analyzeChr: Run the derfinder analysis on a chromosome
In lcolladotor/derfinder: Annotation-agnostic differential expression analysis of RNA-seq data at base-pair resolution via the DER Finder approach

analyzeChr

R Documentation

Run the derfinder analysis on a chromosome

Description

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.

Usage

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,
  ...
)

Arguments

`chr`	Used for naming the output files when `writeOutput=TRUE` and the resulting `GRanges` object.
`coverageInfo`	A list containing a DataFrame –`⁠$coverage⁠`– with the coverage data and a logical Rle –`⁠$position⁠`– with the positions that passed the cutoff. This object is generated using loadCoverage. You should have specified a cutoff value for loadCoverage unless that you are using `colsubset` which will force a filtering step with filterData when running preprocessCoverage.
`models`	The output from makeModels.
`cutoffPre`	This argument is passed to preprocessCoverage (`cutoff`).
`cutoffFstat`	This is used to determine the cutoff argument of calculatePvalues and it's behaviour is determined by `cutoffType`.
`cutoffType`	If set to `empirical`, the `cutoffFstat` (example: 0.99) quantile is used via quantile. If set to `theoretical`, the theoretical `cutoffFstats` (example: 1e-08) is calculated via qf. If set to `manual`, `cutoffFstats` is passed to calculatePvalues without any other calculation.
`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 `⁠$regions⁠` component is created.
`seeds`	An integer vector of length `nPermute` specifying the seeds to be used for each permutation. If `NULL` no seeds are used.
`groupInfo`	A factor specifying the group membership of each sample that can later be used with the plotting functions in the `derfinderPlot` package.
`txdb`	This argument is passed to annotateTranscripts. If `NULL`, TxDb.Hsapiens.UCSC.hg19.knownGene is used.
`writeOutput`	If `TRUE`, output Rdata files are created at each step inside a directory with the chromosome name (example: 'chr21' if `chrnum='21'`). One Rdata file is created for each component described in the return section.
`runAnnotation`	If `TRUE` annotateTranscripts and matchGenes are run. Otherwise these steps are skipped.
`lowMemDir`	If specified, each chunk is saved into a separate Rdata file under `lowMemDir` and later loaded in fstats.apply when running calculateStats and calculatePvalues. Using this option helps reduce the memory load as each fork in bplapply loads only the data needed for the chunk processing. The downside is a bit longer computation time due to input/output.
`smooth`	Whether to smooth the F-statistics (`fstats`) or not. This is by default `FALSE`. For RNA-seq data we recommend using `FALSE`.
`weights`	Weights used by the smoother as described in smoother.
`smoothFunction`	A function to be used for smoothing the F-statistics. Two functions are provided by the `bumphunter` package: loessByCluster and runmedByCluster. If you are using your own custom function, it has to return a named list with an element called `⁠$fitted⁠` that contains the smoothed F-statistics and an element claled `⁠$smoothed⁠` that is a logical vector indicating whether the F-statistics were smoothed or not. If they are not smoothed, the original values will be used.
`...`	Arguments passed to other methods and/or advanced arguments. Advanced arguments: verbose If `TRUE` basic status updates will be printed along the way. Default `TRUE`. scalefac This argument is passed to preprocessCoverage. chunksize This argument is passed to preprocessCoverage. returnOutput If `TRUE`, it returns a list with the results from each step. Otherwise, it returns `NULL`. Default: the opposite of `writeOutput`. Passed to extendedMapSeqlevels, preprocessCoverage, calculateStats, calculatePvalues, annotateTranscripts, matchGenes, and define_cluster.

Details

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')

Value

If returnOutput=TRUE, a list with six components:

timeinfo: The wallclock timing information for each step.
optionsStats: The main options used when running this function.
coveragePrep: The output from preprocessCoverage.
fstats: The output from calculateStats.
regions: The output from calculatePvalues.
annotation: The output from matchGenes.

These are the same components that are written to Rdata files if writeOutput=TRUE.

Author(s)

Leonardo Collado-Torres

Examples

## 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)

lcolladotor/derfinder documentation built on May 4, 2024, 5:38 p.m.