Nothing
inst/doc/vidger.R
In vidger: Create rapid visualizations of RNAseq data in R
## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(
fig.path='figure/graphics-',
cache.path='cache/graphics-',
fig.align='center',
external=TRUE,
echo=TRUE,
warning=FALSE
# fig.pos="H"
)
## ---- echo=FALSE, message=FALSE-----------------------------------------------
library(vidger)
library(DESeq2)
library(edgeR)
data("df.cuff")
data("df.deseq")
data("df.edger")
## ---- eval=FALSE, message=FALSE-----------------------------------------------
# if (!requireNamespace("BiocManager", quietly=TRUE))
# install.packages("BiocManager")
# BiocManager::install("vidger")
## ---- eval=FALSE, message=FALSE-----------------------------------------------
# if (!require("devtools")) install.packages("devtools")
# devtools::install_github("btmonier/vidger", ref = "devel")
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
vsBoxPlot(
data = df.cuff, d.factor = NULL, type = 'cuffdiff', title = TRUE,
legend = TRUE, grid = TRUE
)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
vsBoxPlot(
data = df.deseq, d.factor = 'condition', type = 'deseq',
title = TRUE, legend = TRUE, grid = TRUE
)
## ---- echo=FALSE--------------------------------------------------------------
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"
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
vsBoxPlot(
data = df.edger, d.factor = NULL, type = 'edger',
title = TRUE, legend = TRUE, grid = TRUE
)
## ---- echo=FALSE--------------------------------------------------------------
my.cap <- "A box plot example using the `aes` parameter: `box`."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
data("df.edger")
vsBoxPlot(
data = df.edger, d.factor = NULL, type = "edger", title = TRUE,
legend = TRUE, grid = TRUE, aes = "box"
)
## ---- echo=FALSE--------------------------------------------------------------
my.cap <- "A box plot example using the `aes` parameter: `violin`."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
data("df.edger")
vsBoxPlot(
data = df.edger, d.factor = NULL, type = "edger", title = TRUE,
legend = TRUE, grid = TRUE, aes = "violin"
)
## ---- echo=FALSE--------------------------------------------------------------
my.cap <- "A box plot example using the `aes` parameter: `boxdot`."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
data("df.edger")
vsBoxPlot(
data = df.edger, d.factor = NULL, type = "edger", title = TRUE,
legend = TRUE, grid = TRUE, aes = "boxdot"
)
## ---- echo=FALSE--------------------------------------------------------------
my.cap <- "A box plot example using the `aes` parameter: `viodot`."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
data("df.edger")
vsBoxPlot(
data = df.edger, d.factor = NULL, type = "edger", title = TRUE,
legend = TRUE, grid = TRUE, aes = "viodot"
)
## ---- echo=FALSE--------------------------------------------------------------
my.cap <- "A box plot example using the `aes` parameter: `viosumm`."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
data("df.edger")
vsBoxPlot(
data = df.edger, d.factor = NULL, type = "edger", title = TRUE,
legend = TRUE, grid = TRUE, aes = "viosumm"
)
## ---- echo=FALSE--------------------------------------------------------------
my.cap <- "A box plot example using the `aes` parameter: `notch`."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
data("df.edger")
vsBoxPlot(
data = df.edger, d.factor = NULL, type = "edger", title = TRUE,
legend = TRUE, grid = TRUE, aes = "notch"
)
## ---- echo=FALSE--------------------------------------------------------------
my.cap <- "Color variant 1. A box plot example using the `fill.color`
parameter: `RdGy`."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
data("df.edger")
vsBoxPlot(
data = df.edger, d.factor = NULL, type = "edger", title = TRUE,
legend = TRUE, grid = TRUE, aes = "box", fill.color = "RdGy"
)
## ---- echo=FALSE--------------------------------------------------------------
my.cap <- "Color variant 2. A violin plot example using the `fill.color`
parameter: `Paired` with the `aes` parameter: `viosumm`."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
data("df.edger")
vsBoxPlot(
data = df.edger, d.factor = NULL, type = "edger", title = TRUE,
legend = TRUE, grid = TRUE, aes = "viosumm", fill.color = "Paired"
)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
data("df.edger")
vsBoxPlot(
data = df.edger, d.factor = NULL, type = "edger", title = TRUE,
legend = TRUE, grid = TRUE, aes = "notch", fill.color = "Greys"
)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap-------------------------------------------
vsScatterPlot(
x = 'hESC', y = 'iPS', data = df.cuff, type = 'cuffdiff',
d.factor = NULL, title = TRUE, grid = TRUE
)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap-------------------------------------------
vsScatterPlot(
x = 'treated_paired.end', y = 'untreated_paired.end',
data = df.deseq, type = 'deseq', d.factor = 'condition',
title = TRUE, grid = TRUE
)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap-------------------------------------------
vsScatterPlot(
x = 'WM', y = 'MM', data = df.edger, type = 'edger',
d.factor = NULL, title = TRUE, grid = TRUE
)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap-------------------------------------------
vsScatterMatrix(
data = df.cuff, d.factor = NULL, type = 'cuffdiff',
comp = NULL, title = TRUE, grid = TRUE, man.title = NULL
)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap-------------------------------------------
vsScatterMatrix(
data = df.deseq, d.factor = 'condition', type = 'deseq',
comp = NULL, title = TRUE, grid = TRUE, man.title = NULL
)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap-------------------------------------------
vsScatterMatrix(
data = df.edger, d.factor = NULL, type = 'edger', comp = NULL,
title = TRUE, grid = TRUE, man.title = NULL
)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap-------------------------------------------
vsDEGMatrix(
data = df.cuff, padj = 0.05, d.factor = NULL, type = 'cuffdiff',
title = TRUE, legend = TRUE, grid = TRUE
)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap-------------------------------------------
vsDEGMatrix(
data = df.deseq, padj = 0.05, d.factor = 'condition',
type = 'deseq', title = TRUE, legend = TRUE, grid = TRUE
)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap-------------------------------------------
vsDEGMatrix(
data = df.edger, padj = 0.05, d.factor = NULL, type = 'edger',
title = TRUE, legend = TRUE, grid = TRUE
)
## -----------------------------------------------------------------------------
vsDEGMatrix(data = df.deseq, d.factor = "condition", type = "deseq",
grey.scale = TRUE
)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
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
)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
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
)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
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
)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
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
)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
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
)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
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
)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
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
)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
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
)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
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
)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
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
)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
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
)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
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
)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
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
)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
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
)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
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
)
## -----------------------------------------------------------------------------
important_ids <- c(
"ID_001",
"ID_002",
"ID_003",
"ID_004",
"ID_005"
)
important_ids
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
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
)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
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
)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
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
)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
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
)
## -----------------------------------------------------------------------------
# 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
)
## -----------------------------------------------------------------------------
df_scatter <- tmp[[1]] ## or use tmp$data
head(df_scatter)
## -----------------------------------------------------------------------------
my_plot <- tmp[[2]] ## or use tmp$plot
my_plot
## ---- echo=FALSE--------------------------------------------------------------
my.cap <- "A visual guide to text size parameters. Users can modify these
components which are highlighted by their respective parameter."
## ---- echo=FALSE, fig.cap=my.cap----------------------------------------------
knitr::include_graphics("img/text-size-parameters-01.png", auto_pdf = TRUE)
## ---- echo=FALSE--------------------------------------------------------------
my.cap <- "Location of facet titles. Facet title sizes can be modified using
the `facet.title.size` parameter."
## ---- echo=FALSE, fig.cap=my.cap----------------------------------------------
knitr::include_graphics("img/text-size-parameters-02.png", auto_pdf = TRUE)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- echo=FALSE, fig.cap=my.cap----------------------------------------------
knitr::include_graphics("img/text-size-parameters-03.png", auto_pdf = TRUE)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- echo=FALSE, fig.cap=my.cap, out.width = "75%"---------------------------
knitr::include_graphics("img/lfc-shape.png")
## ---- echo=FALSE--------------------------------------------------------------
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. "
## ---- echo=FALSE, fig.cap=my.cap, out.width = "75%"---------------------------
knitr::include_graphics("img/eff-scatter.png", auto_pdf = TRUE)
## ---- echo=FALSE--------------------------------------------------------------
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. "
## ---- echo=FALSE, fig.cap=my.cap, out.width = "75%"---------------------------
knitr::include_graphics("img/eff-smatrix.png", auto_pdf = TRUE)
## ---- echo=FALSE--------------------------------------------------------------
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. "
## ---- echo=FALSE, fig.cap=my.cap, out.width = "75%"---------------------------
knitr::include_graphics("img/eff-box.png", auto_pdf = TRUE)
## ---- echo=FALSE--------------------------------------------------------------
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. "
## ---- echo=FALSE, fig.cap=my.cap, out.width = "75%"---------------------------
knitr::include_graphics("img/eff-deg.png", auto_pdf = TRUE)
## ---- echo=FALSE--------------------------------------------------------------
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. "
## ---- echo=FALSE, fig.cap=my.cap, out.width = "75%"---------------------------
knitr::include_graphics("img/eff-volcano.png", auto_pdf = TRUE)
## ---- echo=FALSE--------------------------------------------------------------
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. "
## ---- echo=FALSE, fig.cap=my.cap, out.width = "75%"---------------------------
knitr::include_graphics("img/eff-vmatrix.png", auto_pdf = TRUE)
## ---- echo=FALSE--------------------------------------------------------------
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. "
## ---- echo=FALSE, fig.cap=my.cap, out.width = "75%"---------------------------
knitr::include_graphics("img/eff-maplot.png", auto_pdf = TRUE)
## ---- echo=FALSE--------------------------------------------------------------
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. "
## ---- echo=FALSE, fig.cap=my.cap, out.width = "75%"---------------------------
knitr::include_graphics("img/eff-mamatrix.png", auto_pdf = TRUE)
## ---- echo=FALSE--------------------------------------------------------------
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. "
## ---- echo=FALSE, fig.cap=my.cap, out.width = "75%"---------------------------
knitr::include_graphics("img/eff-four.png", auto_pdf = TRUE)
## ---- echo=FALSE--------------------------------------------------------------
sessionInfo()
Try the vidger package in your browser
Any scripts or data that you put into this service are public.
vidger documentation built on Nov. 8, 2020, 5:27 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(
fig.path='figure/graphics-',
cache.path='cache/graphics-',
fig.align='center',
external=TRUE,
echo=TRUE,
warning=FALSE
# fig.pos="H"
)
## ---- echo=FALSE, message=FALSE-----------------------------------------------
library(vidger)
library(DESeq2)
library(edgeR)
data("df.cuff")
data("df.deseq")
data("df.edger")
## ---- eval=FALSE, message=FALSE-----------------------------------------------
# if (!requireNamespace("BiocManager", quietly=TRUE))
# install.packages("BiocManager")
# BiocManager::install("vidger")
## ---- eval=FALSE, message=FALSE-----------------------------------------------
# if (!require("devtools")) install.packages("devtools")
# devtools::install_github("btmonier/vidger", ref = "devel")
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
vsBoxPlot(
data = df.cuff, d.factor = NULL, type = 'cuffdiff', title = TRUE,
legend = TRUE, grid = TRUE
)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
vsBoxPlot(
data = df.deseq, d.factor = 'condition', type = 'deseq',
title = TRUE, legend = TRUE, grid = TRUE
)
## ---- echo=FALSE--------------------------------------------------------------
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"
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
vsBoxPlot(
data = df.edger, d.factor = NULL, type = 'edger',
title = TRUE, legend = TRUE, grid = TRUE
)
## ---- echo=FALSE--------------------------------------------------------------
my.cap <- "A box plot example using the `aes` parameter: `box`."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
data("df.edger")
vsBoxPlot(
data = df.edger, d.factor = NULL, type = "edger", title = TRUE,
legend = TRUE, grid = TRUE, aes = "box"
)
## ---- echo=FALSE--------------------------------------------------------------
my.cap <- "A box plot example using the `aes` parameter: `violin`."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
data("df.edger")
vsBoxPlot(
data = df.edger, d.factor = NULL, type = "edger", title = TRUE,
legend = TRUE, grid = TRUE, aes = "violin"
)
## ---- echo=FALSE--------------------------------------------------------------
my.cap <- "A box plot example using the `aes` parameter: `boxdot`."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
data("df.edger")
vsBoxPlot(
data = df.edger, d.factor = NULL, type = "edger", title = TRUE,
legend = TRUE, grid = TRUE, aes = "boxdot"
)
## ---- echo=FALSE--------------------------------------------------------------
my.cap <- "A box plot example using the `aes` parameter: `viodot`."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
data("df.edger")
vsBoxPlot(
data = df.edger, d.factor = NULL, type = "edger", title = TRUE,
legend = TRUE, grid = TRUE, aes = "viodot"
)
## ---- echo=FALSE--------------------------------------------------------------
my.cap <- "A box plot example using the `aes` parameter: `viosumm`."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
data("df.edger")
vsBoxPlot(
data = df.edger, d.factor = NULL, type = "edger", title = TRUE,
legend = TRUE, grid = TRUE, aes = "viosumm"
)
## ---- echo=FALSE--------------------------------------------------------------
my.cap <- "A box plot example using the `aes` parameter: `notch`."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
data("df.edger")
vsBoxPlot(
data = df.edger, d.factor = NULL, type = "edger", title = TRUE,
legend = TRUE, grid = TRUE, aes = "notch"
)
## ---- echo=FALSE--------------------------------------------------------------
my.cap <- "Color variant 1. A box plot example using the `fill.color`
parameter: `RdGy`."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
data("df.edger")
vsBoxPlot(
data = df.edger, d.factor = NULL, type = "edger", title = TRUE,
legend = TRUE, grid = TRUE, aes = "box", fill.color = "RdGy"
)
## ---- echo=FALSE--------------------------------------------------------------
my.cap <- "Color variant 2. A violin plot example using the `fill.color`
parameter: `Paired` with the `aes` parameter: `viosumm`."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
data("df.edger")
vsBoxPlot(
data = df.edger, d.factor = NULL, type = "edger", title = TRUE,
legend = TRUE, grid = TRUE, aes = "viosumm", fill.color = "Paired"
)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
data("df.edger")
vsBoxPlot(
data = df.edger, d.factor = NULL, type = "edger", title = TRUE,
legend = TRUE, grid = TRUE, aes = "notch", fill.color = "Greys"
)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap-------------------------------------------
vsScatterPlot(
x = 'hESC', y = 'iPS', data = df.cuff, type = 'cuffdiff',
d.factor = NULL, title = TRUE, grid = TRUE
)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap-------------------------------------------
vsScatterPlot(
x = 'treated_paired.end', y = 'untreated_paired.end',
data = df.deseq, type = 'deseq', d.factor = 'condition',
title = TRUE, grid = TRUE
)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap-------------------------------------------
vsScatterPlot(
x = 'WM', y = 'MM', data = df.edger, type = 'edger',
d.factor = NULL, title = TRUE, grid = TRUE
)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap-------------------------------------------
vsScatterMatrix(
data = df.cuff, d.factor = NULL, type = 'cuffdiff',
comp = NULL, title = TRUE, grid = TRUE, man.title = NULL
)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap-------------------------------------------
vsScatterMatrix(
data = df.deseq, d.factor = 'condition', type = 'deseq',
comp = NULL, title = TRUE, grid = TRUE, man.title = NULL
)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap-------------------------------------------
vsScatterMatrix(
data = df.edger, d.factor = NULL, type = 'edger', comp = NULL,
title = TRUE, grid = TRUE, man.title = NULL
)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap-------------------------------------------
vsDEGMatrix(
data = df.cuff, padj = 0.05, d.factor = NULL, type = 'cuffdiff',
title = TRUE, legend = TRUE, grid = TRUE
)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap-------------------------------------------
vsDEGMatrix(
data = df.deseq, padj = 0.05, d.factor = 'condition',
type = 'deseq', title = TRUE, legend = TRUE, grid = TRUE
)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap-------------------------------------------
vsDEGMatrix(
data = df.edger, padj = 0.05, d.factor = NULL, type = 'edger',
title = TRUE, legend = TRUE, grid = TRUE
)
## -----------------------------------------------------------------------------
vsDEGMatrix(data = df.deseq, d.factor = "condition", type = "deseq",
grey.scale = TRUE
)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
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
)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
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
)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
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
)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
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
)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
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
)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
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
)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
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
)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
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
)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
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
)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
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
)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
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
)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
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
)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
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
)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
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
)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
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
)
## -----------------------------------------------------------------------------
important_ids <- c(
"ID_001",
"ID_002",
"ID_003",
"ID_004",
"ID_005"
)
important_ids
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
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
)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
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
)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
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
)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- message=FALSE, fig.cap=my.cap------------------------------------------
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
)
## -----------------------------------------------------------------------------
# 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
)
## -----------------------------------------------------------------------------
df_scatter <- tmp[[1]] ## or use tmp$data
head(df_scatter)
## -----------------------------------------------------------------------------
my_plot <- tmp[[2]] ## or use tmp$plot
my_plot
## ---- echo=FALSE--------------------------------------------------------------
my.cap <- "A visual guide to text size parameters. Users can modify these
components which are highlighted by their respective parameter."
## ---- echo=FALSE, fig.cap=my.cap----------------------------------------------
knitr::include_graphics("img/text-size-parameters-01.png", auto_pdf = TRUE)
## ---- echo=FALSE--------------------------------------------------------------
my.cap <- "Location of facet titles. Facet title sizes can be modified using
the `facet.title.size` parameter."
## ---- echo=FALSE, fig.cap=my.cap----------------------------------------------
knitr::include_graphics("img/text-size-parameters-02.png", auto_pdf = TRUE)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- echo=FALSE, fig.cap=my.cap----------------------------------------------
knitr::include_graphics("img/text-size-parameters-03.png", auto_pdf = TRUE)
## ---- echo=FALSE--------------------------------------------------------------
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."
## ---- echo=FALSE, fig.cap=my.cap, out.width = "75%"---------------------------
knitr::include_graphics("img/lfc-shape.png")
## ---- echo=FALSE--------------------------------------------------------------
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. "
## ---- echo=FALSE, fig.cap=my.cap, out.width = "75%"---------------------------
knitr::include_graphics("img/eff-scatter.png", auto_pdf = TRUE)
## ---- echo=FALSE--------------------------------------------------------------
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. "
## ---- echo=FALSE, fig.cap=my.cap, out.width = "75%"---------------------------
knitr::include_graphics("img/eff-smatrix.png", auto_pdf = TRUE)
## ---- echo=FALSE--------------------------------------------------------------
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. "
## ---- echo=FALSE, fig.cap=my.cap, out.width = "75%"---------------------------
knitr::include_graphics("img/eff-box.png", auto_pdf = TRUE)
## ---- echo=FALSE--------------------------------------------------------------
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. "
## ---- echo=FALSE, fig.cap=my.cap, out.width = "75%"---------------------------
knitr::include_graphics("img/eff-deg.png", auto_pdf = TRUE)
## ---- echo=FALSE--------------------------------------------------------------
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. "
## ---- echo=FALSE, fig.cap=my.cap, out.width = "75%"---------------------------
knitr::include_graphics("img/eff-volcano.png", auto_pdf = TRUE)
## ---- echo=FALSE--------------------------------------------------------------
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. "
## ---- echo=FALSE, fig.cap=my.cap, out.width = "75%"---------------------------
knitr::include_graphics("img/eff-vmatrix.png", auto_pdf = TRUE)
## ---- echo=FALSE--------------------------------------------------------------
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. "
## ---- echo=FALSE, fig.cap=my.cap, out.width = "75%"---------------------------
knitr::include_graphics("img/eff-maplot.png", auto_pdf = TRUE)
## ---- echo=FALSE--------------------------------------------------------------
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. "
## ---- echo=FALSE, fig.cap=my.cap, out.width = "75%"---------------------------
knitr::include_graphics("img/eff-mamatrix.png", auto_pdf = TRUE)
## ---- echo=FALSE--------------------------------------------------------------
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. "
## ---- echo=FALSE, fig.cap=my.cap, out.width = "75%"---------------------------
knitr::include_graphics("img/eff-four.png", auto_pdf = TRUE)
## ---- echo=FALSE--------------------------------------------------------------
sessionInfo()
Try the vidger package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.