@@ -29,7 +29,7 @@ A list is returned containing:

 }

 \description{

 \code{ggplot2_intersectPlot} function generates and plots the list of

-differentially expressed (DE) genes that are commonly found in all

+differentially expressed (DE) genes that are found in all

 combinations at any particular replicate level. Often in small-scale

 RNA-seq experiments, the inclusion or exclusion of any paricular sample can

 result in a very different list of DE genes. To reduce the influence of any

@@ -47,7 +47,7 @@ been found.

 }

 \examples{

 # load edgeR deg object generated by erssa_edger using example dataset

-# example dataset containing 1000 genes, 4 replicates and 10 comb. per rep.

+# example dataset containing 1000 genes, 4 replicates and 5 comb. per rep.

 # level

 data(deg.partial, package = "ERSSA")

@@ -4,13 +4,15 @@

 \alias{ggplot2_intersectPlot}

 \title{Plot number of DE genes that is common across combinations}

 \usage{

-ggplot2_intersectPlot(deg = NULL, path = ".")

+ggplot2_intersectPlot(deg = NULL, path = ".", save_plot = TRUE)

 }

 \arguments{

 \item{deg}{The list of DE genes generated by one of ERSSA::DE_*.R scripts.}

 \item{path}{Path to which the plot will be saved. Default to current working

 directory.}

+

+\item{save_plot}{Boolean. Whether to save plot to drive. Default to TRUE.}

 }

 \value{

 A list is returned containing:

@@ -18,7 +20,8 @@ A list is returned containing:

   \item{gg_object} {the ggplot2 object, which can then be further

   customized.}

   \item{intersect.dataframe} {the tidy table version used for plotting.}

-  \item{deg_dataframe} {the tidy table version of DEG numbers for plotting mean.}

+  \item{deg_dataframe} {the tidy table version of DEG numbers for

+  plotting mean.}

   \item{intersect_genes} {list of vectors containing DE genes with vector

   name indicating the associated replicate level.}

   \item{full_num_DEG} {The number of DE genes with all samples included.}

@@ -29,7 +32,7 @@ A list is returned containing:

 differentially expressed (DE) genes that are commonly found in all

 combinations at any particular replicate level. Often in small-scale

 RNA-seq experiments, the inclusion or exclusion of any paricular sample can

-result in a highly different list of DE genes. To reduce the influence of any

+result in a very different list of DE genes. To reduce the influence of any

 particular sample in the entire dataset analysis, it may be desirable to

 identify the list of DE genes that are enriched regardless of any specific

 sample(s) inclusion. This approach may be most useful analyzing the list of

@@ -41,16 +44,6 @@ Similar to how increasing number of detected DE genes can be found with more

 biological replicates, the list of common DE genes is expected to increase

 with more replicates. This eventually levels off as majority of DE genes have

 been found.

-

-Another similar analytical approach is to repeat DE analysis with all

-combinations of samples with one sample left out. In typical datasets with

-equal number of samples in both conditions, this approach will include one

-additional sample in the comparison, potentially leading to improvement in

-detection. However, the number of possible combinations with this approach

-will be less than the ones possible with ERSSA. As a result, the identified

-common gene list may not be as robust as the one identified with ERSSA.

-Regardless, in practice, both approaches are likely to generate comparable

-DE gene lists.

 }

 \examples{

 # load edgeR deg object generated by erssa_edger using example dataset

@@ -18,6 +18,7 @@ A list is returned containing:

   \item{gg_object} {the ggplot2 object, which can then be further

   customized.}

   \item{intersect.dataframe} {the tidy table version used for plotting.}

+  \item{deg_dataframe} {the tidy table version of DEG numbers for plotting mean.}

   \item{intersect_genes} {list of vectors containing DE genes with vector

   name indicating the associated replicate level.}

   \item{full_num_DEG} {The number of DE genes with all samples included.}

@@ -52,12 +52,17 @@ Regardless, in practice, both approaches are likely to generate comparable

 DE gene lists.

 }

 \examples{

-#load edgeR deg object generated by erssa_edger using example dataset

-#test dataset with 1000 genes, 4 replicates and 20 comb. per rep. level

+# load edgeR deg object generated by erssa_edger using example dataset

+# example dataset containing 1000 genes, 4 replicates and 10 comb. per rep.

+# level

 data(deg.partial, package = "ERSSA")

 gg_intersect = ggplot2_intersectPlot(deg.partial)

+}

+\references{

+H. Wickham. ggplot2: Elegant Graphics for Data Analysis.

+Springer-Verlag New York, 2009.

 }

 \author{

 Zixuan Shao, \email{[email protected]}

@@ -53,7 +53,7 @@ DE gene lists.

 }

 \examples{

 #load edgeR deg object generated by erssa_edger using example dataset

-#only 5000 genes and 4 replicates tested to speed up runtime

+#test dataset with 1000 genes, 4 replicates and 20 comb. per rep. level

 data(deg.partial, package = "ERSSA")

 gg_intersect = ggplot2_intersectPlot(deg.partial)

new file mode 100644

@@ -0,0 +1,64 @@

+% Generated by roxygen2: do not edit by hand

+% Please edit documentation in R/plot_intersectPlot.R

+\name{ggplot2_intersectPlot}

+\alias{ggplot2_intersectPlot}

+\title{Plot number of DE genes that is common across combinations}

+\usage{

+ggplot2_intersectPlot(deg = NULL, path = ".")

+}

+\arguments{

+\item{deg}{The list of DE genes generated by one of ERSSA::DE_*.R scripts.}

+

+\item{path}{Path to which the plot will be saved. Default to current working

+directory.}

+}

+\value{

+A list is returned containing:

+ \itemize{

+  \item{gg_object} {the ggplot2 object, which can then be further

+  customized.}

+  \item{intersect.dataframe} {the tidy table version used for plotting.}

+  \item{intersect_genes} {list of vectors containing DE genes with vector

+  name indicating the associated replicate level.}

+  \item{full_num_DEG} {The number of DE genes with all samples included.}

+}

+}

+\description{

+\code{ggplot2_intersectPlot} function generates and plots the list of

+differentially expressed (DE) genes that are commonly found in all

+combinations at any particular replicate level. Often in small-scale

+RNA-seq experiments, the inclusion or exclusion of any paricular sample can

+result in a highly different list of DE genes. To reduce the influence of any

+particular sample in the entire dataset analysis, it may be desirable to

+identify the list of DE genes that are enriched regardless of any specific

+sample(s) inclusion. This approach may be most useful analyzing the list of

+common DE genes at the greatest possible replicate to take advantage of the

+robust feature as well as employing typically the longest list of DE genes.

+}

+\details{

+Similar to how increasing number of detected DE genes can be found with more

+biological replicates, the list of common DE genes is expected to increase

+with more replicates. This eventually levels off as majority of DE genes have

+been found.

+

+Another similar analytical approach is to repeat DE analysis with all

+combinations of samples with one sample left out. In typical datasets with

+equal number of samples in both conditions, this approach will include one

+additional sample in the comparison, potentially leading to improvement in

+detection. However, the number of possible combinations with this approach

+will be less than the ones possible with ERSSA. As a result, the identified

+common gene list may not be as robust as the one identified with ERSSA.

+Regardless, in practice, both approaches are likely to generate comparable

+DE gene lists.

+}

+\examples{

+#load edgeR deg object generated by erssa_edger using example dataset

+#only 5000 genes and 4 replicates tested to speed up runtime

+data(deg.partial, package = "ERSSA")

+

+gg_intersect = ggplot2_intersectPlot(deg.partial)

+

+}

+\author{

+Zixuan Shao, \email{[email protected]}

+}