R/control_metrics.R
In ttriche/sesamizeGEO: the poor man's methylation suite (cf. `sesamizer`)
Defines functions
.get_dft
.getetaddr
.getsub
control_metrics
Documented in
control_metrics
#' bead array control processing/QC: get metrics from control probe signals
#'
#' mostly lifted from Sean Maden's recountmethylation supplement
#' https://github.com/metamaden/recountmethylationManuscriptSupplement
#' modified slightly to take advantage of rgSet annotations and to Go Big,
#' i.e. this works reasonably well on fairly large rgSets (like all of GEO)
#'
#' @param rgSet an rgSet with control probe intensities and annotation
#' @param dft optional single-row data.frame of thresholds (default)
#' @param baseline offset for Extension green based background (3000)
#' @param biotin.baseline baseline offset for biotin staining probes (1)
#'
#' @return a matrix (cols = metrics, rows = samples)
#'
#' @examples
#'
#' if (exists("MPAL_rgSet")) {
#'
#' # TARGET MPAL data, from Alexander et al, Nature 2018
#' cm <- control_metrics(MPAL_rgSet)
#'
#' library(ComplexHeatmap)
#' Heatmap(t(cm), name="metric", column_names_side="top",
#' column_names_gp=gpar(fontsize=6), row_names_side="left")
#'
#' library(circlize)
#' flagcols <- colorRamp2(c(0, 1), c("white", "darkred"))
#' flagged <- flag_control_failures(cm, platform="epic")
#' all_samples_passed <- ifelse(colSums(flagged) == 0, 1, 0)
#' all_probes_passed <- ifelse(rowSums(flagged) == 0, 1, 0)
#' Heatmap(t(flagged), name="failed", col=flagcols, column_names_side="top",
#' column_split=all_probes_passed, column_names_gp=gpar(fontsize=6),
#' row_split=all_samples_passed, row_names_side="left")
#'
#' }
#'
#' @seealso flag_control_failures
#'
#' @import minfi
#'
#' @export
control_metrics <- function(rgSet, dft=NULL, baseline=3000, biotin.baseline=1) {
# make this easier on RAM by NOT grabbing hundreds of unneeded control probes
#
cdf <- subset(getProbeInfo(rgSet, "Control"), !grepl("(NORM|NEGATIVE)", Type))
message("Retrieving control probe red intensities...")
rs <- as.matrix(t(getRed(rgSet[cdf$Address,])))
message("Retrieving control probe green intensities...")
gs <- as.matrix(t(getGreen(rgSet[cdf$Address,])))
message("Loading thresholds...")
if (is.null(dft)) dft <- .get_dft()
cnl <- names(dft)
# just dimnames
rmat <- rs[, 0]
#---------------------
# Background addresses
#---------------------
# 1 metric, grn channel
#
ci <- .getsub(cdf, "EXTENSION")
addr.bkg <- subset(ci, grepl("\\([AT]\\)", ExtendedType))$Address
#---------------------
# FFPE Restoration kit
#---------------------
# 1 metric, grn channel
#
message("calculating restore metric...")
ci = .getsub(cdf, "RESTORATION")
which.rest = which(colnames(gs) %in% ci$Address)
m1 = apply(gs, 1, function(x) x[which.rest]/(max(x[addr.bkg]) + baseline))
mi = 1
rmat = cbind(rmat, m1)
colnames(rmat)[ncol(rmat)] = cnl[mi]
#----------------
# BIOTIN STAINING
#----------------
# 2 metrics, 1 per chan
#
message("calculating biotin staining metrics...")
ci = cdf[grepl("Biotin|DNP", cdf$ExtendedType),]
# red
which.stain = which(colnames(rs) %in% .getetaddr(ci, "DNP \\(High"))
which.bkg = which(colnames(rs) %in% .getetaddr(ci, "DNP \\(Bkg"))
m1 = apply(rs, 1, function(x) x[which.stain]/(x[which.bkg] + biotin.baseline))
mi = 2
rmat = cbind(rmat, m1)
colnames(rmat)[ncol(rmat)] = cnl[mi]
# grn
which.stain = which(colnames(gs) %in% .getetaddr(ci, "Biotin \\(High"))
which.bkg = which(colnames(gs) %in% .getetaddr(ci, "Biotin \\(Bkg"))
m2 = apply(gs, 1, function(x) x[which.stain]/(x[which.bkg] + biotin.baseline))
mi = 3
rmat = cbind(rmat, m2)
colnames(rmat)[ncol(rmat)] = cnl[mi]
#--------------
# SPECIFICITY I
#--------------
# 2 metrics, 1 per channel
#
message("calculating specificity I...")
ci1 <- .getsub(cdf, "SPECIFICITY I")
# red
ci = ci1[grepl("Mismatch (4|5|6)", ci1$ExtendedType),]
addr.mm.index = which(colnames(rs) %in% .getetaddr(ci, "MM"))
addr.pm.index = which(colnames(rs) %in% .getetaddr(ci, "PM"))
m1 = apply(rs, 1, function(x) min(x[addr.pm.index])/max(x[addr.mm.index]))
mi = 4
rmat = cbind(rmat, m1)
colnames(rmat)[ncol(rmat)] = cnl[mi]
# grn
ci = ci1[grepl("Mismatch (1|2|3)", ci1$ExtendedType),]
addr.mm.index = which(colnames(gs) %in% .getetaddr(ci, "MM"))
addr.pm.index = which(colnames(gs) %in% .getetaddr(ci, "PM"))
m2 = apply(gs, 1, function(x) min(x[addr.pm.index])/max(x[addr.mm.index]))
mi = 5
rmat = cbind(rmat, m2)
colnames(rmat)[ncol(rmat)] = cnl[mi]
#----------------
# SPECIFICITY II
#----------------
# 1 metric, uses both channels
#
message("calculating specificityII metric...")
ci = .getsub(cdf, "SPECIFICITY II")
which.addr.red = which(colnames(rs) %in% ci$Address)
which.addr.grn = which(colnames(gs) %in% ci$Address)
m0.1 = apply(rs, 1, function(x) min(x[which.addr.red]))
m0.2 = apply(gs, 1, function(x) max(x[which.addr.grn]))
m1 = m0.1/m0.2
mi = 6
rmat = cbind(rmat, m1)
colnames(rmat)[ncol(rmat)] = cnl[mi]
#-----------
# EXTENSION
#-----------
# 2 metrics, 1 per channel
#
message("calculating extension metrics...")
ci = .getsub(cdf, "EXTENSION")
addr.ext.cg = ci$Address[grepl("(C)|(G)", ci$ExtendedType)]
addr.ext.at = ci$Address[grepl("(A)|(T)", ci$ExtendedType)]
# red
which.cg = which(colnames(rs) %in% addr.ext.cg)
which.at = which(colnames(rs) %in% addr.ext.at)
m1 = apply(rs, 1, function(x) min(x[which.at])/max(x[which.cg]))
mi = 7
rmat = cbind(rmat, m1)
colnames(rmat)[ncol(rmat)] = cnl[mi]
# grn
which.cg = which(colnames(gs) %in% addr.ext.cg)
which.at = which(colnames(gs) %in% addr.ext.at)
m2 = apply(gs, 1, function(x) min(x[which.cg])/max(x[which.at]))
mi = 8
rmat = cbind(rmat, m2)
colnames(rmat)[ncol(rmat)] = cnl[mi]
#---------------
# HYBRIDIZATION
#---------------
# 2 metrics, 1 per channel
#
message("calculating hybridization metrics...")
ci = .getsub(cdf, "HYBRIDIZATION")
which.hi = which(colnames(gs) %in% .getetaddr(ci, "High"))
which.med = which(colnames(gs) %in% .getetaddr(ci, "Med"))
which.low = which(colnames(gs) %in% .getetaddr(ci, "Low"))
m1 = apply(gs, 1, function(x) x[which.hi]/x[which.med])
m2 = apply(gs, 1, function(x) x[which.med]/x[which.low])
mi = 9
rmat = cbind(rmat, m1)
colnames(rmat)[ncol(rmat)] = cnl[mi]
mi = 10
rmat = cbind(rmat, m2)
colnames(rmat)[ncol(rmat)] = cnl[mi]
#----------------
# TARGET REMOVAL
#----------------
# 2 metrics, both from grn channel
#
message("calculating target removal metics...")
ci = .getsub(cdf, "EXTENSION")
which.bkg = which(colnames(gs) %in% .getetaddr(ci, "(A)|(T)"))
ci = .getsub(cdf, "TARGET REMOVAL")
which.t1 = which(colnames(gs) %in% .getetaddr(ci, "Removal 1"))
which.t2 = which(colnames(gs) %in% .getetaddr(ci, "Removal 2"))
# rem 1
m1 = apply(gs, 1, function(x) (max(x[which.bkg]) + baseline)/x[which.t1])
mi = 11
rmat = cbind(rmat, m1)
colnames(rmat)[ncol(rmat)] = cnl[mi]
# rem 2
m2 = apply(gs, 1, function(x) (max(x[which.bkg]) + baseline)/x[which.t2])
mi = 12
rmat = cbind(rmat, m2)
colnames(rmat)[ncol(rmat)] = cnl[mi]
#------------------------
# BISULFITE CONVERSION I
#------------------------
# 2 metrics, 1 per channel
#
# Somewhat useless (see Zhou et al, NAR 2017)
#
message("calculating bisulfite conversion I metrics...")
ci = .getsub(cdf, "BISULFITE CONVERSION I")
which.c123 = which(colnames(gs) %in% .getetaddr(ci, "C[123]"))
which.u123 = which(colnames(gs) %in% .getetaddr(ci, "U[123]"))
which.c456 = which(colnames(gs) %in% .getetaddr(ci, "C[456]"))
which.u456 = which(colnames(gs) %in% .getetaddr(ci, "U[456]"))
# red
m1 = apply(rs, 1, function(x) min(x[which.c456])/max(x[which.u456]))
mi = 13
rmat = cbind(rmat, m1)
colnames(rmat)[ncol(rmat)] = cnl[mi]
# grn
m2 = apply(gs, 1, function(x) min(x[which.c123])/max(x[which.u123]))
mi = 14
rmat = cbind(rmat, m2)
colnames(rmat)[ncol(rmat)] = cnl[mi]
#-------------------------
# BISULFITE CONVERSION II
#-------------------------
# 2 metrics, 1 per channel
#
# Somewhat useless (see Zhou et al, NAR 2017)
#
message("calculating bisulfite conversion II metric...")
ci = .getsub(cdf, "BISULFITE CONVERSION II")
which.ci.red = which(colnames(rs) %in% ci$Address)
which.ci.grn = which(colnames(gs) %in% ci$Address)
m0.1 = apply(rs, 1, function(x) min(x[which.ci.red]))
m0.2 = apply(gs, 1, function(x) max(x[which.ci.grn]))
m1 = m0.1/m0.2
mi = 15
rmat = cbind(rmat, m1)
colnames(rmat)[ncol(rmat)] = cnl[mi]
#----------------
# NON-POLYMORPHIC
#----------------
# 2 metrics, 1 per channel
#
message("calculating non-polymorphic metrics...")
ci = .getsub(cdf, "NON-POLYMORPHIC")
# red
which.cg = which(colnames(rs) %in% .getetaddr(ci, "(C|G)"))
which.at = which(colnames(rs) %in% .getetaddr(ci, "(A|T)"))
m1 = apply(rs, 1, function(x) min(x[which.at])/max(x[which.cg]))
mi = 16
rmat = cbind(rmat, m1)
colnames(rmat)[ncol(rmat)] = cnl[mi]
# grn
which.cg = which(colnames(rs) %in% .getetaddr(ci, "(C|G)"))
which.at = which(colnames(rs) %in% .getetaddr(ci, "(A|T)"))
m2 = apply(gs, 1, function(x) min(x[which.cg])/max(x[which.at]))
mi = 17
rmat = cbind(rmat, m2)
colnames(rmat)[ncol(rmat)] = cnl[mi]
#-----
# DONE
#-----
return(rmat)
}
# helper
.getsub <- function(cdf, ct) subset(cdf, Type == ct)
# helper
.getetaddr <- function(cdf, patt) subset(cdf, grepl(patt, ExtendedType))$Address
# helper
.get_dft <- function() {
# should go into data(dft) or similar
dft <- data.frame(restoration.grn = 0,
biotin.stain.red = 5,
biotin.stain.grn = 5,
specificityI.red = 1,
specificityI.grn = 1,
specificityII = 1,
extension.red = 5,
extension.grn = 5,
hyb.hi.med = 1,
hyb.med.low = 1,
target.removal.1 = 1,
target.removal.2 = 1,
bisulfite.conv.I.red = 1,
bisulfite.conv.I.grn = 1,
bisulfite.conv.II = 1,
nonpolymorphic.red = 5,
nonpolymorphic.grn = 5)
return(dft)
}
ttriche/sesamizeGEO documentation built on Nov. 12, 2023, 5:42 p.m.
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Defines functions .get_dft .getetaddr .getsub control_metrics
Documented in control_metrics
#' bead array control processing/QC: get metrics from control probe signals
#'
#' mostly lifted from Sean Maden's recountmethylation supplement
#' https://github.com/metamaden/recountmethylationManuscriptSupplement
#' modified slightly to take advantage of rgSet annotations and to Go Big,
#' i.e. this works reasonably well on fairly large rgSets (like all of GEO)
#'
#' @param rgSet an rgSet with control probe intensities and annotation
#' @param dft optional single-row data.frame of thresholds (default)
#' @param baseline offset for Extension green based background (3000)
#' @param biotin.baseline baseline offset for biotin staining probes (1)
#'
#' @return a matrix (cols = metrics, rows = samples)
#'
#' @examples
#'
#' if (exists("MPAL_rgSet")) {
#'
#' # TARGET MPAL data, from Alexander et al, Nature 2018
#' cm <- control_metrics(MPAL_rgSet)
#'
#' library(ComplexHeatmap)
#' Heatmap(t(cm), name="metric", column_names_side="top",
#' column_names_gp=gpar(fontsize=6), row_names_side="left")
#'
#' library(circlize)
#' flagcols <- colorRamp2(c(0, 1), c("white", "darkred"))
#' flagged <- flag_control_failures(cm, platform="epic")
#' all_samples_passed <- ifelse(colSums(flagged) == 0, 1, 0)
#' all_probes_passed <- ifelse(rowSums(flagged) == 0, 1, 0)
#' Heatmap(t(flagged), name="failed", col=flagcols, column_names_side="top",
#' column_split=all_probes_passed, column_names_gp=gpar(fontsize=6),
#' row_split=all_samples_passed, row_names_side="left")
#'
#' }
#'
#' @seealso flag_control_failures
#'
#' @import minfi
#'
#' @export
control_metrics <- function(rgSet, dft=NULL, baseline=3000, biotin.baseline=1) {
# make this easier on RAM by NOT grabbing hundreds of unneeded control probes
#
cdf <- subset(getProbeInfo(rgSet, "Control"), !grepl("(NORM|NEGATIVE)", Type))
message("Retrieving control probe red intensities...")
rs <- as.matrix(t(getRed(rgSet[cdf$Address,])))
message("Retrieving control probe green intensities...")
gs <- as.matrix(t(getGreen(rgSet[cdf$Address,])))
message("Loading thresholds...")
if (is.null(dft)) dft <- .get_dft()
cnl <- names(dft)
# just dimnames
rmat <- rs[, 0]
#---------------------
# Background addresses
#---------------------
# 1 metric, grn channel
#
ci <- .getsub(cdf, "EXTENSION")
addr.bkg <- subset(ci, grepl("\\([AT]\\)", ExtendedType))$Address
#---------------------
# FFPE Restoration kit
#---------------------
# 1 metric, grn channel
#
message("calculating restore metric...")
ci = .getsub(cdf, "RESTORATION")
which.rest = which(colnames(gs) %in% ci$Address)
m1 = apply(gs, 1, function(x) x[which.rest]/(max(x[addr.bkg]) + baseline))
mi = 1
rmat = cbind(rmat, m1)
colnames(rmat)[ncol(rmat)] = cnl[mi]
#----------------
# BIOTIN STAINING
#----------------
# 2 metrics, 1 per chan
#
message("calculating biotin staining metrics...")
ci = cdf[grepl("Biotin|DNP", cdf$ExtendedType),]
# red
which.stain = which(colnames(rs) %in% .getetaddr(ci, "DNP \\(High"))
which.bkg = which(colnames(rs) %in% .getetaddr(ci, "DNP \\(Bkg"))
m1 = apply(rs, 1, function(x) x[which.stain]/(x[which.bkg] + biotin.baseline))
mi = 2
rmat = cbind(rmat, m1)
colnames(rmat)[ncol(rmat)] = cnl[mi]
# grn
which.stain = which(colnames(gs) %in% .getetaddr(ci, "Biotin \\(High"))
which.bkg = which(colnames(gs) %in% .getetaddr(ci, "Biotin \\(Bkg"))
m2 = apply(gs, 1, function(x) x[which.stain]/(x[which.bkg] + biotin.baseline))
mi = 3
rmat = cbind(rmat, m2)
colnames(rmat)[ncol(rmat)] = cnl[mi]
#--------------
# SPECIFICITY I
#--------------
# 2 metrics, 1 per channel
#
message("calculating specificity I...")
ci1 <- .getsub(cdf, "SPECIFICITY I")
# red
ci = ci1[grepl("Mismatch (4|5|6)", ci1$ExtendedType),]
addr.mm.index = which(colnames(rs) %in% .getetaddr(ci, "MM"))
addr.pm.index = which(colnames(rs) %in% .getetaddr(ci, "PM"))
m1 = apply(rs, 1, function(x) min(x[addr.pm.index])/max(x[addr.mm.index]))
mi = 4
rmat = cbind(rmat, m1)
colnames(rmat)[ncol(rmat)] = cnl[mi]
# grn
ci = ci1[grepl("Mismatch (1|2|3)", ci1$ExtendedType),]
addr.mm.index = which(colnames(gs) %in% .getetaddr(ci, "MM"))
addr.pm.index = which(colnames(gs) %in% .getetaddr(ci, "PM"))
m2 = apply(gs, 1, function(x) min(x[addr.pm.index])/max(x[addr.mm.index]))
mi = 5
rmat = cbind(rmat, m2)
colnames(rmat)[ncol(rmat)] = cnl[mi]
#----------------
# SPECIFICITY II
#----------------
# 1 metric, uses both channels
#
message("calculating specificityII metric...")
ci = .getsub(cdf, "SPECIFICITY II")
which.addr.red = which(colnames(rs) %in% ci$Address)
which.addr.grn = which(colnames(gs) %in% ci$Address)
m0.1 = apply(rs, 1, function(x) min(x[which.addr.red]))
m0.2 = apply(gs, 1, function(x) max(x[which.addr.grn]))
m1 = m0.1/m0.2
mi = 6
rmat = cbind(rmat, m1)
colnames(rmat)[ncol(rmat)] = cnl[mi]
#-----------
# EXTENSION
#-----------
# 2 metrics, 1 per channel
#
message("calculating extension metrics...")
ci = .getsub(cdf, "EXTENSION")
addr.ext.cg = ci$Address[grepl("(C)|(G)", ci$ExtendedType)]
addr.ext.at = ci$Address[grepl("(A)|(T)", ci$ExtendedType)]
# red
which.cg = which(colnames(rs) %in% addr.ext.cg)
which.at = which(colnames(rs) %in% addr.ext.at)
m1 = apply(rs, 1, function(x) min(x[which.at])/max(x[which.cg]))
mi = 7
rmat = cbind(rmat, m1)
colnames(rmat)[ncol(rmat)] = cnl[mi]
# grn
which.cg = which(colnames(gs) %in% addr.ext.cg)
which.at = which(colnames(gs) %in% addr.ext.at)
m2 = apply(gs, 1, function(x) min(x[which.cg])/max(x[which.at]))
mi = 8
rmat = cbind(rmat, m2)
colnames(rmat)[ncol(rmat)] = cnl[mi]
#---------------
# HYBRIDIZATION
#---------------
# 2 metrics, 1 per channel
#
message("calculating hybridization metrics...")
ci = .getsub(cdf, "HYBRIDIZATION")
which.hi = which(colnames(gs) %in% .getetaddr(ci, "High"))
which.med = which(colnames(gs) %in% .getetaddr(ci, "Med"))
which.low = which(colnames(gs) %in% .getetaddr(ci, "Low"))
m1 = apply(gs, 1, function(x) x[which.hi]/x[which.med])
m2 = apply(gs, 1, function(x) x[which.med]/x[which.low])
mi = 9
rmat = cbind(rmat, m1)
colnames(rmat)[ncol(rmat)] = cnl[mi]
mi = 10
rmat = cbind(rmat, m2)
colnames(rmat)[ncol(rmat)] = cnl[mi]
#----------------
# TARGET REMOVAL
#----------------
# 2 metrics, both from grn channel
#
message("calculating target removal metics...")
ci = .getsub(cdf, "EXTENSION")
which.bkg = which(colnames(gs) %in% .getetaddr(ci, "(A)|(T)"))
ci = .getsub(cdf, "TARGET REMOVAL")
which.t1 = which(colnames(gs) %in% .getetaddr(ci, "Removal 1"))
which.t2 = which(colnames(gs) %in% .getetaddr(ci, "Removal 2"))
# rem 1
m1 = apply(gs, 1, function(x) (max(x[which.bkg]) + baseline)/x[which.t1])
mi = 11
rmat = cbind(rmat, m1)
colnames(rmat)[ncol(rmat)] = cnl[mi]
# rem 2
m2 = apply(gs, 1, function(x) (max(x[which.bkg]) + baseline)/x[which.t2])
mi = 12
rmat = cbind(rmat, m2)
colnames(rmat)[ncol(rmat)] = cnl[mi]
#------------------------
# BISULFITE CONVERSION I
#------------------------
# 2 metrics, 1 per channel
#
# Somewhat useless (see Zhou et al, NAR 2017)
#
message("calculating bisulfite conversion I metrics...")
ci = .getsub(cdf, "BISULFITE CONVERSION I")
which.c123 = which(colnames(gs) %in% .getetaddr(ci, "C[123]"))
which.u123 = which(colnames(gs) %in% .getetaddr(ci, "U[123]"))
which.c456 = which(colnames(gs) %in% .getetaddr(ci, "C[456]"))
which.u456 = which(colnames(gs) %in% .getetaddr(ci, "U[456]"))
# red
m1 = apply(rs, 1, function(x) min(x[which.c456])/max(x[which.u456]))
mi = 13
rmat = cbind(rmat, m1)
colnames(rmat)[ncol(rmat)] = cnl[mi]
# grn
m2 = apply(gs, 1, function(x) min(x[which.c123])/max(x[which.u123]))
mi = 14
rmat = cbind(rmat, m2)
colnames(rmat)[ncol(rmat)] = cnl[mi]
#-------------------------
# BISULFITE CONVERSION II
#-------------------------
# 2 metrics, 1 per channel
#
# Somewhat useless (see Zhou et al, NAR 2017)
#
message("calculating bisulfite conversion II metric...")
ci = .getsub(cdf, "BISULFITE CONVERSION II")
which.ci.red = which(colnames(rs) %in% ci$Address)
which.ci.grn = which(colnames(gs) %in% ci$Address)
m0.1 = apply(rs, 1, function(x) min(x[which.ci.red]))
m0.2 = apply(gs, 1, function(x) max(x[which.ci.grn]))
m1 = m0.1/m0.2
mi = 15
rmat = cbind(rmat, m1)
colnames(rmat)[ncol(rmat)] = cnl[mi]
#----------------
# NON-POLYMORPHIC
#----------------
# 2 metrics, 1 per channel
#
message("calculating non-polymorphic metrics...")
ci = .getsub(cdf, "NON-POLYMORPHIC")
# red
which.cg = which(colnames(rs) %in% .getetaddr(ci, "(C|G)"))
which.at = which(colnames(rs) %in% .getetaddr(ci, "(A|T)"))
m1 = apply(rs, 1, function(x) min(x[which.at])/max(x[which.cg]))
mi = 16
rmat = cbind(rmat, m1)
colnames(rmat)[ncol(rmat)] = cnl[mi]
# grn
which.cg = which(colnames(rs) %in% .getetaddr(ci, "(C|G)"))
which.at = which(colnames(rs) %in% .getetaddr(ci, "(A|T)"))
m2 = apply(gs, 1, function(x) min(x[which.cg])/max(x[which.at]))
mi = 17
rmat = cbind(rmat, m2)
colnames(rmat)[ncol(rmat)] = cnl[mi]
#-----
# DONE
#-----
return(rmat)
}
# helper
.getsub <- function(cdf, ct) subset(cdf, Type == ct)
# helper
.getetaddr <- function(cdf, patt) subset(cdf, grepl(patt, ExtendedType))$Address
# helper
.get_dft <- function() {
# should go into data(dft) or similar
dft <- data.frame(restoration.grn = 0,
biotin.stain.red = 5,
biotin.stain.grn = 5,
specificityI.red = 1,
specificityI.grn = 1,
specificityII = 1,
extension.red = 5,
extension.grn = 5,
hyb.hi.med = 1,
hyb.med.low = 1,
target.removal.1 = 1,
target.removal.2 = 1,
bisulfite.conv.I.red = 1,
bisulfite.conv.I.grn = 1,
bisulfite.conv.II = 1,
nonpolymorphic.red = 5,
nonpolymorphic.grn = 5)
return(dft)
}
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