In btmonier/vidger-nd: Create rapid visualizations of RNAseq data in R
^a^Department of Biology and Microbiology, South Dakota State University,
Brookings, SD, USA; ^b,c^Bioinformatics and Mathematical Biosciences Lab,
Department of Agronomy, Horticulture and Plant Science,
South Dakota State University, Brookings, SD, USA; ^d^Population Health Group
at Sanford Research, Sioux Falls, SD, USA; ^e^BioSNTR, Brookings, SD, USA.
knitr::opts_chunk$set(
fig.path='figure/graphics-',
cache.path='cache/graphics-',
fig.align='center',
external=TRUE,
echo=TRUE,
warning=FALSE,
fig.pos="H"
)
require(vidger)
require(DESeq2)
require(edgeR)
data("df.cuff")
data("df.deseq")
data("df.edger")
Example S1: Installation and data examples
The latest version of ViDGER can be installed via GitHub using the devtools
package. If you do not have devtools installed in your R environment, you
will need to obtain it through the install.packages() function (see
commented code):
# Install from GitHub
# if (!require("devtools")) install.packages("devtools")
# devtools::install_github("btmonier/vidger")
# Load vidger
require(vidger)
Once installed, you will have access to the following functions:
vsBoxplot()vsScatterPlot()vsScatterMatrix()vsDEGMatrix()vsMAPlot()vsMAMatrix()vsVolcano()vsVolcanoMatrix()vsFourWay()
Further explanation will be given to how these functions work later on in the
documentation. For the following examples, three toy data sets will be used:
df.cuff, df.deseq, and df.edger. Each of these data sets reflect the
three RNA-seq analyses this package covers. These can be loaded in the R
workspace by using the following command:
data(<data_set>)
Where <data_set> is one of the previously mentioned data sets. Some of the
recurring elements that are found in each of these functions are the type
and d.factor arguments. The type argument tells the function how to
process the data for each analytical type (i.e. "cuffdiff", "deseq", or
"edger"). The d.factor argument is used specifically for DESeq2 objects
which we will discuss in the DESeq2 section. All other arguments are discussed
in further detail by looking at the respective help file for each functions
(i.e. ?vsScatterPlot).
\newpage
An overview of the data used
As mentioned earlier, three toy data sets are included with this package. In
addition to these data sets, 5 "real-world" data sets were also used. All
real-world data used is currently unpublished from ongoing collaborations.
Summaries of this data can be found in the following tables:
Table 1: An overview of the toy data sets included in this package. In this
table, each data set is summarized in terms of what analytical software was
used, organism ID, experimental layout (replicates and treatments), number of
transcripts (IDs), and size of the data object in terms of megabytes (MB).
| Data | Software | Organism | Reps | Treat. | IDs | Size (MB) |
|------------|----------|-----------------|------|--------|-------|-----------|
| df.cuff | CuffDiff | H | 2 | 3 | 1200 | 0.2 |
| | | sapiens | | | | |
| df.deseq | DESeq2 | D. | 2 | 3 | 29391 | 2.3 |
| | | melanogaster | | | | |
| df.deseq | edgeR | A. | 2 | 3 | 724 | 0.1 |
| | | thaliana | | | | |
Table 2: "Real-world" (RW) data set statistics. To test the reliability of our
package, real data was used from human collections and several plant samples.
Each data set is summarized in terms of organism ID, number of experimental
samples (n), experimental conditions, and number of transcripts (IDs).
| Data | Organism | n | Exp. Conditions | IDs |
|------|------------|----|--------------------------------------|--------|
| RW-1 | H. | 10 | Two treatment dosages taken at two | 198002 |
| | sapiens | | time points and one control sample | |
| | | | taken at one time point | |
| RW-2 | M. | 24 | Two phenotypes taken at four time | 63517 |
| | domestia | | points (three replicates each) | |
| RW-3 | V. | 6 | Two conditions (three replicates | 59262 |
| | ripria: | | each). | |
| | bud | | | |
| RW-4 | V. | 6 | Two conditions (three replicates | 17962 |
| | ripria: | | each). | |
| | shoot-tip | | | |
| | (7 days) | | | |
| RW-5 | V. | 6 | Two conditions (three replicates | 19064 |
| | ripria: | | each). | |
| | shoot-tip | | | |
| | (21 days) | | | |
\newpage
Example S2: Creating box plots
Box plots are a useful way to determine the distribution of data. In this case
we can determine the distribution of FPKM or CPM values by using the
vsBoxPlot() function. This function allows you to extract necessary
results-based data from analytical objects to create a box plot comparing
$log_{10}$ (FPKM or CPM) distributions for experimental treatments.
With Cuffdiff
my.cap <- "A boxplot example using the `vsBoxPlot()` function with
`cuffdiff` data. In this example, FPKM distributions for each treatment within
an experiment are shown in the form of a box and whisker plot."
vsBoxPlot(
data = df.cuff, d.factor = NULL, type = 'cuffdiff', title = TRUE,
legend = TRUE, grid = TRUE
)
\newpage
With DESeq2
my.cap <- "A boxplot example using the `vsBoxPlot()` function with
`DESeq2` data. In this example, FPKM distributions for each treatment within
an experiment are shown in the form of a box and whisker plot."
vsBoxPlot(
data = df.deseq, d.factor = 'condition', type = 'deseq',
title = TRUE, legend = TRUE, grid = TRUE
)
\newpage
With edgeR
my.cap <- "A boxplot example using the `vsBoxPlot()` function with `edgeR`
data. In this example, CPM distributions for each treatment within an
experiment are shown in the form of a box and whisker plot"
vsBoxPlot(
data = df.edger, d.factor = NULL, type = 'edger',
title = TRUE, legend = TRUE, grid = TRUE
)
\newpage
Example S3: Creating scatter plots
This example will look at a basic scatter plot function, vsScatterPlot().
This function allows you to visualize comparisons of $log_{10}$ values of
either FPKM or CPM measurements of two treatments depending on analytical type.
With Cuffdiff
my.cap <- "A scatterplot example using the `vsScatterPlot()` function with
`Cuffdiff` data. In this visualization, $log_{10}$ comparisons are made of
fragments per kilobase of transcript per million mapped reads (FPKM)
measurments. The dashed line represents regression line for the comparison."
vsScatterPlot(
x = 'hESC', y = 'iPS', data = df.cuff, type = 'cuffdiff',
d.factor = NULL, title = TRUE, grid = TRUE
)
\newpage
With DESeq2
my.cap <- "A scatterplot example using the `vsScatterPlot()` function with
`DESeq2` data. In this visualization, $log_{10}$ comparisons are made of
fragments per kilobase of transcript per million mapped reads (FPKM)
measurments. The dashed line represents regression line for the comparison."
vsScatterPlot(
x = 'treated_paired.end', y = 'untreated_paired.end',
data = df.deseq, type = 'deseq', d.factor = 'condition',
title = TRUE, grid = TRUE
)
\newpage
With edgeR
my.cap <- "A scatterplot example using the `vsScatterPlot()` function with
`edgeR` data. In this visualization, $log_{10}$ comparisons are made of
fragments per kilobase of transcript per million mapped reads (FPKM)
measurments. The dashed line represents regression line for the comparison."
vsScatterPlot(
x = 'WM', y = 'MM', data = df.edger, type = 'edger',
d.factor = NULL, title = TRUE, grid = TRUE
)
\newpage
Example S4: Creating scatter plot matrices
This example will look at an extension of the vsScatterPlot() function which
is vsScatterMatrix(). This function will create a matrix of all possible
comparisons of treatments within an experiment with additional info.
With Cuffdiff
my.cap <- "A scatterplot matrix example using the `vsScatterMatrix()`
function with `Cuffdiff` data. Similar to the scatterplot function, this
visualization allows for all comparisons to be made within an experiment. In
addition to the scatterplot visuals, FPKM distributions (histograms) and
correlation (Corr) values are generated."
vsScatterMatrix(
data = df.cuff, d.factor = NULL, type = 'cuffdiff',
comp = NULL, title = TRUE, grid = TRUE, man.title = NULL
)
\newpage
With DESeq2
my.cap <- "A scatterplot matrix example using the `vsScatterMatrix()`
function with `DESeq2` data. Similar to the scatterplot function, this
visualization allows for all comparisons to be made within an experiment. In
addition to the scatterplot visuals, FPKM distributions (histograms) and
correlation (Corr) values are generated."
vsScatterMatrix(
data = df.deseq, d.factor = 'condition', type = 'deseq',
comp = NULL, title = TRUE, grid = TRUE, man.title = NULL
)
\newpage
With edgeR
my.cap <- "A scatterplot matrix example using the `vsScatterMatrix()`
function with `edgeR` data. Similar to the scatterplot function, this
visualization allows for all comparisons to be made within an experiment. In
addition to the scatterplot visuals, FPKM distributions (histograms) and
correlation (Corr) values are generated."
vsScatterMatrix(
data = df.edger, d.factor = NULL, type = 'edger', comp = NULL,
title = TRUE, grid = TRUE, man.title = NULL
)
\newpage
Example S5: Creating differential gene expression matrices
Using the vsDEGMatrix() function allows the user to visualize the number of
differentially expressed genes (DEGs) at a given adjusted p-value (padj =)
for each experimental treatment level. Higher color intensity correlates to a
higher number of DEGs.
With Cuffdiff
my.cap <- "A matrix of differentially expressed genes (DEGs) at a given
*p*-value using the `vsDEGMatrix()` function with `Cuffdiff` data. With this
function, the user is able to visualize the number of DEGs ata given adjusted
*p*-value for each experimental treatment level. Higher color intensity
correlates to a higher number of DEGs."
vsDEGMatrix(
data = df.cuff, padj = 0.05, d.factor = NULL, type = 'cuffdiff',
title = TRUE, legend = TRUE, grid = TRUE
)
\newpage
With DESeq2
my.cap <- "A matrix of differentially expressed genes (DEGs) at a given
*p*-value using the `vsDEGMatrix()` function with `DESeq2` data. With this
function, the user is able to visualize the number of DEGs ata given adjusted
*p*-value for each experimental treatment level. Higher color intensity
correlates to a higher number of DEGs."
vsDEGMatrix(
data = df.deseq, padj = 0.05, d.factor = 'condition',
type = 'deseq', title = TRUE, legend = TRUE, grid = TRUE
)
\newpage
With edgeR
my.cap <- "A matrix of differentially expressed genes (DEGs) at a given
*p*-value using the `vsDEGMatrix()` function with `edgeR` data. With this
function, the user is able to visualize the number of DEGs ata given adjusted
*p*-value for each experimental treatment level. Higher color intensity
correlates to a higher number of DEGs."
vsDEGMatrix(
data = df.edger, padj = 0.05, d.factor = NULL, type = 'edger',
title = TRUE, legend = TRUE, grid = TRUE
)
\newpage
Example S6: Creating MA plots
vsMAPlot() visualizes the variance between two samples in terms of gene
expression values where logarithmic fold changes of count data are plotted
against mean counts. For more information on how each of the aesthetics are
plotted, please refer to the figure captions and Method S1.
With Cuffdiff
my.cap <- "MA plot visualization using the `vsMAPLot()` function with
`Cuffdiff` data. LFCs are plotted mean counts to determine the variance between
two treatments in terms of gene expression. Blue nodes on the graph represent
statistically significant LFCs which are greater than a given value than a
user-defined LFC parameter. Green nodes indicate statistically significant LFCs
which are less than the user-defined LFC parameter. Gray nodes are data points
that are not statistically significant. Numerical values in parantheses for
each legend color indicate the number of transcripts that meet the prior
conditions. Triangular shapes represent values that exceed the viewing area of
the graph. Node size changes represent the magnitude of the LFC values (i.e.
larger shapes reflect larger LFC values). Dashed lines indicate user-defined
LFC values."
vsMAPlot(
x = 'iPS', y = 'hESC', data = df.cuff, d.factor = NULL,
type = 'cuffdiff', padj = 0.05, y.lim = NULL, lfc = NULL,
title = TRUE, legend = TRUE, grid = TRUE
)
\newpage
With DESeq2
my.cap <- "MA plot visualization using the `vsMAPLot()` function with
`DESeq2` data. LFCs are plotted mean counts to determine the variance between
two treatments in terms of gene expression. Blue nodes on the graph represent
statistically significant LFCs which are greater than a given value than a
user-defined LFC parameter. Green nodes indicate statistically significant LFCs
which are less than the user-defined LFC parameter. Gray nodes are data points
that are not statistically significant. Numerical values in parantheses for
each legend color indicate the number of transcripts that meet the prior
conditions. Triangular shapes represent values that exceed the viewing area of
the graph. Node size changes represent the magnitude of the LFC values (i.e.
larger shapes reflect larger LFC values). Dashed lines indicate user-defined
LFC values."
vsMAPlot(
x = 'treated_paired.end', y = 'untreated_paired.end',
data = df.deseq, d.factor = 'condition', type = 'deseq',
padj = 0.05, y.lim = NULL, lfc = NULL, title = TRUE,
legend = TRUE, grid = TRUE
)
\newpage
With edgeR
my.cap <- "MA plot visualization using the `vsMAPLot()` function with
`edgeR` data. LFCs are plotted mean counts to determine the variance between
two treatments in terms of gene expression. Blue nodes on the graph represent
statistically significant LFCs which are greater than a given value than a
user-defined LFC parameter. Green nodes indicate statistically significant LFCs
which are less than the user-defined LFC parameter. Gray nodes are data points
that are not statistically significant. Numerical values in parantheses for
each legend color indicate the number of transcripts that meet the prior
conditions. Triangular shapes represent values that exceed the viewing area of
the graph. Node size changes represent the magnitude of the LFC values (i.e.
larger shapes reflect larger LFC values). Dashed lines indicate user-defined
LFC values."
vsMAPlot(
x = 'WW', y = 'MM', data = df.edger, d.factor = NULL,
type = 'edger', padj = 0.05, y.lim = NULL, lfc = NULL,
title = TRUE, legend = TRUE, grid = TRUE
)
\newpage
Example S7: Creating MA plot matrices
Similar to a scatter plot matrix, vsMAMatrix() will produce visualizations
for all comparisons within your data set. For more information on how the
aesthetics are plotted in these visualizations, please refer to the figure
caption and Method S1.
With Cuffdiff
my.cap <- "A MA plot matrix using the `vsMAMatrix()` function with `Cuffdiff`
data. Similar to the `vsMAPlot()` function, `vsMAMatrix()` will generate a
matrix of MA plots for all comparisons within an experiment. LFCs are plotted
mean counts to determine the variance between two treatments in terms of gene
expression. Blue nodes on the graph represent statistically significant LFCs
which are greater than a given value than a user-defined LFC parameter. Green
nodes indicate statistically significant LFCs which are less than the
user-defined LFC parameter. Gray nodes are data points that are not
statistically significant. Numerical values in parantheses for each legend
color indicate the number of transcripts that meet the prior conditions.
Triangular shapes represent values that exceed the viewing area of the graph.
Node size changes represent the magnitude of the LFC values (i.e. larger shapes
reflect larger LFC values). Dashed lines indicate user-defined LFC values."
vsMAMatrix(
data = df.cuff, d.factor = NULL, type = 'cuffdiff',
padj = 0.05, y.lim = NULL, lfc = 1, title = TRUE,
grid = TRUE, counts = TRUE, data.return = FALSE
)
\newpage
With DESeq2
my.cap <- "A MA plot matrix using the `vsMAMatrix()` function with `DESeq2`
data. Similar to the `vsMAPlot()` function, `vsMAMatrix()` will generate a
matrix of MA plots for all comparisons within an experiment. LFCs are plotted
mean counts to determine the variance between two treatments in terms of gene
expression. Blue nodes on the graph represent statistically significant LFCs
which are greater than a given value than a user-defined LFC parameter. Green
nodes indicate statistically significant LFCs which are less than the
user-defined LFC parameter. Gray nodes are data points that are not
statistically significant. Numerical values in parantheses for each legend
color indicate the number of transcripts that meet the prior conditions.
Triangular shapes represent values that exceed the viewing area of the graph.
Node size changes represent the magnitude of the LFC values (i.e. larger shapes
reflect larger LFC values). Dashed lines indicate user-defined LFC values."
vsMAMatrix(
data = df.deseq, d.factor = 'condition', type = 'deseq',
padj = 0.05, y.lim = NULL, lfc = 1, title = TRUE,
grid = TRUE, counts = TRUE, data.return = FALSE
)
\newpage
With edgeR
my.cap <- "A MA plot matrix using the `vsMAMatrix()` function with `edgeR`
data. Similar to the `vsMAPlot()` function, `vsMAMatrix()` will generate a
matrix of MA plots for all comparisons within an experiment. LFCs are plotted
mean counts to determine the variance between two treatments in terms of gene
expression. Blue nodes on the graph represent statistically significant LFCs
which are greater than a given value than a user-defined LFC parameter. Green
nodes indicate statistically significant LFCs which are less than the
user-defined LFC parameter. Gray nodes are data points that are not
statistically significant. Numerical values in parantheses for each legend
color indicate the number of transcripts that meet the prior conditions.
Triangular shapes represent values that exceed the viewing area of the graph.
Node size changes represent the magnitude of the LFC values (i.e. larger shapes
reflect larger LFC values). Dashed lines indicate user-defined LFC values."
vsMAMatrix(
data = df.edger, d.factor = NULL, type = 'edger',
padj = 0.05, y.lim = NULL, lfc = 1, title = TRUE,
grid = TRUE, counts = TRUE, data.return = FALSE
)
\newpage
Example S8: Creating volcano plots
The next few visualizations will focus on ways to display differential gene
expression between two or more treatments. Volcano plots visualize the variance
between two samples in terms of gene expression values where the $-log_{10}$ of
calculated p-values (y-axis) are a plotted against the $log_2$ changes
(x-axis). These plots can be visualized with the vsVolcano() function.
For more information on how each of the aesthetics are plotted, please refer
to the figure captions and Method S1.
With Cuffdiff
my.cap <- "A volcano plot example using the `vsVolcano()` function with
`Cuffdiff` data. In this visualization, comparisons are made between the
$-log_{10}$ *p*-value versus the $log_2$ fold change (LFC) between two
treatments. Blue nodes on the graph represent statistically significant LFCs
which are greater than a given value than a user-defined LFC parameter. Green
nodes indicate statistically significant LFCs which are less than the
user-defined LFC parameter. Gray nodes are data points that are not
statistically significant. Numerical values in parantheses for each legend
color indicate the number of transcripts that meet the prior conditions. Left
and right brackets (< and >) represent values that exceed the viewing area of
the graph. Node size changes represent the magnitude of the LFC values (i.e.
larger shapes reflect larger LFC values). Vertical and horizontal lines
indicate user-defined LFC and adjusted *p*-values, respectively."
vsVolcano(
x = 'iPS', y = 'hESC', data = df.cuff, d.factor = NULL,
type = 'cuffdiff', padj = 0.05, x.lim = NULL, lfc = NULL,
title = TRUE, legend = TRUE, grid = TRUE, data.return = FALSE
)
\newpage
With DESeq2
my.cap <- "A volcano plot example using the `vsVolcano()` function with
`DESeq2` data. In this visualization, comparisons are made between the
$-log_{10}$ *p*-value versus the $log_2$ fold change (LFC) between two
treatments. Blue nodes on the graph represent statistically significant LFCs
which are greater than a given value than a user-defined LFC parameter. Green
nodes indicate statistically significant LFCs which are less than the
user-defined LFC parameter. Gray nodes are data points that are not
statistically significant. Numerical values in parantheses for each legend
color indicate the number of transcripts that meet the prior conditions. Left
and right brackets (< and >) represent values that exceed the viewing area of
the graph. Node size changes represent the magnitude of the LFC values (i.e.
larger shapes reflect larger LFC values). Vertical and horizontal lines
indicate user-defined LFC and adjusted *p*-values, respectively."
vsVolcano(
x = 'treated_paired.end', y = 'untreated_paired.end',
data = df.deseq, d.factor = 'condition', type = 'deseq',
padj = 0.05, x.lim = NULL, lfc = NULL, title = TRUE,
legend = TRUE, grid = TRUE, data.return = FALSE
)
\newpage
With edgeR
my.cap <- "A volcano plot example using the `vsVolcano()` function with
`edgeR` data. In this visualization, comparisons are made between the
$-log_{10}$ *p*-value versus the $log_2$ fold change (LFC) between two
treatments. Blue nodes on the graph represent statistically significant LFCs
which are greater than a given value than a user-defined LFC parameter. Green
nodes indicate statistically significant LFCs which are less than the
user-defined LFC parameter. Gray nodes are data points that are not
statistically significant. Numerical values in parantheses for each legend
color indicate the number of transcripts that meet the prior conditions. Left
and right brackets (< and >) represent values that exceed the viewing area of
the graph. Node size changes represent the magnitude of the LFC values (i.e.
larger shapes reflect larger LFC values). Vertical and horizontal lines
indicate user-defined LFC and adjusted *p*-values, respectively."
vsVolcano(
x = 'WW', y = 'MM', data = df.edger, d.factor = NULL,
type = 'edger', padj = 0.05, x.lim = NULL, lfc = NULL,
title = TRUE, legend = TRUE, grid = TRUE, data.return = FALSE
)
\newpage
Example S9: Creating volcano plot matrices
Similar to the prior matrix functions, vsVolcanoMatrix() will produce
visualizations for all comparisons within your data set. For more information
on how the aesthetics are plotted in these visualizations, please refer to the
figure caption and Method S1.
With Cuffdiff
my.cap <- "A volcano plot matrix using the `vsVolcanoMatrix()` function with
`Cuffdiff` data. Similar to the `vsVolcano()` function, `vsVolcanoMatrix()`
will generate a matrix of volcano plots for all comparisons within an
experiment. Comparisons are made between the $-log_{10}$ *p*-value versus the
$log_2$ fold change (LFC) between two treatments. Blue nodes on the graph
represent statistically significant LFCs which are greater than a given value
than a user-defined LFC parameter. Green nodes indicate statistically
significant LFCs which are less than the user-defined LFC parameter. Gray nodes
are data points that are not statistically significant. The blue and green
numbers in each facet represent the number of transcripts that meet the
criteria for blue and green nodes in each comparison. Left and right brackets
(< and >) represent values that exceed the viewing area of the graph. Node size
changes represent the magnitude of the LFC values (i.e. larger shapes reflect
larger LFC values). Vertical and horizontal lines indicate user-defined LFC and
adjusted *p*-values, respectively."
vsVolcanoMatrix(
data = df.cuff, d.factor = NULL, type = 'cuffdiff',
padj = 0.05, x.lim = NULL, lfc = NULL, title = TRUE,
legend = TRUE, grid = TRUE, counts = TRUE
)
\newpage
With DESeq2
my.cap <- "A volcano plot matrix using the `vsVolcanoMatrix()` function with
`DESeq2` data. Similar to the `vsVolcano()` function, `vsVolcanoMatrix()`
will generate a matrix of volcano plots for all comparisons within an
experiment. Comparisons are made between the $-log_{10}$ *p*-value versus the
$log_2$ fold change (LFC) between two treatments. Blue nodes on the graph
represent statistically significant LFCs which are greater than a given value
than a user-defined LFC parameter. Green nodes indicate statistically
significant LFCs which are less than the user-defined LFC parameter. Gray nodes
are data points that are not statistically significant. The blue and green
numbers in each facet represent the number of transcripts that meet the
criteria for blue and green nodes in each comparison. Left and right brackets
(< and >) represent values that exceed the viewing area of the graph. Node size
changes represent the magnitude of the LFC values (i.e. larger shapes reflect
larger LFC values). Vertical and horizontal lines indicate user-defined LFC and
adjusted *p*-values, respectively."
vsVolcanoMatrix(
data = df.deseq, d.factor = 'condition', type = 'deseq',
padj = 0.05, x.lim = NULL, lfc = NULL, title = TRUE,
legend = TRUE, grid = TRUE, counts = TRUE
)
\newpage
With edgeR
my.cap <- "A volcano plot matrix using the `vsVolcanoMatrix()` function with
`edgeR` data. Similar to the `vsVolcano()` function, `vsVolcanoMatrix()`
will generate a matrix of volcano plots for all comparisons within an
experiment. Comparisons are made between the $-log_{10}$ *p*-value versus the
$log_2$ fold change (LFC) between two treatments. Blue nodes on the graph
represent statistically significant LFCs which are greater than a given value
than a user-defined LFC parameter. Green nodes indicate statistically
significant LFCs which are less than the user-defined LFC parameter. Gray nodes
are data points that are not statistically significant. The blue and green
numbers in each facet represent the number of transcripts that meet the
criteria for blue and green nodes in each comparison. Left and right brackets
(< and >) represent values that exceed the viewing area of the graph. Node size
changes represent the magnitude of the LFC values (i.e. larger shapes reflect
larger LFC values). Vertical and horizontal lines indicate user-defined LFC and
adjusted *p*-values, respectively."
vsVolcanoMatrix(
data = df.edger, d.factor = NULL, type = 'edger', padj = 0.05,
x.lim = NULL, lfc = NULL, title = TRUE, legend = TRUE,
grid = TRUE, counts = TRUE
)
\newpage
Example S10: Creating four way plots
To create four-way plots, the function, vsFourWay() is used. This plot
compares the $log_2$ fold changes between two samples and a 'control'. For more
information on how each of the aesthetics are plotted, please refer to the
figure captions and Method S1.
With Cuffdiff
my.cap <- "A four way plot visualization using the `vsFourWay()` function with
`Cuffdiff` data. In this example, LFCs comparisons between two treatments and a
control are made. Blue nodes indicate statistically significant LFCs which are
greater than a given user-defined value for both x and y-axes. Green nodes
reflect statistically significant LFCs which are less than a user-defined value
for treatment y and greater than said value for treatment x. Similar to green
nodes, red nodes reflect statistically significant LFCs which are greater than
a user-defined vlaue treatment y and less than said value for treatment x. Gray
nodes are data points that are not statistically significant for both x and
y-axes. Triangular shapes indicate values which exceed the viewing are for the
graph. Size change reflects the magnitude of LFC values (i.e. larger shapes
reflect larger LFC values). Vertical and horizontal dashed lines indicate
user-defined LFC values."
vsFourWay(
x = 'iPS', y = 'hESC', control = 'Fibroblasts', data = df.cuff,
d.factor = NULL, type = 'cuffdiff', padj = 0.05, x.lim = NULL,
y.lim = NULL, lfc = NULL, legend = TRUE, title = TRUE, grid = TRUE
)
\newpage
With DESeq2
my.cap <- "A four way plot visualization using the `vsFourWay()` function with
`DESeq2` data. In this example, LFCs comparisons between two treatments and a
control are made. Blue nodes indicate statistically significant LFCs which are
greater than a given user-defined value for both x and y-axes. Green nodes
reflect statistically significant LFCs which are less than a user-defined value
for treatment y and greater than said value for treatment x. Similar to green
nodes, red nodes reflect statistically significant LFCs which are greater than
a user-defined vlaue treatment y and less than said value for treatment x. Gray
nodes are data points that are not statistically significant for both x and
y-axes. Triangular shapes indicate values which exceed the viewing are for the
graph. Size change reflects the magnitude of LFC values (i.e. larger shapes
reflect larger LFC values). Vertical and horizontal dashed lines indicate
user-defined LFC values."
vsFourWay(
x = 'treated_paired.end', y = 'untreated_single.read',
control = 'untreated_paired.end', data = df.deseq,
d.factor = 'condition', type = 'deseq', padj = 0.05, x.lim = NULL,
y.lim = NULL, lfc = NULL, legend = TRUE, title = TRUE, grid = TRUE
)
\newpage
With edgeR
my.cap <- "A four way plot visualization using the `vsFourWay()` function with
`DESeq2` data. In this example, LFCs comparisons between two treatments and a
control are made. Blue nodes indicate statistically significant LFCs which are
greater than a given user-defined value for both x and y-axes. Green nodes
reflect statistically significant LFCs which are less than a user-defined value
for treatment y and greater than said value for treatment x. Similar to green
nodes, red nodes reflect statistically significant LFCs which are greater than
a user-defined vlaue treatment y and less than said value for treatment x. Gray
nodes are data points that are not statistically significant for both x and
y-axes. Triangular shapes indicate values which exceed the viewing are for the
graph. Size change reflects the magnitude of LFC values (i.e. larger shapes
reflect larger LFC values). Vertical and horizontal dashed lines indicate
user-defined LFC values."
vsFourWay(
x = 'WW', y = 'WM', control = 'MM', data = df.edger,
d.factor = NULL, type = 'edger', padj = 0.05, x.lim = NULL,
y.lim = NULL, lfc = NULL, legend = TRUE, title = TRUE, grid = TRUE
)
\newpage
Method S1: Determining data point shape and size changes
The shape and size of each data point will also change depending on several
conditions. To maximize the viewing area while retaining high resolution, some
data points will not be present within the viewing area. If they exceed the
viewing area, they will change shape from a circle to a triangular orientation.
The extent (i.e. fold change) to how far these points exceed the viewing area
are based on the following criteria:
- SUB - values that fall within the viewing area of the plot.
- T-1 - values that are greater than the maximum viewing area and are
less than the 25th percentile of values that exceed the viewing area.
- T-2 - Similar to T-1; values fall between the 25th and 50th
percentile.
- T-3 - Similar to T-2; values fall between the 50th and 75th
percentile.
- T-4 - Similar to T-3; values fall between the 75th and 100th
percentile.
To further clarify theses conditions, please refer to the following figure:
my.cap <- "An illustration detailing the principles behind the node size for
the differntial gene expression functions. In this figure, the data points
increase in size depending on which quartile they reside as the absolute LFC
increases (top bar). Data points that fall within the viewing area classified
as SUB while data points that exceed this area are classified as T-1 through
T-4."
knitr::include_graphics("lfc-shape.png", auto_pdf = TRUE)
\newpage
Method S2: Determining function performance
Function efficiencies were determined by calculating system times by using the
microbenchmark R package. Each function was ran 100 times with the prior code
used in the documentation. All benchmarks were determined on a machine running
a 64-bit Windows 10 operating system, 8 GB of RAM, and an Intel Core i5-6400
processor running at 2.7 GHz.
Scatterplots
my.cap <- "Benchmarks for the `vsScatterPlot()` function. Time (ms)
distributions were generated for this function using 100 trials for each of the
three RNAseq data objects. Cuffdiff, DESeq2, and edgeR example data sets
contained 1200, 724, and 29391 transcripts, respectively. "
knitr::include_graphics("eff-scatter.png", auto_pdf = TRUE)
\newpage
Scatterplot matrices
my.cap <- "Benchmarks for the `vsScatterMatrix()` function. Time (ms)
distributions were generated for this function using 100 trials for each of the
three RNAseq data objects. Cuffdiff, DESeq2, and edgeR example data sets
contained 1200, 724, and 29391 transcripts, respectively. "
knitr::include_graphics("eff-smatrix.png", auto_pdf = TRUE)
\newpage
Box plots
my.cap <- "Benchmarks for the `vsBoxPlot()` function. Time (ms)
distributions were generated for this function using 100 trials for each of the
three RNAseq data objects. Cuffdiff, DESeq2, and edgeR example data sets
contained 1200, 724, and 29391 transcripts, respectively. "
knitr::include_graphics("eff-box.png", auto_pdf = TRUE)
\newpage
Differential gene expression matrices
my.cap <- "Benchmarks for the `vsDEGMatrix()` function. Time (ms)
distributions were generated for this function using 100 trials for each of the
three RNAseq data objects. Cuffdiff, DESeq2, and edgeR example data sets
contained 1200, 724, and 29391 transcripts, respectively. "
knitr::include_graphics("eff-deg.png", auto_pdf = TRUE)
\newpage
Volcano plots
my.cap <- "Benchmarks for the `vsVolcano()` function. Time (ms)
distributions were generated for this function using 100 trials for each of the
three RNAseq data objects. Cuffdiff, DESeq2, and edgeR example data sets
contained 1200, 724, and 29391 transcripts, respectively. "
knitr::include_graphics("eff-volcano.png", auto_pdf = TRUE)
\newpage
Volcano plot matrices
my.cap <- "Benchmarks for the `vsVolcanoMatrix()` function. Time (ms)
distributions were generated for this function using 100 trials for each of the
three RNAseq data objects. Cuffdiff, DESeq2, and edgeR example data sets
contained 1200, 724, and 29391 transcripts, respectively. "
knitr::include_graphics("eff-vmatrix.png", auto_pdf = TRUE)
\newpage
MA plots
my.cap <- "Benchmarks for the `vsMAPlot()` function. Time (ms)
distributions were generated for this function using 100 trials for each of the
three RNAseq data objects. Cuffdiff, DESeq2, and edgeR example data sets
contained 1200, 724, and 29391 transcripts, respectively. "
knitr::include_graphics("eff-maplot.png", auto_pdf = TRUE)
\newpage
MA matrices
my.cap <- "Benchmarks for the `vsMAMatrix()` function. Time (s)
distributions were generated for this function using 100 trials for each of the
three RNAseq data objects. Cuffdiff, DESeq2, and edgeR example data sets
contained 1200, 724, and 29391 transcripts, respectively. "
knitr::include_graphics("eff-mamatrix.png", auto_pdf = TRUE)
\newpage
Four way plots
my.cap <- "Benchmarks for the `vsFourWay()` function. Time (ms)
distributions were generated for this function using 100 trials for each of the
three RNAseq data objects. Cuffdiff, DESeq2, and edgeR example data sets
contained 1200, 724, and 29391 transcripts, respectively. "
knitr::include_graphics("eff-four.png", auto_pdf = TRUE)
btmonier/vidger-nd documentation built on May 14, 2019, 12:44 p.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
^a^Department of Biology and Microbiology, South Dakota State University, Brookings, SD, USA; ^b,c^Bioinformatics and Mathematical Biosciences Lab, Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, SD, USA; ^d^Population Health Group at Sanford Research, Sioux Falls, SD, USA; ^e^BioSNTR, Brookings, SD, USA.
knitr::opts_chunk$set( fig.path='figure/graphics-', cache.path='cache/graphics-', fig.align='center', external=TRUE, echo=TRUE, warning=FALSE, fig.pos="H" )
require(vidger) require(DESeq2) require(edgeR) data("df.cuff") data("df.deseq") data("df.edger")
Example S1: Installation and data examples
The latest version of ViDGER can be installed via GitHub using the devtools
package. If you do not have devtools installed in your R environment, you
will need to obtain it through the install.packages() function (see
commented code):
# Install from GitHub # if (!require("devtools")) install.packages("devtools") # devtools::install_github("btmonier/vidger") # Load vidger require(vidger)
Once installed, you will have access to the following functions:
vsBoxplot()vsScatterPlot()vsScatterMatrix()vsDEGMatrix()vsMAPlot()vsMAMatrix()vsVolcano()vsVolcanoMatrix()vsFourWay()
Further explanation will be given to how these functions work later on in the
documentation. For the following examples, three toy data sets will be used:
df.cuff, df.deseq, and df.edger. Each of these data sets reflect the
three RNA-seq analyses this package covers. These can be loaded in the R
workspace by using the following command:
data(<data_set>)
Where <data_set> is one of the previously mentioned data sets. Some of the
recurring elements that are found in each of these functions are the type
and d.factor arguments. The type argument tells the function how to
process the data for each analytical type (i.e. "cuffdiff", "deseq", or
"edger"). The d.factor argument is used specifically for DESeq2 objects
which we will discuss in the DESeq2 section. All other arguments are discussed
in further detail by looking at the respective help file for each functions
(i.e. ?vsScatterPlot).
\newpage
An overview of the data used
As mentioned earlier, three toy data sets are included with this package. In addition to these data sets, 5 "real-world" data sets were also used. All real-world data used is currently unpublished from ongoing collaborations. Summaries of this data can be found in the following tables:
Table 1: An overview of the toy data sets included in this package. In this table, each data set is summarized in terms of what analytical software was used, organism ID, experimental layout (replicates and treatments), number of transcripts (IDs), and size of the data object in terms of megabytes (MB).
| Data | Software | Organism | Reps | Treat. | IDs | Size (MB) |
|------------|----------|-----------------|------|--------|-------|-----------|
| df.cuff | CuffDiff | H | 2 | 3 | 1200 | 0.2 |
| | | sapiens | | | | |
| df.deseq | DESeq2 | D. | 2 | 3 | 29391 | 2.3 |
| | | melanogaster | | | | |
| df.deseq | edgeR | A. | 2 | 3 | 724 | 0.1 |
| | | thaliana | | | | |
Table 2: "Real-world" (RW) data set statistics. To test the reliability of our package, real data was used from human collections and several plant samples. Each data set is summarized in terms of organism ID, number of experimental samples (n), experimental conditions, and number of transcripts (IDs).
| Data | Organism | n | Exp. Conditions | IDs | |------|------------|----|--------------------------------------|--------| | RW-1 | H. | 10 | Two treatment dosages taken at two | 198002 | | | sapiens | | time points and one control sample | | | | | | taken at one time point | | | RW-2 | M. | 24 | Two phenotypes taken at four time | 63517 | | | domestia | | points (three replicates each) | | | RW-3 | V. | 6 | Two conditions (three replicates | 59262 | | | ripria: | | each). | | | | bud | | | | | RW-4 | V. | 6 | Two conditions (three replicates | 17962 | | | ripria: | | each). | | | | shoot-tip | | | | | | (7 days) | | | | | RW-5 | V. | 6 | Two conditions (three replicates | 19064 | | | ripria: | | each). | | | | shoot-tip | | | | | | (21 days) | | | |
\newpage
Example S2: Creating box plots
Box plots are a useful way to determine the distribution of data. In this case
we can determine the distribution of FPKM or CPM values by using the
vsBoxPlot() function. This function allows you to extract necessary
results-based data from analytical objects to create a box plot comparing
$log_{10}$ (FPKM or CPM) distributions for experimental treatments.
With Cuffdiff
my.cap <- "A boxplot example using the `vsBoxPlot()` function with `cuffdiff` data. In this example, FPKM distributions for each treatment within an experiment are shown in the form of a box and whisker plot."
vsBoxPlot( data = df.cuff, d.factor = NULL, type = 'cuffdiff', title = TRUE, legend = TRUE, grid = TRUE )
\newpage
With DESeq2
my.cap <- "A boxplot example using the `vsBoxPlot()` function with `DESeq2` data. In this example, FPKM distributions for each treatment within an experiment are shown in the form of a box and whisker plot."
vsBoxPlot( data = df.deseq, d.factor = 'condition', type = 'deseq', title = TRUE, legend = TRUE, grid = TRUE )
\newpage
With edgeR
my.cap <- "A boxplot example using the `vsBoxPlot()` function with `edgeR` data. In this example, CPM distributions for each treatment within an experiment are shown in the form of a box and whisker plot"
vsBoxPlot( data = df.edger, d.factor = NULL, type = 'edger', title = TRUE, legend = TRUE, grid = TRUE )
\newpage
Example S3: Creating scatter plots
This example will look at a basic scatter plot function, vsScatterPlot().
This function allows you to visualize comparisons of $log_{10}$ values of
either FPKM or CPM measurements of two treatments depending on analytical type.
With Cuffdiff
my.cap <- "A scatterplot example using the `vsScatterPlot()` function with `Cuffdiff` data. In this visualization, $log_{10}$ comparisons are made of fragments per kilobase of transcript per million mapped reads (FPKM) measurments. The dashed line represents regression line for the comparison."
vsScatterPlot( x = 'hESC', y = 'iPS', data = df.cuff, type = 'cuffdiff', d.factor = NULL, title = TRUE, grid = TRUE )
\newpage
With DESeq2
my.cap <- "A scatterplot example using the `vsScatterPlot()` function with `DESeq2` data. In this visualization, $log_{10}$ comparisons are made of fragments per kilobase of transcript per million mapped reads (FPKM) measurments. The dashed line represents regression line for the comparison."
vsScatterPlot( x = 'treated_paired.end', y = 'untreated_paired.end', data = df.deseq, type = 'deseq', d.factor = 'condition', title = TRUE, grid = TRUE )
\newpage
With edgeR
my.cap <- "A scatterplot example using the `vsScatterPlot()` function with `edgeR` data. In this visualization, $log_{10}$ comparisons are made of fragments per kilobase of transcript per million mapped reads (FPKM) measurments. The dashed line represents regression line for the comparison."
vsScatterPlot( x = 'WM', y = 'MM', data = df.edger, type = 'edger', d.factor = NULL, title = TRUE, grid = TRUE )
\newpage
Example S4: Creating scatter plot matrices
This example will look at an extension of the vsScatterPlot() function which
is vsScatterMatrix(). This function will create a matrix of all possible
comparisons of treatments within an experiment with additional info.
With Cuffdiff
my.cap <- "A scatterplot matrix example using the `vsScatterMatrix()` function with `Cuffdiff` data. Similar to the scatterplot function, this visualization allows for all comparisons to be made within an experiment. In addition to the scatterplot visuals, FPKM distributions (histograms) and correlation (Corr) values are generated."
vsScatterMatrix( data = df.cuff, d.factor = NULL, type = 'cuffdiff', comp = NULL, title = TRUE, grid = TRUE, man.title = NULL )
\newpage
With DESeq2
my.cap <- "A scatterplot matrix example using the `vsScatterMatrix()` function with `DESeq2` data. Similar to the scatterplot function, this visualization allows for all comparisons to be made within an experiment. In addition to the scatterplot visuals, FPKM distributions (histograms) and correlation (Corr) values are generated."
vsScatterMatrix( data = df.deseq, d.factor = 'condition', type = 'deseq', comp = NULL, title = TRUE, grid = TRUE, man.title = NULL )
\newpage
With edgeR
my.cap <- "A scatterplot matrix example using the `vsScatterMatrix()` function with `edgeR` data. Similar to the scatterplot function, this visualization allows for all comparisons to be made within an experiment. In addition to the scatterplot visuals, FPKM distributions (histograms) and correlation (Corr) values are generated."
vsScatterMatrix( data = df.edger, d.factor = NULL, type = 'edger', comp = NULL, title = TRUE, grid = TRUE, man.title = NULL )
\newpage
Example S5: Creating differential gene expression matrices
Using the vsDEGMatrix() function allows the user to visualize the number of
differentially expressed genes (DEGs) at a given adjusted p-value (padj =)
for each experimental treatment level. Higher color intensity correlates to a
higher number of DEGs.
With Cuffdiff
my.cap <- "A matrix of differentially expressed genes (DEGs) at a given *p*-value using the `vsDEGMatrix()` function with `Cuffdiff` data. With this function, the user is able to visualize the number of DEGs ata given adjusted *p*-value for each experimental treatment level. Higher color intensity correlates to a higher number of DEGs."
vsDEGMatrix( data = df.cuff, padj = 0.05, d.factor = NULL, type = 'cuffdiff', title = TRUE, legend = TRUE, grid = TRUE )
\newpage
With DESeq2
my.cap <- "A matrix of differentially expressed genes (DEGs) at a given *p*-value using the `vsDEGMatrix()` function with `DESeq2` data. With this function, the user is able to visualize the number of DEGs ata given adjusted *p*-value for each experimental treatment level. Higher color intensity correlates to a higher number of DEGs."
vsDEGMatrix( data = df.deseq, padj = 0.05, d.factor = 'condition', type = 'deseq', title = TRUE, legend = TRUE, grid = TRUE )
\newpage
With edgeR
my.cap <- "A matrix of differentially expressed genes (DEGs) at a given *p*-value using the `vsDEGMatrix()` function with `edgeR` data. With this function, the user is able to visualize the number of DEGs ata given adjusted *p*-value for each experimental treatment level. Higher color intensity correlates to a higher number of DEGs."
vsDEGMatrix( data = df.edger, padj = 0.05, d.factor = NULL, type = 'edger', title = TRUE, legend = TRUE, grid = TRUE )
\newpage
Example S6: Creating MA plots
vsMAPlot() visualizes the variance between two samples in terms of gene
expression values where logarithmic fold changes of count data are plotted
against mean counts. For more information on how each of the aesthetics are
plotted, please refer to the figure captions and Method S1.
With Cuffdiff
my.cap <- "MA plot visualization using the `vsMAPLot()` function with `Cuffdiff` data. LFCs are plotted mean counts to determine the variance between two treatments in terms of gene expression. Blue nodes on the graph represent statistically significant LFCs which are greater than a given value than a user-defined LFC parameter. Green nodes indicate statistically significant LFCs which are less than the user-defined LFC parameter. Gray nodes are data points that are not statistically significant. Numerical values in parantheses for each legend color indicate the number of transcripts that meet the prior conditions. Triangular shapes represent values that exceed the viewing area of the graph. Node size changes represent the magnitude of the LFC values (i.e. larger shapes reflect larger LFC values). Dashed lines indicate user-defined LFC values."
vsMAPlot( x = 'iPS', y = 'hESC', data = df.cuff, d.factor = NULL, type = 'cuffdiff', padj = 0.05, y.lim = NULL, lfc = NULL, title = TRUE, legend = TRUE, grid = TRUE )
\newpage
With DESeq2
my.cap <- "MA plot visualization using the `vsMAPLot()` function with `DESeq2` data. LFCs are plotted mean counts to determine the variance between two treatments in terms of gene expression. Blue nodes on the graph represent statistically significant LFCs which are greater than a given value than a user-defined LFC parameter. Green nodes indicate statistically significant LFCs which are less than the user-defined LFC parameter. Gray nodes are data points that are not statistically significant. Numerical values in parantheses for each legend color indicate the number of transcripts that meet the prior conditions. Triangular shapes represent values that exceed the viewing area of the graph. Node size changes represent the magnitude of the LFC values (i.e. larger shapes reflect larger LFC values). Dashed lines indicate user-defined LFC values."
vsMAPlot( x = 'treated_paired.end', y = 'untreated_paired.end', data = df.deseq, d.factor = 'condition', type = 'deseq', padj = 0.05, y.lim = NULL, lfc = NULL, title = TRUE, legend = TRUE, grid = TRUE )
\newpage
With edgeR
my.cap <- "MA plot visualization using the `vsMAPLot()` function with `edgeR` data. LFCs are plotted mean counts to determine the variance between two treatments in terms of gene expression. Blue nodes on the graph represent statistically significant LFCs which are greater than a given value than a user-defined LFC parameter. Green nodes indicate statistically significant LFCs which are less than the user-defined LFC parameter. Gray nodes are data points that are not statistically significant. Numerical values in parantheses for each legend color indicate the number of transcripts that meet the prior conditions. Triangular shapes represent values that exceed the viewing area of the graph. Node size changes represent the magnitude of the LFC values (i.e. larger shapes reflect larger LFC values). Dashed lines indicate user-defined LFC values."
vsMAPlot( x = 'WW', y = 'MM', data = df.edger, d.factor = NULL, type = 'edger', padj = 0.05, y.lim = NULL, lfc = NULL, title = TRUE, legend = TRUE, grid = TRUE )
\newpage
Example S7: Creating MA plot matrices
Similar to a scatter plot matrix, vsMAMatrix() will produce visualizations
for all comparisons within your data set. For more information on how the
aesthetics are plotted in these visualizations, please refer to the figure
caption and Method S1.
With Cuffdiff
my.cap <- "A MA plot matrix using the `vsMAMatrix()` function with `Cuffdiff` data. Similar to the `vsMAPlot()` function, `vsMAMatrix()` will generate a matrix of MA plots for all comparisons within an experiment. LFCs are plotted mean counts to determine the variance between two treatments in terms of gene expression. Blue nodes on the graph represent statistically significant LFCs which are greater than a given value than a user-defined LFC parameter. Green nodes indicate statistically significant LFCs which are less than the user-defined LFC parameter. Gray nodes are data points that are not statistically significant. Numerical values in parantheses for each legend color indicate the number of transcripts that meet the prior conditions. Triangular shapes represent values that exceed the viewing area of the graph. Node size changes represent the magnitude of the LFC values (i.e. larger shapes reflect larger LFC values). Dashed lines indicate user-defined LFC values."
vsMAMatrix( data = df.cuff, d.factor = NULL, type = 'cuffdiff', padj = 0.05, y.lim = NULL, lfc = 1, title = TRUE, grid = TRUE, counts = TRUE, data.return = FALSE )
\newpage
With DESeq2
my.cap <- "A MA plot matrix using the `vsMAMatrix()` function with `DESeq2` data. Similar to the `vsMAPlot()` function, `vsMAMatrix()` will generate a matrix of MA plots for all comparisons within an experiment. LFCs are plotted mean counts to determine the variance between two treatments in terms of gene expression. Blue nodes on the graph represent statistically significant LFCs which are greater than a given value than a user-defined LFC parameter. Green nodes indicate statistically significant LFCs which are less than the user-defined LFC parameter. Gray nodes are data points that are not statistically significant. Numerical values in parantheses for each legend color indicate the number of transcripts that meet the prior conditions. Triangular shapes represent values that exceed the viewing area of the graph. Node size changes represent the magnitude of the LFC values (i.e. larger shapes reflect larger LFC values). Dashed lines indicate user-defined LFC values."
vsMAMatrix( data = df.deseq, d.factor = 'condition', type = 'deseq', padj = 0.05, y.lim = NULL, lfc = 1, title = TRUE, grid = TRUE, counts = TRUE, data.return = FALSE )
\newpage
With edgeR
my.cap <- "A MA plot matrix using the `vsMAMatrix()` function with `edgeR` data. Similar to the `vsMAPlot()` function, `vsMAMatrix()` will generate a matrix of MA plots for all comparisons within an experiment. LFCs are plotted mean counts to determine the variance between two treatments in terms of gene expression. Blue nodes on the graph represent statistically significant LFCs which are greater than a given value than a user-defined LFC parameter. Green nodes indicate statistically significant LFCs which are less than the user-defined LFC parameter. Gray nodes are data points that are not statistically significant. Numerical values in parantheses for each legend color indicate the number of transcripts that meet the prior conditions. Triangular shapes represent values that exceed the viewing area of the graph. Node size changes represent the magnitude of the LFC values (i.e. larger shapes reflect larger LFC values). Dashed lines indicate user-defined LFC values."
vsMAMatrix( data = df.edger, d.factor = NULL, type = 'edger', padj = 0.05, y.lim = NULL, lfc = 1, title = TRUE, grid = TRUE, counts = TRUE, data.return = FALSE )
\newpage
Example S8: Creating volcano plots
The next few visualizations will focus on ways to display differential gene
expression between two or more treatments. Volcano plots visualize the variance
between two samples in terms of gene expression values where the $-log_{10}$ of
calculated p-values (y-axis) are a plotted against the $log_2$ changes
(x-axis). These plots can be visualized with the vsVolcano() function.
For more information on how each of the aesthetics are plotted, please refer
to the figure captions and Method S1.
With Cuffdiff
my.cap <- "A volcano plot example using the `vsVolcano()` function with `Cuffdiff` data. In this visualization, comparisons are made between the $-log_{10}$ *p*-value versus the $log_2$ fold change (LFC) between two treatments. Blue nodes on the graph represent statistically significant LFCs which are greater than a given value than a user-defined LFC parameter. Green nodes indicate statistically significant LFCs which are less than the user-defined LFC parameter. Gray nodes are data points that are not statistically significant. Numerical values in parantheses for each legend color indicate the number of transcripts that meet the prior conditions. Left and right brackets (< and >) represent values that exceed the viewing area of the graph. Node size changes represent the magnitude of the LFC values (i.e. larger shapes reflect larger LFC values). Vertical and horizontal lines indicate user-defined LFC and adjusted *p*-values, respectively."
vsVolcano( x = 'iPS', y = 'hESC', data = df.cuff, d.factor = NULL, type = 'cuffdiff', padj = 0.05, x.lim = NULL, lfc = NULL, title = TRUE, legend = TRUE, grid = TRUE, data.return = FALSE )
\newpage
With DESeq2
my.cap <- "A volcano plot example using the `vsVolcano()` function with `DESeq2` data. In this visualization, comparisons are made between the $-log_{10}$ *p*-value versus the $log_2$ fold change (LFC) between two treatments. Blue nodes on the graph represent statistically significant LFCs which are greater than a given value than a user-defined LFC parameter. Green nodes indicate statistically significant LFCs which are less than the user-defined LFC parameter. Gray nodes are data points that are not statistically significant. Numerical values in parantheses for each legend color indicate the number of transcripts that meet the prior conditions. Left and right brackets (< and >) represent values that exceed the viewing area of the graph. Node size changes represent the magnitude of the LFC values (i.e. larger shapes reflect larger LFC values). Vertical and horizontal lines indicate user-defined LFC and adjusted *p*-values, respectively."
vsVolcano( x = 'treated_paired.end', y = 'untreated_paired.end', data = df.deseq, d.factor = 'condition', type = 'deseq', padj = 0.05, x.lim = NULL, lfc = NULL, title = TRUE, legend = TRUE, grid = TRUE, data.return = FALSE )
\newpage
With edgeR
my.cap <- "A volcano plot example using the `vsVolcano()` function with `edgeR` data. In this visualization, comparisons are made between the $-log_{10}$ *p*-value versus the $log_2$ fold change (LFC) between two treatments. Blue nodes on the graph represent statistically significant LFCs which are greater than a given value than a user-defined LFC parameter. Green nodes indicate statistically significant LFCs which are less than the user-defined LFC parameter. Gray nodes are data points that are not statistically significant. Numerical values in parantheses for each legend color indicate the number of transcripts that meet the prior conditions. Left and right brackets (< and >) represent values that exceed the viewing area of the graph. Node size changes represent the magnitude of the LFC values (i.e. larger shapes reflect larger LFC values). Vertical and horizontal lines indicate user-defined LFC and adjusted *p*-values, respectively."
vsVolcano( x = 'WW', y = 'MM', data = df.edger, d.factor = NULL, type = 'edger', padj = 0.05, x.lim = NULL, lfc = NULL, title = TRUE, legend = TRUE, grid = TRUE, data.return = FALSE )
\newpage
Example S9: Creating volcano plot matrices
Similar to the prior matrix functions, vsVolcanoMatrix() will produce
visualizations for all comparisons within your data set. For more information
on how the aesthetics are plotted in these visualizations, please refer to the
figure caption and Method S1.
With Cuffdiff
my.cap <- "A volcano plot matrix using the `vsVolcanoMatrix()` function with `Cuffdiff` data. Similar to the `vsVolcano()` function, `vsVolcanoMatrix()` will generate a matrix of volcano plots for all comparisons within an experiment. Comparisons are made between the $-log_{10}$ *p*-value versus the $log_2$ fold change (LFC) between two treatments. Blue nodes on the graph represent statistically significant LFCs which are greater than a given value than a user-defined LFC parameter. Green nodes indicate statistically significant LFCs which are less than the user-defined LFC parameter. Gray nodes are data points that are not statistically significant. The blue and green numbers in each facet represent the number of transcripts that meet the criteria for blue and green nodes in each comparison. Left and right brackets (< and >) represent values that exceed the viewing area of the graph. Node size changes represent the magnitude of the LFC values (i.e. larger shapes reflect larger LFC values). Vertical and horizontal lines indicate user-defined LFC and adjusted *p*-values, respectively."
vsVolcanoMatrix( data = df.cuff, d.factor = NULL, type = 'cuffdiff', padj = 0.05, x.lim = NULL, lfc = NULL, title = TRUE, legend = TRUE, grid = TRUE, counts = TRUE )
\newpage
With DESeq2
my.cap <- "A volcano plot matrix using the `vsVolcanoMatrix()` function with `DESeq2` data. Similar to the `vsVolcano()` function, `vsVolcanoMatrix()` will generate a matrix of volcano plots for all comparisons within an experiment. Comparisons are made between the $-log_{10}$ *p*-value versus the $log_2$ fold change (LFC) between two treatments. Blue nodes on the graph represent statistically significant LFCs which are greater than a given value than a user-defined LFC parameter. Green nodes indicate statistically significant LFCs which are less than the user-defined LFC parameter. Gray nodes are data points that are not statistically significant. The blue and green numbers in each facet represent the number of transcripts that meet the criteria for blue and green nodes in each comparison. Left and right brackets (< and >) represent values that exceed the viewing area of the graph. Node size changes represent the magnitude of the LFC values (i.e. larger shapes reflect larger LFC values). Vertical and horizontal lines indicate user-defined LFC and adjusted *p*-values, respectively."
vsVolcanoMatrix( data = df.deseq, d.factor = 'condition', type = 'deseq', padj = 0.05, x.lim = NULL, lfc = NULL, title = TRUE, legend = TRUE, grid = TRUE, counts = TRUE )
\newpage
With edgeR
my.cap <- "A volcano plot matrix using the `vsVolcanoMatrix()` function with `edgeR` data. Similar to the `vsVolcano()` function, `vsVolcanoMatrix()` will generate a matrix of volcano plots for all comparisons within an experiment. Comparisons are made between the $-log_{10}$ *p*-value versus the $log_2$ fold change (LFC) between two treatments. Blue nodes on the graph represent statistically significant LFCs which are greater than a given value than a user-defined LFC parameter. Green nodes indicate statistically significant LFCs which are less than the user-defined LFC parameter. Gray nodes are data points that are not statistically significant. The blue and green numbers in each facet represent the number of transcripts that meet the criteria for blue and green nodes in each comparison. Left and right brackets (< and >) represent values that exceed the viewing area of the graph. Node size changes represent the magnitude of the LFC values (i.e. larger shapes reflect larger LFC values). Vertical and horizontal lines indicate user-defined LFC and adjusted *p*-values, respectively."
vsVolcanoMatrix( data = df.edger, d.factor = NULL, type = 'edger', padj = 0.05, x.lim = NULL, lfc = NULL, title = TRUE, legend = TRUE, grid = TRUE, counts = TRUE )
\newpage
Example S10: Creating four way plots
To create four-way plots, the function, vsFourWay() is used. This plot
compares the $log_2$ fold changes between two samples and a 'control'. For more
information on how each of the aesthetics are plotted, please refer to the
figure captions and Method S1.
With Cuffdiff
my.cap <- "A four way plot visualization using the `vsFourWay()` function with `Cuffdiff` data. In this example, LFCs comparisons between two treatments and a control are made. Blue nodes indicate statistically significant LFCs which are greater than a given user-defined value for both x and y-axes. Green nodes reflect statistically significant LFCs which are less than a user-defined value for treatment y and greater than said value for treatment x. Similar to green nodes, red nodes reflect statistically significant LFCs which are greater than a user-defined vlaue treatment y and less than said value for treatment x. Gray nodes are data points that are not statistically significant for both x and y-axes. Triangular shapes indicate values which exceed the viewing are for the graph. Size change reflects the magnitude of LFC values (i.e. larger shapes reflect larger LFC values). Vertical and horizontal dashed lines indicate user-defined LFC values."
vsFourWay( x = 'iPS', y = 'hESC', control = 'Fibroblasts', data = df.cuff, d.factor = NULL, type = 'cuffdiff', padj = 0.05, x.lim = NULL, y.lim = NULL, lfc = NULL, legend = TRUE, title = TRUE, grid = TRUE )
\newpage
With DESeq2
my.cap <- "A four way plot visualization using the `vsFourWay()` function with `DESeq2` data. In this example, LFCs comparisons between two treatments and a control are made. Blue nodes indicate statistically significant LFCs which are greater than a given user-defined value for both x and y-axes. Green nodes reflect statistically significant LFCs which are less than a user-defined value for treatment y and greater than said value for treatment x. Similar to green nodes, red nodes reflect statistically significant LFCs which are greater than a user-defined vlaue treatment y and less than said value for treatment x. Gray nodes are data points that are not statistically significant for both x and y-axes. Triangular shapes indicate values which exceed the viewing are for the graph. Size change reflects the magnitude of LFC values (i.e. larger shapes reflect larger LFC values). Vertical and horizontal dashed lines indicate user-defined LFC values."
vsFourWay( x = 'treated_paired.end', y = 'untreated_single.read', control = 'untreated_paired.end', data = df.deseq, d.factor = 'condition', type = 'deseq', padj = 0.05, x.lim = NULL, y.lim = NULL, lfc = NULL, legend = TRUE, title = TRUE, grid = TRUE )
\newpage
With edgeR
my.cap <- "A four way plot visualization using the `vsFourWay()` function with `DESeq2` data. In this example, LFCs comparisons between two treatments and a control are made. Blue nodes indicate statistically significant LFCs which are greater than a given user-defined value for both x and y-axes. Green nodes reflect statistically significant LFCs which are less than a user-defined value for treatment y and greater than said value for treatment x. Similar to green nodes, red nodes reflect statistically significant LFCs which are greater than a user-defined vlaue treatment y and less than said value for treatment x. Gray nodes are data points that are not statistically significant for both x and y-axes. Triangular shapes indicate values which exceed the viewing are for the graph. Size change reflects the magnitude of LFC values (i.e. larger shapes reflect larger LFC values). Vertical and horizontal dashed lines indicate user-defined LFC values."
vsFourWay( x = 'WW', y = 'WM', control = 'MM', data = df.edger, d.factor = NULL, type = 'edger', padj = 0.05, x.lim = NULL, y.lim = NULL, lfc = NULL, legend = TRUE, title = TRUE, grid = TRUE )
\newpage
Method S1: Determining data point shape and size changes
The shape and size of each data point will also change depending on several conditions. To maximize the viewing area while retaining high resolution, some data points will not be present within the viewing area. If they exceed the viewing area, they will change shape from a circle to a triangular orientation.
The extent (i.e. fold change) to how far these points exceed the viewing area are based on the following criteria:
- SUB - values that fall within the viewing area of the plot.
- T-1 - values that are greater than the maximum viewing area and are less than the 25th percentile of values that exceed the viewing area.
- T-2 - Similar to T-1; values fall between the 25th and 50th percentile.
- T-3 - Similar to T-2; values fall between the 50th and 75th percentile.
- T-4 - Similar to T-3; values fall between the 75th and 100th percentile.
To further clarify theses conditions, please refer to the following figure:
my.cap <- "An illustration detailing the principles behind the node size for the differntial gene expression functions. In this figure, the data points increase in size depending on which quartile they reside as the absolute LFC increases (top bar). Data points that fall within the viewing area classified as SUB while data points that exceed this area are classified as T-1 through T-4."
knitr::include_graphics("lfc-shape.png", auto_pdf = TRUE)
\newpage
Method S2: Determining function performance
Function efficiencies were determined by calculating system times by using the
microbenchmark R package. Each function was ran 100 times with the prior code
used in the documentation. All benchmarks were determined on a machine running
a 64-bit Windows 10 operating system, 8 GB of RAM, and an Intel Core i5-6400
processor running at 2.7 GHz.
Scatterplots
my.cap <- "Benchmarks for the `vsScatterPlot()` function. Time (ms) distributions were generated for this function using 100 trials for each of the three RNAseq data objects. Cuffdiff, DESeq2, and edgeR example data sets contained 1200, 724, and 29391 transcripts, respectively. "
knitr::include_graphics("eff-scatter.png", auto_pdf = TRUE)
\newpage
Scatterplot matrices
my.cap <- "Benchmarks for the `vsScatterMatrix()` function. Time (ms) distributions were generated for this function using 100 trials for each of the three RNAseq data objects. Cuffdiff, DESeq2, and edgeR example data sets contained 1200, 724, and 29391 transcripts, respectively. "
knitr::include_graphics("eff-smatrix.png", auto_pdf = TRUE)
\newpage
Box plots
my.cap <- "Benchmarks for the `vsBoxPlot()` function. Time (ms) distributions were generated for this function using 100 trials for each of the three RNAseq data objects. Cuffdiff, DESeq2, and edgeR example data sets contained 1200, 724, and 29391 transcripts, respectively. "
knitr::include_graphics("eff-box.png", auto_pdf = TRUE)
\newpage
Differential gene expression matrices
my.cap <- "Benchmarks for the `vsDEGMatrix()` function. Time (ms) distributions were generated for this function using 100 trials for each of the three RNAseq data objects. Cuffdiff, DESeq2, and edgeR example data sets contained 1200, 724, and 29391 transcripts, respectively. "
knitr::include_graphics("eff-deg.png", auto_pdf = TRUE)
\newpage
Volcano plots
my.cap <- "Benchmarks for the `vsVolcano()` function. Time (ms) distributions were generated for this function using 100 trials for each of the three RNAseq data objects. Cuffdiff, DESeq2, and edgeR example data sets contained 1200, 724, and 29391 transcripts, respectively. "
knitr::include_graphics("eff-volcano.png", auto_pdf = TRUE)
\newpage
Volcano plot matrices
my.cap <- "Benchmarks for the `vsVolcanoMatrix()` function. Time (ms) distributions were generated for this function using 100 trials for each of the three RNAseq data objects. Cuffdiff, DESeq2, and edgeR example data sets contained 1200, 724, and 29391 transcripts, respectively. "
knitr::include_graphics("eff-vmatrix.png", auto_pdf = TRUE)
\newpage
MA plots
my.cap <- "Benchmarks for the `vsMAPlot()` function. Time (ms) distributions were generated for this function using 100 trials for each of the three RNAseq data objects. Cuffdiff, DESeq2, and edgeR example data sets contained 1200, 724, and 29391 transcripts, respectively. "
knitr::include_graphics("eff-maplot.png", auto_pdf = TRUE)
\newpage
MA matrices
my.cap <- "Benchmarks for the `vsMAMatrix()` function. Time (s) distributions were generated for this function using 100 trials for each of the three RNAseq data objects. Cuffdiff, DESeq2, and edgeR example data sets contained 1200, 724, and 29391 transcripts, respectively. "
knitr::include_graphics("eff-mamatrix.png", auto_pdf = TRUE)
\newpage
Four way plots
my.cap <- "Benchmarks for the `vsFourWay()` function. Time (ms) distributions were generated for this function using 100 trials for each of the three RNAseq data objects. Cuffdiff, DESeq2, and edgeR example data sets contained 1200, 724, and 29391 transcripts, respectively. "
knitr::include_graphics("eff-four.png", auto_pdf = TRUE)
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