diff --git a/R/file-location-workaround.R b/R/file-location-workaround.R
new file mode 100644
index 0000000..a50ba95
--- /dev/null
+++ b/R/file-location-workaround.R
@@ -0,0 +1,29 @@
+# We are currently receiving an error when running ParseCAMERA with gen.plots = TRUE
+#Munge Data Modules
+#if positive, run:
+# ParseCAMERA(from.table = "Annotated", to.table = "output_parsed", CAMERA.obj = "anposGa")
+#
+# Error in .local(object, ...) : 
+#   xcmsSource: file not found: E:\ORD Backup\Users\jmosley\Desktop\WLSSD 2014 Livers\mzML/Pooled QCs/Pooled_QC_Pos_10.mzML
+
+#To get around this, set the variables in the script below and run *AFTER* running the InitWorkflow() module. Then execute the remaining script code to bypass the bug.
+
+##Set these variables to be equivalent on your local system
+data_dir <- "O:/Public/jmosl01/LUMA-Data"
+
+
+##Execute this code only after setting the above variables for your local system
+new_filepaths <- list.files(path = data_dir, recursive = TRUE, full.names = TRUE)
+
+if(BLANK) new_filepaths <- new_filepaths[grepl("Blanks", new_filepaths)]
+if(!BLANK) new_filepaths <- new_filepaths[!grepl("Blanks", new_filepaths)]
+
+#If Positive mode, run this:
+new_filepaths <- new_filepaths[grepl(ion.id[1],new_filepaths)]
+
+anposGa@xcmsSet@filepaths <- new_filepaths
+
+#If Negative mode, run this:
+new_filepaths <- new_filepaths[grepl(ion.id[2],new_filepaths)]
+
+annegGa@xcmsSet@filepaths <- new_filepaths
diff --git a/R/my_plotEICs.R b/R/my_plotEICs.R
new file mode 100644
index 0000000..943591d
--- /dev/null
+++ b/R/my_plotEICs.R
@@ -0,0 +1,136 @@
+#
+# This function is a modified version of CAMERA's plotEICs.  It implements a bug fix that
+# addresses an error with the color labels in the plots. Bug fix was first proposed by
+# Jonathan Mosley.
+#
+my_plotEICs <- function (object, pspec = 1:length(object@pspectra), maxlabel = 0,
+                         sleep = 0, method = "bin")
+{
+    smpls <- unique(object@psSamples[pspec])
+    xeic <- new("xcmsEIC")
+    xeic@rtrange <- matrix(nrow = length(pspec), ncol = 2)
+    xeic@mzrange <- matrix(nrow = length(pspec), ncol = 2)
+    pcpos <- 1
+    rtmargin <- 1
+    for (a in seq(along = smpls)) {
+      xraw <- xcmsRaw(object@xcmsSet@filepaths[smpls[a]],
+                      profmethod = method)
+      pspecS <- pspec[which(object@psSamples[pspec] == smpls[a])]
+      peaks <- CAMERA:::getPeaks(object@xcmsSet, smpls[a])
+      eic <- lapply(pspecS, function(pc) {
+        pidx <- object@pspectra[[pc]]
+        pks <- peaks[pidx, , drop = FALSE]
+        gks <- object@groupInfo[pidx, , drop = FALSE]
+        nap <- which(is.na(pks[, 1]))
+        pks[nap, ] <- cbind(gks[nap, c(1:6), drop = FALSE],
+                            matrix(nrow = length(nap), ncol = 5, 0))
+        bbox <- c(rtmin = min(pks[, "rtmin"]) - rtmargin,
+                  rtmax = max(pks[, "rtmax"]) + rtmargin,
+                  mzmin = min(pks[,"mzmin"]),
+                  mzmax = max(pks[, "mzmax"]))
+        eic <- xcms:::getEIC(xraw, rtrange = pks[, c("rtmin","rtmax"), drop = FALSE],
+                             mzrange = pks[, c("mzmin","mzmax"), drop = FALSE])
+        xeic@rtrange[pcpos, ] <<- bbox[c("rtmin", "rtmax")]
+        xeic@mzrange[pcpos, ] <<- bbox[c("mzmin", "mzmax")]
+        cat("-->", pcpos, "\n")
+        pcpos <<- pcpos + 1
+        eic@eic[[1]]
+      })
+      xeic@eic <- c(xeic@eic, eic)
+    }
+    names(xeic@eic) <- paste("Pseudospectrum ", pspec, sep = "")
+    if (maxlabel > 0) {
+      col <- rainbow(maxlabel)
+    }
+    else {
+      col <- c()
+    }
+    for (ps in seq(along = pspec)) {
+      EIC <- xeic@eic[[ps]]
+      pidx <- object@pspectra[[pspec[ps]]]
+      peaks <- CAMERA:::getPeaks(object@xcmsSet, object@psSamples[pspec[ps]])[pidx,, drop = FALSE]
+      grps <- object@groupInfo[pidx, ]
+      nap <- which(is.na(peaks[, 1]))
+      naps <- rep(FALSE, nrow(peaks))
+      if (length(nap) > 0) {
+        naps[nap] <- TRUE
+        peaks[nap, ] <- cbind(grps[nap, c(1:6), drop = FALSE],
+                              matrix(nrow = length(nap), ncol = 5, 0))
+      }
+      main <- paste("Pseudospectrum ", pspec[ps], sep = "")
+      neics <- length(pidx)
+      lmaxlabel <- min(maxlabel, neics)
+      eicidx <- 1:neics
+      maxint <- numeric(length(eicidx))
+      for (j in eicidx) {
+        maxint[j] <- max(EIC[[j]][, "intensity"])
+      }
+      o <- order(maxint, decreasing = TRUE)
+      rt <- xeic@rtrange[ps, ]
+      rt.min <- round(mean(peaks[, "rtmin"]), digits = 3)
+      rt.med <- round(peaks[o[1], "rt"], digits = 3)
+      rt.max <- round(mean(peaks[, "rtmax"]), digits = 3)
+      plot(0, 0, type = "n", xlim = rt, ylim = c(0, max(maxint)),
+           xaxs = "i", xlab = "Retention Time (seconds)",
+           ylab = "Intensity", main = paste("Extracted Ion Chromatograms for ",
+                                            main, "\nTime: From", rt.min, "to", rt.max,
+                                            ", mean", rt.med))
+      lcol <- rgb(0.6, 0.6, 0.6)
+      lcol <- c(col, rep(lcol, max(nrow(peaks) - maxlabel,0)))
+      cnt <- 1
+      for (j in eicidx[o]) {
+        pts <- xeic@eic[[ps]][[j]]
+        points(pts, type = "l", col = lcol[cnt])
+        peakrange <- peaks[, c("rtmin", "rtmax"), drop = FALSE]
+        ptsidx <- pts[, "rt"] >= peakrange[j, 1] & pts[,"rt"] <= peakrange[j, 2]
+        if (naps[j]) {
+          points(pts[ptsidx, ], type = "l", col = col[cnt],lwd = 1.3, lty = 3)
+        }
+        else {
+          points(pts[ptsidx, ], type = "l", col = col[cnt],lwd = 1.3)
+        }
+        cnt <- cnt + 1
+      }
+      pspectrum <- getpspectra(object, grp = pspec[ps])
+      mz <- pspectrum[o, "mz"]
+      if (lmaxlabel > 0 & "adduct" %in% colnames(pspectrum)) {
+        adduct <- sub("^ ", "", pspectrum[o, "adduct"])
+        mass <- sapply(strsplit(adduct, " "), function(x) {
+          x[2]
+        })
+        adduct <- sapply(strsplit(adduct, " "), function(x) {
+          x[1]
+        })
+        umass <- unique(na.omit(mass[1:maxlabel]))
+        adduct[is.na(adduct)] <- ""
+        test <- vector("list", length = length(mz))
+        mz <- format(pspectrum[o[1:lmaxlabel], "mz"],
+                     digits = 5)
+        if (length(umass) > 0) {
+          for (i in 1:length(umass)) {
+            ini <- which(mass == umass[i])
+            for (ii in 1:length(ini)) {
+              firstpart <- strsplit(adduct[ini[ii]],"M")[[1]][1]
+              secondpart <- strsplit(adduct[ini[ii]],"M")[[1]][2]
+              masspart <- mz[ini[ii]]
+              test[[ini[ii]]] <- substitute(paste(masspart," ", firstpart, M[i], secondpart),
+                                            list(firstpart = firstpart,
+                                                 i = i,
+                                                 secondpart = secondpart,
+                                                 masspart = masspart))
+            }
+          }
+        }
+        for (i in seq(along = lmaxlabel)) {
+          if (is.null(test[[i]])) {
+            test[[i]] <- mz[i]
+          }
+        }
+        leg <- as.expression(test[1:lmaxlabel])
+        legend("topright", legend = leg, col = lcol,lty = 1)
+      }
+      if (sleep > 0) {
+        Sys.sleep(sleep)
+      }
+    }
+}
diff --git a/R/plotting-functions.R b/R/plotting-functions.R
index 5676819..5c6143d 100644
--- a/R/plotting-functions.R
+++ b/R/plotting-functions.R
@@ -1,3 +1,4 @@
+source("my_plotEICs.R")
 #' @title Loop-based EIC Plotter for metabolite groups
 #'
 #' @export
@@ -33,7 +34,6 @@ plot_metgroup = function(CAMERA.obj, Sample.df, Peak.list, center, BLANK, gen.pl
     if (missing(maxlabel))
         maxlabel = 10
 
-
     # Change the psspectra list in the xsAnnotate object to be the unique list of metabolite_groups
     X <- split(Peak.list$EIC_ID, as.numeric(Peak.list$metabolite_group))
     names(X) <- sort(unique(Peak.list$metabolite_group))
@@ -93,8 +93,8 @@ plot_metgroup = function(CAMERA.obj, Sample.df, Peak.list, center, BLANK, gen.pl
                   test.mat <- as.matrix(t(my.mat))
                   dimnames(test.mat) <- list(colnames(my.df[, sample.cols]), my.df$EIC_ID)
                   plot(c(0, 1), c(0, 1), ann = F, bty = "n", type = "n", xaxt = "n", yaxt = "n")
-                  text(x = 0.5, y = 0.5, paste("Dendrograms can't be plotted for two features.\n", "Use the correlation plot to the left to \n",
-                    "Tell if two features are from the same metabolite"), cex = 1.6, col = "black")
+                  text(x = 0.5, y = 0.5, paste("Dendrograms can't be plotted for two features.\n", "Use the correlation plot to the left to tell \n",
+                    "if two features are from the same metabolite"), cex = 1.6, col = "black")
                   res2 <- rcorr(test.mat)
                   M <- res2$r
                   P <- res2$P
@@ -102,12 +102,17 @@ plot_metgroup = function(CAMERA.obj, Sample.df, Peak.list, center, BLANK, gen.pl
                   order.hc2 <- corrplot::corrMatOrder(M, order = "hclust", hclust.method = "complete")
                   col.new = rainbow(maxlabel)[order.hc2]
                   corrplot(M, type = "upper", order = "hclust", hclust.method = "complete", p.mat = P, sig.level = 0.01, insig = "blank",
-                    tl.col = col.new)
+                    tl.col = col.new, cl.ratio = 0.15, cl.align.text = "l", cl.cex = 0.6, cl.offset = 0.2)
 
                   # Plots the EICs and Pseudo-spectra for each metabolite group containing more than one feature in a for loop
-                  EIC.plots <- plotEICs(new_CAMERA.obj, pspec = get.mg[i], maxlabel = maxlabel, sleep = 0)  ## Plot the EICs
+                  EIC.plots <- my_plotEICs(new_CAMERA.obj, pspec = get.mg[i], maxlabel = maxlabel, sleep = 0)  ## Plot the EICs
+
+                  a1 <- new_CAMERA.obj@pspectra[[get.mg[i]]]
+                  b1 <- new_CAMERA.obj@groupInfo[a1,"mz"]
+                  mz1 <- min(b1) * 0.80
+                  mz2 <- max(b1) * 1.20
 
-                  plotPsSpectrum(new_CAMERA.obj, pspec = get.mg[i], maxlabel = maxlabel, sleep = 0)  #Plot the Mass Spectra
+                  plotPsSpectrum(new_CAMERA.obj, pspec = get.mg[i], maxlabel = maxlabel, sleep = 0, cex.main = 0.8, cexMulti = 1.0, mzrange = c(mz1,mz2))  #Plot the Mass Spectra
 
                 } else {
                   my.mat <- as.matrix(my.df[, grep(paste(QC.id, paste(strsplit(sexes, "(?<=.[_])", perl = TRUE),
@@ -129,11 +134,16 @@ plot_metgroup = function(CAMERA.obj, Sample.df, Peak.list, center, BLANK, gen.pl
                   order.hc2 <- corrplot::corrMatOrder(M, order = "hclust", hclust.method = "complete")
                   col.new = rainbow(maxlabel)[order.hc2]
                   corrplot(M, type = "upper", order = "hclust", hclust.method = "complete", p.mat = P, sig.level = 0.01, insig = "blank",
-                    tl.col = col.new)
+                    tl.col = col.new, cl.ratio = 0.15, cl.align.text = "l", cl.cex = 0.6, cl.offset = 0.2)
                   # Plots the EICs and Pseudo-spectra for each metabolite group containing more than one feature in a for loop
-                  EIC.plots <- plotEICs(new_CAMERA.obj, pspec = get.mg[i], maxlabel = maxlabel, sleep = 0)  ## Plot the EICs
+                  EIC.plots <- my_plotEICs(new_CAMERA.obj, pspec = get.mg[i], maxlabel = maxlabel, sleep = 0)  ## Plot the EICs
+
+                  a1 <- new_CAMERA.obj@pspectra[[get.mg[i]]]
+                  b1 <- new_CAMERA.obj@groupInfo[a1,"mz"]
+                  mz1 <- min(b1) * 0.80
+                  mz2 <- max(b1) * 1.20
 
-                  plotPsSpectrum(new_CAMERA.obj, pspec = get.mg[i], maxlabel = maxlabel, sleep = 0)  #Plot the Mass Spectra
+                  plotPsSpectrum(new_CAMERA.obj, pspec = get.mg[i], maxlabel = maxlabel, sleep = 0, cex.main = 0.8, cexMulti = 1.0, mzrange = c(mz1, mz2))  #Plot the Mass Spectra
                 }
             }
         }