aggregateTagClusters {CAGEr} | R Documentation |
Aggregates tag clusters (TCs) across all CAGE datasets within the CAGEr object to create a referent set of consensus clusters.
aggregateTagClusters(object, tpmThreshold = 5, excludeSignalBelowThreshold = TRUE, qLow = NULL, qUp = NULL, maxDist = 100, useMulticore = FALSE, nrCores = NULL) ## S4 method for signature 'CAGEr' aggregateTagClusters(object, tpmThreshold = 5, excludeSignalBelowThreshold = TRUE, qLow = NULL, qUp = NULL, maxDist = 100, useMulticore = FALSE, nrCores = NULL)
object |
A |
tpmThreshold |
Ignore tag clusters with normalized signal |
excludeSignalBelowThreshold |
When |
qLow, qUp |
Set which "lower" (or "upper") quantile should be used as 5'
(or 3') boundary of the tag cluster. If |
maxDist |
Maximal length of the gap (in base-pairs) between two tag clusters for them to be part of the same consensus clusters. |
useMulticore |
Logical, should multicore be used. |
nrCores |
Number of cores to use when |
Since the tag clusters (TCs) returned by the clusterCTSS
function
are constructed separately for every CAGE sample within the CAGEr object, they can differ
between samples in both their number, genomic coordinates, position of dominant TSS and
overall signal. To be able to compare all samples at the level of clusters of TSSs, TCs
from all CAGE datasets are aggregated into a single set of consensus clusters.
First, TCs with signal >= tpmThreshold
from all CAGE datasets are selected, and their
5' and 3' boundaries are determined based on provided qLow
and qUp
parameter
(or the start and end coordinates, if qLow = NULL
and qUp = NULL
.
Finally, the defined set of TCs from all CAGE datasets is reduced to a non-overlapping set
of consensus clusters by merging overlapping TCs and TCs <= maxDist
base-pairs apart.
Consensus clusters represent a referent set of promoters that can be further used for
expression profiling or detecting "shifting" (differentially used) promoters between different
CAGE samples.
For CAGEset
objects, the consensusClusters
slot will be
populated with a data frame indicating the cluster name, chromosome, start and end
coordinates, the strand, and the normalised expression score of the cluster. This
table is returned by the consensusClusters
function.
For CAGEexp
objects, the experiment consensusClusters
will be occupied by a RangedSummarizedExperiment
containing the cluster
coodinates as row ranges, and their expression levels in the counts
and normalized
assays. These genomic ranges are returned by the consensusClustersGR
function.
The CTSS ranges of the tagCountMatrix
experiment will gain a
cluster
column indicating which cluster they belong to. Lastly, the number of
CTSS outside clusters will be documented in the outOfClusters
column data.
This table is returned by the consensusClusters
function.
Vanja Haberle
Charles Plessy
Other CAGEr object modifiers: CTSStoGenes
,
CustomConsensusClusters
,
annotateCTSS
, clusterCTSS
,
cumulativeCTSSdistribution
,
getCTSS
, normalizeTagCount
,
quantilePositions
,
summariseChrExpr
Other CAGEr clusters functions: CTSSclusteringMethod
,
CTSScumulativesTagClusters
,
CustomConsensusClusters
,
clusterCTSS
,
consensusClustersDESeq2
,
consensusClustersGR
,
cumulativeCTSSdistribution
,
plotInterquantileWidth
,
quantilePositions
,
tagClusters
head(consensusClusters(exampleCAGEset)) aggregateTagClusters( exampleCAGEset, tpmThreshold = 50 , excludeSignalBelowThreshold = FALSE, maxDist = 100) head(consensusClusters(exampleCAGEset)) aggregateTagClusters(object = exampleCAGEset, tpmThreshold = 50, excludeSignalBelowThreshold = FALSE, qLow = 0.1, qUp = 0.9, maxDist = 100) head(consensusClusters(exampleCAGEset)) consensusClustersGR(exampleCAGEexp) aggregateTagClusters( exampleCAGEexp, tpmThreshold = 50 , excludeSignalBelowThreshold = FALSE, maxDist = 100) consensusClustersGR(exampleCAGEexp) aggregateTagClusters( exampleCAGEexp, tpmThreshold = 50 , excludeSignalBelowThreshold = TRUE, maxDist = 100) consensusClustersGR(exampleCAGEexp) aggregateTagClusters( exampleCAGEexp, tpmThreshold = 50 , excludeSignalBelowThreshold = TRUE, maxDist = 100 , qLow = 0.1, qUp = 0.9) consensusClustersGR(exampleCAGEexp)