Description Usage Arguments Details Value Author(s) See Also Examples
This is a major wrapper for running several key functions from this package. It is meant to be used after loadCoverage has been used for a specific chromosome. The steps run include makeModels, preprocessCoverage, calculateStats, calculatePvalues and annotating with annotateTranscripts and matchGenes.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | analyzeChr(
chr,
coverageInfo,
models,
cutoffPre = 5,
cutoffFstat = 1e-08,
cutoffType = "theoretical",
nPermute = 1,
seeds = as.integer(gsub("-", "", Sys.Date())) + seq_len(nPermute),
groupInfo,
txdb = NULL,
writeOutput = TRUE,
runAnnotation = TRUE,
lowMemDir = file.path(chr, "chunksDir"),
smooth = FALSE,
weights = NULL,
smoothFunction = bumphunter::locfitByCluster,
...
)
|
chr |
Used for naming the output files when |
coverageInfo |
A list containing a DataFrame – |
models |
The output from makeModels. |
cutoffPre |
This argument is passed to preprocessCoverage
( |
cutoffFstat |
This is used to determine the cutoff argument of
calculatePvalues and it's behaviour is determined by
|
cutoffType |
If set to |
nPermute |
The number of permutations. Note that for a full chromosome,
a small amount (10) of permutations is sufficient. If set to 0, no
permutations are performed and thus no null regions are used, however, the
|
seeds |
An integer vector of length |
groupInfo |
A factor specifying the group membership of each sample
that can later be used with the plotting functions in the
|
txdb |
This argument is passed to
annotateTranscripts. If |
writeOutput |
If |
runAnnotation |
If |
lowMemDir |
If specified, each chunk is saved into a separate Rdata
file under |
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:
Passed to extendedMapSeqlevels, preprocessCoverage, calculateStats, calculatePvalues, annotateTranscripts, matchGenes, and define_cluster. |
If you are working with data from an organism different from 'Homo sapiens'
specify so by setting the global 'species' and 'chrsStyle' options. For
example:
options(species = 'arabidopsis_thaliana')
options(chrsStyle = 'NCBI')
If returnOutput=TRUE
, a list with six components:
The wallclock timing information for each step.
The main options used when running this function.
The output from preprocessCoverage.
The output from calculateStats.
The output from calculatePvalues.
The output from matchGenes.
These are the same components that are written to Rdata files if
writeOutput=TRUE
.
Leonardo Collado-Torres
makeModels, preprocessCoverage, calculateStats, calculatePvalues, annotateTranscripts, matchGenes
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | ## Collapse the coverage information
collapsedFull <- collapseFullCoverage(list(genomeData$coverage),
verbose = TRUE
)
## Calculate library size adjustments
sampleDepths <- sampleDepth(collapsedFull,
probs = c(0.5), nonzero = TRUE,
verbose = TRUE
)
## Build the models
groupInfo <- genomeInfo$pop
adjustvars <- data.frame(genomeInfo$gender)
models <- makeModels(sampleDepths, testvars = groupInfo, adjustvars = adjustvars)
## Analyze the chromosome
results <- analyzeChr(
chr = "21", coverageInfo = genomeData, models = models,
cutoffFstat = 1, cutoffType = "manual", groupInfo = groupInfo, mc.cores = 1,
writeOutput = FALSE, returnOutput = TRUE, method = "regular",
runAnnotation = FALSE
)
names(results)
|
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