Nothing
In vidger: Create rapid visualizations of RNAseq data in R
knitr::opts_chunk$set(
fig.path='figure/graphics-',
cache.path='cache/graphics-',
fig.align='center',
external=TRUE,
echo=TRUE,
warning=FALSE
# fig.pos="H"
)
library(vidger)
library(DESeq2)
library(edgeR)
data("df.cuff")
data("df.deseq")
data("df.edger")
Example S1: Installation and data examples
The stable version of this package is available on
Bioconductor. You can install it by running the following:
if (!requireNamespace("BiocManager", quietly=TRUE))
install.packages("BiocManager")
BiocManager::install("vidger")
The latest developmental version of ViDGER can be installed via GitHub
using the devtools package:
if (!require("devtools")) install.packages("devtools")
devtools::install_github("btmonier/vidger", ref = "devel")
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real-world data used is currently unpublished from ongoing collaborations.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 box plot 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 box plot 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 box plot 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
Aesthetic variants to box plots
vsBoxPlot() can allow for different iterations to showcase data
distribution. These changes can be implemented using the aes parameter.
Currently, there are 6 different variants:
box: standard box plotviolin: violin plotboxdot: box plot with dot plot overlayviodot: violin plot with dot plot overlayviosumm: violin plot with summary stats overlaynotch: box plot with notch
box variant
my.cap <- "A box plot example using the `aes` parameter: `box`."
data("df.edger")
vsBoxPlot(
data = df.edger, d.factor = NULL, type = "edger", title = TRUE,
legend = TRUE, grid = TRUE, aes = "box"
)
\newpage
violin variant
my.cap <- "A box plot example using the `aes` parameter: `violin`."
data("df.edger")
vsBoxPlot(
data = df.edger, d.factor = NULL, type = "edger", title = TRUE,
legend = TRUE, grid = TRUE, aes = "violin"
)
\newpage
boxdot variant
my.cap <- "A box plot example using the `aes` parameter: `boxdot`."
data("df.edger")
vsBoxPlot(
data = df.edger, d.factor = NULL, type = "edger", title = TRUE,
legend = TRUE, grid = TRUE, aes = "boxdot"
)
\newpage
viodot variant
my.cap <- "A box plot example using the `aes` parameter: `viodot`."
data("df.edger")
vsBoxPlot(
data = df.edger, d.factor = NULL, type = "edger", title = TRUE,
legend = TRUE, grid = TRUE, aes = "viodot"
)
\newpage
viosumm variant
my.cap <- "A box plot example using the `aes` parameter: `viosumm`."
data("df.edger")
vsBoxPlot(
data = df.edger, d.factor = NULL, type = "edger", title = TRUE,
legend = TRUE, grid = TRUE, aes = "viosumm"
)
\newpage
notch variant
my.cap <- "A box plot example using the `aes` parameter: `notch`."
data("df.edger")
vsBoxPlot(
data = df.edger, d.factor = NULL, type = "edger", title = TRUE,
legend = TRUE, grid = TRUE, aes = "notch"
)
\newpage
Color palette variants to box plots
In addition to aesthetic changes, the fill color of each variant can
also be changed. This can be implemented by modifiying the fill.color
parameter.
The palettes that can be used for this parameter are based off of the
palettes found in the RColorBrewer
package. A visual list of all the palettes can be found
here.
Color variant example 1
my.cap <- "Color variant 1. A box plot example using the `fill.color`
parameter: `RdGy`."
data("df.edger")
vsBoxPlot(
data = df.edger, d.factor = NULL, type = "edger", title = TRUE,
legend = TRUE, grid = TRUE, aes = "box", fill.color = "RdGy"
)
\newpage
Color variant example 2
my.cap <- "Color variant 2. A violin plot example using the `fill.color`
parameter: `Paired` with the `aes` parameter: `viosumm`."
data("df.edger")
vsBoxPlot(
data = df.edger, d.factor = NULL, type = "edger", title = TRUE,
legend = TRUE, grid = TRUE, aes = "viosumm", fill.color = "Paired"
)
\newpage
Color variant example 3
my.cap <- "Color variant 3. A notched box plot example using the `fill.color`
parameter: `Greys` with the `aes` parameter: `notch`. Using these parameters,
we can also generate grey-scale plots."
data("df.edger")
vsBoxPlot(
data = df.edger, d.factor = NULL, type = "edger", title = TRUE,
legend = TRUE, grid = TRUE, aes = "notch", fill.color = "Greys"
)
\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
Grey-scale DEG matrices
A grey-scale option is available for this function if you wish to use a
grey-to-white gradient instead of the classic blue-to-white gradient. This
can be invoked by setting the grey.scale parameter to TRUE.
vsDEGMatrix(data = df.deseq, d.factor = "condition", type = "deseq",
grey.scale = 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
Example S11: Highlighting data points
Overview
For point-based plots, users can highlight IDs of interest (i.e. genes,
transcripts, etc.). Currently, this functionality is implemented in the
following functions:
vsScatterPlot()vsMAPlot()vsVolcano()vsFourWay()
To use this feature, simply provide a vector of specified IDs to the
highlight parameter found in the prior functions. An example of a typical
vector would be as follows:
important_ids <- c(
"ID_001",
"ID_002",
"ID_003",
"ID_004",
"ID_005"
)
important_ids
For specific examples using the toy data set, please see the proceeding 4
sub-sections.
\newpage
Highlighting with vsScatterPlot()
my.cap <- "Highlighting with `vsScatterPlot()`. IDs of interest can be
identified within basic scatter plots. When highlighted, non-important points
will turn grey while highlighted points will turn blue. Text tags will *try*
to optimize their location within the graph without trying to overlap each
other."
data("df.cuff")
hl <- c(
"XLOC_000033",
"XLOC_000099",
"XLOC_001414",
"XLOC_001409"
)
vsScatterPlot(
x = "hESC", y = "iPS", data = df.cuff, d.factor = NULL,
type = "cuffdiff", title = TRUE, grid = TRUE, highlight = hl
)
\newpage
Highlighting with vsMAPlot()
my.cap <- "Highlighting with `vsMAPlot()`. IDs of interest can be
identified within MA plots. When highlighted, non-important points
will decrease in transparency (i.e. lower alpha values) while highlighted
points will turn red. Text tags will *try* to optimize their location within
the graph without trying to overlap each other."
hl <- c(
"FBgn0022201",
"FBgn0003042",
"FBgn0031957",
"FBgn0033853",
"FBgn0003371"
)
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, data.return = FALSE, highlight = hl
)
\newpage
Highlighting with vsVolcano()
my.cap <- "Highlighting with `vsVolcano()`. IDs of interest can be
identified within volcano plots. When highlighted, non-important points
will decrease in transparency (i.e. lower alpha values) while highlighted
points will turn red. Text tags will *try* to optimize their location within
the graph without trying to overlap each other."
hl <- c(
"FBgn0036248",
"FBgn0026573",
"FBgn0259742",
"FBgn0038961",
"FBgn0038928"
)
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, grid = TRUE, data.return = FALSE, highlight = hl
)
\newpage
Highlighting with vsFourWay()
my.cap <- "Highlighting with `vsFourWay()`. IDs of interest can be
identified within four-way plots. When highlighted, non-important points
will decrease in transparency (i.e. lower alpha values) while highlighted
points will turn dark grey. Text tags will *try* to optimize their location
within the graph without trying to overlap each other."
data("df.edger")
hl <- c(
"ID_639",
"ID_518",
"ID_602",
"ID_449",
"ID_076"
)
vsFourWay(
x = "WM", y = "WW", control = "MM", data = df.edger,
d.factor = NULL, type = "edger", padj = 0.05, x.lim = NULL,
y.lim = NULL, lfc = 2, title = TRUE, grid = TRUE,
data.return = FALSE, highlight = hl
)
\newpage
Example S12: Extracting datasets from plots
Overview
For all plots, users can extract datasets used for the visualizations.
You may want to pursue this option if you want to use a highly customized
plot script or you would like to perform some unmentioned analysis, for
example.
To use this this feature, set the data.return parameter in the function
you are using to TRUE. You will also need to assign the function to an
object. See the following example for further details.
The data extraction process
In this example, we will use the toy data set df.cuff, a cuffdiff output
on the function vsScatterPlot(). Take note that we are assigning the
function to an object tmp:
# Extract data frame from visualization
data("df.cuff")
tmp <- vsScatterPlot(
x = "hESC", y = "iPS", data = df.cuff, d.factor = NULL,
type = "cuffdiff", title = TRUE, grid = TRUE, data.return = TRUE
)
The object we have created is a list with two elements: data and plot.
To extract the data, we can call the first element of the list using the
subset method (<object>[[1]]) or by invoking its element name
(<object>$data):
df_scatter <- tmp[[1]] ## or use tmp$data
head(df_scatter)
Return the plot
By assigning each of these functions to a list, we can also store the plot
as another element. To extract the plot, we can call the second element of
the list using the aformentioned procedures:
my_plot <- tmp[[2]] ## or use tmp$plot
my_plot
\newpage
Example S13: Changing text sizes
Overview
For all functions, users can modify the font size of multiple portions of the
plot. These portions primarily revolve around these components:
- Axis text and titles
- Plot title
- Legend text and titles
- Facet titles
To manipulate these components, users can modify the default values of the
following parameters:
xaxis.text.sizeyaxis.text.sizexaxis.title.sizeyaxis.title.sizemain.title.sizelegend.text.sizelegend.title.sizefacet.title.size
What exactly can you manipulate?
Each of parameters mentioned in the prior section refer to numerical values.
These values correlate to font size in typographic points. To illustrate what
exactly these parameters modify, please refer to the following figure:
my.cap <- "A visual guide to text size parameters. Users can modify these
components which are highlighted by their respective parameter."
knitr::include_graphics("img/text-size-parameters-01.png", auto_pdf = TRUE)
The facet.title.size parameter refers to the facets which are allocated in
the matrix functions (vsScatterMatrix(), vsMAMatrix(),
vsVolcanoMatrix()). This is illustrated in the following figure:
my.cap <- "Location of facet titles. Facet title sizes can be modified using
the `facet.title.size` parameter."
knitr::include_graphics("img/text-size-parameters-02.png", auto_pdf = TRUE)
Since not all functions are equal in their parameters and component layout,
some functions will either have or lack some of the prior parameters. To
get an idea of which have functions have which, please refer to the following
figure:
my.cap <- "An overview of text size parameters for each function. Cells
highlighted in red refer to parameters (columns) which are found in their
respective functions (rows). Cells which are grey indicate parameters which
are not found in each of the functions."
knitr::include_graphics("img/text-size-parameters-03.png", auto_pdf = 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("img/lfc-shape.png")
\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("img/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("img/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("img/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("img/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("img/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("img/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("img/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("img/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("img/eff-four.png", auto_pdf = TRUE)
\newpage
Session info
sessionInfo()
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knitr::opts_chunk$set( fig.path='figure/graphics-', cache.path='cache/graphics-', fig.align='center', external=TRUE, echo=TRUE, warning=FALSE # fig.pos="H" )
library(vidger) library(DESeq2) library(edgeR) data("df.cuff") data("df.deseq") data("df.edger")
Example S1: Installation and data examples
The stable version of this package is available on Bioconductor. You can install it by running the following:
if (!requireNamespace("BiocManager", quietly=TRUE)) install.packages("BiocManager") BiocManager::install("vidger")
The latest developmental version of ViDGER can be installed via GitHub
using the devtools package:
if (!require("devtools")) install.packages("devtools") devtools::install_github("btmonier/vidger", ref = "devel")
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 box plot 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 box plot 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 box plot 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
Aesthetic variants to box plots
vsBoxPlot() can allow for different iterations to showcase data
distribution. These changes can be implemented using the aes parameter.
Currently, there are 6 different variants:
box: standard box plotviolin: violin plotboxdot: box plot with dot plot overlayviodot: violin plot with dot plot overlayviosumm: violin plot with summary stats overlaynotch: box plot with notch
box variant
my.cap <- "A box plot example using the `aes` parameter: `box`."
data("df.edger") vsBoxPlot( data = df.edger, d.factor = NULL, type = "edger", title = TRUE, legend = TRUE, grid = TRUE, aes = "box" )
\newpage
violin variant
my.cap <- "A box plot example using the `aes` parameter: `violin`."
data("df.edger") vsBoxPlot( data = df.edger, d.factor = NULL, type = "edger", title = TRUE, legend = TRUE, grid = TRUE, aes = "violin" )
\newpage
boxdot variant
my.cap <- "A box plot example using the `aes` parameter: `boxdot`."
data("df.edger") vsBoxPlot( data = df.edger, d.factor = NULL, type = "edger", title = TRUE, legend = TRUE, grid = TRUE, aes = "boxdot" )
\newpage
viodot variant
my.cap <- "A box plot example using the `aes` parameter: `viodot`."
data("df.edger") vsBoxPlot( data = df.edger, d.factor = NULL, type = "edger", title = TRUE, legend = TRUE, grid = TRUE, aes = "viodot" )
\newpage
viosumm variant
my.cap <- "A box plot example using the `aes` parameter: `viosumm`."
data("df.edger") vsBoxPlot( data = df.edger, d.factor = NULL, type = "edger", title = TRUE, legend = TRUE, grid = TRUE, aes = "viosumm" )
\newpage
notch variant
my.cap <- "A box plot example using the `aes` parameter: `notch`."
data("df.edger") vsBoxPlot( data = df.edger, d.factor = NULL, type = "edger", title = TRUE, legend = TRUE, grid = TRUE, aes = "notch" )
\newpage
Color palette variants to box plots
In addition to aesthetic changes, the fill color of each variant can
also be changed. This can be implemented by modifiying the fill.color
parameter.
The palettes that can be used for this parameter are based off of the
palettes found in the RColorBrewer
package. A visual list of all the palettes can be found
here.
Color variant example 1
my.cap <- "Color variant 1. A box plot example using the `fill.color` parameter: `RdGy`."
data("df.edger") vsBoxPlot( data = df.edger, d.factor = NULL, type = "edger", title = TRUE, legend = TRUE, grid = TRUE, aes = "box", fill.color = "RdGy" )
\newpage
Color variant example 2
my.cap <- "Color variant 2. A violin plot example using the `fill.color` parameter: `Paired` with the `aes` parameter: `viosumm`."
data("df.edger") vsBoxPlot( data = df.edger, d.factor = NULL, type = "edger", title = TRUE, legend = TRUE, grid = TRUE, aes = "viosumm", fill.color = "Paired" )
\newpage
Color variant example 3
my.cap <- "Color variant 3. A notched box plot example using the `fill.color` parameter: `Greys` with the `aes` parameter: `notch`. Using these parameters, we can also generate grey-scale plots."
data("df.edger") vsBoxPlot( data = df.edger, d.factor = NULL, type = "edger", title = TRUE, legend = TRUE, grid = TRUE, aes = "notch", fill.color = "Greys" )
\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
Grey-scale DEG matrices
A grey-scale option is available for this function if you wish to use a
grey-to-white gradient instead of the classic blue-to-white gradient. This
can be invoked by setting the grey.scale parameter to TRUE.
vsDEGMatrix(data = df.deseq, d.factor = "condition", type = "deseq", grey.scale = 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
Example S11: Highlighting data points
Overview
For point-based plots, users can highlight IDs of interest (i.e. genes, transcripts, etc.). Currently, this functionality is implemented in the following functions:
vsScatterPlot()vsMAPlot()vsVolcano()vsFourWay()
To use this feature, simply provide a vector of specified IDs to the
highlight parameter found in the prior functions. An example of a typical
vector would be as follows:
important_ids <- c( "ID_001", "ID_002", "ID_003", "ID_004", "ID_005" ) important_ids
For specific examples using the toy data set, please see the proceeding 4 sub-sections.
\newpage
Highlighting with vsScatterPlot()
my.cap <- "Highlighting with `vsScatterPlot()`. IDs of interest can be identified within basic scatter plots. When highlighted, non-important points will turn grey while highlighted points will turn blue. Text tags will *try* to optimize their location within the graph without trying to overlap each other."
data("df.cuff") hl <- c( "XLOC_000033", "XLOC_000099", "XLOC_001414", "XLOC_001409" ) vsScatterPlot( x = "hESC", y = "iPS", data = df.cuff, d.factor = NULL, type = "cuffdiff", title = TRUE, grid = TRUE, highlight = hl )
\newpage
Highlighting with vsMAPlot()
my.cap <- "Highlighting with `vsMAPlot()`. IDs of interest can be identified within MA plots. When highlighted, non-important points will decrease in transparency (i.e. lower alpha values) while highlighted points will turn red. Text tags will *try* to optimize their location within the graph without trying to overlap each other."
hl <- c( "FBgn0022201", "FBgn0003042", "FBgn0031957", "FBgn0033853", "FBgn0003371" ) 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, data.return = FALSE, highlight = hl )
\newpage
Highlighting with vsVolcano()
my.cap <- "Highlighting with `vsVolcano()`. IDs of interest can be identified within volcano plots. When highlighted, non-important points will decrease in transparency (i.e. lower alpha values) while highlighted points will turn red. Text tags will *try* to optimize their location within the graph without trying to overlap each other."
hl <- c( "FBgn0036248", "FBgn0026573", "FBgn0259742", "FBgn0038961", "FBgn0038928" ) 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, grid = TRUE, data.return = FALSE, highlight = hl )
\newpage
Highlighting with vsFourWay()
my.cap <- "Highlighting with `vsFourWay()`. IDs of interest can be identified within four-way plots. When highlighted, non-important points will decrease in transparency (i.e. lower alpha values) while highlighted points will turn dark grey. Text tags will *try* to optimize their location within the graph without trying to overlap each other."
data("df.edger") hl <- c( "ID_639", "ID_518", "ID_602", "ID_449", "ID_076" ) vsFourWay( x = "WM", y = "WW", control = "MM", data = df.edger, d.factor = NULL, type = "edger", padj = 0.05, x.lim = NULL, y.lim = NULL, lfc = 2, title = TRUE, grid = TRUE, data.return = FALSE, highlight = hl )
\newpage
Example S12: Extracting datasets from plots
Overview
For all plots, users can extract datasets used for the visualizations. You may want to pursue this option if you want to use a highly customized plot script or you would like to perform some unmentioned analysis, for example.
To use this this feature, set the data.return parameter in the function
you are using to TRUE. You will also need to assign the function to an
object. See the following example for further details.
The data extraction process
In this example, we will use the toy data set df.cuff, a cuffdiff output
on the function vsScatterPlot(). Take note that we are assigning the
function to an object tmp:
# Extract data frame from visualization data("df.cuff") tmp <- vsScatterPlot( x = "hESC", y = "iPS", data = df.cuff, d.factor = NULL, type = "cuffdiff", title = TRUE, grid = TRUE, data.return = TRUE )
The object we have created is a list with two elements: data and plot.
To extract the data, we can call the first element of the list using the
subset method (<object>[[1]]) or by invoking its element name
(<object>$data):
df_scatter <- tmp[[1]] ## or use tmp$data head(df_scatter)
Return the plot
By assigning each of these functions to a list, we can also store the plot as another element. To extract the plot, we can call the second element of the list using the aformentioned procedures:
my_plot <- tmp[[2]] ## or use tmp$plot my_plot
\newpage
Example S13: Changing text sizes
Overview
For all functions, users can modify the font size of multiple portions of the plot. These portions primarily revolve around these components:
- Axis text and titles
- Plot title
- Legend text and titles
- Facet titles
To manipulate these components, users can modify the default values of the following parameters:
xaxis.text.sizeyaxis.text.sizexaxis.title.sizeyaxis.title.sizemain.title.sizelegend.text.sizelegend.title.sizefacet.title.size
What exactly can you manipulate?
Each of parameters mentioned in the prior section refer to numerical values. These values correlate to font size in typographic points. To illustrate what exactly these parameters modify, please refer to the following figure:
my.cap <- "A visual guide to text size parameters. Users can modify these components which are highlighted by their respective parameter."
knitr::include_graphics("img/text-size-parameters-01.png", auto_pdf = TRUE)
The facet.title.size parameter refers to the facets which are allocated in
the matrix functions (vsScatterMatrix(), vsMAMatrix(),
vsVolcanoMatrix()). This is illustrated in the following figure:
my.cap <- "Location of facet titles. Facet title sizes can be modified using the `facet.title.size` parameter."
knitr::include_graphics("img/text-size-parameters-02.png", auto_pdf = TRUE)
Since not all functions are equal in their parameters and component layout, some functions will either have or lack some of the prior parameters. To get an idea of which have functions have which, please refer to the following figure:
my.cap <- "An overview of text size parameters for each function. Cells highlighted in red refer to parameters (columns) which are found in their respective functions (rows). Cells which are grey indicate parameters which are not found in each of the functions."
knitr::include_graphics("img/text-size-parameters-03.png", auto_pdf = 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("img/lfc-shape.png")
\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("img/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("img/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("img/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("img/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("img/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("img/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("img/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("img/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("img/eff-four.png", auto_pdf = TRUE)
\newpage
Session info
sessionInfo()
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