Description Usage Arguments Value See Also Examples
Finds stretches or groups of clusters along the genome, where each cluster is within a certain distance of the next. Once stretches have been identified, the average pairwise correlation between all clusters in the stretch is calculated. A typical use case is to look for stretches of enhancers, often refered to as "super enhancers".
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | findStretches(object, ...)
## S4 method for signature 'GRanges'
findStretches(object, mergeDist = 10000L, minSize = 3L)
## S4 method for signature 'RangedSummarizedExperiment'
findStretches(
object,
inputAssay,
mergeDist = 10000L,
minSize = 3L,
corFun = cor,
...
)
|
object |
GRanges or RangedSummarizedExperiment: Clusters, possibly with expression for calculating correlations. |
... |
additional arguments passed to methods or ultimately corFun. |
mergeDist |
integer: Maximum distance between clusters to be merged into stretches. |
minSize |
integer: Minimum number of clusters in stretches. |
inputAssay |
character: Name of assay holding expression values (if object is a RangedSummarizedExperiment) |
corFun |
function: Function for calculating correlations. Should behave and produce output similar to cor(). |
A GRanges containing stretches with number of clusters and average pairwise correlations calculated. The revmap can be used to retrieve the original clusters (see example below.)
Other Spatial functions:
findLinks()
,
trackLinks()
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | # Calculate TPM values for bidirectional clusters
data(exampleBidirectional)
BCs <- calcTPM(exampleBidirectional)
# Find stretches
pearson_stretches <- findStretches(BCs, inputAssay="TPM")
# Use Kendall instead of pearson and require bigger stretches
kendall_stretches <- findStretches(BCs, inputAssay="TPM",
minSize=5, method="kendall")
# Use the revmap to get stretches as a GRangesList
grl <- extractList(rowRanges(BCs), kendall_stretches$revmap)
names(grl) <- names(kendall_stretches)
|
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