findRegions | R Documentation |

Find genomic regions for which a numeric vector is above (or below) predefined thresholds. In other words, this function finds the candidate Differentially Expressed Regions (candidate DERs). This is similar to regionFinder and is a helper function for calculatePvalues.

```
findRegions(
position = NULL,
fstats,
chr,
oneTable = TRUE,
maxClusterGap = 300L,
cutoff = quantile(fstats, 0.99, na.rm = TRUE),
segmentIR = NULL,
smooth = FALSE,
weights = NULL,
smoothFunction = bumphunter::locfitByCluster,
...
)
```

`position` |
A logical Rle of genomic positions. This is generated in
loadCoverage. Note that it gets updated in preprocessCoverage
if |

`fstats` |
A numeric Rle with the F-statistics. Usually obtained using calculateStats. |

`chr` |
A single element character vector specifying the chromosome name. |

`oneTable` |
If |

`maxClusterGap` |
This determines the maximum gap between candidate DERs.
It should be greater than |

`cutoff` |
Threshold applied to the |

`segmentIR` |
An IRanges object with the genomic positions that are potentials DERs. This is used in calculatePvalues to speed up permutation calculations. |

`smooth` |
Whether to smooth the F-statistics ( |

`weights` |
Weights used by the smoother as described in smoother. |

`smoothFunction` |
A function to be used for smoothing the F-statistics.
Two functions are provided by the |

`...` |
Arguments passed to other methods and/or advanced arguments. Advanced arguments: - verbose
If `TRUE` basic status updates will be printed along the way.- basic
If `TRUE` a DataFrame is returned that has only basic information on the candidate DERs. This is used in calculatePvalues to speed up permutation calculations. Default:`FALSE` .- maxRegionGap
This determines the maximum number of gaps between two genomic positions to be considered part of the same candidate region. The default is 0L.
Passed to extendedMapSeqlevels and the internal function
When |

regionFinder adapted to Rle world.

Either a GRanges or a GRangesList as determined by `oneTable`

.
Each of them has the following metadata variables.

- value
The mean of the values of

`y`

for the given region.- area
The absolute value of the sum of the values of

`y`

for the given region.- indexStart
The start position of the region in terms of the index for

`y`

.- indexEnd
The end position of the region in terms of the index for

`y`

.- cluster
The cluser ID.

- clusterL
The total length of the cluster.

Leonardo Collado-Torres

Rafael A. Irizarry, Martin Aryee, Hector Corrada Bravo, Kasper D. Hansen and Harris A. Jaffee. bumphunter: Bump Hunter. R package version 1.1.10.

calculatePvalues

```
## Preprocess the data
prep <- preprocessCoverage(genomeData,
cutoff = 0, scalefac = 32, chunksize = 1e3,
colsubset = NULL
)
## Get the F statistics
fstats <- genomeFstats
## Find the regions
regs <- findRegions(prep$position, fstats, "chr21", verbose = TRUE)
regs
## Not run:
## Once you have the regions you can proceed to annotate them
library("bumphunter")
genes <- annotateTranscripts(TxDb.Hsapiens.UCSC.hg19.knownGene::TxDb.Hsapiens.UCSC.hg19.knownGene)
annotation <- matchGenes(regs, genes)
annotation
## End(Not run)
# Find regions with smoothing the F-statistics by bumphunter::runmedByCluster
regs_smooth <- findRegions(prep$position, fstats, "chr21",
verbose = TRUE,
smoothFunction = bumphunter::runmedByCluster
)
## Compare against the object regs obtained earlier
regs_smooth
```

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