it will return a list of tracks.
trk <- exportToTrack(CTSSnormalizedTpmGR(ce, "Zf.30p.dome"))
ce |> CTSSnormalizedTpmGR("all") |> exportToTrack(ce, oneTrack = FALSE)## GRangesList object of length 4:
## [[1]]
## UCSC track 'Zf.unfertilized.egg (TC)'
## UCSCData object with 8395 ranges and 6 metadata columns:
## seqnames ranges strand | genes annotation filteredCTSSidx
## <Rle> <IRanges> <Rle> | <Rle> <Rle> <Rle>
## [1] chr17 26050540 + | grid1a promoter FALSE
## [2] chr17 26391265 + | si:ch73-34j14.2 exon FALSE
## [3] chr17 26446219 + | unknown FALSE
## [4] chr17 26453605 + | promoter TRUE
## [5] chr17 26453632 + | promoter TRUE
## ... ... ... ... . ... ... ...
## [8391] chr17 45901781 - | arf6b promoter FALSE
## [8392] chr17 45901784 - | arf6b promoter TRUE
## [8393] chr17 45901800 - | arf6b promoter FALSE
## [8394] chr17 45901814 - | arf6b promoter TRUE
## [8395] chr17 45901816 - | arf6b promoter TRUE
## cluster score itemRgb
## <Rle> <numeric> <character>
## [1] 0.658379 grey50
## [2] 0.658379 grey50
## [3] 0.658379 grey50
## [4] 1.303748 black
## [5] 1.303748 black
## ... ... ... ...
## [8391] 0.658379 grey50
## [8392] 0.658379 black
## [8393] 0.658379 grey50
## [8394] 1.303748 black
## [8395] 1.944295 black
## -------
## seqinfo: 26 sequences (1 circular) from danRer7 genome
##
## ...
## <3 more elements>Some track file format, for instance bigWig or bedGraph require the + and
- strands to be separated, because they do not allow overlapping ranges.
This can be done with the (split) function like in the following
example5 The drop = TRUE option is needed to remove the * level..
split(trk, strand(trk), drop = TRUE)## GRangesList object of length 2:
## $`+`
## UCSC track 'Zf.30p.dome (TC)'
## UCSCData object with 3778 ranges and 6 metadata columns:
## seqnames ranges strand | genes annotation filteredCTSSidx
## <Rle> <IRanges> <Rle> | <Rle> <Rle> <Rle>
## [1] chr17 26222417 + | unknown FALSE
## [2] chr17 26323229 + | unknown FALSE
## [3] chr17 26453603 + | promoter TRUE
## [4] chr17 26453615 + | promoter FALSE
## [5] chr17 26453632 + | promoter TRUE
## ... ... ... ... . ... ... ...
## [3774] chr17 45975288 + | unknown TRUE
## [3775] chr17 45975289 + | unknown TRUE
## [3776] chr17 45975290 + | unknown TRUE
## [3777] chr17 45975292 + | unknown TRUE
## [3778] chr17 45975293 + | unknown TRUE
## cluster score itemRgb
## <Rle> <numeric> <character>
## [1] 0.680723 grey50
## [2] 0.680723 grey50
## [3] 1.394796 black
## [4] 0.680723 grey50
## [5] 2.122024 black
## ... ... ... ...
## [3774] chr17:45975252-45975.. 10.44998 black
## [3775] chr17:45975252-45975.. 1.39480 black
## [3776] chr17:45975252-45975.. 4.34801 black
## [3777] chr17:45975252-45975.. 1.39480 black
## [3778] chr17:45975252-45975.. 2.85793 black
## -------
## seqinfo: 26 sequences (1 circular) from danRer7 genome
##
## $`-`
## UCSC track 'Zf.30p.dome (TC)'
## UCSCData object with 3499 ranges and 6 metadata columns:
## seqnames ranges strand | genes annotation filteredCTSSidx cluster
## <Rle> <IRanges> <Rle> | <Rle> <Rle> <Rle> <Rle>
## [1] chr17 26068225 - | unknown FALSE
## [2] chr17 26068227 - | unknown FALSE
## [3] chr17 26068233 - | unknown FALSE
## [4] chr17 26074127 - | unknown TRUE
## [5] chr17 26113371 - | unknown FALSE
## ... ... ... ... . ... ... ... ...
## [3495] chr17 45901810 - | arf6b promoter FALSE
## [3496] chr17 45901814 - | arf6b promoter TRUE
## [3497] chr17 45901816 - | arf6b promoter TRUE
## [3498] chr17 45975041 - | unknown FALSE
## [3499] chr17 45999921 - | unknown FALSE
## score itemRgb
## <numeric> <character>
## [1] 0.680723 grey50
## [2] 0.680723 grey50
## [3] 0.680723 grey50
## [4] 1.394796 black
## [5] 0.680723 grey50
## ... ... ...
## [3495] 0.680723 grey50
## [3496] 0.680723 black
## [3497] 0.680723 black
## [3498] 0.680723 grey50
## [3499] 0.680723 grey50
## -------
## seqinfo: 26 sequences (1 circular) from danRer7 genomeFor bigWig export, the (rtracklayer::export.bw) needs to be run on each element of the list returned by the (split) command.
For bedGraph export, the (rtracklayer::export.bedGraph) command can take the list as input and will produce a single file containing the two tracks. (Figure 5) shows an example of bedGraph visualisation.
For BED export, the (rtracklayer::export.bed) can operate directly on the track object.
Other export format probably operate in a way similar to one of the cases above.
Figure 5: CAGE data bedGraph track visualized in the UCSC Genome Browser
Interquantile width can also be visualized in a gene-like representation in the genome browsers by passing quantile information during data conversion to the UCSCData format and then exporting it into a BED file:
iqtrack <- exportToTrack(ce, what = "tagClusters", qLow = 0.1, qUp = 0.9, oneTrack = FALSE)
iqtrack## GRangesList object of length 4:
## $Zf.unfertilized.egg
## UCSC track 'TC'
## UCSCData object with 517 ranges and 6 metadata columns:
## seqnames ranges strand | score q_0.1 q_0.9 thick
## <Rle> <IRanges> <Rle> | <integer> <Rle> <Rle> <IRanges>
## [1] chr17 26453632-26453708 + | 0 36 72 26453667
## [2] chr17 26564508-26564610 + | 0 17 81 26564585
## [3] chr17 26595637-26595793 + | 0 37 114 26595750
## [4] chr17 26596033-26596091 + | 0 1 50 26596070
## [5] chr17 26596118-26596127 + | 0 1 10 26596118
## ... ... ... ... . ... ... ... ...
## [513] chr17 45700182-45700187 - | 0 1 6 45700182
## [514] chr17 45901329-45901330 - | 0 1 2 45901329
## [515] chr17 45901698-45901710 - | 0 1 2 45901698
## [516] chr17 45901732-45901784 - | 0 2 21 45901749
## [517] chr17 45901814-45901816 - | 0 1 3 45901816
## name blocks
## <logical> <IRangesList>
## [1] <NA> 1,36-72,77
## [2] <NA> 1,17-81,103
## [3] <NA> 1,37-114,157
## [4] <NA> 1-50,59
## [5] <NA> 1-10
## ... ... ...
## [513] <NA> 1-6
## [514] <NA> 1-2
## [515] <NA> 1-2,13
## [516] <NA> 1,2-21,53
## [517] <NA> 1-3
## -------
## seqinfo: 26 sequences (1 circular) from danRer7 genome
##
## ...
## <3 more elements>#rtracklayer::export.bed(iqtrack, "outputFileName.bed")In this gene-like representation (Figure 6), the oriented line shows the full span of the cluster, filled block marks the interquantile width and a single base-pair thick block denotes the position of the dominant TSS.
Figure 6: Tag clusters visualization in the genome browser
3.11 Expression profiling
The CAGE signal is a quantitative measure of promoter activity. In CAGEr,
normalised expression scores of individual CTSSs or consensus clusters can be
clustered in expression classes. Two unsupervised clustering algorithms are
supported: kmeans and self-organizing maps (SOM). Both require to specify a
number of clusters in advance. Results are stored in the exprClass metadata
column of the CTSS or consensus clusters genomic ranges, and the
expressionClass accessor function is provided for convenience.
In the example below, we perform expression clustering at the level of entire promoters using SOM algorithm with 4 × 2 dimensions and applying it only to consensus clusters with a normalized CAGE signal of at least 10 TPM in at least one sample.
ce <- getExpressionProfiles(ce, what = "consensusClusters", tpmThreshold = 10,
nrPassThreshold = 1, method = "som", xDim = 4, yDim = 2)## Warning: Argument 'center' should be of the same length as number of rows of
## 'x'. Use of a scalar value is deprecated: 1 != 285consensusClustersGR(ce)$exprClass |> table(useNA = "always")##
## 0_0 0_1 1_0 1_1 2_0 2_1 3_0 3_1 <NA>
## 46 49 25 19 15 5 6 45 75Distribution of expression across samples for the 8 clusters returned by SOM can
be visualized using the plotExpressionProfiles function
as shown in Figure ??:
plotExpressionProfiles(ce, what = "consensusClusters")## Warning: Transformation introduced infinite values in continuous x-axis## Warning: Removed 103 rows containing non-finite values (`stat_ydensity()`).
(#fig:ConsensusClustersExpressionProfiles_svg)Expression clusters
Each cluster is shown in different color and is marked by its label and the number of elements (promoters) in the cluster. We can extract promoters belonging to a specific cluster by typing commands like:
consensusClustersGR(ce) |> subset(consensusClustersGR(ce)$exprClass == "0_1")## ConsensusClusters object with 49 ranges and 6 metadata columns:
## seqnames ranges strand | score
## <Rle> <IRanges> <Rle> | <numeric>
## chr17:26645157-26645514:+ chr17 26645157-26645514 + | 2582.780
## chr17:26651964-26652050:+ chr17 26651964-26652050 + | 51.707
## chr17:28161574-28161757:+ chr17 28161574-28161757 + | 1215.010
## chr17:28670871-28670986:+ chr17 28670871-28670986 + | 3063.827
## chr17:28683436-28683585:+ chr17 28683436-28683585 + | 728.827
## ... ... ... ... . ...
## chr17:43639501-43639675:- chr17 43639501-43639675 - | 195.1327
## chr17:43910083-43910371:- chr17 43910083-43910371 - | 2248.6892
## chr17:44487317-44487409:- chr17 44487317-44487409 - | 1555.0730
## chr17:45175977-45175990:- chr17 45175977-45175990 - | 31.9905
## chr17:45545922-45545996:- chr17 45545922-45545996 - | 866.2800
## dominant_ctss tpm.dominant_ctss annotation
## <GRanges> <numeric> <Rle>
## chr17:26645157-26645514:+ chr17:26645160:+ 317.3201 promoter
## chr17:26651964-26652050:+ chr17:26652002:+ 23.1206 exon
## chr17:28161574-28161757:+ chr17:28161692:+ 269.0824 promoter
## chr17:28670871-28670986:+ chr17:28670882:+ 447.2790 promoter
## chr17:28683436-28683585:+ chr17:28683496:+ 270.7466 promoter
## ... ... ... ...
## chr17:43639501-43639675:- chr17:43639621:- 50.1326 promoter
## chr17:43910083-43910371:- chr17:43910245:- 362.3547 promoter
## chr17:44487317-44487409:- chr17:44487371:- 513.7589 promoter
## chr17:45175977-45175990:- chr17:45175986:- 19.9999 promoter
## chr17:45545922-45545996:- chr17:45545991:- 277.0181 promoter
## genes exprClass
## <Rle> <Rle>
## chr17:26645157-26645514:+ pgrmc2 0_1
## chr17:26651964-26652050:+ pgrmc2 0_1
## chr17:28161574-28161757:+ TMEM30B 0_1
## chr17:28670871-28670986:+ MIS18BP1 0_1
## chr17:28683436-28683585:+ heatr5a 0_1
## ... ... ...
## chr17:43639501-43639675:- zfyve28 0_1
## chr17:43910083-43910371:- ahsa1l 0_1
## chr17:44487317-44487409:- exoc5 0_1
## chr17:45175977-45175990:- fam161b 0_1
## chr17:45545922-45545996:- znf106a 0_1
## -------
## seqinfo: 26 sequences (1 circular) from danRer7 genomeConsensus clusters and information on their expression profile can be exported to a BED file, which allows visualization of the promoters in the genome browser colored in the color of the expression cluster they belong to (Figure 7:
cc_iqtrack <- exportToTrack(ce, what = "consensusClusters", colorByExpressionProfile = TRUE)
cc_iqtrack## UCSC track 'TC'
## UCSCData object with 285 ranges and 6 metadata columns:
## seqnames ranges strand | score
## <Rle> <IRanges> <Rle> | <integer>
## chr17:26453647-26453719:+ chr17 26453647-26453719 + | 0
## chr17:26564524-26564591:+ chr17 26564524-26564591 + | 0
## chr17:26595673-26595750:+ chr17 26595673-26595750 + | 0
## chr17:26596033-26596339:+ chr17 26596033-26596339 + | 0
## chr17:26645157-26645514:+ chr17 26645157-26645514 + | 0
## ... ... ... ... . ...
## chr17:45534727-45534729:- chr17 45534727-45534729 - | 0
## chr17:45545922-45545996:- chr17 45545922-45545996 - | 0
## chr17:45554314-45554345:- chr17 45554314-45554345 - | 0
## chr17:45700092-45700187:- chr17 45700092-45700187 - | 0
## chr17:45901695-45901752:- chr17 45901695-45901752 - | 0
## dominant_ctss tpm.dominant_ctss annotation
## <GRanges> <numeric> <Rle>
## chr17:26453647-26453719:+ chr17:26453701:+ 37.9264 promoter
## chr17:26564524-26564591:+ chr17:26564585:+ 65.7220 promoter
## chr17:26595673-26595750:+ chr17:26595750:+ 368.6590 promoter
## chr17:26596033-26596339:+ chr17:26596198:+ 55.5861 promoter
## chr17:26645157-26645514:+ chr17:26645160:+ 317.3201 promoter
## ... ... ... ...
## chr17:45534727-45534729:- chr17:45534727:- 146.95219 intron
## chr17:45545922-45545996:- chr17:45545991:- 277.01814 promoter
## chr17:45554314-45554345:- chr17:45554339:- 17.51376 promoter
## chr17:45700092-45700187:- chr17:45700182:- 9.21467 exon
## chr17:45901695-45901752:- chr17:45901749:- 283.80412 promoter
## genes exprClass
## <Rle> <Rle>
## chr17:26453647-26453719:+ ttc7b 1_1
## chr17:26564524-26564591:+ nrde2 0_0
## chr17:26595673-26595750:+ larp1b 1_0
## chr17:26596033-26596339:+ larp1b 1_0
## chr17:26645157-26645514:+ pgrmc2 0_1
## ... ... ...
## chr17:45534727-45534729:- znf106a 3_1
## chr17:45545922-45545996:- znf106a 0_1
## chr17:45554314-45554345:- tmem206 2_0
## chr17:45700092-45700187:- susd4 <NA>
## chr17:45901695-45901752:- arf6b 1_1
## -------
## seqinfo: 26 sequences (1 circular) from danRer7 genome#rtracklayer::export.bed(cc_iqtrack, "outputFileName.bed")
Figure 7: Consensus clusters colored by expression profile in the genome browser
Expression profiling of individual TSSs is done using the same procedure as
described above for consensus clusters, only by setting wha = "CTSS" in all
of the functions.
3.12 Differential expression analysis
The raw expression table for the consensus clusters can be exported to the DESeq2 package for differential expression analysis. For this, the column data needs to contain factors that can group the samples. They can have any name.
ce$group <- factor(c("a", "a", "b", "b"))
dds <- consensusClustersDESeq2(ce, ~group)3.13 Shifting promoters
As shown in Figure 6, TSSs within the same promoter region can be used differently in different samples. Thus, although the overall transcription level from a promoter does not change between the samples, the differential usage of TSSs or promoter shifting may indicate changes in the regulation of transcription from that promoter, which cannot be detected by expression profiling. To detect this promoter shifting, a method described in @[Haberle:2014] has been implemented in CAGEr. Shifting can be detected between two individual samples or between two groups of samples. In the latter case, samples are first merged into groups and then compared in the same way as two individual samples. For all promoters a shifting score is calculated based on the difference in the cumulative distribution of CAGE signal along that promoter in the two samples. In addition, a more general assessment of differential TSS usage is obtained by performing Kolmogorov-Smirnov test on the cumulative distributions of CAGE signal, as described below. Thus, prior to shifting score calculation and statistical testing, we have to calculate cumulative distribution along all consensus clusters:
ce <- cumulativeCTSSdistribution(ce, clusters = "consensusClusters")Next, we calculate a shifting score and P-value of Kolmogorov-Smirnov test for all promoters comparing two specified samples:
ce <- scoreShift(ce, groupX = "Zf.unfertilized.egg", groupY = "Zf.prim6",
testKS = TRUE, useTpmKS = FALSE)## Warning in min(x[nrow(x), ]): no non-missing arguments to min; returning Inf## Warning in max(x[, less.tpm]): no non-missing arguments to max; returning -Inf## Warning in min(x[nrow(x), ]): no non-missing arguments to min; returning Inf## Warning in max(x[, less.tpm]): no non-missing arguments to max; returning -Inf## Warning in min(x[nrow(x), ]): no non-missing arguments to min; returning Inf## Warning in max(x[, less.tpm]): no non-missing arguments to max; returning -Inf## Warning in min(x[nrow(x), ]): no non-missing arguments to min; returning Inf## Warning in max(x[, less.tpm]): no non-missing arguments to max; returning -Inf## Warning in min(x[nrow(x), ]): no non-missing arguments to min; returning Inf## Warning in max(x[, less.tpm]): no non-missing arguments to max; returning -Inf## Warning in min(x[nrow(x), ]): no non-missing arguments to min; returning Inf## Warning in max(x[, less.tpm]): no non-missing arguments to max; returning -Inf## Warning in min(x[nrow(x), ]): no non-missing arguments to min; returning Inf## Warning in max(x[, less.tpm]): no non-missing arguments to max; returning -Inf## Warning in min(x[nrow(x), ]): no non-missing arguments to min; returning Inf## Warning in max(x[, less.tpm]): no non-missing arguments to max; returning -Inf## Warning in min(x[nrow(x), ]): no non-missing arguments to min; returning Inf## Warning in max(x[, less.tpm]): no non-missing arguments to max; returning -Inf## Warning in min(x[nrow(x), ]): no non-missing arguments to min; returning Inf## Warning in max(x[, less.tpm]): no non-missing arguments to max; returning -Inf## Warning in min(x[nrow(x), ]): no non-missing arguments to min; returning Inf## Warning in max(x[, less.tpm]): no non-missing arguments to max; returning -Inf## Warning in min(x[nrow(x), ]): no non-missing arguments to min; returning Inf## Warning in max(x[, less.tpm]): no non-missing arguments to max; returning -Inf## Warning in min(x[nrow(x), ]): no non-missing arguments to min; returning Inf## Warning in max(x[, less.tpm]): no non-missing arguments to max; returning -Inf## Warning in max(x[, "groupX"]): no non-missing arguments to max; returning -Inf
## Warning in max(x[, "groupX"]): no non-missing arguments to max; returning -Inf
## Warning in max(x[, "groupX"]): no non-missing arguments to max; returning -Inf
## Warning in max(x[, "groupX"]): no non-missing arguments to max; returning -Inf
## Warning in max(x[, "groupX"]): no non-missing arguments to max; returning -Inf
## Warning in max(x[, "groupX"]): no non-missing arguments to max; returning -Inf
## Warning in max(x[, "groupX"]): no non-missing arguments to max; returning -Inf
## Warning in max(x[, "groupX"]): no non-missing arguments to max; returning -Inf
## Warning in max(x[, "groupX"]): no non-missing arguments to max; returning -Inf
## Warning in max(x[, "groupX"]): no non-missing arguments to max; returning -Inf
## Warning in max(x[, "groupX"]): no non-missing arguments to max; returning -Inf
## Warning in max(x[, "groupX"]): no non-missing arguments to max; returning -Inf
## Warning in max(x[, "groupX"]): no non-missing arguments to max; returning -Inf## Warning in max(x[, "groupY"]): no non-missing arguments to max; returning -Inf
## Warning in max(x[, "groupY"]): no non-missing arguments to max; returning -Inf
## Warning in max(x[, "groupY"]): no non-missing arguments to max; returning -Inf
## Warning in max(x[, "groupY"]): no non-missing arguments to max; returning -Inf
## Warning in max(x[, "groupY"]): no non-missing arguments to max; returning -Inf
## Warning in max(x[, "groupY"]): no non-missing arguments to max; returning -Inf
## Warning in max(x[, "groupY"]): no non-missing arguments to max; returning -Inf
## Warning in max(x[, "groupY"]): no non-missing arguments to max; returning -Inf
## Warning in max(x[, "groupY"]): no non-missing arguments to max; returning -Inf
## Warning in max(x[, "groupY"]): no non-missing arguments to max; returning -Inf
## Warning in max(x[, "groupY"]): no non-missing arguments to max; returning -Inf
## Warning in max(x[, "groupY"]): no non-missing arguments to max; returning -Inf
## Warning in max(x[, "groupY"]): no non-missing arguments to max; returning -Inf## Warning in max(cumsum.matrix[, 1]): no non-missing arguments to max; returning
## -Inf## Warning in max(cumsum.matrix[, 2]): no non-missing arguments to max; returning
## -Inf## Warning in max(abs(z)): no non-missing arguments to max; returning -Inf## Warning in max(cumsum.matrix[, 1]): no non-missing arguments to max; returning
## -Inf## Warning in max(cumsum.matrix[, 2]): no non-missing arguments to max; returning
## -Inf## Warning in max(abs(z)): no non-missing arguments to max; returning -Inf## Warning in max(cumsum.matrix[, 1]): no non-missing arguments to max; returning
## -Inf## Warning in max(cumsum.matrix[, 2]): no non-missing arguments to max; returning
## -Inf## Warning in max(abs(z)): no non-missing arguments to max; returning -Inf## Warning in max(cumsum.matrix[, 1]): no non-missing arguments to max; returning
## -Inf## Warning in max(cumsum.matrix[, 2]): no non-missing arguments to max; returning
## -Inf## Warning in max(abs(z)): no non-missing arguments to max; returning -Inf## Warning in max(cumsum.matrix[, 1]): no non-missing arguments to max; returning
## -Inf## Warning in max(cumsum.matrix[, 2]): no non-missing arguments to max; returning
## -Inf## Warning in max(abs(z)): no non-missing arguments to max; returning -Inf## Warning in max(cumsum.matrix[, 1]): no non-missing arguments to max; returning
## -Inf## Warning in max(cumsum.matrix[, 2]): no non-missing arguments to max; returning
## -Inf## Warning in max(abs(z)): no non-missing arguments to max; returning -Inf## Warning in max(cumsum.matrix[, 1]): no non-missing arguments to max; returning
## -Inf## Warning in max(cumsum.matrix[, 2]): no non-missing arguments to max; returning
## -Inf## Warning in max(abs(z)): no non-missing arguments to max; returning -Inf## Warning in max(cumsum.matrix[, 1]): no non-missing arguments to max; returning
## -Inf## Warning in max(cumsum.matrix[, 2]): no non-missing arguments to max; returning
## -Inf## Warning in max(abs(z)): no non-missing arguments to max; returning -Inf## Warning in max(cumsum.matrix[, 1]): no non-missing arguments to max; returning
## -Inf## Warning in max(cumsum.matrix[, 2]): no non-missing arguments to max; returning
## -Inf## Warning in max(abs(z)): no non-missing arguments to max; returning -Inf## Warning in max(cumsum.matrix[, 1]): no non-missing arguments to max; returning
## -Inf## Warning in max(cumsum.matrix[, 2]): no non-missing arguments to max; returning
## -Inf## Warning in max(abs(z)): no non-missing arguments to max; returning -Inf## Warning in max(cumsum.matrix[, 1]): no non-missing arguments to max; returning
## -Inf## Warning in max(cumsum.matrix[, 2]): no non-missing arguments to max; returning
## -Inf## Warning in max(abs(z)): no non-missing arguments to max; returning -Inf## Warning in max(cumsum.matrix[, 1]): no non-missing arguments to max; returning
## -Inf## Warning in max(cumsum.matrix[, 2]): no non-missing arguments to max; returning
## -Inf## Warning in max(abs(z)): no non-missing arguments to max; returning -Inf## Warning in max(cumsum.matrix[, 1]): no non-missing arguments to max; returning
## -Inf## Warning in max(cumsum.matrix[, 2]): no non-missing arguments to max; returning
## -Inf## Warning in max(abs(z)): no non-missing arguments to max; returning -InfThis function will calculate shifting score as illustrated in
Figure 8. Values of shifting score are in range between
-Inf and 1. Positive values can be interpreted as the proportion of
transcription initiation in the sample with lower expression that is happening
“outside” (either upstream or downstream) of the region used for transcription
initiation in the other sample. In contrast, negative values indicate no
physical separation, i.e. the region used for transcription initiation in the
sample with lower expression is completely contained within the region used for
transcription initiation in the other sample. Thus, shifting score detects only
the degree of upstream or downstream shifting, but does not detect more general
changes in TSS rearrangement in the region, e.g. narrowing or broadening of
the region used for transcription.
To assess any general change in the TSS usage within the promoter region,
a two-sample Kolmogorov-Smirnov (K-S) test on cumulative sums of CAGE signal
along the consensus cluster is performed. Cumulative sums in both samples are
scaled to range between 0 and 1 and are considered to be empirical cumulative
distribution functions (ECDF) reflecting sampling of TSS positions during
transcription initiation. K-S test is performed to assess whether the two
underlying probability distributions differ. To obtain a P-value i.e. the
level at which the null-hypothesis can be rejected), sample sizes that generated
the ECDFs are required, in addition to actual K-S statistics calculated from
ECDFs. These are derived either from raw tag counts, i.e. exact number of
times each TSS in the cluster was sampled during sequencing (when
useTpmKS = FALSE), or from normalized tpm values (when useTpmKS = TRUE).
P-values obtained from K-S tests are further corrected for multiple testing
using Benjamini and Hochberg (BH) method and for each P-value a corresponding
false-discovery rate (FDR) is also reported.
Figure 8: Calculation of shifting score
We can select a subset of promoters with shifting score and/or FDR above specified threshold:
# Not supported yet for CAGEexp objects, sorry.
shifting.promoters <- getShiftingPromoters(ce,
groupX = "Zf.unfertilized.egg", groupY = "Zf.prim6",
tpmThreshold = 5, scoreThreshold = 0.6,
fdrThreshold = 0.01)
head(shifting.promoters)## DataFrame with 6 rows and 10 columns
## consensus.cluster score score
## <character> <numeric> <numeric>
## chr17:26595673-26595750:+ chr17:26595673-26595.. 835.8583 835.8583
## chr17:33502378-33502474:+ chr17:33502378-33502.. 21.7111 21.7111
## chr17:33581354-33581420:+ chr17:33581354-33581.. 46.4559 46.4559
## chr17:35410890-35410920:+ chr17:35410890-35410.. 258.8781 258.8781
## chr17:37383275-37383398:+ chr17:37383275-37383.. 62.6232 62.6232
## chr17:37395388-37395497:+ chr17:37395388-37395.. 225.8784 225.8784
## shifting.score.Zf.unfertilized.egg.Zf.prim6
## <numeric>
## chr17:26595673-26595750:+ 0.703858
## chr17:33502378-33502474:+ 0.654196
## chr17:33581354-33581420:+ 0.602743
## chr17:35410890-35410920:+ 0.609506
## chr17:37383275-37383398:+ 0.733289
## chr17:37395388-37395497:+ 0.659035
## groupX.pos.Zf.unfertilized.egg groupY.pos.Zf.prim6
## <integer> <integer>
## chr17:26595673-26595750:+ 26595751 26595710
## chr17:33502378-33502474:+ 33502455 33502393
## chr17:33581354-33581420:+ 33581375 33581408
## chr17:35410890-35410920:+ 35410921 35410894
## chr17:37383275-37383398:+ 37383278 37383376
## chr17:37395388-37395497:+ 37395411 37395469
## groupX.tpm.Zf.unfertilized.egg groupY.tpm.Zf.prim6
## <numeric> <numeric>
## chr17:26595673-26595750:+ 188.0397 233.7861
## chr17:33502378-33502474:+ 15.2911 9.3698
## chr17:33581354-33581420:+ 22.5912 15.1074
## chr17:35410890-35410920:+ 93.1385 67.0356
## chr17:37383275-37383398:+ 14.0695 44.9775
## chr17:37395388-37395497:+ 58.4980 134.5016
## pvalue.KS.Zf.unfertilized.egg.Zf.prim6
## <numeric>
## chr17:26595673-26595750:+ 0.00000e+00
## chr17:33502378-33502474:+ 2.09475e-05
## chr17:33581354-33581420:+ 1.04406e-05
## chr17:35410890-35410920:+ 0.00000e+00
## chr17:37383275-37383398:+ 2.68232e-11
## chr17:37395388-37395497:+ 0.00000e+00
## fdr.KS.Zf.unfertilized.egg.Zf.prim6
## <numeric>
## chr17:26595673-26595750:+ 0.00000e+00
## chr17:33502378-33502474:+ 4.94267e-05
## chr17:33581354-33581420:+ 2.49150e-05
## chr17:35410890-35410920:+ 0.00000e+00
## chr17:37383275-37383398:+ 1.11803e-10
## chr17:37395388-37395497:+ 0.00000e+00The getShiftingPromoters function returns genomic coordinates, shifting score
and P-value (FDR) of the promoters, as well as the value of CAGE signal and
position of the dominant TSS in the two compared (groups of) samples.
Figure 9 shows the difference in the CAGE signal
between the two compared samples for one of the selected high-scoring shifting
promoters.
Figure 9: Example of shifting promoter
3.14 Enhancers
The FANTOM5 project reported that “enhancer activity can be detected through
the presence of balanced bidirectional capped transcripts” (Andersson et al. 2014).
The CAGEr package is providing a wrapper to the CAGEfightR package’s
function quickEnhancers so that it can run directly on CAGEexp objects.
The wrapper returns a modified CAGEexp object in which the results are stored
in its enhancers experiment slot.
ce <- quickEnhancers(ce)
ce[["enhancers"]]## class: RangedSummarizedExperiment
## dim: 33 4
## metadata(0):
## assays(1): counts
## rownames(33): chr17:26690165-26690757 chr17:27120436-27120991 ...
## chr17:45175861-45176390 chr17:45611150-45611574
## rowData names(4): score thick balance bidirectionality
## colnames(4): Zf.unfertilized.egg Zf.high Zf.30p.dome Zf.prim6
## colData names(12): inputFiles inputFilesType ... Name totalTags4 Appendix
4.1 Creating a CAGEexp object by coercing a data frame
A CAGEexp object can also be created directly by coercing a data frame containing single base-pair TSS information. To be able to do the coercion into a CAGEexp, the data frame must conform with the following:
The data frame must have at least 4 columns;
the first three columns must be named
chr,posandstrand, and contain chromosome name, 1-based genomic coordinate of the TSS (positive integer) and TSS strand information (+or-), respectively;these first three columns must be of the class
character,integerandcharacter, respectively;all additional columns must be of the class
integerand should contain raw CAGE tag counts (non-negative integer) supporting each TSS in different samples (columns). At least one such column with tag counts must be present;the names of the columns containing tag counts must begin with a letter, and these column names are used as sample labels in the resulting CAGEexp object.
An example of such data frame is shown below:
TSS.df <- read.table(system.file( "extdata/Zf.unfertilized.egg.chr17.ctss"
, package = "CAGEr"))
# make sure the column names are as required
colnames(TSS.df) <- c("chr", "pos", "strand", "Zf.unfertilized.egg")
# make sure the column classes are as required
TSS.df$chr <- as.character(TSS.df$chr)
TSS.df$pos <- as.integer(TSS.df$pos)
TSS.df$strand <- as.character(TSS.df$strand)
TSS.df$Zf.unfertilized.egg <- as.integer(TSS.df$Zf.unfertilized.egg)
head(TSS.df)## chr pos strand Zf.unfertilized.egg
## 1 chr17 26050540 + 1
## 2 chr17 26074127 - 2
## 3 chr17 26074129 - 3
## 4 chr17 26222545 - 1
## 5 chr17 26322780 - 1
## 6 chr17 26322832 - 2This data.frame can now be coerced to a CAGEexp object, which will fill the corresponding slots of the object with provided TSS information:
ce.coerced <- as(TSS.df, "CAGEexp")
ce.coerced## A CAGEexp object of 1 listed
## experiment with a user-defined name and respective class.
## Containing an ExperimentList class object of length 1:
## [1] tagCountMatrix: RangedSummarizedExperiment with 8395 rows and 1 columns
## Functionality:
## experiments() - obtain the ExperimentList instance
## colData() - the primary/phenotype DataFrame
## sampleMap() - the sample coordination DataFrame
## `$`, `[`, `[[` - extract colData columns, subset, or experiment
## *Format() - convert into a long or wide DataFrame
## assays() - convert ExperimentList to a SimpleList of matrices
## exportClass() - save data to flat files4.2 Summary of the CAGEr accessor functions
Originally there was one accessor per slot in CAGEset objects (the original CAGEr format), but now that I added the CAGEexp class, that uses different underlying formats, the number of accessors increased because a) I provide the old ones for backwards compatibility and b) there multiple possible output formats.
Before releasing this CAGEr update in Bioconductor, I would like to be sure that the number of accessors and the name scheme are good enough.
Please let me know your opinion about the names and scope of the accessors below:
4.2.1 CTSS data
| Name | Output |
|---|---|
| CTSScoordinatesGR | Coordinate table in GRanges format. |
| CTSStagCountDA | Raw CTSS counts in DelayedArray format wrapping a integer Rle DataFrame. |
| CTSStagCountDF | Raw CTSS counts in integer Rle DataFrame format. |
| CTSStagCountGR | Raw CTSS counts in GRanges format (single samples). |
| CTSStagCountSE | RangedSummarizedExperiment containing an assay for the Raw CTSS counts. |
| CTSSnormalizedTpmDF | Normalised CTSS values in Rle DataFrame format. |
| CTSSnormalizedTpmGR | Normalised CTSS values in GRanges format (single samples). |
4.2.2 Cluster data
| Name | Output |
|---|---|
| consensusClustersDESeq2 | Consensus cluster coordinates and expression matrix in DESeq2 format. |
| consensusClustersGR | Consensus cluster coordinates in GRanges format. |
| consensusClustersSE | Consensus cluster coordinates and expression matrix in RangedSummarizedExperiment format. |
| consensusClustersTpm | Consensus cluster coordinates and raw expression matrix. |
| tagClustersGR | Per-sample GRangesList of tag cluster coordinates. |
4.2.3 Gene data
| Name | Output |
|---|---|
| GeneExpDESeq2 | Gene expression data in DESeq2 format. |
| GeneExpSE | Gene expression data in SummarizedExperiment format. |
4.3 Summary of the CAGEexp experiment slots and assays
A CAGEexp object can contain the following experiments.
Please let me know your opinion about the names
| Name | Assays | Comment |
|---|---|---|
| tagCountMatrix | counts, normalizedTpmMatrix | RangedSummarizedExperiment |
| seqNameTotals | counts, norm | SummarizedExperiment |
| consensusClusters | counts, normalized, q_x, q_y | RangedSummarizedExperiment |
| geneExpMatrix | counts | SummarizedExperiment |
4.3.1 CAGEexp assays
| Name | Experiment | Comment |
|---|---|---|
| counts | tagCountMatrix | Integer Rle DataFrame of CTSS raw counts. |
| counts | seqNameTotals | Numeric matrix of total counts per chromosome. |
| counts | consensusClusters | Integer matrix of consensus cluster expression counts. |
| counts | geneExpMatrix | Integer matrix of gene expression counts. |
| normalizedTpmMatrix | tagCountMatrix | Numeric matrix of normalised CTSS expression scores. |
| norm | seqNameTotals | Numeric matrix of percent total counts per chromosome. |
| normalized | consensusClusters | Numeric matrix of normalised CC expression scores. |
| q_x, q_y, q_z, … | consensusClusters | Interger Rle DataFrame of relative quantile positions |
4.4 Summary of the CAGEr classes
The CTSS, CTSS.chr, TagCluster and ConsensusClsuters are mostly used internally or type safety and preventing me (Charles) from mixing up inputs. They are visible from the outside. Should they be used more extensively ? Can they be replaced by more “core” Bioconductor classes ?
| Name | Comment |
|---|---|
| CAGEset | The original CAGEr class, based on data frames and matrices. |
| CAGEexp | The new CAGEr class, based on GRanges, DataFrames, etc. |
| CAGEr | Union class for functions that accept both CAGEset and CAGEexp. |
| CTSS | Wraps GRanges and guarantees that width equals 1. |
| CTSS.chr | Same as CTSS but also guarantees the there is only one chromosome (useful in some loops) |
| TagClusters | Wraps GRanges, represents the fact that each sample has their own tag clusters. |
| ConsensusClusters | Wraps GRanges, represents the fact that they are valid for all the samples. |
| CAGErCluster | Union class for functions that accept both TagClusters and ConsensusClusters. |
Session info
sessionInfo()## R version 4.3.1 (2023-06-16)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.2 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.17-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] FANTOM3and4CAGE_1.36.0 CAGEr_2.6.1
## [3] MultiAssayExperiment_1.26.0 SummarizedExperiment_1.30.2
## [5] Biobase_2.60.0 GenomicRanges_1.52.0
## [7] GenomeInfoDb_1.36.1 IRanges_2.34.1
## [9] S4Vectors_0.38.1 BiocGenerics_0.46.0
## [11] MatrixGenerics_1.12.2 matrixStats_1.0.0
## [13] BiocStyle_2.28.0
##
## loaded via a namespace (and not attached):
## [1] RColorBrewer_1.1-3 rstudioapi_0.14
## [3] jsonlite_1.8.7 magrittr_2.0.3
## [5] magick_2.7.4 GenomicFeatures_1.52.1
## [7] farver_2.1.1 rmarkdown_2.23
## [9] BiocIO_1.10.0 zlibbioc_1.46.0
## [11] vctrs_0.6.3 memoise_2.0.1
## [13] Rsamtools_2.16.0 DelayedMatrixStats_1.22.1
## [15] RCurl_1.98-1.12 base64enc_0.1-3
## [17] BiocBaseUtils_1.2.0 htmltools_0.5.5
## [19] S4Arrays_1.0.4 progress_1.2.2
## [21] curl_5.0.1 Formula_1.2-5
## [23] sass_0.4.6 KernSmooth_2.23-21
## [25] bslib_0.5.0 htmlwidgets_1.6.2
## [27] plyr_1.8.8 Gviz_1.44.0
## [29] cachem_1.0.8 GenomicAlignments_1.36.0
## [31] lifecycle_1.0.3 pkgconfig_2.0.3
## [33] Matrix_1.5-4.1 R6_2.5.1
## [35] fastmap_1.1.1 GenomeInfoDbData_1.2.10
## [37] digest_0.6.32 colorspace_2.1-0
## [39] BSgenome.Drerio.UCSC.danRer7_1.4.0 AnnotationDbi_1.62.2
## [41] DESeq2_1.40.2 Hmisc_5.1-0
## [43] RSQLite_2.3.1 vegan_2.6-4
## [45] labeling_0.4.2 filelock_1.0.2
## [47] fansi_1.0.4 mgcv_1.8-42
## [49] httr_1.4.6 compiler_4.3.1
## [51] withr_2.5.0 bit64_4.0.5
## [53] htmlTable_2.4.1 backports_1.4.1
## [55] CAGEfightR_1.20.0 BiocParallel_1.34.2
## [57] DBI_1.1.3 highr_0.10
## [59] biomaRt_2.56.1 MASS_7.3-60
## [61] rappdirs_0.3.3 DelayedArray_0.26.6
## [63] rjson_0.2.21 permute_0.9-7
## [65] gtools_3.9.4 tools_4.3.1
## [67] foreign_0.8-84 nnet_7.3-19
## [69] glue_1.6.2 restfulr_0.0.15
## [71] nlme_3.1-162 stringdist_0.9.10
## [73] grid_4.3.1 checkmate_2.2.0
## [75] reshape2_1.4.4 cluster_2.1.4
## [77] generics_0.1.3 operator.tools_1.6.3
## [79] gtable_0.3.3 BSgenome_1.68.0
## [81] formula.tools_1.7.1 ensembldb_2.24.0
## [83] data.table_1.14.8 hms_1.1.3
## [85] xml2_1.3.4 utf8_1.2.3
## [87] XVector_0.40.0 pillar_1.9.0
## [89] stringr_1.5.0 splines_4.3.1
## [91] dplyr_1.1.2 BiocFileCache_2.8.0
## [93] lattice_0.21-8 rtracklayer_1.60.0
## [95] bit_4.0.5 deldir_1.0-9
## [97] biovizBase_1.48.0 tidyselect_1.2.0
## [99] locfit_1.5-9.8 Biostrings_2.68.1
## [101] knitr_1.43 gridExtra_2.3
## [103] bookdown_0.34 ProtGenerics_1.32.0
## [105] xfun_0.39 VGAM_1.1-8
## [107] stringi_1.7.12 lazyeval_0.2.2
## [109] yaml_2.3.7 som_0.3-5.1
## [111] evaluate_0.21 codetools_0.2-19
## [113] interp_1.1-4 tibble_3.2.1
## [115] BiocManager_1.30.21 cli_3.6.1
## [117] rpart_4.1.19 munsell_0.5.0
## [119] jquerylib_0.1.4 dichromat_2.0-0.1
## [121] Rcpp_1.0.10 dbplyr_2.3.2
## [123] png_0.1-8 XML_3.99-0.14
## [125] parallel_4.3.1 ggplot2_3.4.2
## [127] assertthat_0.2.1 blob_1.2.4
## [129] prettyunits_1.1.1 latticeExtra_0.6-30
## [131] jpeg_0.1-10 AnnotationFilter_1.24.0
## [133] sparseMatrixStats_1.12.2 bitops_1.0-7
## [135] VariantAnnotation_1.46.0 scales_1.2.1
## [137] crayon_1.5.2 rlang_1.1.1
## [139] KEGGREST_1.40.0References
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