ROUT outlier testing added
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################################################################################
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# F.Innerbichler
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# Jun 2026
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# Robust Outlier detection
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################################################################################
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# Code acc. to Mutulsky and Brown hpptps://pubmed.ncbi.nlm.nih.gov/16526949/
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# except line 175 p <- 2*(1-pt(z, length(residuals)-5)) was exchanged with p <- 2*(1-pcauchy(z)) to be robust
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library(minpack.lm)
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library(ggplot2)
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library(rstudioapi)
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# Getting path for current open file
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current_path = rstudioapi::getActiveDocumentContext()$path
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setwd(dirname(current_path))
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outsPlot_FUN <- function(all_l,OUTs_, TS, PROC = "not specified", PROBE="", Q=0.01, par) {
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#browser()
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#all_l_rout <- all_l[KEEP,]
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klein <- min(log(all_l$conc))
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gross <- max(log(all_l$conc))
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x_seq <- seq(klein, gross, (gross-klein)/100)
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samTRUE <- f4pl(x=x_seq, bottom=par["a"], top=par["d"],hill=par["b"], logEC50=par["cs"]-par["r"])
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refTRUE <- f4pl(x=x_seq, bottom=par["a"], top=par["d"],hill=par["b"], logEC50=par["cs"])
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pl_T <- data.frame(cbind(x_seq, refTRUE, samTRUE))
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p <- ggplot(all_l) +
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geom_point(aes(x=log(conc), y=y, shape=factor(isRef)), size=2) +
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theme_bw()
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p2 <- p + geom_point(OUTs_, mapping=aes(x=log(conc), y=y, shape=factor(isRef)),
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size=5, color="violetred", stroke=2, inherit.aes = FALSE) +
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scale_shape_manual(label=c("R",TS), values = c(21,25)) +
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ggtitle(paste(PROC, PROBE, TS, "Threshold=", Q)) +
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theme(legend.title=element_blank())
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p3 <- p2 + geom_line(data=pl_T, aes(x=x_seq, y=refTRUE), color="blue", inherit.aes = F) +
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geom_line(data=pl_T, aes(x=x_seq, y=samTRUE), color="red", inherit.aes = F)
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p3
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}
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#### 4PL model ----
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f4pl <- function(x, bottom, top, logEC50, hill) {
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bottom+(top-bottom)/(1+exp((logEC50-x)*hill))
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}
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#### residuals for joint REF/TEST fit ----
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resid_4pl_joint <- function(par,x,y, is_ref) {
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bottom <- par["bottom"]
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top <- par["top"]
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hill <- par["hill"]
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le50_r <- par["logEC50_ref"]
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le50_t <- par["logEC50_test"]
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le50 <- ifelse(is_ref, le50_r, le50_t)
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yhat <- f4pl(x, bottom, top, le50, hill)
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y - yhat
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}
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jac_4pl_joint <- function(par, x,y,is_ref) {
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bottom <- par["bottom"]
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top <- par["top"]
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hill <- par["hill"]
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le50_r <- par["logEC50_ref"]
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le50_t <- par["logEC50_test"]
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le50 <- ifelse(is_ref, le50_r, le50_t)
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# useful intermediates
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texp <- exp((le50-x)*hill) # t = 10^((le50-x)*hill))
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den <- (1-texp)
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frac <- (top-bottom)/den
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dyhat_dbottom <- 1-1/den
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dyhat_dtop <- 1/den
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d_invden_dle50 <- -(texp*log(2.718282)*hill)/(den^2)
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d_invden_dhill <- -(texp*log(2.718282)*(le50-x))/(den^2)
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dyhat_dle50 <- (top-bottom)* d_invden_dle50
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dyhat_dhill <- (top-bottom)* d_invden_dhill
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J <- cbind(
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bottom = -dyhat_dbottom,
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topm = -dyhat_dtop,
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hill = -dyhat_dhill,
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logEC50_ref = ifelse(is_ref, -dyhat_dle50,0),
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logEC50_test = ifelse(!is_ref, -dyhat_dle50,0)
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)
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J
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}
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robust_fit_irls <- function(par_start, x,y, is_ref,
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max_outer = 30, control = nls.lm.control(maxiter=200, ftol=1e-10, ptol=1e-10)) {
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par <- par_start
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#browser()
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for (k in seq_len(max_outer)) {
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r <- resid_4pl_joint(par,x,y,is_ref)
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# Mot Brown
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rSD <- quantile(r, 0.6827)*length(is_ref)/(length(is_ref)-5)
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# Robust scale (MAD); fallback if MAD ~0
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# s <- median(abs(r))/0.6745
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# Lorentzian / Cauchy weights acc. Mot/Brown
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w <- 1/log(1+(abs(r)/rSD)^2)
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# weighted residual function lor LM: sqrt(w)*r
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fn_w <- function(p) {
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rr <- resid_4pl_joint(p,x,y,is_ref)
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sqrt(w)*rr
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# w*rr
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}
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jac_w <- function(p) {
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JJ <- jac_4pl_joint(p,x,y,is_ref)
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JJ*sqrt(w) # row-wise scaling
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# JJ*w
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}
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#browser()
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fit <- tryCatch(nls.lm(par=par, fn=fn_w, jac=jac_w, control=control),
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error=function(e) {
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paste("error at robust fit irls", k)
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# tau <<- tau *10
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#Return zero delta to avoid NaN updates
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return(rep(0, length(par)))
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})
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if (max(abs(fit$par -par)) < 1e-8) {
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par <- fit$par
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break
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}
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par <- fit$par
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}
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r_final <- resid_4pl_joint(par,x,y,is_ref)
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# s_final <- median(abs(r_final))/0.6745
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rSD <- quantile(r, 0.6827)*length(r)/(length(r)-5)
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# Robust scale (MAD); fallback if MAD ~0
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# s <- median(abs(r))/0.6745
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list(par=par, residuals=r_final, scale=rSD)
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}
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# ROUT outlier detection via BH-FDR [1] https://cran.r-project.org/web//packages/minpack.lm/refman/minpack.lm.html)[6]
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rout_detect <- function(residuals, scale, Q=0.01) {
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z <- abs(residuals/scale)
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p <- 2*(1 - pcauchy(z))
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# p <- 2*(1-pt(z, length(residuals)-5))
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o <- order(p)
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p_sorted <- p[o]
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m <- length(p)
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# thresh <- (seq_len(m)/m) # *Q
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keep <- p_sorted <= Q # thresh
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(out_idx <- o[keep])
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sort(out_idx)
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}
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# Final OLS LM refit after removing outliers
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ols_refit <- function(par_start, x,y,is_ref, keep_idx,
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control = nls.lm.control(maxiter=400, ftol=1e-12, ptol=1e-12)) {
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xk <- x[keep_idx]; yk <- y[keep_idx]; rk <- is_ref[keep_idx]
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fn_w <- function(p) resid_4pl_joint(p,xk,yk,rk)
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jac_w <- function(p) jac_4pl_joint(p,xk,yk,rk)
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#browser()
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fit <- tryCatch(nls.lm(par=par_start, fn=fn_w, jac=jac_w, control=control),
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error=function(e) {
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paste("error at ols_refits")
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# tau <<- tau *10
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#Return zero delta to avoid NaN updates
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return(rep(0, length(par)))
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})
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list(par = fit$par, residuals = fn_w(fit$par), keep=keep_idx)
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}
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#. high-level ROUT 4PL analysis
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rout_4pl_potency <- function(df, Q=0.01, par_start=NULL, max_outer = 30) {
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stopifnot(all(c("sample","conc","y") %in% names(df))) # "rep",
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#browser()
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x <- log(df$conc)
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y <- df$y
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is_ref <- df$isRef == 1
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# start values if not provided
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if (is.null(par_start)) {
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bottom0 = min(y, na.rm = T)
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top0 = max(y, na.rm = T)
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if (df$y[1]>df$y[8]) hill0 <- 1 else hill0 <- -1
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le50_r0 <- median(x[is_ref], na.rm = T)
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le50_t0 <- median(x[!is_ref], na.rm = T)
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par_start <- c(bottom=bottom0, top=top0, hill=hill0, logEC50_ref = le50_r0 , logEC50_test = le50_t0)
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}
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# Step1 : robust fit
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robust <- robust_fit_irls(par_start, x,y,is_ref, max_outer = max_outer)
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# Step 2: outlier detection
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out_idx <- rout_detect(robust$residuals, robust$scale, Q=Q)
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keep_idx <- setdiff(seq_len(nrow(df)), out_idx)
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# Step 3: OLS refit
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ols <- ols_refit(robust$par, x,y,is_ref, keep_idx)
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# Relative potency
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rp <- exp(ols$par["logEC50_ref"] - ols$par["logEC50_test"])*100
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list(Q=Q, outliers=out_idx,
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kept=keep_idx,
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par_robust = robust$par,
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par_final = ols$par,
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rp_percent = rp,
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df=df)
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}
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# sheets <- openxlsx::getSheetNames("~/plateflow/outlierFUB.xlsx")
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# all_dat <- lapply(sheets, openxlsx::read.xlsx, xlsxFile="~/plateflow/outlierFUB.xlsx")
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# names(all_dat) <- sheets
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#
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# Plate <- 1
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#
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# PlanteN <- sheets[Plate]
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# NoDils <- 8
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# Nreps <- 3
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# DAT <- all_dat[[Plate]]
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# colnames(DAT) <- DAT[2,]
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# REF <- DAT[3:10,1:4]
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# SAM1 <- DAT[13:20,1:4]
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# SAM2 <- DAT[23:30,1:4]
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# SAM3 <- DAT[33:40,1:4]
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#
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# REF_ <- sapply(REF, as.numeric)
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# colnames(REF_) <- colnames(REF)
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# SAM1_ <- sapply(SAM1, as.numeric)
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# colnames(SAM1_) <- colnames(SAM1)
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# SAM2_ <- sapply(SAM2, as.numeric)
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# colnames(SAM2_) <- colnames(SAM2)
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# SAM3_ <- sapply(SAM3, as.numeric)
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# colnames(SAM3_) <- colnames(SAM3)
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#
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# plot_df <- rbind(REF_, SAM1_, SAM2_,SAM3_)
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#
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# plot_df_ <- cbind(as.data.frame(plot_df), sample=c(rep("R", NoDils),rep("S1R", NoDils),rep("S2", NoDils),rep("S3", NoDils)))
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# test_df <- plot_df_[1:16,]
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# test_df2 <- plot_df_[c(1:8, 17:24),]
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# test_df3 <- plot_df_[c(1:8, 25:32),]
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#
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# all_l <- melt(test_df, id.vars = c("sample","Dose"), variable.name="replname", value.name="readout")
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# colnames(all_l) <- c("sample","conc","rep","y")
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#
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# for (Q in c(0.01,0.015,0.02)) {
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# res <- rout_4pl_potency(all_l, Q)
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# OUTs_ <- all_l[res$outliers,]
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# print(outsPlot_FUN(all_l, OUTs_, TS=all_l$sample[9], PROC="ROUT",PROBE="Sheet 1",Q,par=res$par_final))
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# }
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@@ -31,7 +31,7 @@ library(scales)
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library(tolerance)
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source("../R/Global.R")
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source("ROUT.R")
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#### ui ----
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@@ -177,7 +177,6 @@ server <- function(input, output, session) {
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),
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uiOutput(outputId = "sheetName"),
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"For data format in the EXCEL file see Data template",
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"If no data are uploaded, the settings to the right are used for calculations.",
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tags$head(tags$style(HTML("label {font-size:80%;margin-bottom: 3px;margin-top: 3px;}"))),
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div(checkboxInput("PureErr", "Should pure error be used for calculation of CIs?", FALSE),
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style = "font-size: 24px !important;color: #C2173F"
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@@ -300,7 +299,7 @@ server <- function(input, output, session) {
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)
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),
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tabPanel(
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"Tests and ANOVAA",
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"Tests and ANOVA",
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column(
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12,
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h3("Tests for linear PLA:"),
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@@ -325,6 +324,16 @@ server <- function(input, output, session) {
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)
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)
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),
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tabPanel("robust outlier testing",
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sliderInput("Qslider", "adjust Q-value in %",min=0.1, max = 20, value = 1, step=0.1),
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plotOutput("OutlierPlot"),
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tableOutput("OutlierDF"),
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"GUIDANCE: The procedure of Motulsky & Brown allows for robust outlier testing.",
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"Adjust the slider to mark the suspected outliers. Mostly, a Q-value of 2% is sufficient.",
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"If the general variability of the data is high, many datapoints will be flagged, also ones that are not deemed to be outliers",
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"An indicator for high variability is, when the Q-value needs to be increased above 5%, to flag suspected outliers.",
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"Then, please re-consider, if the suspected outlier is not 'just' normal variability."
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),
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tabPanel(
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"parameter estimates",
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htmlOutput("PureErrWParEst"),
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@@ -729,7 +738,7 @@ server <- function(input, output, session) {
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reset(id = "") # from shinyjs package
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})
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#### input optim XL file ----
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#### input Wizard XL file ----
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observe({
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if (!is.null(input$MiFile)) {
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MinFile <- input$MiFile
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@@ -804,7 +813,50 @@ server <- function(input, output, session) {
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# all_l$readout[all_l$readout < 0] <- 0.01
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REP$all_l <- all_l
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#### XLSX eval ----
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##### ROUT outlier testing ----
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#browser()
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if(!is.null(input$Qslider)) {
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all_lROUT <- all_l
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colnames(all_lROUT) <- c("log_dose","sample","y","isRef","isSample","conc")
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res <- rout_4pl_potency(all_lROUT, Q=input$Qslider/100)
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OUTs_ <- all_lROUT[res$outliers,]
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all_l_rout <- all_lROUT[res$kept,]
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# all_l_rout$log_dose <- log(all_l_rout$Conc)
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# colnames(all_l_rout) <- c("log_dose","sample","y","isRef","isSample","conc")
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if (all_l_rout$conc[1]>all_l_rout$conc[6]) {
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if (all_l_rout$y[1]>all_l_rout$y[6]) SLOPE <- 1 else SLOPE<- -1
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} else {
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if (all_l_rout$y[1]>all_l_rout$y[6]) SLOPE <- -1 else SLOPE<- 1
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}
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startlist <- list(a = min(all_l_rout$y), b = SLOPE, d = max(all_l_rout$y), cs = mean(log(all_l_rout$conc)), r = 0)
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mr <- tryCatch(
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{
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gsl_nls(
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fn = y ~ a + (d - a) / (1 + exp(b * ((cs - r * isSample) - log_dose))),
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data = all_l_rout,
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start = startlist, # race=T,
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control = gsl_nls_control(xtol = 1e-6, ftol = 1e-6, gtol = 1e-6)
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)
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},
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warning = function(e) {
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mr <<- "In nlsModel singular gradient matrix"
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})
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PAR <- summary(mr)$coefficients[,1]
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ROUTplot <- outsPlot_FUN(all_l_rout, OUTs_, TS=all_l_rout$sample[13], PROC="ROUT",PROBE=input$sheet,Q=input$Qslider/100,par=PAR)
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output$OutlierDF <- renderTable({
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OUTs_
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})
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output$OutlierPlot <- renderPlot({
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print(ROUTplot)
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})
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}
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##### XLSX eval ----
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#if (CORro < 0) SLOPE <- -1 else SLOPE <- 1
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FITs <- Fitting_FUNC(XLdat2, TransFlag = FALSE, nameWS="")
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