R/configOpts.R
In gschofl/DR2S: Dual redundant reference sequencing
#' @include zzz.R utils.R
NULL
normaliseOpts <- function(opts, pipeline = "LR") {
if (has_attr(opts, "valid")) {
return(opts)
}
pipeline <- match.arg(pipeline, c("SR", "LR"))
## camelCasify option names of passed-in options
opts <- setNames(opts, .camelCasify(names(opts)))
## set up defaults according to the pipeline type
opts0 <- list()
##
## mapInit() defaults ####
##
opts0$mapInit <- compact(list(
##
## <includeDeletions>: include deletions in pileup.
includeDeletions = TRUE,
##
## <includeInsertions>: include insertions in pileup.
includeInsertions = TRUE,
##
## <callInsertionThreshold>: if <includeInsertions == TRUE>, an insertion
## needs to be at frequency <callInsertionThreshold> for it to be included
## in the pileup.
callInsertionThreshold = 1/5,
##
## <microsatellite>: if <pipeline == "SR">, perform a second mapping of
## shortreads to the inferred reference. Set to TRUE if you suspect
## microsatellites or repetitive regions in your sequence. This extends
## the reference to a maximum length and enables a better mapping.
microsatellite = FALSE,
##
## <forceMapping>: set to TRUE if you want to force processing of "bad"
## shortreads when the distribution of coverage is heavily unequal.
## Aborts the program if maximum coverage > 75 % quantile * 5.
forceMapping = FALSE,
##
## <minMapq>: don't filter longreads for mapping quality unless specified.
## NOTE: for shortreads <minMapq = 50> is hardcoded.
minMapq = 0,
##
## <topx>: pick the x top-scoring reads. Set to an integer value to pick
## a fixed number of reads. Set to "auto" to use a dynamically determined
## number of reads to be selected.
topx = FALSE,
##
## <pickiness>: if <topx == "auto">: <pickiness < 1>: bias towards higher
## scores/less reads; <pickiness > 1>: bias towards lower scores/more reads
pickiness = 1,
##
## <increasePickiness>: if <topx == "auto">: increase pickiness for the
## second iteration of LR mapping
increasePickiness = 1,
##
## <lowerLimit>: if <topx == "auto"> or <topx > 0>: the minimum number
## of reads to pick if available.
lowerLimit = 200,
##
## <updateBackgroundModel>: estimate the indel noise in a pileup and use
## this information to update the background model for PWM scoring
updateBackgroundModel = FALSE,
##
## <createIgv>: subsample bam files for visualisation with IgvJs in the
## DR2S shiny app.
createIgv = TRUE,
##
## <plot>: generate diagnostic plots.
plot = TRUE
))
##
## partitionLongreads() defaults ####
##
opts0$partitionLongreads <- compact(list(
## Threshold to call a polymorphic position. A minority nucleotide frequency
## below this threshold is considered noise rather than a valid polymorphism.
threshold = 1/5,
## The expected number of distinct alleles in the sample. This should be 2
## for heterozygous samples, 1 for homozygous samples may be >2 for some
## KIR loci.
distAlleles = 2,
## The minumum frequency of the gap character required to call a gap position.
skipGapFreq = 2/3,
## Don't partition based on gaps. Useful for samples with only few SNPs but
## with homopolymers. The falsely called gaps could mask the real variation.
## Set to override global default.
noGapPartitioning = TRUE,
## Correlate polymorphic positions and cluster based on the absolute
## correlation coefficient. Extract positions from the cluster with the
## higher absolute mean correlation coefficient. This gets rid of positions
## that are not well distributed across the two alleles.
selectCorrelatedPositions = FALSE,
## if <selectCorrelatedPositions> == TRUE, use <measureOfAssociation>
## ("cramer.V" or "spearman") to determine linkage between all polymorphic
## positions.
measureOfAssociation = "cramer.V",
## We perform an equivalence test on clusters of polymorhic positions:
## Calculate the lower 1-sigma bound of the high-association cluster i.
## Calculate the upper 1-sigma bound of the low-association cluster j.
## Reject the clusters, if this bounds overlap by more than <proportionOfOverlap>
## of the average distance (dij) between clusters.
proportionOfOverlap = 1/3,
## By how much do we expect 2 clusters to minimally differ in mean Cramér's V.
## BIC-informed model-based clustering tends to split rather than lump
## and this is a heuristical attempt to forestall this.
minimumExpectedDifference = 0.06,
## If more than <distAlleles> clusters are found select clusters based on:
## (1) "distance": The hamming distance of the resulting variant consensus
## sequences or (2) "count": Take the clusters with the most reads as the
## true alleles.
selectAllelesBy = "distance",
## Minimum size of an allele cluster
minClusterSize = 20,
## When selecting reads from allele clusters using a dynamic threshold:
## pickiness < 1: bias towards higher scores/less reads
## pickiness > 1: bias towards lower scores/more reads
pickiness = 1,
## When selecting reads from allele clusters the minimum number of
## reads to pick if available.
lowerLimit = 40,
## Generate diagnostic plots.
plot = TRUE
))
##
## mapIter() defaults ####
##
opts0$mapIter <- compact(list(
## Number of <mapIter> iterations. How often are the
## clustered reads remapped to updated reference sequences.
iterations = 1,
## Minimum occupancy (1 - fraction of gap) below which
## bases at insertion position are excluded from from consensus calling.
columnOccupancy = 2/5,
## an insertion needs to be at frequency <callInsertionThreshold> for it
## to be included in the pileup.
callInsertionThreshold = 1/5,
## Generate diagnostic plots.
plot = TRUE
))
##
## partitionShortreads() defaults ####
##
if (pipeline == "SR") {
opts0$partitionShortreads <- compact(list(
))
}
##
## mapFinal() defaults ####
##
opts0$mapFinal <- compact(list(
## include deletions in pileup.
includeDeletions = TRUE,
## include insertions in pileup.
includeInsertions = TRUE,
## an insertion needs to be at frequency <callInsertionThreshold> for it
## to be included in the pileup.
callInsertionThreshold = 1/5,
## (for shortreads only) trim softclips and polymorphic ends of reads before
## the final mapping
trimPolymorphicEnds = FALSE,
## Subsample bam files for visualisation with IgvJs in the
## DR2S shiny app.
createIgv = TRUE,
## Generate diagnostic plots.
plot = TRUE
))
##
## polish() defaults ####
##
opts0$polish <- compact(list(
## Threshold to call a polymorphic position. Set to override global default.
threshold = NULL,
## Check the number of homopolymer counts. Compare the resulting sequence
## with the mode value and report differences.
checkHpCount = TRUE,
## The minimal length of a homopolymer to be checked.
hpCount = 10
))
##
## report() defaults ####
##
opts0$report <- compact(list(
## Maximum number of sequence letters per line in pairwise alignment.
blockWidth = 80,
## Suppress remapping of reads against final consensus.
noRemap = FALSE,
## Subsample bam files for visualisation with IgvJs in the
## DR2S shiny app.
createIgv = TRUE
))
opts0 <- compact(opts0)
## update default with config settings
opts1 <- utils::modifyList(compact(opts0), opts, keep.null = FALSE)
validateOpts(opts = opts1)
}
MANDATORY_OPTS <- function() {
c("mapInit", "partitionLongreads", "mapIter", "mapFinal", "polish", "report")
}
validateOpts <- function(opts) {
fields <- MANDATORY_OPTS()
assert_that(all(fields %in% names(opts)),
msg = paste0("Missing opts for <",
comma(fields[!fields %in% names(opts)]),
"> in config"))
##
## mapInit() asserts ####
##
assert_that(
is.flag(opts$mapInit$includeDeletions),
is.flag(opts$mapInit$includeInsertions),
is.flag(opts$mapInit$microsatellite),
is.flag(opts$mapInit$forceMapping),
is.flag(opts$mapInit$updateBackgroundModel),
is.number(opts$mapInit$minMapq),
is.number(opts$mapInit$pickiness),
is.number(opts$mapInit$increasePickiness),
is.number(opts$mapInit$lowerLimit),
is.flag(opts$mapInit$createIgv),
is.flag(opts$mapInit$plot)
)
assert_that(
is.number(opts$mapInit$callInsertionThreshold),
opts$mapInit$callInsertionThreshold >= 0,
opts$mapInit$callInsertionThreshold <= 1,
msg = "<callInsertionThreshold> in mapInit() is not a number between 0 and 1")
assert_that(
opts$mapInit$topx == "auto" || is.number(opts$mapInit$topx) || is.flag(opts$mapInit$topx),
msg = "<topx> in mapInit() is not \"auto\" or a number >= 0")
##
## partitionLonreads() asserts ####
##
assert_that(
is.flag(opts$partitionLongreads$noGapPartitioning),
is.flag(opts$partitionLongreads$selectCorrelatedPositions),
is.number(opts$partitionLongreads$minClusterSize),
is.number(opts$partitionLongreads$pickiness),
is.number(opts$partitionLongreads$lowerLimit),
is.flag(opts$partitionLongreads$plot)
)
assert_that(
is.number(opts$partitionLongreads$threshold),
opts$partitionLongreads$threshold >= 0,
opts$partitionLongreads$threshold <= 1,
msg = "<threshold> in partitionLongreads() is not a number between 0 and 1")
assert_that(
is.count(opts$partitionLongreads$distAlleles),
opts$partitionLongreads$distAlleles > 0,
opts$partitionLongreads$distAlleles < 5,
msg = "<distAlleles> in partitionLongreads() is not a count between 1 and 4")
assert_that(
is.number(opts$partitionLongreads$skipGapFreq),
opts$partitionLongreads$skipGapFreq >= 0,
opts$partitionLongreads$skipGapFreq <= 1,
msg = "<skipGapFreq> in partitionLongreads() is not a number between 0 and 1")
assert_that(
is.string(opts$partitionLongreads$selectAllelesBy),
opts$partitionLongreads$selectAllelesBy %in% c("count", "distance"),
msg = "<selectAllelesBy> in partitionLongreads() is not 'count' nor 'distance'"
)
assert_that(
is.string(opts$partitionLongreads$measureOfAssociation),
opts$partitionLongreads$measureOfAssociation %in% c("cramer.V", "spearman"),
msg = "<measureOfAssociation> in partitionLongreads() is not 'cramer.V' nor 'spearman'"
)
assert_that(
is.number(opts$partitionLongreads$proportionOfOverlap),
opts$partitionLongreads$proportionOfOverlap >= 0,
opts$partitionLongreads$proportionOfOverlap <= 1,
msg = "<proportionOfOverlap> in partitionLongreads() is not a number between 0 and 1")
assert_that(
is.number(opts$partitionLongreads$minimumExpectedDifference),
opts$partitionLongreads$minimumExpectedDifference >= 0,
opts$partitionLongreads$minimumExpectedDifference <= 1,
msg = "<minimumExpectedDifference> in partitionLongreads() is not a number between 0 and 1")
##
## mapIter() asserts ####
##
assert_that(
is.flag(opts$mapIter$plot)
)
assert_that(
is.count(opts$mapIter$iterations),
opts$mapIter$iterations > 0,
opts$mapIter$iterations < 10,
msg = "<iterations> in mapIter() is not a count between 1 and 9"
)
assert_that(
is.number(opts$mapIter$columnOccupancy),
opts$mapIter$columnOccupancy >= 0,
opts$mapIter$columnOccupancy <= 1,
msg = "<columnOccupancy> in mapIter() is not a number between 0 and 1")
assert_that(
is.number(opts$mapIter$callInsertionThreshold),
opts$mapIter$callInsertionThreshold >= 0,
opts$mapIter$callInsertionThreshold <= 1,
msg = "<callInsertionThreshold> in mapIter() is not a number between 0 and 1")
##
## mapFinal() asserts ####
##
assert_that(
is.flag(opts$mapFinal$includeDeletions),
is.flag(opts$mapFinal$includeInsertions),
is.flag(opts$mapFinal$trimPolymorphicEnds),
is.flag(opts$mapFinal$createIgv),
is.flag(opts$mapFinal$plot)
)
assert_that(
is.number(opts$mapFinal$callInsertionThreshold),
opts$mapFinal$callInsertionThreshold >= 0,
opts$mapFinal$callInsertionThreshold <= 1,
msg = "<callInsertionThreshold> in mapFinal() is not a number between 0 and 1")
##
## polish() asserts ####
##
assert_that(
is.flag(opts$polish$checkHpCount),
is.count(opts$polish$hpCount)
)
assert_that(
is.null(opts$polish$threshold) || (
is.number(opts$threshold$threshold) && opts$polish$threshold >= 0 && opts$polish$threshold <= 1
),
msg = "<threshold> in polish() is not NULL or a number between 0 and 1")
##
## report() asserts ####
##
assert_that(
is.count(opts$report$blockWidth),
is.flag(opts$report$noRemap),
is.flag(opts$report$createIgv)
)
attr(opts, "valid") <- TRUE
return(opts)
}
.capitalise <- function(x) {
capped <- grep("^[A-Z]", x, invert = TRUE)
substr(x[capped], 1, 1) <- toupper(substr(x[capped], 1, 1))
x
}
.camelCasify <- function(x, sep = "_") {
xs <- strsplit(x, sep, fixed = TRUE)
vapply(xs, function(x) {
paste0(x[1L], paste0(.capitalise(x[-1L]), collapse = ""))
}, FUN.VALUE = character(1))
}
gschofl/DR2S documentation built on May 17, 2019, 8:40 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
#' @include zzz.R utils.R
NULL
normaliseOpts <- function(opts, pipeline = "LR") {
if (has_attr(opts, "valid")) {
return(opts)
}
pipeline <- match.arg(pipeline, c("SR", "LR"))
## camelCasify option names of passed-in options
opts <- setNames(opts, .camelCasify(names(opts)))
## set up defaults according to the pipeline type
opts0 <- list()
##
## mapInit() defaults ####
##
opts0$mapInit <- compact(list(
##
## <includeDeletions>: include deletions in pileup.
includeDeletions = TRUE,
##
## <includeInsertions>: include insertions in pileup.
includeInsertions = TRUE,
##
## <callInsertionThreshold>: if <includeInsertions == TRUE>, an insertion
## needs to be at frequency <callInsertionThreshold> for it to be included
## in the pileup.
callInsertionThreshold = 1/5,
##
## <microsatellite>: if <pipeline == "SR">, perform a second mapping of
## shortreads to the inferred reference. Set to TRUE if you suspect
## microsatellites or repetitive regions in your sequence. This extends
## the reference to a maximum length and enables a better mapping.
microsatellite = FALSE,
##
## <forceMapping>: set to TRUE if you want to force processing of "bad"
## shortreads when the distribution of coverage is heavily unequal.
## Aborts the program if maximum coverage > 75 % quantile * 5.
forceMapping = FALSE,
##
## <minMapq>: don't filter longreads for mapping quality unless specified.
## NOTE: for shortreads <minMapq = 50> is hardcoded.
minMapq = 0,
##
## <topx>: pick the x top-scoring reads. Set to an integer value to pick
## a fixed number of reads. Set to "auto" to use a dynamically determined
## number of reads to be selected.
topx = FALSE,
##
## <pickiness>: if <topx == "auto">: <pickiness < 1>: bias towards higher
## scores/less reads; <pickiness > 1>: bias towards lower scores/more reads
pickiness = 1,
##
## <increasePickiness>: if <topx == "auto">: increase pickiness for the
## second iteration of LR mapping
increasePickiness = 1,
##
## <lowerLimit>: if <topx == "auto"> or <topx > 0>: the minimum number
## of reads to pick if available.
lowerLimit = 200,
##
## <updateBackgroundModel>: estimate the indel noise in a pileup and use
## this information to update the background model for PWM scoring
updateBackgroundModel = FALSE,
##
## <createIgv>: subsample bam files for visualisation with IgvJs in the
## DR2S shiny app.
createIgv = TRUE,
##
## <plot>: generate diagnostic plots.
plot = TRUE
))
##
## partitionLongreads() defaults ####
##
opts0$partitionLongreads <- compact(list(
## Threshold to call a polymorphic position. A minority nucleotide frequency
## below this threshold is considered noise rather than a valid polymorphism.
threshold = 1/5,
## The expected number of distinct alleles in the sample. This should be 2
## for heterozygous samples, 1 for homozygous samples may be >2 for some
## KIR loci.
distAlleles = 2,
## The minumum frequency of the gap character required to call a gap position.
skipGapFreq = 2/3,
## Don't partition based on gaps. Useful for samples with only few SNPs but
## with homopolymers. The falsely called gaps could mask the real variation.
## Set to override global default.
noGapPartitioning = TRUE,
## Correlate polymorphic positions and cluster based on the absolute
## correlation coefficient. Extract positions from the cluster with the
## higher absolute mean correlation coefficient. This gets rid of positions
## that are not well distributed across the two alleles.
selectCorrelatedPositions = FALSE,
## if <selectCorrelatedPositions> == TRUE, use <measureOfAssociation>
## ("cramer.V" or "spearman") to determine linkage between all polymorphic
## positions.
measureOfAssociation = "cramer.V",
## We perform an equivalence test on clusters of polymorhic positions:
## Calculate the lower 1-sigma bound of the high-association cluster i.
## Calculate the upper 1-sigma bound of the low-association cluster j.
## Reject the clusters, if this bounds overlap by more than <proportionOfOverlap>
## of the average distance (dij) between clusters.
proportionOfOverlap = 1/3,
## By how much do we expect 2 clusters to minimally differ in mean Cramér's V.
## BIC-informed model-based clustering tends to split rather than lump
## and this is a heuristical attempt to forestall this.
minimumExpectedDifference = 0.06,
## If more than <distAlleles> clusters are found select clusters based on:
## (1) "distance": The hamming distance of the resulting variant consensus
## sequences or (2) "count": Take the clusters with the most reads as the
## true alleles.
selectAllelesBy = "distance",
## Minimum size of an allele cluster
minClusterSize = 20,
## When selecting reads from allele clusters using a dynamic threshold:
## pickiness < 1: bias towards higher scores/less reads
## pickiness > 1: bias towards lower scores/more reads
pickiness = 1,
## When selecting reads from allele clusters the minimum number of
## reads to pick if available.
lowerLimit = 40,
## Generate diagnostic plots.
plot = TRUE
))
##
## mapIter() defaults ####
##
opts0$mapIter <- compact(list(
## Number of <mapIter> iterations. How often are the
## clustered reads remapped to updated reference sequences.
iterations = 1,
## Minimum occupancy (1 - fraction of gap) below which
## bases at insertion position are excluded from from consensus calling.
columnOccupancy = 2/5,
## an insertion needs to be at frequency <callInsertionThreshold> for it
## to be included in the pileup.
callInsertionThreshold = 1/5,
## Generate diagnostic plots.
plot = TRUE
))
##
## partitionShortreads() defaults ####
##
if (pipeline == "SR") {
opts0$partitionShortreads <- compact(list(
))
}
##
## mapFinal() defaults ####
##
opts0$mapFinal <- compact(list(
## include deletions in pileup.
includeDeletions = TRUE,
## include insertions in pileup.
includeInsertions = TRUE,
## an insertion needs to be at frequency <callInsertionThreshold> for it
## to be included in the pileup.
callInsertionThreshold = 1/5,
## (for shortreads only) trim softclips and polymorphic ends of reads before
## the final mapping
trimPolymorphicEnds = FALSE,
## Subsample bam files for visualisation with IgvJs in the
## DR2S shiny app.
createIgv = TRUE,
## Generate diagnostic plots.
plot = TRUE
))
##
## polish() defaults ####
##
opts0$polish <- compact(list(
## Threshold to call a polymorphic position. Set to override global default.
threshold = NULL,
## Check the number of homopolymer counts. Compare the resulting sequence
## with the mode value and report differences.
checkHpCount = TRUE,
## The minimal length of a homopolymer to be checked.
hpCount = 10
))
##
## report() defaults ####
##
opts0$report <- compact(list(
## Maximum number of sequence letters per line in pairwise alignment.
blockWidth = 80,
## Suppress remapping of reads against final consensus.
noRemap = FALSE,
## Subsample bam files for visualisation with IgvJs in the
## DR2S shiny app.
createIgv = TRUE
))
opts0 <- compact(opts0)
## update default with config settings
opts1 <- utils::modifyList(compact(opts0), opts, keep.null = FALSE)
validateOpts(opts = opts1)
}
MANDATORY_OPTS <- function() {
c("mapInit", "partitionLongreads", "mapIter", "mapFinal", "polish", "report")
}
validateOpts <- function(opts) {
fields <- MANDATORY_OPTS()
assert_that(all(fields %in% names(opts)),
msg = paste0("Missing opts for <",
comma(fields[!fields %in% names(opts)]),
"> in config"))
##
## mapInit() asserts ####
##
assert_that(
is.flag(opts$mapInit$includeDeletions),
is.flag(opts$mapInit$includeInsertions),
is.flag(opts$mapInit$microsatellite),
is.flag(opts$mapInit$forceMapping),
is.flag(opts$mapInit$updateBackgroundModel),
is.number(opts$mapInit$minMapq),
is.number(opts$mapInit$pickiness),
is.number(opts$mapInit$increasePickiness),
is.number(opts$mapInit$lowerLimit),
is.flag(opts$mapInit$createIgv),
is.flag(opts$mapInit$plot)
)
assert_that(
is.number(opts$mapInit$callInsertionThreshold),
opts$mapInit$callInsertionThreshold >= 0,
opts$mapInit$callInsertionThreshold <= 1,
msg = "<callInsertionThreshold> in mapInit() is not a number between 0 and 1")
assert_that(
opts$mapInit$topx == "auto" || is.number(opts$mapInit$topx) || is.flag(opts$mapInit$topx),
msg = "<topx> in mapInit() is not \"auto\" or a number >= 0")
##
## partitionLonreads() asserts ####
##
assert_that(
is.flag(opts$partitionLongreads$noGapPartitioning),
is.flag(opts$partitionLongreads$selectCorrelatedPositions),
is.number(opts$partitionLongreads$minClusterSize),
is.number(opts$partitionLongreads$pickiness),
is.number(opts$partitionLongreads$lowerLimit),
is.flag(opts$partitionLongreads$plot)
)
assert_that(
is.number(opts$partitionLongreads$threshold),
opts$partitionLongreads$threshold >= 0,
opts$partitionLongreads$threshold <= 1,
msg = "<threshold> in partitionLongreads() is not a number between 0 and 1")
assert_that(
is.count(opts$partitionLongreads$distAlleles),
opts$partitionLongreads$distAlleles > 0,
opts$partitionLongreads$distAlleles < 5,
msg = "<distAlleles> in partitionLongreads() is not a count between 1 and 4")
assert_that(
is.number(opts$partitionLongreads$skipGapFreq),
opts$partitionLongreads$skipGapFreq >= 0,
opts$partitionLongreads$skipGapFreq <= 1,
msg = "<skipGapFreq> in partitionLongreads() is not a number between 0 and 1")
assert_that(
is.string(opts$partitionLongreads$selectAllelesBy),
opts$partitionLongreads$selectAllelesBy %in% c("count", "distance"),
msg = "<selectAllelesBy> in partitionLongreads() is not 'count' nor 'distance'"
)
assert_that(
is.string(opts$partitionLongreads$measureOfAssociation),
opts$partitionLongreads$measureOfAssociation %in% c("cramer.V", "spearman"),
msg = "<measureOfAssociation> in partitionLongreads() is not 'cramer.V' nor 'spearman'"
)
assert_that(
is.number(opts$partitionLongreads$proportionOfOverlap),
opts$partitionLongreads$proportionOfOverlap >= 0,
opts$partitionLongreads$proportionOfOverlap <= 1,
msg = "<proportionOfOverlap> in partitionLongreads() is not a number between 0 and 1")
assert_that(
is.number(opts$partitionLongreads$minimumExpectedDifference),
opts$partitionLongreads$minimumExpectedDifference >= 0,
opts$partitionLongreads$minimumExpectedDifference <= 1,
msg = "<minimumExpectedDifference> in partitionLongreads() is not a number between 0 and 1")
##
## mapIter() asserts ####
##
assert_that(
is.flag(opts$mapIter$plot)
)
assert_that(
is.count(opts$mapIter$iterations),
opts$mapIter$iterations > 0,
opts$mapIter$iterations < 10,
msg = "<iterations> in mapIter() is not a count between 1 and 9"
)
assert_that(
is.number(opts$mapIter$columnOccupancy),
opts$mapIter$columnOccupancy >= 0,
opts$mapIter$columnOccupancy <= 1,
msg = "<columnOccupancy> in mapIter() is not a number between 0 and 1")
assert_that(
is.number(opts$mapIter$callInsertionThreshold),
opts$mapIter$callInsertionThreshold >= 0,
opts$mapIter$callInsertionThreshold <= 1,
msg = "<callInsertionThreshold> in mapIter() is not a number between 0 and 1")
##
## mapFinal() asserts ####
##
assert_that(
is.flag(opts$mapFinal$includeDeletions),
is.flag(opts$mapFinal$includeInsertions),
is.flag(opts$mapFinal$trimPolymorphicEnds),
is.flag(opts$mapFinal$createIgv),
is.flag(opts$mapFinal$plot)
)
assert_that(
is.number(opts$mapFinal$callInsertionThreshold),
opts$mapFinal$callInsertionThreshold >= 0,
opts$mapFinal$callInsertionThreshold <= 1,
msg = "<callInsertionThreshold> in mapFinal() is not a number between 0 and 1")
##
## polish() asserts ####
##
assert_that(
is.flag(opts$polish$checkHpCount),
is.count(opts$polish$hpCount)
)
assert_that(
is.null(opts$polish$threshold) || (
is.number(opts$threshold$threshold) && opts$polish$threshold >= 0 && opts$polish$threshold <= 1
),
msg = "<threshold> in polish() is not NULL or a number between 0 and 1")
##
## report() asserts ####
##
assert_that(
is.count(opts$report$blockWidth),
is.flag(opts$report$noRemap),
is.flag(opts$report$createIgv)
)
attr(opts, "valid") <- TRUE
return(opts)
}
.capitalise <- function(x) {
capped <- grep("^[A-Z]", x, invert = TRUE)
substr(x[capped], 1, 1) <- toupper(substr(x[capped], 1, 1))
x
}
.camelCasify <- function(x, sep = "_") {
xs <- strsplit(x, sep, fixed = TRUE)
vapply(xs, function(x) {
paste0(x[1L], paste0(.capitalise(x[-1L]), collapse = ""))
}, FUN.VALUE = character(1))
}
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