Merge bug fix

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: MSstatsTMT
 Title: Protein Significance Analysis in shotgun mass spectrometry-based proteomic experiments with tandem mass tag (TMT) labeling
-Version: 2.12.0
+Version: 2.13.1
 Date: 2024-04-23
 Description: The package provides statistical tools for detecting differentially abundant proteins in shotgun mass spectrometry-based proteomic experiments with tandem mass tag (TMT) labeling. It provides multiple functionalities, including aata visualization, protein quantification and normalization, and statistical modeling and inference. Furthermore, it is inter-operable with other data processing tools, such as Proteome Discoverer, MaxQuant, OpenMS and SpectroMine.
 Authors@R: c(person("Devon", "Kohler", email = "[email protected]", role=c("aut","cre")),
@@ -13,12 +13,12 @@ License: Artistic-2.0
 Depends: R (>= 4.2)
 Imports: limma, lme4, lmerTest, methods, data.table, 
          stats, utils, ggplot2, grDevices, graphics, MSstats,
-         MSstatsConvert, checkmate
+         MSstatsConvert, checkmate, plotly, htmltools
 Suggests: BiocStyle, knitr, rmarkdown, testthat
 VignetteBuilder: knitr
 biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Software
 Encoding: UTF-8
 LazyData: true
 URL: http://msstats.org/msstatstmt/
 BugReports: https://groups.google.com/forum/#!forum/msstats
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.1

NAMESPACE

@@ -6,6 +6,7 @@ export(PDtoMSstatsTMTFormat)
 export(PhilosophertoMSstatsTMTFormat)
 export(SpectroMinetoMSstatsTMTFormat)
 export(dataProcessPlotsTMT)
+export(designSampleSizeTMT)
 export(groupComparisonTMT)
 export(proteinSummarization)
 import(data.table)
@@ -33,8 +34,17 @@ importFrom(graphics,par)
 importFrom(graphics,plot)
 importFrom(graphics,plot.new)
 importFrom(graphics,title)
+importFrom(htmltools,div)
+importFrom(htmltools,save_html)
+importFrom(htmltools,tagList)
 importFrom(lmerTest,lmer)
 importFrom(methods,new)
+importFrom(plotly,add_trace)
+importFrom(plotly,ggplotly)
+importFrom(plotly,layout)
+importFrom(plotly,plot_ly)
+importFrom(plotly,style)
+importFrom(plotly,subplot)
 importFrom(stats,anova)
 importFrom(stats,coef)
 importFrom(stats,lm)
@@ -44,5 +54,6 @@ importFrom(stats,model.matrix)
 importFrom(stats,na.omit)
 importFrom(stats,p.adjust)
 importFrom(stats,pt)
+importFrom(stats,qnorm)
 importFrom(utils,setTxtProgressBar)
 importFrom(utils,txtProgressBar)

R/designSampleSizeTMT.R

@@ -0,0 +1,221 @@
+#' Planning future experimental designs of Tandem Mass Tag (TMT) experiments acquired with Data-Dependent Acquisition (DDA or shotgun)
+#'
+#' @description Calculate sample size for future experiments of a TMT experiment 
+#' based on intensity-based linear model. Two options of the calculation: 
+#' (1) number of biological replicates per condition, 
+#' (2) power.
+#' 
+#' @param data 'FittedModel' in testing output from function groupComparisonTMT.
+#' @param desiredFC the range of a desired fold change which includes the lower 
+#' and upper values of the desired fold change.
+#' @param FDR a pre-specified false discovery ratio (FDR) to control the overall 
+#' false positive rate. Default is 0.05
+#' @param numSample minimal number of biological replicates per condition. 
+#' TRUE represents you require to calculate the sample size for this category, 
+#' else you should input the exact number of biological replicates.
+#' @param power a pre-specified statistical power which defined as the probability 
+#' of detecting a true fold change. TRUE represent you require to calculate the power 
+#' for this category, else you should input the average of power you expect. Default is 0.9
+#' @inheritParams .documentFunction
+#' 
+#' @details The function fits the model and uses variance components to calculate 
+#' sample size. The underlying model fitting with intensity-based linear model with 
+#' technical MS run replication. Estimated sample size is rounded to 0 decimal.
+#' The function can only obtain either one of the categories of the sample size 
+#' calculation (numSample, numPep, numTran, power) at the same time.
+#' 
+#' @return data.frame - sample size calculation results including varibles:
+#' desiredFC, numSample, FDR,  and power.
+#' 
+#' @importFrom stats median
+#' @importFrom plotly ggplotly style add_trace plot_ly subplot layout
+#' 
+#' @export
+#' 
+#' @examples
+#' data(input.pd)
+#' # use protein.summarization() to get protein abundance data
+#' quant.pd.msstats = proteinSummarization(input.pd,
+#'                                         method="msstats",
+#'                                         global_norm=TRUE,
+#'                                         reference_norm=TRUE)
+#' 
+#' test.pairwise = groupComparisonTMT(quant.pd.msstats, save_fitted_models = TRUE)
+#' head(test.pairwise$ComparisonResult)
+#' 
+#' ## Calculate sample size for future experiments:
+#' #(1) Minimal number of biological replicates per condition
+#' designSampleSizeTMT(data=test.pairwise$FittedModel, numSample=TRUE,
+#'                  desiredFC=c(1.25,1.75), FDR=0.05, power=0.8)
+#' #(2) Power calculation
+#' designSampleSizeTMT(data=test.pairwise$FittedModel, numSample=2,
+#'                  desiredFC=c(1.25,1.75), FDR=0.05, power=TRUE)
+#'           
+designSampleSizeTMT = function(
+    data, desiredFC, FDR = 0.05, numSample = TRUE, power = 0.9,
+    use_log_file = TRUE, append = FALSE, verbose = TRUE, log_file_path = NULL
+) {
+
+  var_component = .getVarComponentTMT(data)
+
+  median_sigma_error = median(var_component[["Error"]], na.rm = TRUE)
+  median_sigma_subject = .getMedianSigmaSubject(var_component)
+  median_sigma_run = .getMedianSigmaRun(var_component)
+
+  ## power calculation
+  if (isTRUE(power)) {
+    delta = log2(seq(desiredFC[1], desiredFC[2], 0.025))
+    desiredFC = 2 ^ delta
+    power_output = .calculatePower(desiredFC, FDR, delta, median_sigma_error, 
+                                   median_sigma_subject, median_sigma_run, 
+                                   numSample)        
+    var_comp = (median_sigma_error / numSample + median_sigma_subject / numSample + median_sigma_run / numSample)
+    CV = round( (2 * var_comp) / desiredFC, 3)
+    sample_size = data.frame(desiredFC, numSample, FDR, 
+                             power = power_output, CV)
+  }	
+
+  if (is.numeric(power)) {
+    # delta = log2(seq(desiredFC[1], desiredFC[2], 0.025))
+    delta = log2(seq(desiredFC[1], desiredFC[2], 0.001))
+    desiredFC = 2 ^ delta
+    ## Large portion of proteins are not changing
+    m0_m1 = 99 ## it means m0/m1=99, m0/(m0+m1)=0.99
+    alpha = power * FDR / (1 + (1 - FDR) * m0_m1)
+    if (isTRUE(numSample)) {
+      numSample = .getNumSample(desiredFC, power, alpha, delta,
+                                median_sigma_error, median_sigma_subject,
+                                median_sigma_run)
+      var_comp = (median_sigma_error / numSample + median_sigma_subject / numSample + median_sigma_run / numSample)
+      CV = round(2 * var_comp / desiredFC, 3)
+      sample_size = data.frame(desiredFC, numSample, FDR, power, CV)
+    }
+  } 
+  sample_size
+}
+
+#' Get variances from models fitted by the groupComparison function
+#' @param fitted_models FittedModels element of groupComparison output
+#' @keywords internal
+.getVarComponentTMT = function(fitted_models) {
+  Protein = NULL
+
+  result = data.table::rbindlist(
+    lapply(fitted_models, function(fit) {
+      if (!is.null(fit)) {
+        if (!is(fit, "lmerMod")) {
+          error = summary(fit)$sigma^2
+          subject <- NA
+          group_subject <- NA
+          run <- NA
+          mix_run <- NA
+        } else {
+          stddev = c(sapply(lme4::VarCorr(fit), function(el) attr(el, "stddev")), 
+                     attr(lme4::VarCorr(fit), "sc"))
+          error = stddev[names(stddev) == ""]^2
+          if (any(names(stddev) %in% "SUBJECT.(Intercept)")) {
+            subject = stddev["SUBJECT.(Intercept)"]^2
+          } else {
+            subject = NA
+          }
+          if (any(names(stddev) %in% "SUBJECT:GROUP.(Intercept)")) {
+            group_subject = stddev["SUBJECT:GROUP.(Intercept)"]^2
+          } else {
+            group_subject = NA
+          }
+          if (any(names(stddev) %in% "Run.(Intercept)")) {
+            run = stddev["Run.(Intercept)"]^2
+          } else {
+            run = NA
+          }
+          if (any(names(stddev) %in% "Mixture:TechRepMixture.(Intercept)")) {
+            mix_run = stddev["Mixture:TechRepMixture.(Intercept)"]^2
+          } else {
+            mix_run = NA
+          }
+
+        }
+        list(Error = error,
+             Subject = subject,
+             GroupBySubject = group_subject,
+             Run = run,
+             RunByMixture=mix_run)
+      } else {
+        NULL
+      }
+    })
+  )
+  result[, Protein := 1:.N]
+  result
+}
+
+#' Get median per subject or group by subject
+#' @param var_component data.frame, output of .getVarComponent
+#' @importFrom stats median
+#' @keywords internal
+.getMedianSigmaRun = function(var_component) {
+  if (sum(!is.na(var_component[, "RunByMixture"])) > 0) {
+    median_sigma_run = median(var_component[["RunByMixture"]], na.rm=TRUE)
+  } else {
+    if (sum(!is.na(var_component[, "Run"])) > 0) {
+      median_sigma_run = median(var_component[["Run"]], na.rm=TRUE)
+    } else {
+      median_sigma_run = 0
+    }
+  }
+  median_sigma_run
+}
+
+#' Get median per run or run by mix
+#' @param var_component data.frame, output of .getVarComponent
+#' @importFrom stats median
+#' @keywords internal
+.getMedianSigmaSubject = function(var_component) {
+  if (sum(!is.na(var_component[, "GroupBySubject"])) > 0) {
+    median_sigma_subject = median(var_component[["GroupBySubject"]], na.rm=TRUE)
+  } else {
+    if (sum(!is.na(var_component[, "Subject"])) > 0) {
+      median_sigma_subject = median(var_component[["Subject"]], na.rm=TRUE)
+    } else {
+      median_sigma_subject = 0
+    }
+  }
+  median_sigma_subject
+}
+
+#' Power calculation
+#' @inheritParams designSampleSizeTMT
+#' @param delta difference between means (?)
+#' @param median_sigma_error median of error standard deviation
+#' @param median_sigma_subject median standard deviation per subject
+#' @importFrom stats qnorm
+#' @keywords internal
+.calculatePower = function(desiredFC, FDR, delta, median_sigma_error, 
+                           median_sigma_subject, median_sigma_run, numSample) {
+  m0_m1 = 99
+  t = delta / sqrt(2 * (median_sigma_error / numSample + median_sigma_subject / numSample + median_sigma_run / numSample))
+  powerTemp = seq(0, 1, 0.01)
+  power = numeric(length(t))
+  for (i in seq_along(t)) {
+    diff = qnorm(powerTemp) + qnorm(1 - powerTemp * FDR / (1 + (1 - FDR) * m0_m1) / 2) - t[i]
+    min(abs(diff), na.rm = TRUE)
+    power[i] = powerTemp[order(abs(diff))][1]
+  }
+  power
+}
+
+#' Get sample size
+#' @inheritParams designSampleSizeTMT
+#' @inheritParams .calculatePower
+#' @param alpha significance level
+#' @param delta difference between means (?)
+#' @importFrom stats qnorm
+#' @keywords internal
+.getNumSample = function(desiredFC, power, alpha, delta, median_sigma_error, 
+                         median_sigma_subject, median_sigma_run){
+  z_alpha = qnorm(1 - alpha / 2)
+  z_beta = qnorm(power)
+  aa = (delta / (z_alpha + z_beta)) ^ 2
+  numSample = round(2 * (median_sigma_error + median_sigma_subject + median_sigma_run) / aa, 0)
+  numSample
+}

R/utils_group_comparison.R

@@ -85,7 +85,7 @@
 
           ####### TODO: need to double check the full model ######
           fit = fit_Mix_TechRep_Group_Sub_model(single_protein) # fit the full model with mixture, techrep, subject effects
-          if (!inherits(fit, "try-error")) { # full model is not applicable, fit the model with run and subject effects
+          if (inherits(fit, "try-error")) { # full model is not applicable, fit the model with run and subject effects
             fit = fit_Run_Group_Sub_model(single_protein) 
           }
           if (inherits(fit, "try-error")) { # second model not applicable - fit model with only subject effect
@@ -120,7 +120,7 @@
       if (has_biomixtures) { # multiple mixtures
         if (has_techreps) { # multiple tech MS runs
           fit = fit_Mix_TechRep_Group_Sub_model(single_protein) # fit the full model with mixture, techrep, subject effects
-          if (!inherits(fit, "try-error")) { # full model is not applicable
+          if (inherits(fit, "try-error")) { # full model is not applicable
             fit = fit_Run_Group_Sub_model(single_protein) # fit the reduced model with run and subject effects
           }
           if (inherits(fit, "try-error")) { # full model not applicable - fit model with only run effect