R/mc6_mthds.R
In USEPA/CompTox-ToxCast-tcpl: ToxCast Data Analysis Pipeline
Defines functions
mc6_mthds
Documented in
mc6_mthds
#-------------------------------------------------------------------------------
# mc6_mthds: Load list of flag methods (to be used at level 6)
#-------------------------------------------------------------------------------
#' @name MC6_Methods
#' @title Load list of level 6 multiple-concentration flag methods
#'
#' @description
#' \code{mc6_mthds} returns a list of flag methods to be used
#' during level 6 multiple-concentration processing.
#'
#' @return A list functions
#'
#' @seealso \code{\link{mc6}}, \code{\link{Method functions}} to query what
#' methods get applied to each aeid.
#'
#' @section Available Methods:
#'
#' More information about the level 6 multiple-concentration processing is
#' available in the package vignette, "Data_processing."
#'
#' \describe{
#' \item{modl.directionality.fail}{Flag series if model directionality is questionable, i.e. if
#' the winning model direction was opposite, more responses (resp) would have exceeded the cutoff
#' (coff). If loss was winning directionality (top < 0), flag if
#' \eqn{count(resp<-1*coff)<2*count(resp>coff)}{count(resp < -1(coff)) < 2(count(resp > coff))}.
#' If gain was winning directionality (top > 0), flag if
#' \eqn{count(resp>coff)<2*count(resp<-1*coff)}.}
#' \item{low.nrep}{Flag series if the average number of replicates per concentration is less than
#' 2; \eqn{nrep < 2}{nrep < 2}.}
#' \item{low.nconc}{Flag series if 4 concentrations or less were tested; \eqn{nconc<=4}{nconc<=4}.
#' }
#' \item{bmd.high}{Flag series if modeled benchmark dose (BMD) is greater than AC50
#' (concentration at 50 percent maximal response). This is indicates high variability in baseline
#' response in excess of more than half of the maximal response.}
#' \item{singlept.hit.high}{Flag single-point hit that's only at the highest conc tested, where
#' series is an active hit call (hitc >= 0.9) with the median response observed above baseline
#' occurring only at the highest tested concentration tested.}
#' \item{singlept.hit.mid}{Flag single-point hit that's not at the highest conc tested, where
#' series is an active hit call (hitc >= 0.9) with the median response observed above baseline
#' occurring only at one concentration and not the highest concentration tested.}
#' \item{multipoint.neg}{Flag multi-point miss, where series is an inactive hit call (hitc < 0.9)
#' with multiple median responses observed above baseline.}
#' \item{gnls.lowconc}{Flag series where winning model is gain-loss (gnls) and the gain AC50 is
#' less than the minimum tested concentration, and the loss AC50 is less than the mean tested
#' concentration.}
#' \item{noise}{Flag series as noisy if the quality of fit as calculated by the root mean square
#' error (rmse) for the series is greater than the cutoff (coff); \eqn{rmse > coff}{rmse > coff}.}
#' \item{border}{Flag series if borderline activity is suspected based on modeled top parameter
#' (top) relative to cutoff (coff); \eqn{|top|<=1.2*coff~or~|top|>=0.8*coff}{|top| <= 1.2(coff) or
#' |top| >= 0.8(coff)}.}
#' \item{overfit.hit}{Method not yet updated for tcpl implementation. Flag hit-calls that would
#' get changed after doing the small N correction to the aic values.}
#' \item{efficacy.50}{Flag low efficacy hits if series has an active hit call (hitc >= 0.9) and
#' efficacy values (e.g. top and maximum median response) less than 50 percent; intended for
#' biochemical assays. If \eqn{hitc>=0.9}{hitc>=0.9} and \eqn{coff>=5}{coff>=5}, then flag when
#' \eqn{top<50}{top<50} or \eqn{maxmed < 50}{ma_med < 50}. If \eqn{hitc>=0.9}{hitc>=0.9} and
#' \eqn{coff<5}{coff<5}, then flag when \eqn{top<\log_{2}{1.5}}{top<log2(1.5)} or
#' \eqn{maxmed<\log_{2}{1.5}}{max_med<log2(1.5)}.}
#' \item{ac50.lowconc}{Flag series with an active hit call (hitc >= 0.9) if AC50 (concentration
#' at 50 percent maximal response) is less than the lowest concentration tested; if
#' \eqn{hitc>=0.9}{hitc>=0.9} and \eqn{ac50<10^{log_{c}{min}}}{ac50<10^logc_min}, then flag.}
#' \item{viability.gnls}{Flag series with an active hit call (hitc >= 0.9) if denoted as cell
#' viability assay with winning model is gain-loss (gnls); if hitc >= 0.9, modl = "gnls" and
#' cell_viability_assay = 1, then flag.}
#' \item{no.med.gt.3bmad}{Flag series where no median response values are greater than baseline as
#' defined by 3 times the baseline median absolute deviation (bmad); nmed_gtbl_pos and
#' nmed_gtbl_neg both = 0, where nmed_gtbl_pos/_neg is the number of medians greater than 3 *
#' bmad/less than -3 * bmad.}
#' \item{no.med.single.dir.gt.3bmad}{Flag series where no median response values in the intended
#' fit direction are greater than baseline as defined by 3 times the baseline median absolute
#' deviation (bmad); Depending on intended direction, either nmed_gtbl_pos or nmed_gtbl_neg are
#' = 0, where nmed_gtbl_pos/_neg is the number of medians greater than 3 * bmad/less than -3
#' * bmad.}
#' }
#'
#' @note
#' This function is not exported and is not intended to be used by the user.
mc6_mthds <- function() {
list(
modl.directionality.fail = function(mthd) {
flag <- "Model directionality questionable"
out <- c("m5id", "m4id", "aeid", "mc6_mthd_id",
"flag")
init <- bquote(list(.(mthd), .(flag), FALSE))
e1 <- bquote(dr[ , .(c(out[4:5], "test")) := .(init)])
e2 <- bquote(dr[ , coffsign := ifelse(top < 0, -1*coff, coff)])
e3 <- bquote(dr[ , gtabscoff := abs(resp) > abs(coffsign)])
e4 <- bquote(dr[ , nrsp_gtabscoff := sum(gtabscoff), by = m4id])
e5 <- bquote(dr[ , gtcoff := resp > coffsign])
e6 <- bquote(dr[ , ltcoff := resp < coffsign])
e7 <- bquote(dr[ , nrsp_gtcoff := sum(gtcoff), by = m4id])
e8 <- bquote(dr[ , nrsp_ltcoff := sum(ltcoff), by = m4id])
e9 <- bquote(dr[ , test := ifelse(coffsign > 0, nrsp_gtabscoff > 2*nrsp_gtcoff, nrsp_gtabscoff > 2*nrsp_ltcoff)])
e10 <- bquote(f[[.(mthd)]] <- unique(dr[which(test), .SD, .SDcols = .(out)], by = NULL))
cr <- c("mc6_mthd_id", "flag", "test", "coffsign", "gtabscoff", "nrsp_gtabscoff", "gtcoff", "ltcoff", "nrsp_gtcoff", "nrsp_ltcoff")
e11 <- bquote(dr[ , .(cr) := NULL])
list(e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11)
},
low.nrep = function(mthd) {
flag <- "Average number of replicates per conc < 2"
out <- c("m5id", "m4id", "aeid", "mc6_mthd_id",
"flag")
init <- bquote(list(.(mthd), .(flag), FALSE))
e1 <- bquote(ft[ , .(c(out[4:5], "test")) := .(init)])
e2 <- bquote(ft[ , test := nrep < 2])
e3 <- bquote(f[[.(mthd)]] <- ft[which(test), .SD, .SDcols = .(out)])
cr <- c("mc6_mthd_id", "flag", "test")
e4 <- bquote(ft[ , .(cr) := NULL])
list(e1, e2, e3, e4)
},
low.nconc = function(mthd) {
flag <- "Number of concentrations tested < 4"
out <- c("m5id", "m4id", "aeid", "mc6_mthd_id",
"flag")
init <- bquote(list(.(mthd), .(flag), FALSE))
e1 <- bquote(ft[ , .(c(out[4:5], "test")) := .(init)])
e2 <- bquote(ft[ , test := modl != "none" & nconc <= 4])
e3 <- bquote(f[[.(mthd)]] <- ft[which(test), .SD, .SDcols = .(out)])
cr <- c("mc6_mthd_id", "flag", "test")
e4 <- bquote(ft[ , .(cr) := NULL])
list(e1, e2, e3, e4)
},
bmd.high = function(mthd) {
flag <- "Bmd > ac50, indication of high baseline variability"
out <- c("m5id", "m4id", "aeid", "mc6_mthd_id",
"flag")
init <- bquote(list(.(mthd), .(flag), FALSE))
e1 <- bquote(ft[ , .(c(out[4:5], "test")) := .(init)])
e2 <- bquote(ifelse(all(c("ac50","bmd") %in% names(ft)),ft[ , test := bmd > ac50],ft))
e3 <- bquote(f[[.(mthd)]] <- ft[which(test), .SD, .SDcols = .(out)])
cr <- c("mc6_mthd_id", "flag", "test")
e4 <- bquote(ft[ , .(cr) := NULL])
list(e1, e2, e3, e4)
},
singlept.hit.high = function(mthd) {
flag <- "Active with only highest conc above baseline (3*bmad)"
out <- c("m5id", "m4id", "aeid", "mc6_mthd_id",
"flag")
init <- bquote(list(.(mthd), .(flag), FALSE))
e1 <- bquote(ft[ , .(c(out[4:5], "test")) := .(init)])
e2 <- bquote(ft[ , lstc := max_med_diff_conc == conc_max])
e3 <- bquote(ft[ , test := ((nmed_gtbl_pos == 1 & model_type == 2 & top >= 0) |
(nmed_gtbl_neg == 1 & model_type == 2 & top <= 0) |
(nmed_gtbl_pos == 1 & model_type == 3) |
(nmed_gtbl_neg == 1 & model_type == 4)) & hitc >= 0.9 & lstc])
e4 <- bquote(f[[.(mthd)]] <- ft[which(test), .SD, .SDcols = .(out)])
cr <- c("mc6_mthd_id", "flag", "test", "lstc")
e5 <- bquote(ft[ , .(cr) := NULL])
list(e1, e2, e3, e4, e5)
},
singlept.hit.mid = function(mthd) {
flag <- "Active with one conc (not highest) above baseline (3*bmad)"
out <- c("m5id", "m4id", "aeid", "mc6_mthd_id",
"flag")
init <- bquote(list(.(mthd), .(flag), FALSE))
e1 <- bquote(ft[ , .(c(out[4:5], "test")) := .(init)])
e2 <- bquote(ft[ , lstc := max_med_diff_conc == conc_max])
e3 <- bquote(ft[ , test := ((nmed_gtbl_pos == 1 & model_type == 2 & top >= 0) |
(nmed_gtbl_neg == 1 & model_type == 2 & top <= 0) |
(nmed_gtbl_pos == 1 & model_type == 3) |
(nmed_gtbl_neg == 1 & model_type == 4)) & hitc >= 0.9 & !lstc])
e4 <- bquote(f[[.(mthd)]] <- ft[which(test), .SD, .SDcols = .(out)])
cr <- c("mc6_mthd_id", "flag", "test", "lstc")
e5 <- bquote(ft[ , .(cr) := NULL])
list(e1, e2, e3, e4, e5)
},
multipoint.neg = function(mthd) {
flag <- "Inactive with multiple concs above baseline (3*bmad)"
out <- c("m5id", "m4id", "aeid", "mc6_mthd_id",
"flag")
init <- bquote(list(.(mthd), .(flag), FALSE))
e1 <- bquote(ft[ , .(c(out[4:5], "test")) := .(init)])
e2 <- bquote(ft[ , test := ((nmed_gtbl_pos > 1 & model_type == 2 & top >= 0) |
(nmed_gtbl_neg > 1 & model_type == 2 & top <= 0) |
(nmed_gtbl_pos > 1 & model_type == 3) |
(nmed_gtbl_neg > 1 & model_type == 4)) & hitc < 0.9 & hitc >= 0])
e3 <- bquote(f[[.(mthd)]] <- ft[which(test), .SD, .SDcols = .(out)])
cr <- c("mc6_mthd_id", "flag", "test")
e4 <- bquote(ft[ , .(cr) := NULL])
list(e1, e2, e3, e4)
},
gnls.lowconc = function(mthd) {
flag <- "Gain AC50 < lowest conc & loss AC50 < mean conc"
out <- c("m5id", "m4id", "aeid", "mc6_mthd_id",
"flag")
init <- bquote(list(.(mthd), .(flag), FALSE))
e1 <- bquote(ft[ , .(c(out[4:5], "test")) := .(init)])
e2 <- bquote(ft[ , c_min := conc_min])
e3 <- bquote(ft[ , c_max := conc_max])
conc_cols <- c("c_min", "c_max")
e4 <- bquote(ft[ , cmen := rowMeans(.SD), .SDcols = .(conc_cols)])
e5 <- bquote(ifelse("ac50_loss" %in% names(ft), ft[ , test := modl == "gnls" & ac50 < c_min & ac50_loss < cmen], ft))
e6 <- bquote(f[[.(mthd)]] <- ft[which(test), .SD, .SDcols = .(out)])
cr <- c("mc6_mthd_id", "flag", "test", "c_min", "c_max","cmen")
e7 <- bquote(ft[ , .(cr) := NULL])
list(e1, e2, e3, e4, e5, e6, e7)
},
noise = function(mthd) {
flag <- "Noisy data"
out <- c("m5id", "m4id", "aeid", "mc6_mthd_id",
"flag")
init <- bquote(list(.(mthd), .(flag), FALSE))
e1 <- bquote(ft[ , .(c(out[4:5], "test")) := .(init)])
e2 <- bquote(ft[ , test := rmse > coff])
e3 <- bquote(f[[.(mthd)]] <- ft[which(test), .SD, .SDcols = .(out)])
cr <- c("mc6_mthd_id", "flag", "test")
e4 <- bquote(ft[ , .(cr) := NULL])
list(e1, e2, e3, e4)
},
border = function(mthd) {
flag <- "Borderline"
out <- c("m5id", "m4id", "aeid", "mc6_mthd_id",
"flag")
init <- bquote(list(.(mthd), .(flag), FALSE))
e1 <- bquote(ft[ , .(c(out[4:5], "test")) := .(init)])
e2 <- bquote(ft[ , test := abs(top) <= 1.2*coff & abs(top) >= 0.8*coff])
e3 <- bquote(f[[.(mthd)]] <- ft[which(test), .SD, .SDcols = .(out)])
cr <- c("mc6_mthd_id", "flag", "test")
e4 <- bquote(ft[ , .(cr) := NULL])
list(e1, e2, e3, e4)
},
#overfit.hit = function(mthd) {
#flag <- "Hit-call potentially confounded by overfitting"
#out <- c("m5id", "m4id", "aeid", "mc6_mthd_id",
#"flag")
#init <- bquote(list(.(mthd), .(flag), FALSE))
#e1 <- bquote(ft[ , .(c(out[4:5], "test")) := .(init)])
#e2 <- bquote(ft[modl == "hill" & npts < 5 & hitc == 1, test := TRUE])
#e3 <- bquote(ft[modl == "gnls" & npts < 7 & hitc == 1, test := TRUE])
#e4 <- bquote(ft[npts > 1, cna := cnst_aic + 4/(npts - 2)])
#e5 <- bquote(ft[npts > 4, hna := hill_aic + 40/(npts - 4)])
#e6 <- bquote(ft[npts > 6, gna := gnls_aic + 84/(npts - 7)])
#e7 <- bquote(ft[ , nma := pmin(cna, hna, gna, na.rm = TRUE)])
#e8 <- bquote(ft[gna == nma, nmdl := "gnls"])
#e9 <- bquote(ft[hna == nma, nmdl := "hill"])
#e10 <- bquote(ft[cna == nma, nmdl := "cnst"])
#e11 <- bquote(ft[ , nhc := FALSE])
#e12 <- bquote(ft[nmdl == "hill" & hill_tp >= coff & max_med >= coff,
#nhc := TRUE])
#e13 <- bquote(ft[nmdl == "gnls" & gnls_tp >= coff & max_med >= coff,
#nhc := TRUE])
#e14 <- bquote(ft[hitc == 1 & !nhc, test := TRUE])
#e15 <- bquote(f[[.(mthd)]] <- ft[which(test), .SD, .SDcols = .(out)])
#cr <- c("mc6_mthd_id", "flag", "test",
#"cna", "hna", "gna", "nma", "nmdl", "nhc")
#e16 <- bquote(ft[ , .(cr) := NULL])
#list(e1, e2, e3, e4, e5, e6, e7, e8, e9, e10,
#e11, e12, e13, e14, e15, e16)
#
#},
efficacy.50 = function(mthd) {
flag <- "Efficacy < 50%"
out <- c("m5id", "m4id", "aeid", "mc6_mthd_id",
"flag")
init <- bquote(list(.(mthd), .(flag), FALSE))
e1 <- bquote(ft[ , .(c(out[4:5], "test")) := .(init)])
e2 <- bquote(ft[hitc >= 0.9 & coff >= 5,
test := abs(top) < 50 | abs(max_med_diff) < 50])
e3 <- bquote(ft[hitc >= 0.9 & coff < 5,
test := abs(top) < log2(1.5) | abs(max_med_diff) < log2(1.5)])
e4 <- bquote(f[[.(mthd)]] <- ft[which(test), .SD, .SDcols = .(out)])
cr <- c("mc6_mthd_id", "flag", "test")
e5 <- bquote(ft[ , .(cr) := NULL])
list(e1, e2, e3, e4, e5)
},
ac50.lowconc = function(mthd) {
flag <- "AC50 < lowest concentration tested"
out <- c("m5id", "m4id", "aeid", "mc6_mthd_id",
"flag")
init <- bquote(list(.(mthd), .(flag), FALSE))
e1 <- bquote(ft[ , .(c(out[4:5], "test")) := .(init)])
e2 <- bquote(ft[hitc >= 0.9, test := ac50 < conc_min])
e3 <- bquote(f[[.(mthd)]] <- ft[which(test), .SD, .SDcols = .(out)])
cr <- c("mc6_mthd_id", "flag", "test")
e4 <- bquote(ft[ , .(cr) := NULL])
list(e1, e2, e3, e4)
},
viability.gnls = function(mthd) {
flag <- "Cell viability assay fit with gnls winning model"
out <- c("m5id", "m4id", "aeid", "mc6_mthd_id",
"flag")
init <- bquote(list(.(mthd), .(flag), FALSE))
e1 <- bquote(ft[ , .(c(out[4:5], "test")) := .(init)])
e2 <- bquote(ft[hitc >= 0.9, test := modl=="gnls" & cell_viability_assay == 1])
e3 <- bquote(f[[.(mthd)]] <- ft[which(test), .SD, .SDcols = .(out)])
cr <- c("mc6_mthd_id", "flag", "test")
e4 <- bquote(ft[ , .(cr) := NULL])
list(e1, e2, e3, e4)
},
no.med.gt.3bmad = function(mthd) {
flag <- "No median responses above baseline (3*bmad)"
out <- c("m5id", "m4id", "aeid", "mc6_mthd_id",
"flag")
init <- bquote(list(.(mthd), .(flag), FALSE))
e1 <- bquote(ft[ , .(c(out[4:5], "test")) := .(init)])
e2 <- bquote(ft[ , test := (model_type == 2 & top > 0 & nmed_gtbl_pos == 0) |
(model_type == 2 & top < 0 & nmed_gtbl_neg == 0) |
(model_type == 3 & nmed_gtbl_pos == 0) |
(model_type == 4 & nmed_gtbl_neg == 0)])
e3 <- bquote(f[[.(mthd)]] <- ft[which(test), .SD, .SDcols = .(out)])
cr <- c("mc6_mthd_id", "flag", "test")
e4 <- bquote(ft[ , .(cr) := NULL])
list(e1, e2, e3, e4)
}
)
}
#-------------------------------------------------------------------------------
USEPA/CompTox-ToxCast-tcpl documentation built on April 22, 2024, 12:37 p.m.
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Defines functions mc6_mthds
Documented in mc6_mthds
#-------------------------------------------------------------------------------
# mc6_mthds: Load list of flag methods (to be used at level 6)
#-------------------------------------------------------------------------------
#' @name MC6_Methods
#' @title Load list of level 6 multiple-concentration flag methods
#'
#' @description
#' \code{mc6_mthds} returns a list of flag methods to be used
#' during level 6 multiple-concentration processing.
#'
#' @return A list functions
#'
#' @seealso \code{\link{mc6}}, \code{\link{Method functions}} to query what
#' methods get applied to each aeid.
#'
#' @section Available Methods:
#'
#' More information about the level 6 multiple-concentration processing is
#' available in the package vignette, "Data_processing."
#'
#' \describe{
#' \item{modl.directionality.fail}{Flag series if model directionality is questionable, i.e. if
#' the winning model direction was opposite, more responses (resp) would have exceeded the cutoff
#' (coff). If loss was winning directionality (top < 0), flag if
#' \eqn{count(resp<-1*coff)<2*count(resp>coff)}{count(resp < -1(coff)) < 2(count(resp > coff))}.
#' If gain was winning directionality (top > 0), flag if
#' \eqn{count(resp>coff)<2*count(resp<-1*coff)}.}
#' \item{low.nrep}{Flag series if the average number of replicates per concentration is less than
#' 2; \eqn{nrep < 2}{nrep < 2}.}
#' \item{low.nconc}{Flag series if 4 concentrations or less were tested; \eqn{nconc<=4}{nconc<=4}.
#' }
#' \item{bmd.high}{Flag series if modeled benchmark dose (BMD) is greater than AC50
#' (concentration at 50 percent maximal response). This is indicates high variability in baseline
#' response in excess of more than half of the maximal response.}
#' \item{singlept.hit.high}{Flag single-point hit that's only at the highest conc tested, where
#' series is an active hit call (hitc >= 0.9) with the median response observed above baseline
#' occurring only at the highest tested concentration tested.}
#' \item{singlept.hit.mid}{Flag single-point hit that's not at the highest conc tested, where
#' series is an active hit call (hitc >= 0.9) with the median response observed above baseline
#' occurring only at one concentration and not the highest concentration tested.}
#' \item{multipoint.neg}{Flag multi-point miss, where series is an inactive hit call (hitc < 0.9)
#' with multiple median responses observed above baseline.}
#' \item{gnls.lowconc}{Flag series where winning model is gain-loss (gnls) and the gain AC50 is
#' less than the minimum tested concentration, and the loss AC50 is less than the mean tested
#' concentration.}
#' \item{noise}{Flag series as noisy if the quality of fit as calculated by the root mean square
#' error (rmse) for the series is greater than the cutoff (coff); \eqn{rmse > coff}{rmse > coff}.}
#' \item{border}{Flag series if borderline activity is suspected based on modeled top parameter
#' (top) relative to cutoff (coff); \eqn{|top|<=1.2*coff~or~|top|>=0.8*coff}{|top| <= 1.2(coff) or
#' |top| >= 0.8(coff)}.}
#' \item{overfit.hit}{Method not yet updated for tcpl implementation. Flag hit-calls that would
#' get changed after doing the small N correction to the aic values.}
#' \item{efficacy.50}{Flag low efficacy hits if series has an active hit call (hitc >= 0.9) and
#' efficacy values (e.g. top and maximum median response) less than 50 percent; intended for
#' biochemical assays. If \eqn{hitc>=0.9}{hitc>=0.9} and \eqn{coff>=5}{coff>=5}, then flag when
#' \eqn{top<50}{top<50} or \eqn{maxmed < 50}{ma_med < 50}. If \eqn{hitc>=0.9}{hitc>=0.9} and
#' \eqn{coff<5}{coff<5}, then flag when \eqn{top<\log_{2}{1.5}}{top<log2(1.5)} or
#' \eqn{maxmed<\log_{2}{1.5}}{max_med<log2(1.5)}.}
#' \item{ac50.lowconc}{Flag series with an active hit call (hitc >= 0.9) if AC50 (concentration
#' at 50 percent maximal response) is less than the lowest concentration tested; if
#' \eqn{hitc>=0.9}{hitc>=0.9} and \eqn{ac50<10^{log_{c}{min}}}{ac50<10^logc_min}, then flag.}
#' \item{viability.gnls}{Flag series with an active hit call (hitc >= 0.9) if denoted as cell
#' viability assay with winning model is gain-loss (gnls); if hitc >= 0.9, modl = "gnls" and
#' cell_viability_assay = 1, then flag.}
#' \item{no.med.gt.3bmad}{Flag series where no median response values are greater than baseline as
#' defined by 3 times the baseline median absolute deviation (bmad); nmed_gtbl_pos and
#' nmed_gtbl_neg both = 0, where nmed_gtbl_pos/_neg is the number of medians greater than 3 *
#' bmad/less than -3 * bmad.}
#' \item{no.med.single.dir.gt.3bmad}{Flag series where no median response values in the intended
#' fit direction are greater than baseline as defined by 3 times the baseline median absolute
#' deviation (bmad); Depending on intended direction, either nmed_gtbl_pos or nmed_gtbl_neg are
#' = 0, where nmed_gtbl_pos/_neg is the number of medians greater than 3 * bmad/less than -3
#' * bmad.}
#' }
#'
#' @note
#' This function is not exported and is not intended to be used by the user.
mc6_mthds <- function() {
list(
modl.directionality.fail = function(mthd) {
flag <- "Model directionality questionable"
out <- c("m5id", "m4id", "aeid", "mc6_mthd_id",
"flag")
init <- bquote(list(.(mthd), .(flag), FALSE))
e1 <- bquote(dr[ , .(c(out[4:5], "test")) := .(init)])
e2 <- bquote(dr[ , coffsign := ifelse(top < 0, -1*coff, coff)])
e3 <- bquote(dr[ , gtabscoff := abs(resp) > abs(coffsign)])
e4 <- bquote(dr[ , nrsp_gtabscoff := sum(gtabscoff), by = m4id])
e5 <- bquote(dr[ , gtcoff := resp > coffsign])
e6 <- bquote(dr[ , ltcoff := resp < coffsign])
e7 <- bquote(dr[ , nrsp_gtcoff := sum(gtcoff), by = m4id])
e8 <- bquote(dr[ , nrsp_ltcoff := sum(ltcoff), by = m4id])
e9 <- bquote(dr[ , test := ifelse(coffsign > 0, nrsp_gtabscoff > 2*nrsp_gtcoff, nrsp_gtabscoff > 2*nrsp_ltcoff)])
e10 <- bquote(f[[.(mthd)]] <- unique(dr[which(test), .SD, .SDcols = .(out)], by = NULL))
cr <- c("mc6_mthd_id", "flag", "test", "coffsign", "gtabscoff", "nrsp_gtabscoff", "gtcoff", "ltcoff", "nrsp_gtcoff", "nrsp_ltcoff")
e11 <- bquote(dr[ , .(cr) := NULL])
list(e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11)
},
low.nrep = function(mthd) {
flag <- "Average number of replicates per conc < 2"
out <- c("m5id", "m4id", "aeid", "mc6_mthd_id",
"flag")
init <- bquote(list(.(mthd), .(flag), FALSE))
e1 <- bquote(ft[ , .(c(out[4:5], "test")) := .(init)])
e2 <- bquote(ft[ , test := nrep < 2])
e3 <- bquote(f[[.(mthd)]] <- ft[which(test), .SD, .SDcols = .(out)])
cr <- c("mc6_mthd_id", "flag", "test")
e4 <- bquote(ft[ , .(cr) := NULL])
list(e1, e2, e3, e4)
},
low.nconc = function(mthd) {
flag <- "Number of concentrations tested < 4"
out <- c("m5id", "m4id", "aeid", "mc6_mthd_id",
"flag")
init <- bquote(list(.(mthd), .(flag), FALSE))
e1 <- bquote(ft[ , .(c(out[4:5], "test")) := .(init)])
e2 <- bquote(ft[ , test := modl != "none" & nconc <= 4])
e3 <- bquote(f[[.(mthd)]] <- ft[which(test), .SD, .SDcols = .(out)])
cr <- c("mc6_mthd_id", "flag", "test")
e4 <- bquote(ft[ , .(cr) := NULL])
list(e1, e2, e3, e4)
},
bmd.high = function(mthd) {
flag <- "Bmd > ac50, indication of high baseline variability"
out <- c("m5id", "m4id", "aeid", "mc6_mthd_id",
"flag")
init <- bquote(list(.(mthd), .(flag), FALSE))
e1 <- bquote(ft[ , .(c(out[4:5], "test")) := .(init)])
e2 <- bquote(ifelse(all(c("ac50","bmd") %in% names(ft)),ft[ , test := bmd > ac50],ft))
e3 <- bquote(f[[.(mthd)]] <- ft[which(test), .SD, .SDcols = .(out)])
cr <- c("mc6_mthd_id", "flag", "test")
e4 <- bquote(ft[ , .(cr) := NULL])
list(e1, e2, e3, e4)
},
singlept.hit.high = function(mthd) {
flag <- "Active with only highest conc above baseline (3*bmad)"
out <- c("m5id", "m4id", "aeid", "mc6_mthd_id",
"flag")
init <- bquote(list(.(mthd), .(flag), FALSE))
e1 <- bquote(ft[ , .(c(out[4:5], "test")) := .(init)])
e2 <- bquote(ft[ , lstc := max_med_diff_conc == conc_max])
e3 <- bquote(ft[ , test := ((nmed_gtbl_pos == 1 & model_type == 2 & top >= 0) |
(nmed_gtbl_neg == 1 & model_type == 2 & top <= 0) |
(nmed_gtbl_pos == 1 & model_type == 3) |
(nmed_gtbl_neg == 1 & model_type == 4)) & hitc >= 0.9 & lstc])
e4 <- bquote(f[[.(mthd)]] <- ft[which(test), .SD, .SDcols = .(out)])
cr <- c("mc6_mthd_id", "flag", "test", "lstc")
e5 <- bquote(ft[ , .(cr) := NULL])
list(e1, e2, e3, e4, e5)
},
singlept.hit.mid = function(mthd) {
flag <- "Active with one conc (not highest) above baseline (3*bmad)"
out <- c("m5id", "m4id", "aeid", "mc6_mthd_id",
"flag")
init <- bquote(list(.(mthd), .(flag), FALSE))
e1 <- bquote(ft[ , .(c(out[4:5], "test")) := .(init)])
e2 <- bquote(ft[ , lstc := max_med_diff_conc == conc_max])
e3 <- bquote(ft[ , test := ((nmed_gtbl_pos == 1 & model_type == 2 & top >= 0) |
(nmed_gtbl_neg == 1 & model_type == 2 & top <= 0) |
(nmed_gtbl_pos == 1 & model_type == 3) |
(nmed_gtbl_neg == 1 & model_type == 4)) & hitc >= 0.9 & !lstc])
e4 <- bquote(f[[.(mthd)]] <- ft[which(test), .SD, .SDcols = .(out)])
cr <- c("mc6_mthd_id", "flag", "test", "lstc")
e5 <- bquote(ft[ , .(cr) := NULL])
list(e1, e2, e3, e4, e5)
},
multipoint.neg = function(mthd) {
flag <- "Inactive with multiple concs above baseline (3*bmad)"
out <- c("m5id", "m4id", "aeid", "mc6_mthd_id",
"flag")
init <- bquote(list(.(mthd), .(flag), FALSE))
e1 <- bquote(ft[ , .(c(out[4:5], "test")) := .(init)])
e2 <- bquote(ft[ , test := ((nmed_gtbl_pos > 1 & model_type == 2 & top >= 0) |
(nmed_gtbl_neg > 1 & model_type == 2 & top <= 0) |
(nmed_gtbl_pos > 1 & model_type == 3) |
(nmed_gtbl_neg > 1 & model_type == 4)) & hitc < 0.9 & hitc >= 0])
e3 <- bquote(f[[.(mthd)]] <- ft[which(test), .SD, .SDcols = .(out)])
cr <- c("mc6_mthd_id", "flag", "test")
e4 <- bquote(ft[ , .(cr) := NULL])
list(e1, e2, e3, e4)
},
gnls.lowconc = function(mthd) {
flag <- "Gain AC50 < lowest conc & loss AC50 < mean conc"
out <- c("m5id", "m4id", "aeid", "mc6_mthd_id",
"flag")
init <- bquote(list(.(mthd), .(flag), FALSE))
e1 <- bquote(ft[ , .(c(out[4:5], "test")) := .(init)])
e2 <- bquote(ft[ , c_min := conc_min])
e3 <- bquote(ft[ , c_max := conc_max])
conc_cols <- c("c_min", "c_max")
e4 <- bquote(ft[ , cmen := rowMeans(.SD), .SDcols = .(conc_cols)])
e5 <- bquote(ifelse("ac50_loss" %in% names(ft), ft[ , test := modl == "gnls" & ac50 < c_min & ac50_loss < cmen], ft))
e6 <- bquote(f[[.(mthd)]] <- ft[which(test), .SD, .SDcols = .(out)])
cr <- c("mc6_mthd_id", "flag", "test", "c_min", "c_max","cmen")
e7 <- bquote(ft[ , .(cr) := NULL])
list(e1, e2, e3, e4, e5, e6, e7)
},
noise = function(mthd) {
flag <- "Noisy data"
out <- c("m5id", "m4id", "aeid", "mc6_mthd_id",
"flag")
init <- bquote(list(.(mthd), .(flag), FALSE))
e1 <- bquote(ft[ , .(c(out[4:5], "test")) := .(init)])
e2 <- bquote(ft[ , test := rmse > coff])
e3 <- bquote(f[[.(mthd)]] <- ft[which(test), .SD, .SDcols = .(out)])
cr <- c("mc6_mthd_id", "flag", "test")
e4 <- bquote(ft[ , .(cr) := NULL])
list(e1, e2, e3, e4)
},
border = function(mthd) {
flag <- "Borderline"
out <- c("m5id", "m4id", "aeid", "mc6_mthd_id",
"flag")
init <- bquote(list(.(mthd), .(flag), FALSE))
e1 <- bquote(ft[ , .(c(out[4:5], "test")) := .(init)])
e2 <- bquote(ft[ , test := abs(top) <= 1.2*coff & abs(top) >= 0.8*coff])
e3 <- bquote(f[[.(mthd)]] <- ft[which(test), .SD, .SDcols = .(out)])
cr <- c("mc6_mthd_id", "flag", "test")
e4 <- bquote(ft[ , .(cr) := NULL])
list(e1, e2, e3, e4)
},
#overfit.hit = function(mthd) {
#flag <- "Hit-call potentially confounded by overfitting"
#out <- c("m5id", "m4id", "aeid", "mc6_mthd_id",
#"flag")
#init <- bquote(list(.(mthd), .(flag), FALSE))
#e1 <- bquote(ft[ , .(c(out[4:5], "test")) := .(init)])
#e2 <- bquote(ft[modl == "hill" & npts < 5 & hitc == 1, test := TRUE])
#e3 <- bquote(ft[modl == "gnls" & npts < 7 & hitc == 1, test := TRUE])
#e4 <- bquote(ft[npts > 1, cna := cnst_aic + 4/(npts - 2)])
#e5 <- bquote(ft[npts > 4, hna := hill_aic + 40/(npts - 4)])
#e6 <- bquote(ft[npts > 6, gna := gnls_aic + 84/(npts - 7)])
#e7 <- bquote(ft[ , nma := pmin(cna, hna, gna, na.rm = TRUE)])
#e8 <- bquote(ft[gna == nma, nmdl := "gnls"])
#e9 <- bquote(ft[hna == nma, nmdl := "hill"])
#e10 <- bquote(ft[cna == nma, nmdl := "cnst"])
#e11 <- bquote(ft[ , nhc := FALSE])
#e12 <- bquote(ft[nmdl == "hill" & hill_tp >= coff & max_med >= coff,
#nhc := TRUE])
#e13 <- bquote(ft[nmdl == "gnls" & gnls_tp >= coff & max_med >= coff,
#nhc := TRUE])
#e14 <- bquote(ft[hitc == 1 & !nhc, test := TRUE])
#e15 <- bquote(f[[.(mthd)]] <- ft[which(test), .SD, .SDcols = .(out)])
#cr <- c("mc6_mthd_id", "flag", "test",
#"cna", "hna", "gna", "nma", "nmdl", "nhc")
#e16 <- bquote(ft[ , .(cr) := NULL])
#list(e1, e2, e3, e4, e5, e6, e7, e8, e9, e10,
#e11, e12, e13, e14, e15, e16)
#
#},
efficacy.50 = function(mthd) {
flag <- "Efficacy < 50%"
out <- c("m5id", "m4id", "aeid", "mc6_mthd_id",
"flag")
init <- bquote(list(.(mthd), .(flag), FALSE))
e1 <- bquote(ft[ , .(c(out[4:5], "test")) := .(init)])
e2 <- bquote(ft[hitc >= 0.9 & coff >= 5,
test := abs(top) < 50 | abs(max_med_diff) < 50])
e3 <- bquote(ft[hitc >= 0.9 & coff < 5,
test := abs(top) < log2(1.5) | abs(max_med_diff) < log2(1.5)])
e4 <- bquote(f[[.(mthd)]] <- ft[which(test), .SD, .SDcols = .(out)])
cr <- c("mc6_mthd_id", "flag", "test")
e5 <- bquote(ft[ , .(cr) := NULL])
list(e1, e2, e3, e4, e5)
},
ac50.lowconc = function(mthd) {
flag <- "AC50 < lowest concentration tested"
out <- c("m5id", "m4id", "aeid", "mc6_mthd_id",
"flag")
init <- bquote(list(.(mthd), .(flag), FALSE))
e1 <- bquote(ft[ , .(c(out[4:5], "test")) := .(init)])
e2 <- bquote(ft[hitc >= 0.9, test := ac50 < conc_min])
e3 <- bquote(f[[.(mthd)]] <- ft[which(test), .SD, .SDcols = .(out)])
cr <- c("mc6_mthd_id", "flag", "test")
e4 <- bquote(ft[ , .(cr) := NULL])
list(e1, e2, e3, e4)
},
viability.gnls = function(mthd) {
flag <- "Cell viability assay fit with gnls winning model"
out <- c("m5id", "m4id", "aeid", "mc6_mthd_id",
"flag")
init <- bquote(list(.(mthd), .(flag), FALSE))
e1 <- bquote(ft[ , .(c(out[4:5], "test")) := .(init)])
e2 <- bquote(ft[hitc >= 0.9, test := modl=="gnls" & cell_viability_assay == 1])
e3 <- bquote(f[[.(mthd)]] <- ft[which(test), .SD, .SDcols = .(out)])
cr <- c("mc6_mthd_id", "flag", "test")
e4 <- bquote(ft[ , .(cr) := NULL])
list(e1, e2, e3, e4)
},
no.med.gt.3bmad = function(mthd) {
flag <- "No median responses above baseline (3*bmad)"
out <- c("m5id", "m4id", "aeid", "mc6_mthd_id",
"flag")
init <- bquote(list(.(mthd), .(flag), FALSE))
e1 <- bquote(ft[ , .(c(out[4:5], "test")) := .(init)])
e2 <- bquote(ft[ , test := (model_type == 2 & top > 0 & nmed_gtbl_pos == 0) |
(model_type == 2 & top < 0 & nmed_gtbl_neg == 0) |
(model_type == 3 & nmed_gtbl_pos == 0) |
(model_type == 4 & nmed_gtbl_neg == 0)])
e3 <- bquote(f[[.(mthd)]] <- ft[which(test), .SD, .SDcols = .(out)])
cr <- c("mc6_mthd_id", "flag", "test")
e4 <- bquote(ft[ , .(cr) := NULL])
list(e1, e2, e3, e4)
}
)
}
#-------------------------------------------------------------------------------
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