diff --git a/DESCRIPTION b/DESCRIPTION
index b67819e..7c9fb28 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -31,8 +31,8 @@ Imports:
     cowplot (>= 1.1.1),
     dplyr (>= 1.0.9),
     GeomxTools (>= 3.1.1),
-    ggforce (== 0.3.4),
-    ggplot2 (== 3.3.6),
+    ggforce (>= 0.3.4),
+    ggplot2 (>= 3.3.6),
     gridExtra (>= 2.3),
     grid (>= 4.1.3),
     gtable (>= 0.3.0),
diff --git a/NAMESPACE b/NAMESPACE
index 86b2fc3..e91555b 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -45,6 +45,7 @@ importFrom(dplyr,count)
 importFrom(dplyr,filter)
 importFrom(dplyr,group_by)
 importFrom(dplyr,pull)
+importFrom(dplyr,rename)
 importFrom(dplyr,select)
 importFrom(ggforce,gather_set_data)
 importFrom(ggforce,geom_parallel_sets)
diff --git a/R/filtering.R b/R/filtering.R
index db967c0..c128a1f 100644
--- a/R/filtering.R
+++ b/R/filtering.R
@@ -10,6 +10,19 @@
 #' @details This function will run various filtering parameters for NanoStringGeoMxSet datasets
 #' 
 #' @param object A NanoStringGeoMxSet dataset
+#' @param loq.cutoff The number of standard deviations above the negative probe 
+#' geometric mean to use as a cutoff for the limit of quantification
+#' @param loq.min The minimum value for the limit of quantification
+#' @param segment.gene.rate.cutoff A decimal for the minimum cutoff for the 
+#' genes detected in a given segment over the total number of genes in the 
+#' probe set
+#' @param study.gene.rate.cutoff = A decimal for the minimum cutoff for the 
+#' average amount a given gene is detected in all segments
+#' @param sankey.exclude.slide A toggle for including the slide name in the 
+#' Sankey Plot
+#' @param goi A list of genes of interest to evaluate for their study-wide 
+#' detection rate
+#' 
 #' @importFrom scales percent
 #' @importFrom Biobase pData
 #' @importFrom Biobase fData
@@ -18,12 +31,6 @@
 #' @export
 #' @return A list containing the ....
 
-# To call function, must have data = raw object, dsp.obj = QC demoData, 
-# loq.cutoff 2 is recommended,
-# loq.min 2 is recommend, 
-# segment.gene.rate.cutoff = remove segments with less than x% of the gene set detected; .05-.1 recommended,
-# study.gene.rate.cutoff = remove genes detected in less than x% of segments; .05-.2 recommended,
-# goi = goi (genes of interest). Must be a vector of genes (i.e c("PDCD1", "CD274")),
 filtering <- function(object, 
                       loq.cutoff = 2, 
                       loq.min = 2, 
@@ -135,32 +142,46 @@ filtering <- function(object,
     stop(paste0("Error: You have the wrong data class, must be NanoStringGeoMxSet" ))
   }
   
-  # Gather the data and plot in order: class, slide name, region, segment
   # gather_set_data creates x, id, y, and n fields within sankey.count.data
   # Establish the levels of the Sankey with or without the slide name
   if(sankey.exclude.slide == TRUE){
+    # Create a dataframe used to make the Sankey plot
     sankey.count.data <- gather_set_data(count.mat, 1:3)
-    sankey.count.data$x <-
-      factor(
-        sankey.count.data$x,
-        levels = c("class", "region", "segment")
-      )
+    
+    # Define the annotations to use for the Sankey x axis labels
+    sankey.count.data$x[sankey.count.data$x == 1] <- "class"
+    sankey.count.data$x[sankey.count.data$x == 2] <- "region"
+    sankey.count.data$x[sankey.count.data$x == 3] <- "segment"
+    
+    factor(
+      sankey.count.data$x,
+      levels = c("class", "region", "segment")
+    )
+    
     # For position of Sankey 100 segment scale
     adjust.scale.pos = 1
   } else {
+    # Create a dataframe used to make the Sankey plot
     sankey.count.data <- gather_set_data(count.mat, 1:4)
-    sankey.count.data$x <-
-      factor(
-        sankey.count.data$x,
-        levels = c("class", "slide_name", "region", "segment")
-      )
+    
+    # Define the annotations to use for the Sankey x axis labels
+    sankey.count.data$x[sankey.count.data$x == 1] <- "slide_name"
+    sankey.count.data$x[sankey.count.data$x == 2] <- "class"
+    sankey.count.data$x[sankey.count.data$x == 3] <- "region"
+    sankey.count.data$x[sankey.count.data$x == 4] <- "segment"
+    
+    factor(
+      sankey.count.data$x,
+      levels = c("class", "slide_name", "region", "segment")
+    )
+    
     # For position of Sankey 100 segment scale
     adjust.scale.pos = 0
   }
   
   # plot Sankey
   sankey.plot <- ggplot(sankey.count.data, aes(x, id = id, split = y, value = n)) +
-    geom_parallel_sets(aes(fill = region), alpha = 0.5, axis.width = 0.1) +
+    geom_parallel_sets(aes(fill = class), alpha = 0.5, axis.width = 0.1) +
     geom_parallel_sets_axes(axis.width = 0.2) +
     geom_parallel_sets_labels(color = "gray", size = 5, angle = 0) +
     theme_classic(base_size = 17) + 
diff --git a/R/spatial_deconvolution.R b/R/spatial_deconvolution.R
index b6e8fcd..b6beeb5 100644
--- a/R/spatial_deconvolution.R
+++ b/R/spatial_deconvolution.R
@@ -1,8 +1,8 @@
-#' @title Spatial Deconvolution
-#'
 #' Helper functions comes from
 #' https://bioconductor.org/packages/release/bioc/vignettes/SpatialDecon/inst/doc/SpatialDecon_vignette_NSCLC.html
-#'
+#' 
+#' @title Spatial Deconvolution
+#' 
 #' @description spatialDeconvolution estimate cell composition across DSP
 #'              samples from reference expression matrix
 #'
@@ -41,18 +41,6 @@
 #' @importFrom ComplexHeatmap pheatmap
 #'
 #' @export
-#' @example Do not run: spatialDeconvolution(object = NanostringGeomx,
-#'                                           expr.type = "q_norm",
-#'                                           prof.mtx = profile_matrix,
-#'                                           clust.rows = TRUE,
-#'                                           clust.cols = TRUE,
-#'                                           group.by = "none",
-#'                                           plot.fontsize = 5,
-#'                                           use.custom.prof.mtx = FALSE,
-#'                                           ref.mtx = reference_matrix,
-#'                                           ref.annot = reference_annotation,
-#'                                           cell.id.col = "CellID",
-#'                                           celltype.col = "LabeledCellType")
 #'
 #' @return A list dsp.data containing the results of spatial deconvolution,
 #' res$beta: matrix of estimated cell abundances
@@ -66,7 +54,7 @@
 
 
 spatialDeconvolution <- function(object,
-                                 expr.type,
+                                 expr.type = "q_norm",
                                  prof.mtx,
                                  clust.rows = TRUE,
                                  clust.cols = TRUE,
@@ -79,8 +67,8 @@ spatialDeconvolution <- function(object,
                                  min.genes = 10,
                                  ref.mtx,
                                  ref.annot,
-                                 cell.id.col,
-                                 celltype.col) {
+                                 cell.id.col = "CellID",
+                                 celltype.col = "LabeledCellType") {
   
   # Check for Parameter Misspecification Error(s)
   if (!expr.type %in% names(object@assayData)) {
diff --git a/R/study_design.R b/R/study_design.R
index f02f1f1..dc6404e 100644
--- a/R/study_design.R
+++ b/R/study_design.R
@@ -27,10 +27,14 @@
 #' phenoDataFile containing data about the experiment's meta-data.
 #' @param slide.name.col The name of the field that contains the slide names
 #' @param class.col The name of the field that contains the class annotation
-#' @param region.col The name of the field that contains the class annotation
-#' @param segment.col The name of the field that contains the class annotation
-#' 
-#'
+#' @param region.col The name of the field that contains the region annotation
+#' @param segment.col The name of the field that contains the segment name
+#' @param area.col The name of the field that contains the segment area 
+#' @param nuclei.col The name of the field that contains the nuclei number
+#' @param sankey.exclude.slide A toggle for including the slide name in the
+#'  Sankey plot
+#' @param segment.id.length The number of characters to use from each of the 
+#' annotation fields class, region, and segment to create the segment ID
 #'
 #' @importFrom GeomxTools readNanoStringGeoMxSet
 #' @importFrom knitr kable
@@ -52,8 +56,6 @@
 #' @export
 #' @return A list containing the NanoString Object and the Sankey plot.
 
-
-
 studyDesign <- function(dcc.files,
                         pkc.files,
                         pheno.data.file,
@@ -177,26 +179,39 @@ studyDesign <- function(dcc.files,
     rownames(count.mat) <- 1:nrow(count.mat)
   }
    
-  
-  # Gather the data and plot in order: class, slide name, region, segment
   # gather_set_data creates x, id, y, and n fields within sankey.count.data
   # Establish the levels of the Sankey with or without the slide name
   if(sankey.exclude.slide == TRUE){
+    # Create a dataframe used to make the Sankey plot
     sankey.count.data <- gather_set_data(count.mat, 1:3)
-    sankey.count.data$x <-
-      factor(
-        sankey.count.data$x,
-        levels = c("class", "region", "segment")
-      )
+    
+    # Define the annotations to use for the Sankey x axis labels
+    sankey.count.data$x[sankey.count.data$x == 1] <- "class"
+    sankey.count.data$x[sankey.count.data$x == 2] <- "region"
+    sankey.count.data$x[sankey.count.data$x == 3] <- "segment"
+    
+    factor(
+      sankey.count.data$x,
+      levels = c("class", "region", "segment")
+    )
+    
     # For position of Sankey 100 segment scale
     adjust.scale.pos = 1
   } else {
+    # Create a dataframe used to make the Sankey plot
     sankey.count.data <- gather_set_data(count.mat, 1:4)
-    sankey.count.data$x <-
-      factor(
-        sankey.count.data$x,
-        levels = c("class", "slide_name", "region", "segment")
-      )
+    
+    # Define the annotations to use for the Sankey x axis labels
+    sankey.count.data$x[sankey.count.data$x == 1] <- "slide_name"
+    sankey.count.data$x[sankey.count.data$x == 2] <- "class"
+    sankey.count.data$x[sankey.count.data$x == 3] <- "region"
+    sankey.count.data$x[sankey.count.data$x == 4] <- "segment"
+
+    factor(
+      sankey.count.data$x,
+      levels = c("class", "slide_name", "region", "segment")
+    )
+    
     # For position of Sankey 100 segment scale
     adjust.scale.pos = 0
   }
@@ -210,7 +225,7 @@ studyDesign <- function(dcc.files,
              split = y,
              value = n
            )) +
-    geom_parallel_sets(aes(fill = region), alpha = 0.5, axis.width = 0.1) +
+    geom_parallel_sets(aes(fill = class), alpha = 0.5, axis.width = 0.1) +
     geom_parallel_sets_axes(axis.width = 0.2) +
     geom_parallel_sets_labels(color = "gray",
                               size = 5,
diff --git a/R/violin_plot.R b/R/violin_plot.R
index 895435a..b5c4513 100644
--- a/R/violin_plot.R
+++ b/R/violin_plot.R
@@ -19,18 +19,13 @@
 #' @importFrom gridExtra arrangeGrob
 #'
 #' @export
-#' @example Do not run: violinPlot(object = NanostringGeomx, 
-#'                                 expr.type = "q_norm", 
-#'                                 genes = c("FOXP3","CD4"), 
-#'                                 group = "CellType", 
-#'                                 facet.by = "segment")
 #'
 #' @return an arranged grob of violin plots
 
 violinPlot <- function(object,
-                       expr.type,
-                       genes,
-                       group,
+                       expr.type = "q_norm",
+                       genes = c("FOXP3","CD4"),
+                       group = "CellType",
                        facet.by = "none") {
   
   # Check for Parameter Misspecification Error(s)
diff --git a/man/diffExpr.Rd b/man/diffExpr.Rd
index 10222ca..1c489d0 100644
--- a/man/diffExpr.Rd
+++ b/man/diffExpr.Rd
@@ -11,11 +11,11 @@ diffExpr(
   group.col,
   regions,
   region.col,
-  slide.col = "slide name",
+  slide.col = "slide_name",
   element = "q_norm",
   multi.core = TRUE,
   n.cores = 1,
-  p.adjust = "BY",
+  p.adjust = "fdr",
   pairwise = TRUE,
   fc.lim = 1.2,
   pval.lim.1 = 0.05,
@@ -50,7 +50,7 @@ slide)}
 (default is 1)}
 
 \item{p.adjust}{Method to use for pvalue adjustment. Choices are "holm",
-"hochberg","hommel","bonferroni","BH","BY","fdr","none". (default is "BY")}
+"hochberg","hommel","bonferroni","BH","BY","fdr","none". (default is "BH")}
 
 \item{pairwise}{Boolean to calculate least-square means pairwise differences
 (default is TRUE)}
@@ -66,7 +66,7 @@ is 1.2)}
 }
 \value{
 a list containing mixed model output data frame, grid tables for
-samples and summary of genelists
+samples used in analysis and summary of significant genelists
 }
 \description{
 diffExpr returns a DEG table with fold changes and p-values and
diff --git a/man/filtering.Rd b/man/filtering.Rd
index df45836..1c9a93e 100644
--- a/man/filtering.Rd
+++ b/man/filtering.Rd
@@ -4,10 +4,36 @@
 \alias{filtering}
 \title{Filtering NanoStringGeoMxSet dataset}
 \usage{
-filtering(object, loq.cutoff, loq.min, cut.segment, goi)
+filtering(
+  object,
+  loq.cutoff = 2,
+  loq.min = 2,
+  segment.gene.rate.cutoff = 0.05,
+  study.gene.rate.cutoff = 0.05,
+  sankey.exclude.slide = FALSE,
+  goi
+)
 }
 \arguments{
 \item{object}{A NanoStringGeoMxSet dataset}
+
+\item{loq.cutoff}{The number of standard deviations above the negative probe
+geometric mean to use as a cutoff for the limit of quantification}
+
+\item{loq.min}{The minimum value for the limit of quantification}
+
+\item{segment.gene.rate.cutoff}{A decimal for the minimum cutoff for the
+genes detected in a given segment over the total number of genes in the
+probe set}
+
+\item{study.gene.rate.cutoff}{= A decimal for the minimum cutoff for the
+average amount a given gene is detected in all segments}
+
+\item{sankey.exclude.slide}{A toggle for including the slide name in the
+Sankey Plot}
+
+\item{goi}{A list of genes of interest to evaluate for their study-wide
+detection rate}
 }
 \value{
 A list containing the ....
diff --git a/man/geomxNorm.Rd b/man/geomxNorm.Rd
index 36c4ebe..c3bea8d 100644
--- a/man/geomxNorm.Rd
+++ b/man/geomxNorm.Rd
@@ -9,7 +9,7 @@ geomxNorm(object, norm)
 \arguments{
 \item{object}{A NanoStringGeoMxSet dataset}
 
-\item{norm}{A vector with options of c(quant or neg)}
+\item{norm}{A vector with options of c(q3 or neg)}
 }
 \value{
 A list containing the ggplot grid, a boxplot, an normalized dataframe.
diff --git a/man/spatialDeconvolution.Rd b/man/spatialDeconvolution.Rd
index 43e8ce4..c17775f 100644
--- a/man/spatialDeconvolution.Rd
+++ b/man/spatialDeconvolution.Rd
@@ -2,25 +2,25 @@
 % Please edit documentation in R/spatial_deconvolution.R
 \name{spatialDeconvolution}
 \alias{spatialDeconvolution}
-\title{Spatial Deconvolution
-
-Helper functions comes from
-https://bioconductor.org/packages/release/bioc/vignettes/SpatialDecon/inst/doc/SpatialDecon_vignette_NSCLC.html}
+\title{Spatial Deconvolution}
 \usage{
 spatialDeconvolution(
   object,
-  expr.type,
-  ref.mtx,
-  ref.annot,
-  cell.id.col,
-  celltype.col,
-  group.by = NULL,
-  out.directory = NULL,
-  matrix.name = "customDSPmtx",
+  expr.type = "q_norm",
+  prof.mtx,
+  clust.rows = TRUE,
+  clust.cols = TRUE,
+  group.by = "none",
+  plot.fontsize = 5,
+  use.custom.prof.mtx = FALSE,
+  discard.celltype = FALSE,
   normalize = FALSE,
   min.cell.num = 0,
   min.genes = 10,
-  discard.celltype = FALSE
+  ref.mtx,
+  ref.annot,
+  cell.id.col = "CellID",
+  celltype.col = "LabeledCellType"
 )
 }
 \arguments{
@@ -28,29 +28,39 @@ spatialDeconvolution(
 
 \item{expr.type}{Name of slot containing normalized gene expression data}
 
-\item{ref.mtx}{Reference expression matrix (Gene x Reference_Samples)}
-
-\item{ref.annot}{Reference data.frame with cell.id and celltype information}
+\item{prof.mtx}{Use stored profile matrix}
 
-\item{cell.id.col}{Column of data.frame containing cell.id.col info}
+\item{clust.rows}{Cluster rows in heatmap (Default: TRUE)}
 
-\item{celltype.col}{Column of data.frame containing celltype info}
+\item{clust.cols}{Cluster columns in heatmap (Default: TRUE)}
 
-\item{group.by}{Organize heatmap / barplot columns by metadata group}
+\item{group.by}{Organize heatmap / barplot columns by metadata group
+(Default: "none")}
 
-\item{out.directory}{Path to desired output directory, set to NULL if matrix
-should not be written}
+\item{plot.fontsize}{Set size of labels on all figures (Default: 5)}
 
-\item{matrix.name}{Name given to deconvolution signature matrix}
+\item{discard.celltype}{Remove any celltype(s) that is not of interest
+(Default: FALSE)}
 
-\item{normalize}{Scale profile matrix gene expression according to gene count}
+\item{normalize}{Scale profile matrix gene expression according to gene count
+(Default: FALSE)}
 
 \item{min.cell.num}{Prevent deconvolution of celltype(s) if number of
-corresponding cells is below this threshold}
+corresponding cells is below this threshold (Default: 0)}
 
-\item{min.genes}{Filter cells based on minimum number of genes expressed}
+\item{min.genes}{Filter cells based on minimum number of genes expressed
+(Default: 10)}
 
-\item{discard.celltype}{Remove any celltype(s) that is not of interest}
+\item{ref.mtx}{Custom reference expression matrix (Gene x Reference_Samples)}
+
+\item{ref.annot}{Custom reference data.frame with cell.id and celltype
+information}
+
+\item{cell.id.col}{Column of data.frame containing cell.id.col info}
+
+\item{celltype.col}{Column of data.frame containing celltype info}
+
+\item{use.custom.matrix}{Generate custom profile matrix (Default: FALSE)}
 }
 \value{
 A list dsp.data containing the results of spatial deconvolution,
@@ -68,6 +78,9 @@ spatialDeconvolution estimate cell composition across DSP
 samples from reference expression matrix
 }
 \details{
+Helper functions comes from
+https://bioconductor.org/packages/release/bioc/vignettes/SpatialDecon/inst/doc/SpatialDecon_vignette_NSCLC.html
+
 Uses Nanostring developed functions to compute estimated cell
 fractions in DSP samples. Allows for users to group samples based
 on metadata information
diff --git a/man/studyDesign.Rd b/man/studyDesign.Rd
index a94766f..e16541b 100644
--- a/man/studyDesign.Rd
+++ b/man/studyDesign.Rd
@@ -11,7 +11,15 @@ studyDesign(
   pheno.data.sheet = "Template",
   pheno.data.dcc.col.name = "Sample_ID",
   protocol.data.col.names = c("aoi", "roi"),
-  experiment.data.col.names = c("panel")
+  experiment.data.col.names = c("panel"),
+  slide.name.col = "slide name",
+  class.col = "class",
+  region.col = "region",
+  segment.col = "segment",
+  area.col = "area",
+  nuclei.col = "nuclei",
+  sankey.exclude.slide = FALSE,
+  segment.id.length = 4
 )
 }
 \arguments{
@@ -36,6 +44,24 @@ sequencing data.}
 
 \item{experiment.data.col.names}{Character list of column names from
 phenoDataFile containing data about the experiment's meta-data.}
+
+\item{slide.name.col}{The name of the field that contains the slide names}
+
+\item{class.col}{The name of the field that contains the class annotation}
+
+\item{region.col}{The name of the field that contains the region annotation}
+
+\item{segment.col}{The name of the field that contains the segment name}
+
+\item{area.col}{The name of the field that contains the segment area}
+
+\item{nuclei.col}{The name of the field that contains the nuclei number}
+
+\item{sankey.exclude.slide}{A toggle for including the slide name in the
+Sankey plot}
+
+\item{segment.id.length}{The number of characters to use from each of the
+annotation fields class, region, and segment to create the segment ID}
 }
 \value{
 A list containing the NanoString Object and the Sankey plot.
diff --git a/man/violinPlot.Rd b/man/violinPlot.Rd
index 70542d5..1f7ae8c 100644
--- a/man/violinPlot.Rd
+++ b/man/violinPlot.Rd
@@ -4,7 +4,13 @@
 \alias{violinPlot}
 \title{Violin plot of spatially profiled genes}
 \usage{
-violinPlot(object, expr.type, genes, group, facet.by = NULL)
+violinPlot(
+  object,
+  expr.type = "q_norm",
+  genes = c("FOXP3", "CD4"),
+  group = "CellType",
+  facet.by = "none"
+)
 }
 \arguments{
 \item{object}{Name of NanostringGeoMxSet to perform analysis on}
diff --git a/vignettes/Integration_Test_Kidney.Rmd b/vignettes/Integration_Test_Kidney.Rmd
index 4c5e372..cee8b38 100644
--- a/vignettes/Integration_Test_Kidney.Rmd
+++ b/vignettes/Integration_Test_Kidney.Rmd
@@ -71,7 +71,7 @@ This runs the DSPworkflow package to completion using the Human Kidney Dataset:
                                 nuclei.col = "nuclei")
   
   # For creating fixture RDS
-  create.rds <- TRUE
+  create.rds <- FALSE
   if(create.rds) {
     study.design.human.kidney <- sdesign.list$object
     saveRDS(study.design.human.kidney, file = "tests/testthat/fixtures/Human_Kidney/studyDesignHumanKidney.RDS")
@@ -101,7 +101,7 @@ qc.output <-  qcProc(object = sdesign.list$object,
                         print.plots = TRUE)
     print(qc.output$segments.qc)
     
-  create.rds <- TRUE
+  create.rds <- FALSE
   if(create.rds) {
     qc.human.kidney <- qc.output$object
     saveRDS(qc.human.kidney, file = "tests/testthat/fixtures/Human_Kidney/qcHumanKidney.RDS")
@@ -118,11 +118,12 @@ qc.output <-  qcProc(object = sdesign.list$object,
     filtering.output <- filtering(object = qc.output$object,
                             loq.cutoff = 2, 
                             loq.min = 2, 
-                            cut.segment = .1, 
+                            segment.gene.rate.cutoff = 0.05,
+                            study.gene.rate.cutoff = 0.05, 
                             goi = goi)
     
     print(filtering.output$`stacked.bar.plot`)
-    print(filtering.output$`tab`)
+    print(filtering.output$`segment.table`)
     print(filtering.output$`sankey.plot`)
     print(filtering.output$`genes.detected.plot`)
     print(filtering.output$'goi.table', row.names = FALSE)