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).

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 All real-world data used is currently unpublished from ongoing collaborations.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)

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

vsBoxPlot(
    data = df.cuff, d.factor = NULL, type = 'cuffdiff', title = TRUE, 
    legend = TRUE, grid = TRUE
)
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.

Figure 1: 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.

With DESeq2

vsBoxPlot(
    data = df.deseq, d.factor = 'condition', type = 'deseq', 
    title = TRUE, legend = TRUE, grid = TRUE
)
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.

Figure 2: 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.

With edgeR

vsBoxPlot(
    data = df.edger, d.factor = NULL, type = 'edger', 
    title = TRUE, legend = TRUE, grid = TRUE
)
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

Figure 3: 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

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 plot
  • violin: violin plot
  • boxdot: box plot with dot plot overlay
  • viodot: violin plot with dot plot overlay
  • viosumm: violin plot with summary stats overlay
  • notch: box plot with notch

box variant

data("df.edger")
vsBoxPlot(
   data = df.edger, d.factor = NULL, type = "edger", title = TRUE,
   legend = TRUE, grid = TRUE, aes = "box"
)
A box plot example using the `aes` parameter: `box`.

Figure 4: A box plot example using the aes parameter: box

violin variant

data("df.edger")
vsBoxPlot(
   data = df.edger, d.factor = NULL, type = "edger", title = TRUE,
   legend = TRUE, grid = TRUE, aes = "violin"
)
A box plot example using the `aes` parameter: `violin`.

Figure 5: A box plot example using the aes parameter: violin

boxdot variant

data("df.edger")
vsBoxPlot(
   data = df.edger, d.factor = NULL, type = "edger", title = TRUE,
   legend = TRUE, grid = TRUE, aes = "boxdot"
)
A box plot example using the `aes` parameter: `boxdot`.

Figure 6: A box plot example using the aes parameter: boxdot

viodot variant

data("df.edger")
vsBoxPlot(
   data = df.edger, d.factor = NULL, type = "edger", title = TRUE,
   legend = TRUE, grid = TRUE, aes = "viodot"
)
A box plot example using the `aes` parameter: `viodot`.

Figure 7: A box plot example using the aes parameter: viodot

viosumm variant

data("df.edger")
vsBoxPlot(
   data = df.edger, d.factor = NULL, type = "edger", title = TRUE,
   legend = TRUE, grid = TRUE, aes = "viosumm"
)
A box plot example using the `aes` parameter: `viosumm`.

Figure 8: A box plot example using the aes parameter: viosumm

notch variant

data("df.edger")
vsBoxPlot(
   data = df.edger, d.factor = NULL, type = "edger", title = TRUE,
   legend = TRUE, grid = TRUE, aes = "notch"
)
A box plot example using the `aes` parameter: `notch`.

Figure 9: A box plot example using the aes parameter: notch

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

data("df.edger")
vsBoxPlot(
   data = df.edger, d.factor = NULL, type = "edger", title = TRUE,
   legend = TRUE, grid = TRUE, aes = "box", fill.color = "RdGy"
)
Color variant 1. A box plot example using the `fill.color` parameter: `RdGy`.

Figure 10: Color variant 1
A box plot example using the fill.color parameter: RdGy.

Color variant example 2

data("df.edger")
vsBoxPlot(
   data = df.edger, d.factor = NULL, type = "edger", title = TRUE,
   legend = TRUE, grid = TRUE, aes = "viosumm", fill.color = "Paired"
)
Color variant 2. A violin plot example using the `fill.color` parameter: `Paired` with the `aes` parameter: `viosumm`.

Figure 11: Color variant 2
A violin plot example using the fill.color parameter: Paired with the aes parameter: viosumm.

Color variant example 3

data("df.edger")
vsBoxPlot(
   data = df.edger, d.factor = NULL, type = "edger", title = TRUE,
   legend = TRUE, grid = TRUE, aes = "notch", fill.color = "Greys"
)
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.

Figure 12: 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.

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

vsScatterPlot(
    x = 'hESC', y = 'iPS', data = df.cuff, type = 'cuffdiff',
    d.factor = NULL, title = TRUE, grid = TRUE
)
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.

Figure 13: 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.

With DESeq2

vsScatterPlot(
    x = 'treated_paired.end', y = 'untreated_paired.end', 
    data = df.deseq, type = 'deseq', d.factor = 'condition', 
    title = TRUE, grid = TRUE
)
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.

Figure 14: 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.

With edgeR

vsScatterPlot(
    x = 'WM', y = 'MM', data = df.edger, type = 'edger',
    d.factor = NULL, title = TRUE, grid = TRUE
)
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.

Figure 15: 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.

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

vsScatterMatrix(
    data = df.cuff, d.factor = NULL, type = 'cuffdiff', 
    comp = NULL, title = TRUE, grid = TRUE, man.title = NULL
)
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.

Figure 16: 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.

With DESeq2

vsScatterMatrix(
    data = df.deseq, d.factor = 'condition', type = 'deseq',
    comp = NULL, title = TRUE, grid = TRUE, man.title = NULL
)
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.

Figure 17: 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.

With edgeR

vsScatterMatrix(
    data = df.edger, d.factor = NULL, type = 'edger', comp = NULL,
    title = TRUE, grid = TRUE, man.title = NULL
)
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.

Figure 18: 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.

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

vsDEGMatrix(
    data = df.cuff, padj = 0.05, d.factor = NULL, type = 'cuffdiff', 
    title = TRUE, legend = TRUE, grid = TRUE
)
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.

Figure 19: 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.

With DESeq2

vsDEGMatrix(
    data = df.deseq, padj = 0.05, d.factor = 'condition', 
    type = 'deseq', title = TRUE, legend = TRUE, grid = TRUE
)
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.

Figure 20: 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.

With edgeR

vsDEGMatrix(
    data = df.edger, padj = 0.05, d.factor = NULL, type = 'edger', 
    title = TRUE, legend = TRUE, grid = TRUE
)
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.

Figure 21: 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.

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
)

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

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
)
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.

Figure 22: 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.

With DESeq2

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
)
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.

Figure 23: 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.

With edgeR

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
)
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.

Figure 24: 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.

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

 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
)
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.

Figure 25: 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.

With DESeq2

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
)
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.

Figure 26: 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.

With edgeR

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
)
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.

Figure 27: 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.

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

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
)
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.

Figure 28: 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.

With DESeq2

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
)
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.

Figure 29: 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.

With edgeR

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
)
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.

Figure 30: 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.

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

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
)
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.

Figure 31: 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.

With DESeq2

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
)
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.

Figure 32: 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.

With edgeR

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
)
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.

Figure 33: 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.

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

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
)
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.

Figure 34: 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.

With DESeq2

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
)
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.

Figure 35: 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.

With edgeR

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
)
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.

Figure 36: 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.

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
## [1] "ID_001" "ID_002" "ID_003" "ID_004" "ID_005"

For specific examples using the toy data set, please see the proceeding 4 sub-sections.

Highlighting with vsScatterPlot()

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
)
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.

Figure 37: 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.

Highlighting with vsMAPlot()

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
)
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.

Figure 38: 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.

Highlighting with vsVolcano()

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
)
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.

Figure 39: 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.

Highlighting with vsFourWay()

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
)
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.

Figure 40: 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.

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)
##            id           x         y
## 1 XLOC_000001 3.47386e-01  20.21750
## 2 XLOC_000002 0.00000e+00   0.00000
## 3 XLOC_000003 0.00000e+00   0.00000
## 4 XLOC_000004 6.97259e+05   0.00000
## 5 XLOC_000005 6.96704e+02 355.82300
## 6 XLOC_000006 0.00000e+00   1.51396

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

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.size
  • yaxis.text.size
  • xaxis.title.size
  • yaxis.title.size
  • main.title.size
  • legend.text.size
  • legend.title.size
  • facet.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:

A visual guide to text size parameters. Users can modify these components which are highlighted by their respective parameter.

Figure 41: A visual guide to text size parameters
Users can modify these components which are highlighted by their respective parameter.

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:

Location of facet titles. Facet title sizes can be modified using the `facet.title.size` parameter.

Figure 42: Location of facet titles
Facet title sizes can be modified using the facet.title.size parameter.

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:

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.

Figure 43: 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.

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:

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.

Figure 44: 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.

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

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.

Figure 45: 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.

Scatterplot matrices

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.

Figure 46: 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.

Box plots

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.

Figure 47: 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.

Differential gene expression matrices

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.

Figure 48: 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.

Volcano plots

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.

Figure 49: 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.

Volcano plot matrices

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.

Figure 50: 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.

MA plots

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.

Figure 51: 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.

MA matrices

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.

Figure 52: 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.

Four way plots

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.

Figure 53: 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.

Session info

## R version 3.6.1 (2019-07-05)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.2 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.9-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.9-bioc/R/lib/libRlapack.so
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=C              
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] parallel  stats4    stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] edgeR_3.26.5                limma_3.40.2               
##  [3] DESeq2_1.24.0               SummarizedExperiment_1.14.0
##  [5] DelayedArray_0.10.0         BiocParallel_1.18.0        
##  [7] matrixStats_0.54.0          Biobase_2.44.0             
##  [9] GenomicRanges_1.36.0        GenomeInfoDb_1.20.0        
## [11] IRanges_2.18.1              S4Vectors_0.22.0           
## [13] BiocGenerics_0.30.0         vidger_1.4.4               
## [15] BiocStyle_2.12.0           
## 
## loaded via a namespace (and not attached):
##  [1] bitops_1.0-6           bit64_0.9-7            RColorBrewer_1.1-2    
##  [4] tools_3.6.1            backports_1.1.4        R6_2.4.0              
##  [7] rpart_4.1-15           Hmisc_4.2-0            DBI_1.0.0             
## [10] lazyeval_0.2.2         colorspace_1.4-1       nnet_7.3-12           
## [13] withr_2.1.2            tidyselect_0.2.5       gridExtra_2.3         
## [16] GGally_1.4.0           bit_1.1-14             compiler_3.6.1        
## [19] htmlTable_1.13.1       labeling_0.3           bookdown_0.12         
## [22] scales_1.0.0           checkmate_1.9.4        genefilter_1.66.0     
## [25] stringr_1.4.0          digest_0.6.20          foreign_0.8-71        
## [28] rmarkdown_1.14         XVector_0.24.0         base64enc_0.1-3       
## [31] pkgconfig_2.0.2        htmltools_0.3.6        highr_0.8             
## [34] htmlwidgets_1.3        rlang_0.4.0            rstudioapi_0.10       
## [37] RSQLite_2.1.1          acepack_1.4.1          dplyr_0.8.3           
## [40] RCurl_1.95-4.12        magrittr_1.5           GenomeInfoDbData_1.2.1
## [43] Formula_1.2-3          Matrix_1.2-17          Rcpp_1.0.1            
## [46] munsell_0.5.0          stringi_1.4.3          yaml_2.2.0            
## [49] zlibbioc_1.30.0        plyr_1.8.4             grid_3.6.1            
## [52] blob_1.2.0             ggrepel_0.8.1          crayon_1.3.4          
## [55] lattice_0.20-38        splines_3.6.1          annotate_1.62.0       
## [58] locfit_1.5-9.1         zeallot_0.1.0          knitr_1.23            
## [61] pillar_1.4.2           reshape2_1.4.3         geneplotter_1.62.0    
## [64] XML_3.98-1.20          glue_1.3.1             evaluate_0.14         
## [67] latticeExtra_0.6-28    data.table_1.12.2      BiocManager_1.30.4    
## [70] vctrs_0.2.0            gtable_0.3.0           purrr_0.3.2           
## [73] tidyr_0.8.3            reshape_0.8.8          assertthat_0.2.1      
## [76] ggplot2_3.2.0          xfun_0.8               xtable_1.8-4          
## [79] survival_2.44-1.1      tibble_2.1.3           AnnotationDbi_1.46.0  
## [82] memoise_1.1.0          cluster_2.1.0