diff --git a/R/DEqual.R b/R/DEqual.R
index 488e2ed..9ea4a67 100644
--- a/R/DEqual.R
+++ b/R/DEqual.R
@@ -16,6 +16,14 @@
 #' @param deg_tstats an optional`data.frame()` with a column "t" containing
 #' t-statistics resulted from a degradation experiment. Default is the
 #' internal `qsvaR::degradation_tstats` from the package authors.
+#' @param show.legend logical (default TRUE) to show legend in the plot
+#' @param show.cor specify where to show the correlation value. Can be one of
+#' "caption", "corner-top", "corner-bottom", or "none".
+#' @param font.size numeric value to set the base font size of the plot
+#' @param cor.size numeric (default font.size/2) to set the font size for the
+#' correlation text
+#' @param cor.label character (default "cor: ") to set the text preceding the
+#'  correlation value
 #'
 #' @return a `ggplot` object of the DE t-statistic vs
 #' the DE statistic from degradation
diff --git a/man/DEqual.Rd b/man/DEqual.Rd
new file mode 100644
index 0000000..a47fbd1
--- /dev/null
+++ b/man/DEqual.Rd
@@ -0,0 +1,69 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/DEqual.R
+\name{DEqual}
+\alias{DEqual}
+\title{Differential expression quality (DEqual) plot}
+\usage{
+DEqual(
+  DE,
+  deg_tstats = qsvaR::degradation_tstats,
+  show.legend = TRUE,
+  show.cor = c("caption", "corner-top", "corner-bottom", "none"),
+  font.size = 12,
+  cor.size = font.size/2,
+  cor.label = "cor: "
+)
+}
+\arguments{
+\item{DE}{a \code{data.frame()} with a column "t" containing the t-statistics
+from Differential Expression, typically generated with \code{limma::topTable()}.
+\code{rownames(DE)} must have transcript Ensembl/Gencode IDs.}
+
+\item{deg_tstats}{an optional\code{data.frame()} with a column "t" containing
+t-statistics resulted from a degradation experiment. Default is the
+internal \code{qsvaR::degradation_tstats} from the package authors.}
+
+\item{show.legend}{logical (default TRUE) to show legend in the plot}
+
+\item{show.cor}{specify where to show the correlation value. Can be one of
+"caption", "corner-top", "corner-bottom", or "none".}
+
+\item{font.size}{numeric value to set the base font size of the plot}
+
+\item{cor.size}{numeric (default font.size/2) to set the font size for the
+correlation text}
+
+\item{cor.label}{character (default "cor: ") to set the text preceding the
+correlation value}
+}
+\value{
+a \code{ggplot} object of the DE t-statistic vs
+the DE statistic from degradation
+}
+\description{
+A DEqual plot compares the effect of RNA degradation from an independent
+degradation experiment on the y axis to the effect of the outcome of
+interest. They were orignally described by Jaffe et al, PNAS, 2017
+\url{https://doi.org/10.1073/pnas.1617384114}. Other DEqual versions are
+included in Collado-Torres et al, Neuron, 2019
+\url{https://doi.org/10.1016/j.neuron.2019.05.013}. This function compares your
+t-statistics of interest computed on transcripts against the
+t-statistics from degradation time adjusting for the six brain regions from
+degradation experiment data used for determining \code{rse_tx}.
+}
+\examples{
+
+## Random differential expression t-statistics for the same transcripts
+## we have degradation t-statistics for in `degradation_tstats`.
+set.seed(101)
+random_de <- data.frame(
+    t = rt(nrow(degradation_tstats), 5),
+    row.names = sample(
+        rownames(degradation_tstats),
+        nrow(degradation_tstats)
+    )
+)
+
+## Create the DEqual plot
+DEqual(random_de)
+}
diff --git a/man/degradation_tstats.Rd b/man/degradation_tstats.Rd
new file mode 100644
index 0000000..545c578
--- /dev/null
+++ b/man/degradation_tstats.Rd
@@ -0,0 +1,21 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/degradation_tstats-data.R
+\docType{data}
+\name{degradation_tstats}
+\alias{degradation_tstats}
+\title{Degradation time t-statistics}
+\format{
+A \code{data.frame()} with the \code{t} statistics for degradation time. The
+\code{rownames()} are the GENCODE transcript IDs.
+}
+\description{
+These t-statistics are derived from the degradation timepoints data
+built into qsvaR. They are the results from multiple models where
+we determined the association of transcripts with degradation time
+adjusting for brain region (so parallel degradation effects across
+brain regions). They are used for plotting in \code{DEqual()}.
+}
+\seealso{
+\link{DEqual}
+}
+\keyword{datasets}
diff --git a/man/getDegTx.Rd b/man/getDegTx.Rd
new file mode 100644
index 0000000..baefbbb
--- /dev/null
+++ b/man/getDegTx.Rd
@@ -0,0 +1,50 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/getDegTx.R
+\name{getDegTx}
+\alias{getDegTx}
+\title{Obtain expression matrix for degraded transcripts}
+\usage{
+getDegTx(
+  rse_tx,
+  type = c("cell_component", "standard", "top1500"),
+  sig_transcripts = NULL,
+  assayname = "tpm",
+  verbose = TRUE
+)
+}
+\arguments{
+\item{rse_tx}{A \link[SummarizedExperiment:RangedSummarizedExperiment-class]{RangedSummarizedExperiment-class}
+object containing the transcript data desired to be studied.}
+
+\item{type}{A \code{character(1)} specifying the transcripts set type.
+These were determined by Joshua M. Stolz et al, 2022. Here the names "cell_component", "top1500",
+and "standard" refer to models that were determined to be effective in removing degradation effects.
+The "standard" model involves taking the union of the top 1000 transcripts
+associated with degradation from the interaction model and the main effect model.
+The "top1500" model is the same as the "standard model except the
+union of the top 1500 genes associated with degradation is selected.
+The most effective of our models, "cell_component", involved deconvolution of
+the degradation matrix to determine the proportion of cell types within our studied tissue.
+These proportions were then added to our \code{model.matrix()} and the union of the top 1000 transcripts in the interaction model,
+the main effect model, and the cell proportions model were used to generate this model of qSVs.}
+
+\item{sig_transcripts}{A list of transcripts determined to have degradation signal in the qsva expanded paper.}
+
+\item{assayname}{character string specifying the name of the assay desired in rse_tx}
+
+\item{verbose}{specify if the function should report how many model transcripts were matched}
+}
+\value{
+A
+\link[SummarizedExperiment:RangedSummarizedExperiment-class]{RangedSummarizedExperiment-class}
+object.
+}
+\description{
+This function is used to obtain a \link[SummarizedExperiment:RangedSummarizedExperiment-class]{RangedSummarizedExperiment-class}
+of transcripts and their expression values #' These transcripts are selected based on a prior study of RNA degradation in
+postmortem brain tissues. This object can later be used to obtain the principle components
+necessary to remove the effect of degradation in differential expression.
+}
+\examples{
+degTx <- getDegTx(rse_tx, "standard")
+}
diff --git a/man/getPCs.Rd b/man/getPCs.Rd
new file mode 100644
index 0000000..9a50d78
--- /dev/null
+++ b/man/getPCs.Rd
@@ -0,0 +1,22 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/getPCs.R
+\name{getPCs}
+\alias{getPCs}
+\title{PCs from transcripts}
+\usage{
+getPCs(rse_tx, assayname = "tpm")
+}
+\arguments{
+\item{rse_tx}{Ranged Summarizeed Experiment with only trancsripts selected for qsva}
+
+\item{assayname}{character string specifying the name of the assay desired in rse_tx}
+}
+\value{
+prcomp object generated by taking the pcs of degraded transcripts
+}
+\description{
+This function returns the pcs from the obtained RangedSummarizedExperiment object of selected transcripts
+}
+\examples{
+getPCs(rse_tx, "tpm")
+}
diff --git a/man/get_qsvs.Rd b/man/get_qsvs.Rd
new file mode 100644
index 0000000..68a51d2
--- /dev/null
+++ b/man/get_qsvs.Rd
@@ -0,0 +1,25 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/get_qsvs.R
+\name{get_qsvs}
+\alias{get_qsvs}
+\title{Generate matrix of qsvs}
+\usage{
+get_qsvs(qsvPCs, k)
+}
+\arguments{
+\item{qsvPCs}{prcomp object generated by taking
+the pcs of degraded transcripts}
+
+\item{k}{number of qsvs to be included.}
+}
+\value{
+matrix with k principal components for each sample.
+}
+\description{
+Using the pcs and the k number of components be included,
+we generate the qsva matrix.
+}
+\examples{
+qsv <- getPCs(rse_tx, "tpm")
+get_qsvs(qsv, 2)
+}
diff --git a/man/k_qsvs.Rd b/man/k_qsvs.Rd
new file mode 100644
index 0000000..90cb1a4
--- /dev/null
+++ b/man/k_qsvs.Rd
@@ -0,0 +1,38 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/k_qsvs.R
+\name{k_qsvs}
+\alias{k_qsvs}
+\title{Apply num.sv algorithm to determine the number of pcs to be included}
+\usage{
+k_qsvs(rse_tx, mod, assayname)
+}
+\arguments{
+\item{rse_tx}{A \link[SummarizedExperiment:RangedSummarizedExperiment-class]{RangedSummarizedExperiment-class} object containing
+the transcript data desired to be studied.}
+
+\item{mod}{Model Matrix with necessary variables the you would
+model for in differential expression}
+
+\item{assayname}{character string specifying the name of the assay desired in rse_tx}
+}
+\value{
+integer representing number of pcs to be included
+}
+\description{
+Apply num.sv algorithm to determine the number of pcs to be included
+}
+\examples{
+## First we need to define a statistical model. We'll use the example
+## rse_tx data. Note that the model you'll use in your own data
+## might look different from this model.
+mod <- model.matrix(~ mitoRate + Region + rRNA_rate + totalAssignedGene + RIN,
+    data = colData(rse_tx)
+)
+
+## To ensure that the results are reproducible, you will need to set a
+## random seed with the set.seed() function. Internally, we are using
+## sva::num.sv() which needs a random seed to ensure reproducibility of the
+## results.
+set.seed(20230621)
+k_qsvs(rse_tx, mod, "tpm")
+}
diff --git a/man/normalize_tx_names.Rd b/man/normalize_tx_names.Rd
new file mode 100644
index 0000000..5e4174b
--- /dev/null
+++ b/man/normalize_tx_names.Rd
@@ -0,0 +1,21 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/utils.R
+\name{normalize_tx_names}
+\alias{normalize_tx_names}
+\title{Remove version number from Gencode/Ensembl transcript names}
+\usage{
+normalize_tx_names(txnames)
+}
+\arguments{
+\item{txnames}{A \code{character()} vector of GENCODE or ENSEMBL transcript IDs}
+}
+\value{
+A
+\code{character()} vector of transcript names without versioning
+}
+\description{
+This function removes the Gencode/ENSEMBL version from the transcript ID, while protecting _PAR_Y suffixes if present
+}
+\examples{
+ensIDs <-  normalize_tx_names(rownames(rse_tx))
+}
diff --git a/man/qSVA.Rd b/man/qSVA.Rd
new file mode 100644
index 0000000..30bae9b
--- /dev/null
+++ b/man/qSVA.Rd
@@ -0,0 +1,57 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/qSVA.R
+\name{qSVA}
+\alias{qSVA}
+\title{A wrapper function used to perform qSVA in one step.}
+\usage{
+qSVA(
+  rse_tx,
+  type = c("cell_component", "standard", "top1500"),
+  sig_transcripts = NULL,
+  mod,
+  assayname
+)
+}
+\arguments{
+\item{rse_tx}{A \link[SummarizedExperiment:RangedSummarizedExperiment-class]{RangedSummarizedExperiment-class} object containing
+the transcript data desired to be studied.}
+
+\item{type}{a character string specifying which model you would
+like to use from the sets of signature transcripts identified
+by the qsvaR package. This can be omitted if a custom set of
+transcripts is provided to sig_transcripts.}
+
+\item{sig_transcripts}{A list of transcript IDs that are associated
+with degradation signal. Specifying a \code{character()} input with ENSEMBL
+transcript IDs (whose values should match entries in \code{rownames(rse_tx)}).
+This argument provides a custom list of transcripts for adjusting
+for degradation; this should be used instead of the \code{type} argument.}
+
+\item{mod}{Model Matrix with necessary variables the you would
+model for in differential expression}
+
+\item{assayname}{character string specifying the name of
+the assay desired in rse_tx}
+}
+\value{
+matrix with k principal components for each sample
+}
+\description{
+A wrapper function used to perform qSVA in one step.
+}
+\examples{
+## First we need to define a statistical model. We'll use the example
+## rse_tx data. Note that the model you'll use in your own data
+## might look different from this model.
+mod <- model.matrix(~ mitoRate + Region + rRNA_rate + totalAssignedGene + RIN,
+    data = colData(rse_tx)
+)
+
+## To ensure that the results are reproducible, you will need to set a
+## random seed with the set.seed() function. Internally, we are using
+## sva::num.sv() which needs a random seed to ensure reproducibility of the
+## results.
+set.seed(20230621)
+qSVA(rse_tx = rse_tx, type = "cell_component", mod = mod, assayname = "tpm")
+
+}
diff --git a/man/rse_tx.Rd b/man/rse_tx.Rd
new file mode 100644
index 0000000..4edc43e
--- /dev/null
+++ b/man/rse_tx.Rd
@@ -0,0 +1,18 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/rse_tx-data.R
+\docType{data}
+\name{rse_tx}
+\alias{rse_tx}
+\title{Example of RSE object with RNA-seq transcript quantification data}
+\format{
+A \link[SummarizedExperiment:RangedSummarizedExperiment-class]{RangedSummarizedExperiment-class}
+}
+\description{
+This data is a \link[SummarizedExperiment:RangedSummarizedExperiment-class]{RangedSummarizedExperiment-class}
+with transcript quantification data stored in an "tpm" assay. It is
+used to demonstrate the use of qsvaR in bulk RNA-seq data.
+}
+\seealso{
+\link{getPCs} \link{k_qsvs} \link{getDegTx} \link{qSVA}
+}
+\keyword{datasets}
diff --git a/man/select_transcripts.Rd b/man/select_transcripts.Rd
new file mode 100644
index 0000000..cbbe671
--- /dev/null
+++ b/man/select_transcripts.Rd
@@ -0,0 +1,31 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/select_transcripts.R
+\name{select_transcripts}
+\alias{select_transcripts}
+\title{Select transcripts associated with degradation}
+\usage{
+select_transcripts(type = c("cell_component", "top1500", "standard"))
+}
+\arguments{
+\item{type}{A \code{character(1)} specifying the transcripts set type.
+These were determined by Joshua M. Stolz et al, 2022. Here the names "cell_component", "top1500", and "standard" refer to models that were determined to be effective in removing degradation effects.
+The "standard" model involves taking the union of the top 1000 transcripts associated with degradation from the interaction model and the main effect model.
+The "top1500" model is the same as the "standard model except the union of the top 1500 genes associated with degradation is selected.
+The most effective of our models, "cell_component", involved deconvolution of the degradation matrix to determine the proportion of cell types within our studied tissue.
+These proportions were then added to our \code{model.matrix()} and the union of the top 1000 transcripts in the interaction model, the main effect model, and the cell proportions model were used to generate this model of qSVs.}
+}
+\value{
+A \code{character()} with the transcript IDs.
+}
+\description{
+Helper function to select which experimental model will be used to generate the qSVs.
+}
+\examples{
+## Default set of transcripts associated with degradation
+sig_transcripts <- select_transcripts()
+length(sig_transcripts)
+head(sig_transcripts)
+
+## Example where match.arg() auto-completes
+select_transcripts("top")
+}
diff --git a/man/transcripts.Rd b/man/transcripts.Rd
new file mode 100644
index 0000000..1721a58
--- /dev/null
+++ b/man/transcripts.Rd
@@ -0,0 +1,25 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/transcripts-data.R
+\docType{data}
+\name{transcripts}
+\alias{transcripts}
+\title{Transcripts for Degradation Models}
+\format{
+A \code{list()} with character strings containing the transcripts selected by each model.
+Each string is a GENCODE transcript IDs.
+}
+\usage{
+transcripts
+}
+\description{
+An object storing three lists of transcripts each corresponding to a model used in the degradation experiment.
+These were determined by Joshua M. Stolz et al, 2022. Here the names "cell_component", "top1500", and "standard" refer to models that were determined to be effective in removing degradation effects.
+The "standard" model involves taking the union of the top 1000 transcripts associated with degradation from the interaction model and the main effect model.
+The "top1500" model is the same as the "standard" model except the union of the top 1500 genes associated with degradation is selected.
+The most effective of our models, "cell_component", involved deconvolution of the degradation matrix to determine the proportion of cell types within our studied tissue.
+These proportions were then added to our \code{model.matrix()} and the union of the top 1000 transcripts in the interaction model, the main effect model, and the cell proportions model were used to generate this model of qSVs.
+}
+\seealso{
+\link{select_transcripts}
+}
+\keyword{datasets}
diff --git a/man/which_tx_names.Rd b/man/which_tx_names.Rd
new file mode 100644
index 0000000..e0f7f37
--- /dev/null
+++ b/man/which_tx_names.Rd
@@ -0,0 +1,25 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/utils.R
+\name{which_tx_names}
+\alias{which_tx_names}
+\title{Check validity of transcript vectors and return a vector matching indexes in tx1}
+\usage{
+which_tx_names(txnames, sig_tx)
+}
+\arguments{
+\item{txnames}{A \code{character()} vector of GENCODE or ENSEMBL transcript IDs.}
+
+\item{sig_tx}{A \code{character()} vector of GENCODE or ENSEMBL signature transcript IDs.}
+}
+\value{
+A
+\code{integer()} vector of \code{txnames} transcript indexes in \code{sig_tx}.
+}
+\description{
+This function is used to check if tx1 and tx2 are GENCODE or ENSEMBL transcript IDs
+and return an integer vector of tx1 transcript indexes that are in tx2.
+}
+\examples{
+sig_tx <-  select_transcripts("cell_component")
+whichTx <- which_tx_names(rownames(rse_tx), sig_tx)
+}