Package 'Glimma'

Title: Interactive visualizations for gene expression analysis
Description: This package produces interactive visualizations for RNA-seq data analysis, utilizing output from limma, edgeR, or DESeq2. It produces interactive htmlwidgets versions of popular RNA-seq analysis plots to enhance the exploration of analysis results by overlaying interactive features. The plots can be viewed in a web browser or embedded in notebook documents.
Authors: Shian Su [aut, cre], Hasaru Kariyawasam [aut], Oliver Voogd [aut], Matthew Ritchie [aut], Charity Law [aut], Stuart Lee [ctb], Isaac Virshup [ctb]
Maintainer: Shian Su <[email protected]>
License: GPL-3
Version: 2.15.0
Built: 2024-07-02 05:40:05 UTC
Source: https://github.com/bioc/Glimma

Help Index

Example microarray for the study of Ezh2.

Description

Example microarray for the study of Ezh2.

Author(s)

Bhupinder Pal, Toula Bouras, Wei Shi, Francois Vaillant, Julie M. Sheridan, Naiyang Fu, Kelsey Breslin, Kun Jiang, Matthew E. Ritchie, Matthew Young, Geoffrey J. Lindeman, Gordon K. Smyth, Jane E. Visvader

References

http://www.cell.com/cell-reports/abstract/S2211-1247(13)00007-7

Numeric to hex colour converter

Description

Convert numbers and R colour strings into corresponding hex codes for colours

Usage

as.hexcol(x)

Arguments

x

the colour value(s) to be converted to hex values.

Value

hex codes for colours

Glimma: interactive graphics from limma

Description

The Glimma package provides iteractive versions of plots frequently used in the limma package. Currently the MDS and MD plots have been implemented. The functions can be used with both limma, edgeR and DESeq objecs.

Main functions

glMDSPlot, glMDPlot, glXYPlot

Glimma MA Plot

Description

Generic function for drawing a two-panel interactive MA plot, a special case of the glimmaXY plot. The function invokes the following methods which depend on the class of the first argument:

glimmaMD is an alias for glimmaMA.

Usage

glimmaMA(x, ...)

glimmaMD(x, ...)

Arguments

x

the DE object to plot.
...

additional arguments affecting the plots produced. See specific methods for detailed arguments.

Details

The summary plot on the left represents gene-wise log-fold-change (logFC) on the y-axis versus average gene expression calculated as log-counts-per-million (logCPM) values. We call our summary plot an MA plot because this type of plot was originally referred to as an MA plot in the limma package, with the M-value representing logFC and A-value representing average expression - it has since been renamed to MD plot in the limma package. The expression plot on the right displays sample expression values for a single gene. Interactions with the htmlwidget include clicking on genes (points) in the summary plot to bring up associated sample expression values in the expression plot, as well as the summary statistics in the table below. Alternatively, users can interact with the table by clicking on genes (rows) to highlight genes in the summary plot, as well as bring up associated sample expression values in the expression plot. Briefly, other interactive features include a search box for the table, buttons to save plots and data (summary statistics and expression values), additional pop-up information when hovering on points in plots, and rescaling of the y-axis in the expression plot.

Value

htmlwidget object or NULL if html argument is specified.

Author(s)

Hasaru Kariyawasam, Shian Su and Oliver Voogd

Examples

methods(glimmaMA) # show methods for glimmaMA

Glimma MA Plot

Description

Draws a two-panel interactive MA plot from an DESeqDataSet object. This is a special case of the glimmaXY plot.

Usage

## S3 method for class 'DESeqDataSet'
glimmaMA(
  x,
  counts = DESeq2::counts(x),
  groups = extractGroups(colData(x)),
  status = NULL,
  anno = NULL,
  display.columns = NULL,
  status.cols = c("#1052bd", "silver", "#cc212f"),
  sample.cols = NULL,
  transform.counts = c("logcpm", "cpm", "rpkm", "logrpkm", "none"),
  main = "MA Plot",
  xlab = "logCPM",
  ylab = "logFC",
  html = NULL,
  width = 920,
  height = 920,
  ...
)

Arguments

x

DESeqDataSet object from which summary statistics are extracted from to create summary (left) plot.
counts

numeric matrix with nrow(x) rows containing gene expression values.
groups

vector/factor representing the experimental group for each sample; see extractGroups for default value.
status

vector of length nrow(x) indicating the status of each gene.
anno

dataframe with nrow(x) rows containing gene annotations.
display.columns

character vector containing names of columns from anno from which to display in mouseover tooltips and table.
status.cols

vector of length 3 containing valid CSS strings for colours associated with status in the order of -1, 0 and 1.
sample.cols

character vector of length ncol(counts) containing valid CSS strings for colours associated with each sample to be displayed on the expression plot. If left unspecified, samples will be coloured according to groups.
transform.counts

the type of transformation used on the counts - "logcpm" for using edgeR::cpm(counts, log=TRUE); "cpm" for edgeR::cpm(counts); "rpkm" for edgeR::rpkm(counts); "logrpkm" for edgeR::rpkm(counts, log=TRUE); and "none" for no transformation). Defaults to "logcpm".
main

character string for the main title of summary plot.
xlab

character string for the x-axis label of summary plot.
ylab

character string for the y-axis label of summary plot.
html

character string for naming HTML file for exportation of widget. The extension should be included in the file name e.g. "file.html".
width

numeric value indicating width of widget in pixels.
height

numeric value indicating width of height in pixels.
...

additional unused arguments.

Details

The summary plot on the left represents gene-wise log-fold-change (logFC) on the y-axis versus average gene expression calculated as log-counts-per-million (logCPM) values. We call our summary plot an MA plot because this type of plot was originally referred to as an MA plot in the limma package, with the M-value representing logFC and A-value representing average expression - it has since been renamed to MD plot in the limma package. The expression plot on the right displays sample expression values for a single gene. Interactions with the htmlwidget include clicking on genes (points) in the summary plot to bring up associated sample expression values in the expression plot, as well as the summary statistics in the table below. Alternatively, users can interact with the table by clicking on genes (rows) to highlight genes in the summary plot, as well as bring up associated sample expression values in the expression plot. Briefly, other interactive features include a search box for the table, buttons to save plots and data (summary statistics and expression values), additional pop-up information when hovering on points in plots, and rescaling of the y-axis in the expression plot.

Value

htmlwidget object or NULL if html argument is specified.

Author(s)

Hasaru Kariyawasam, Shian Su and Oliver Voogd

See Also

glimmaMA, glimmaMA.MArrayLM, glimmaMA.DGEExact, glimmaMA.DGELRT

Examples

dge <- readRDS(
  system.file("RNAseq123/dge.rds", package = "Glimma"))

dds <- DESeq2::DESeqDataSetFromMatrix(
  countData = dge$counts,
  colData = dge$samples,
  rowData = dge$genes,
  design = ~group
)

dds <- DESeq2::DESeq(dds, quiet=TRUE)
glimmaMA(dds)

Glimma MA Plot

Description

Draws a two-panel interactive MA plot from an DGEExact object. This is a special case of the glimmaXY plot.

Usage

## S3 method for class 'DGEExact'
glimmaMA(
  x,
  dge = NULL,
  counts = dge$counts,
  groups = dge$samples$group,
  status = edgeR::decideTestsDGE(x),
  anno = x$genes,
  display.columns = NULL,
  status.cols = c("#1052bd", "silver", "#cc212f"),
  sample.cols = NULL,
  p.adj.method = "BH",
  transform.counts = c("logcpm", "cpm", "rpkm", "logrpkm", "none"),
  main = paste(x$comparison[2], "vs", x$comparison[1]),
  xlab = "logCPM",
  ylab = "logFC",
  html = NULL,
  width = 920,
  height = 920,
  ...
)

Arguments

x

DGEExact object from which summary statistics are extracted from to create summary (left) plot.
dge

DGEList object with nrow(x) rows from which expression values are extracted from to create expression (right) plot. Gene counts are taken from dge$counts and sample groups from dge$samples$group. By default raw counts are transformed to log-cpm values (see more in the transform.counts argument).
counts

numeric matrix with nrow(x) rows containing gene expression values. This can be used to replace the gene counts from dge$counts, i.e. you may have log-rpkm values stored in a different object that you wish to use.
groups

vector of length ncol(dge) representing categorisation of samples in expression plot.
status

vector of length nrow(x) indicating the status of each gene. By default genes in the summary plot are coloured based on its differential expression status using an adjusted p-value cutoff of 0.05 by calling the edgeR::decideTestsDGE() function, where the value of -1 marks down-regulated genes, 0 marks genes with no expression difference, and 1 marks up-regulated genes.
anno

dataframe with nrow(x) rows containing gene annotations.
display.columns

character vector containing names of columns from anno from which to display in mouseover tooltips and table.
status.cols

vector of length 3 containing valid CSS strings for colours associated with status in the order of -1, 0 and 1.
sample.cols

character vector of length ncol(counts) containing valid CSS strings for colours associated with each sample to be displayed on the expression plot. If left unspecified, samples will be coloured according to groups.
p.adj.method

character string specifying p-value adjustment method.
transform.counts

the type of transformation used on the counts - "logcpm" for using edgeR::cpm(counts, log=TRUE); "cpm" for edgeR::cpm(counts); "rpkm" for edgeR::rpkm(counts); "logrpkm" for edgeR::rpkm(counts, log=TRUE); and "none" for no transformation). Defaults to "logcpm".
main

character string for the main title of summary plot.
xlab

character string for the x-axis label of summary plot.
ylab

character string for the y-axis label of summary plot.
html

character string for naming HTML file for exportation of widget. The extension should be included in the file name e.g. "file.html".
width

numeric value indicating width of widget in pixels.
height

numeric value indicating width of height in pixels.
...

additional unused arguments.

Details

The summary plot on the left represents gene-wise log-fold-change (logFC) on the y-axis versus average gene expression calculated as log-counts-per-million (logCPM) values. We call our summary plot an MA plot because this type of plot was originally referred to as an MA plot in the limma package, with the M-value representing logFC and A-value representing average expression - it has since been renamed to MD plot in the limma package. The expression plot on the right displays sample expression values for a single gene. Interactions with the htmlwidget include clicking on genes (points) in the summary plot to bring up associated sample expression values in the expression plot, as well as the summary statistics in the table below. Alternatively, users can interact with the table by clicking on genes (rows) to highlight genes in the summary plot, as well as bring up associated sample expression values in the expression plot. Briefly, other interactive features include a search box for the table, buttons to save plots and data (summary statistics and expression values), additional pop-up information when hovering on points in plots, and rescaling of the y-axis in the expression plot.

Value

htmlwidget object or NULL if html argument is specified.

Author(s)

Hasaru Kariyawasam, Shian Su and Oliver Voogd

See Also

glimmaMA, glimmaMA.MArrayLM, glimmaMA.DGELRT, glimmaMA.DESeqDataSet

Examples

dge <- readRDS(
  system.file("RNAseq123/dge.rds", package = "Glimma"))
design <- readRDS(
  system.file("RNAseq123/design.rds", package = "Glimma"))
contr.matrix <- readRDS(
  system.file("RNAseq123/contr.matrix.rds", package = "Glimma"))

dge <- edgeR::estimateDisp(dge, design)
gfit <- edgeR::glmFit(dge, design)
glrt <- edgeR::glmLRT(gfit, design, contrast = contr.matrix)

glimmaMA(glrt, dge = dge)

Glimma MA Plot

Description

Draws a two-panel interactive MA plot from an DGELRT object. This is a special case of the glimmaXY plot.

Usage

## S3 method for class 'DGELRT'
glimmaMA(
  x,
  dge = NULL,
  counts = dge$counts,
  groups = dge$samples$group,
  status = edgeR::decideTestsDGE(x),
  anno = x$genes,
  display.columns = NULL,
  status.cols = c("#1052bd", "silver", "#cc212f"),
  sample.cols = NULL,
  p.adj.method = "BH",
  transform.counts = c("logcpm", "cpm", "rpkm", "logrpkm", "none"),
  main = paste(x$comparison[2], "vs", x$comparison[1]),
  xlab = "logCPM",
  ylab = "logFC",
  html = NULL,
  width = 920,
  height = 920,
  ...
)

Arguments

x

DGELRT object from which summary statistics are extracted from to create summary (left) plot.
dge

DGEList object with nrow(x) rows from which expression values are extracted from to create expression (right) plot. Gene counts are taken from dge$counts and sample groups from dge$samples$group. By default raw counts are transformed to log-cpm values (see more in the transform.counts argument).
counts

numeric matrix with nrow(x) rows containing gene expression values. This can be used to replace the gene counts from dge$counts, i.e. you may have log-rpkm values stored in a different object that you wish to use.
groups

vector of length ncol(dge) representing categorisation of samples in expression plot.
status

vector of length nrow(x) indicating the status of each gene. By default genes in the summary plot are coloured based on its differential expression status using an adjusted p-value cutoff of 0.05 by calling the edgeR::decideTestsDGE() function, where the value of -1 marks down-regulated genes, 0 marks genes with no expression difference, and 1 marks up-regulated genes.
anno

dataframe with nrow(x) rows containing gene annotations.
display.columns

character vector containing names of columns from anno from which to display in mouseover tooltips and table.
status.cols

vector of length 3 containing valid CSS strings for colours associated with status in the order of -1, 0 and 1.
sample.cols

character vector of length ncol(counts) containing valid CSS strings for colours associated with each sample to be displayed on the expression plot. If left unspecified, samples will be coloured according to groups.
p.adj.method

character string specifying p-value adjustment method.
transform.counts

the type of transformation used on the counts - "logcpm" for using edgeR::cpm(counts, log=TRUE); "cpm" for edgeR::cpm(counts); "rpkm" for edgeR::rpkm(counts); "logrpkm" for edgeR::rpkm(counts, log=TRUE); and "none" for no transformation). Defaults to "logcpm".
main

character string for the main title of summary plot.
xlab

character string for the x-axis label of summary plot.
ylab

character string for the y-axis label of summary plot.
html

character string for naming HTML file for exportation of widget. The extension should be included in the file name e.g. "file.html".
width

numeric value indicating width of widget in pixels.
height

numeric value indicating width of height in pixels.
...

additional unused arguments.

Details

The summary plot on the left represents gene-wise log-fold-change (logFC) on the y-axis versus average gene expression calculated as log-counts-per-million (logCPM) values. We call our summary plot an MA plot because this type of plot was originally referred to as an MA plot in the limma package, with the M-value representing logFC and A-value representing average expression - it has since been renamed to MD plot in the limma package. The expression plot on the right displays sample expression values for a single gene. Interactions with the htmlwidget include clicking on genes (points) in the summary plot to bring up associated sample expression values in the expression plot, as well as the summary statistics in the table below. Alternatively, users can interact with the table by clicking on genes (rows) to highlight genes in the summary plot, as well as bring up associated sample expression values in the expression plot. Briefly, other interactive features include a search box for the table, buttons to save plots and data (summary statistics and expression values), additional pop-up information when hovering on points in plots, and rescaling of the y-axis in the expression plot.

Value

htmlwidget object or NULL if html argument is specified.

Author(s)

Hasaru Kariyawasam, Shian Su and Oliver Voogd

See Also

glimmaMA, glimmaMA.MArrayLM, glimmaMA.DGEExact, glimmaMA.DESeqDataSet

Glimma MA Plot

Description

Draws a two-panel interactive MA plot from an MArrayLM object. This is a special case of the glimmaXY plot.

Usage

## S3 method for class 'MArrayLM'
glimmaMA(
  x,
  dge = NULL,
  counts = dge$counts,
  groups = dge$samples$group,
  coef = ncol(x$coefficients),
  status = limma::decideTests(x),
  anno = x$genes,
  display.columns = NULL,
  status.cols = c("#1052bd", "silver", "#cc212f"),
  sample.cols = NULL,
  p.adj.method = "BH",
  transform.counts = c("logcpm", "cpm", "rpkm", "logrpkm", "none"),
  main = colnames(x)[coef],
  xlab = "logCPM",
  ylab = "logFC",
  html = NULL,
  width = 920,
  height = 920,
  ...
)

Arguments

x

MArrayLM object from which summary statistics are extracted from to create summary (left) plot.
dge

DGEList object with nrow(x) rows from which expression values are extracted from to create expression (right) plot. Gene counts are taken from dge$counts and sample groups from dge$samples$group. By default raw counts are transformed to log-cpm values (see more in the transform.counts argument).
counts

numeric matrix with nrow(x) rows containing gene expression values. This can be used to replace the gene counts from dge$counts, i.e. you may have log-rpkm values stored in a different object that you wish to use.
groups

vector of length ncol(dge) representing categorisation of samples in expression plot.
coef

integer indicating the column in x from the summary plot is created.
status

vector of length nrow(x) indicating the status of each gene. By default genes in the summary plot are coloured based on its differential expression status using an adjusted p-value cutoff of 5% by calling the limma::decideTests function, where the value of -1 marks down-regulated genes, 0 marks genes with no expression difference, and 1 marks up-regulated genes.
anno

dataframe with nrow(x) rows containing gene annotations.
display.columns

character vector containing names of columns from anno from which to display in mouseover tooltips and table.
status.cols

vector of length 3 containing valid CSS strings for colours associated with status in the order of -1, 0 and 1.
sample.cols

character vector of length ncol(counts) containing valid CSS strings for colours associated with each sample to be displayed on the expression plot. If left unspecified, samples will be coloured according to groups.
p.adj.method

character string specifying p-value adjustment method.
transform.counts

the type of transformation used on the counts - "logcpm" for using edgeR::cpm(counts, log=TRUE); "cpm" for edgeR::cpm(counts); "rpkm" for edgeR::rpkm(counts); "logrpkm" for edgeR::rpkm(counts, log=TRUE); and "none" for no transformation). Defaults to "logcpm".
main

character string for the main title of summary plot.
xlab

character string for the x-axis label of summary plot.
ylab

character string for the y-axis label of summary plot.
html

character string for naming HTML file for exportation of widget. The extension should be included in the file name e.g. "file.html".
width

numeric value indicating width of widget in pixels.
height

numeric value indicating width of height in pixels.
...

additional unused arguments.

Details

The summary plot on the left represents gene-wise log-fold-change (logFC) on the y-axis versus average gene expression calculated as log-counts-per-million (logCPM) values. We call our summary plot an MA plot because this type of plot was originally referred to as an MA plot in the limma package, with the M-value representing logFC and A-value representing average expression - it has since been renamed to MD plot in the limma package. The expression plot on the right displays sample expression values for a single gene. Interactions with the htmlwidget include clicking on genes (points) in the summary plot to bring up associated sample expression values in the expression plot, as well as the summary statistics in the table below. Alternatively, users can interact with the table by clicking on genes (rows) to highlight genes in the summary plot, as well as bring up associated sample expression values in the expression plot. Briefly, other interactive features include a search box for the table, buttons to save plots and data (summary statistics and expression values), additional pop-up information when hovering on points in plots, and rescaling of the y-axis in the expression plot.

Value

htmlwidget object or NULL if html argument is specified.

Author(s)

Hasaru Kariyawasam, Shian Su and Oliver Voogd

See Also

glimmaMA, glimmaMA.DGEExact, glimmaMA.DGELRT, glimmaMA.DESeqDataSet

Examples

dge <- readRDS(
  system.file("RNAseq123/dge.rds", package = "Glimma"))
design <- readRDS(
  system.file("RNAseq123/design.rds", package = "Glimma"))
contr.matrix <- readRDS(
  system.file("RNAseq123/contr.matrix.rds", package = "Glimma"))

v <- limma::voom(dge, design)
vfit <- limma::lmFit(v, design)
vfit <- limma::contrasts.fit(vfit, contrasts = contr.matrix)
efit <- limma::eBayes(vfit)

glimmaMA(efit, dge = dge)

Glimma MDS Plot

Description

Generic function for drawing a two-panel interactive multidimensional scaling (MDS) plot. The function invokes the following methods which depend on the class of the first argument:

Usage

glimmaMDS(x, ...)

Arguments

x

the matrix containing the gene expressions.
...

the additional arguments affecting the plot produced. See specific methods for detailed arguments.

Details

The left plot shows two MDS dimensions, with sample annotations displayed on hover. The right panel contains a bar plot of the eigenvalues of each dimension. The controls beneath the plots can be used to change the dimensions being displayed, and the scale, colour and shape of points. The interactive MDS plot allows users to adjust sample points by scale, colour and shape for multiple vectors associated with sample information. This is carried out most effectively when x$samples includes an abundance of sample information, or when a data frame object is supplied to groups. If a simple character or factor vector is given to groups (with the default of continous.colour=FALSE), then sample points will have no scaling options, but can only be adjusted in colour and shape by groups and labels. Instead, if groups is a numeric vector (e.g. library size or expression level of a specific gene), then the plot can be scaled and coloured by the numeric values with continous.colour=TRUE. For more details, refer to limma::plotMDS .

Value

htmlwidget object or NULL if html argument is specified.

Author(s)

Hasaru Kariyawasam, Shian Su and Oliver Voogd

Examples

dge <- readRDS(system.file("RNAseq123/dge.rds", package = "Glimma"))
glimmaMDS(dge)

# using DESeqDataSet
dds <- DESeq2::DESeqDataSetFromMatrix(
 countData = dge$counts,
 colData = dge$samples,
 rowData = dge$genes,
 design = ~group
)
glimmaMDS(dds)

# using matrix object
expr <- edgeR::cpm(dge, log = TRUE)
glimmaMDS(expr)

Glimma MDS Plot

Description

Draws a two-panel interactive MDS plot.

Usage

## Default S3 method:
glimmaMDS(
  x,
  groups = as.character(rep(1, ncol(x))),
  labels = as.character(seq_len(ncol(x))),
  continuous.colour = FALSE,
  top = 500,
  gene.selection = c("pairwise", "common"),
  html = NULL,
  width = 900,
  height = 500,
  ...
)

Arguments

x

the matrix containing the gene expressions.
groups

vector or data frame object with associated sample information such as experimental groups. The information is displayed in mouseover tooltips, and appropriate vector(s) can be used to adjust the plot using scale_by, colour_by and shape_by drop-down boxes of the widget.
labels

character vector of sample names or labels.
continuous.colour

TRUE if continuous colour schemes should be used. Defaults to FALSE where distinct colour schemes are used.
top

integer indiating number of top genes used to calculate pairwise distances.
gene.selection

character string specifying how genes are selected from the plot - "pairwise" if most variable genes are to be chosen for each pair of samples, or "common" to select the same genes for all comparisons.
html

character string for naming HTML file or exportation of widget. The extension should be included in the file name e.g. "file.hml".
width

numeric value indicating width of widget in pixels.
height

numeric value indicating width of widget in pixels.
...

additional unused arguments.

Details

The left plot shows two MDS dimensions, with sample annotations displayed on hover. The right panel contains a bar plot of the eigenvalues of each dimension. The controls beneath the plots can be used to change the dimensions being displayed, and the scale, colour and shape of points. The interactive MDS plot allows users to adjust sample points by scale, colour and shape for multiple vectors associated with sample information. This is carried out most effectively when x$samples includes an abundance of sample information, or when a data frame object is supplied to groups. If a simple character or factor vector is given to groups (with the default of continous.colour=FALSE), then sample points will have no scaling options, but can only be adjusted in colour and shape by groups and labels. Instead, if groups is a numeric vector (e.g. library size or expression level of a specific gene), then the plot can be scaled and coloured by the numeric values with continous.colour=TRUE. For more details, refer to limma::plotMDS .

Value

htmlwidget object or NULL if html argument is specified.

Author(s)

Hasaru Kariyawasam, Shian Su and Oliver Voogd

See Also

glimmaMDS, glimmaMDS.DGEList, glimmaMDS.DESeqDataSet

Examples

dge <- readRDS(system.file("RNAseq123/dge.rds", package = "Glimma"))
expr <- edgeR::cpm(dge, log = TRUE)
glimmaMDS(expr)

Glimma MDS Plot

Description

Draws a two-panel interactive MDS plot using a DESeqDataset x. Transforms counts using edgeR::cpm(DESeq2::counts(x), log = TRUE, prior.count = prior.count).

Usage

## S3 method for class 'DESeqDataSet'
glimmaMDS(
  x,
  groups = as.data.frame(SummarizedExperiment::colData(x)),
  labels = rownames(SummarizedExperiment::colData(x)),
  continuous.colour = FALSE,
  top = 500,
  gene.selection = c("pairwise", "common"),
  prior.count = 2,
  html = NULL,
  width = 900,
  height = 500,
  ...
)

Arguments

x

DESeqDataSet object containing gene counts.
groups

vector or data frame object with associated sample information such as experimental groups. The information is displayed in mouseover tooltips, and appropriate vector(s) can be used to adjust the plot using scale_by, colour_by and shape_by drop-down boxes of the widget.
labels

character vector of sample names or labels.
continuous.colour

TRUE if continuous colour schemes should be used. Defaults to FALSE where distinct colour schemes are used.
top

integer indiating number of top genes used to calculate pairwise distances.
gene.selection

character string specifying how genes are selected from the plot - "pairwise" if most variable genes are to be chosen for each pair of samples, or "common" to select the same genes for all comparisons.
prior.count

integer indicating the average count to be added to each observation to avoid taking log of zero when raw counts are transformed to log-counts-per-million values (using edgeR::cpm function).
html

character string for naming HTML file or exportation of widget. The extension should be included in the file name e.g. "file.hml".
width

numeric value indicating width of widget in pixels.
height

numeric value indicating width of widget in pixels.
...

additional unused arguments.

Details

The left plot shows two MDS dimensions, with sample annotations displayed on hover. The right panel contains a bar plot of the eigenvalues of each dimension. The controls beneath the plots can be used to change the dimensions being displayed, and the scale, colour and shape of points. The interactive MDS plot allows users to adjust sample points by scale, colour and shape for multiple vectors associated with sample information. This is carried out most effectively when x$samples includes an abundance of sample information, or when a data frame object is supplied to groups. If a simple character or factor vector is given to groups (with the default of continous.colour=FALSE), then sample points will have no scaling options, but can only be adjusted in colour and shape by groups and labels. Instead, if groups is a numeric vector (e.g. library size or expression level of a specific gene), then the plot can be scaled and coloured by the numeric values with continous.colour=TRUE. For more details, refer to limma::plotMDS .

Value

htmlwidget object or NULL if html argument is specified.

Author(s)

Hasaru Kariyawasam, Shian Su and Oliver Voogd

See Also

glimmaMDS, glimmaMDS.default, glimmaMDS.DGEList

Examples

dge <- readRDS(system.file("RNAseq123/dge.rds", package = "Glimma"))
dds <- DESeq2::DESeqDataSetFromMatrix(
 countData = dge$counts,
 colData = dge$samples,
 rowData = dge$genes,
 design = ~group
)
glimmaMDS(dds)

Glimma MDS Plot

Description

Draws a two-panel interactive MDS plot using a DGEList x. Transforms counts using edgeR::cpm(x, log=TRUE, prior.count = prior.count).

Usage

## S3 method for class 'DGEList'
glimmaMDS(
  x,
  groups = x$samples,
  labels = rownames(x$samples),
  continuous.colour = FALSE,
  top = 500,
  gene.selection = c("pairwise", "common"),
  prior.count = 2,
  html = NULL,
  width = 900,
  height = 500,
  ...
)

Arguments

x

DGEList object containing gene counts in x$counts.
groups

vector or data frame object with associated sample information such as experimental groups. The information is displayed in mouseover tooltips, and appropriate vector(s) can be used to adjust the plot using scale_by, colour_by and shape_by drop-down boxes of the widget.
labels

character vector of sample names or labels.
continuous.colour

TRUE if continuous colour schemes should be used. Defaults to FALSE where distinct colour schemes are used.
top

integer indiating number of top genes used to calculate pairwise distances.
gene.selection

character string specifying how genes are selected from the plot - "pairwise" if most variable genes are to be chosen for each pair of samples, or "common" to select the same genes for all comparisons.
prior.count

integer indicating the average count to be added to each observation to avoid taking log of zero when raw counts are transformed to log-counts-per-million values (using edgeR::cpm function).
html

character string for naming HTML file or exportation of widget. The extension should be included in the file name e.g. "file.hml".
width

numeric value indicating width of widget in pixels.
height

numeric value indicating width of widget in pixels.
...

additional unused arguments.

Details

The left plot shows two MDS dimensions, with sample annotations displayed on hover. The right panel contains a bar plot of the eigenvalues of each dimension. The controls beneath the plots can be used to change the dimensions being displayed, and the scale, colour and shape of points. The interactive MDS plot allows users to adjust sample points by scale, colour and shape for multiple vectors associated with sample information. This is carried out most effectively when x$samples includes an abundance of sample information, or when a data frame object is supplied to groups. If a simple character or factor vector is given to groups (with the default of continous.colour=FALSE), then sample points will have no scaling options, but can only be adjusted in colour and shape by groups and labels. Instead, if groups is a numeric vector (e.g. library size or expression level of a specific gene), then the plot can be scaled and coloured by the numeric values with continous.colour=TRUE. For more details, refer to limma::plotMDS .

Value

htmlwidget object or NULL if html argument is specified.

Author(s)

Hasaru Kariyawasam, Shian Su and Oliver Voogd

See Also

glimmaMDS, glimmaMDS.default, glimmaMDS.DESeqDataSet

Examples

dge <- readRDS(system.file("RNAseq123/dge.rds", package = "Glimma"))
glimmaMDS(dge)

Glimma Volcano Plot

Description

Generic function for drawing a two-panel interactive volcano plot, a special case of the glimmaXY plot. The function invokes the following methods which depend on the class of the first argument:

Usage

glimmaVolcano(x, ...)

Arguments

x

the DE object to plot.
...

additional arguments affecting the plots produced. See specific methods for detailed arguments.

Details

The summary plot on the left represents gene-wise log-fold-change (logFC) on the x-axis versus -log10(pvalue). The expression plot on the right displays sample expression values for a single gene. Interactions with the htmlwidget include clicking on genes (points) in the summary plot to bring up associated sample expression values in the expression plot, as well as the summary statistics in the table below. Alternatively, users can interact with the table by clicking on genes (rows) to highlight genes in the summary plot, as well as bring up associated sample expression values in the expression plot. Briefly, other interactive features include a search box for the table, buttons to save plots and data (summary statistics and expression values), additional pop-up information when hovering on points in plots, and rescaling of the y-axis in the expression plot.

Value

htmlwidget object or NULL if html argument is specified.

Author(s)

Hasaru Kariyawasam, Shian Su and Oliver Voogd

Examples

dge <- readRDS(
  system.file("RNAseq123/dge.rds", package = "Glimma"))
design <- readRDS(
  system.file("RNAseq123/design.rds", package = "Glimma"))
contr.matrix <- readRDS(
  system.file("RNAseq123/contr.matrix.rds", package = "Glimma"))

v <- limma::voom(dge, design)
vfit <- limma::lmFit(v, design)
vfit <- limma::contrasts.fit(vfit, contrasts = contr.matrix)
efit <- limma::eBayes(vfit)

glimmaVolcano(efit, dge = dge)

Glimma Volcano Plot

Description

Draws a two-panel interactive volcano plot from an DESeqDataSet object. This is a special case of the glimmaXY plot.

Usage

## S3 method for class 'DESeqDataSet'
glimmaVolcano(
  x,
  counts = DESeq2::counts(x),
  groups = extractGroups(colData(x)),
  status = NULL,
  anno = NULL,
  display.columns = NULL,
  status.cols = c("#1052bd", "silver", "#cc212f"),
  sample.cols = NULL,
  transform.counts = c("logcpm", "cpm", "rpkm", "none"),
  main = "Volcano Plot",
  xlab = "logFC",
  ylab = "negLog10PValue",
  html = NULL,
  width = 920,
  height = 920,
  ...
)

Arguments

x

DESeqDataSet object from which summary statistics are extracted from to create summary (left) plot.
counts

numeric matrix with nrow(x) rows containing gene expression values.
groups

vector/factor representing the experimental group for each sample; see extractGroups for default value.
status

vector of length nrow(x) indicating the status of each gene.
anno

dataframe with nrow(x) rows containing gene annotations.
display.columns

character vector containing names of columns from anno from which to display in mouseover tooltips and table.
status.cols

vector of length 3 containing valid CSS strings for colours associated with status in the order of -1, 0 and 1.
sample.cols

character vector of length ncol(counts) containing valid CSS strings for colours associated with each sample to be displayed on the expression plot. If left unspecified, samples will be coloured according to groups.
transform.counts

the type of transformation used on the counts - "logcpm" for using edgeR::cpm(counts, log=TRUE); "cpm" for edgeR::cpm(counts); "rpkm" for edgeR::rpkm(counts); "logrpkm" for edgeR::rpkm(counts, log=TRUE); and "none" for no transformation). Defaults to "logcpm".
main

character string for the main title of summary plot.
xlab

character string for the x-axis label of summary plot.
ylab

character string for the y-axis label of summary plot.
html

character string for naming HTML file for exportation of widget. The extension should be included in the file name e.g. "file.html".
width

numeric value indicating width of widget in pixels.
height

numeric value indicating width of height in pixels.
...

additional unused arguments.

Details

The summary plot on the left represents gene-wise log-fold-change (logFC) on the x-axis versus -log10(pvalue). The expression plot on the right displays sample expression values for a single gene. Interactions with the htmlwidget include clicking on genes (points) in the summary plot to bring up associated sample expression values in the expression plot, as well as the summary statistics in the table below. Alternatively, users can interact with the table by clicking on genes (rows) to highlight genes in the summary plot, as well as bring up associated sample expression values in the expression plot. Briefly, other interactive features include a search box for the table, buttons to save plots and data (summary statistics and expression values), additional pop-up information when hovering on points in plots, and rescaling of the y-axis in the expression plot.

Value

htmlwidget object or NULL if html argument is specified.

Author(s)

Hasaru Kariyawasam, Shian Su and Oliver Voogd

See Also

glimmaVolcano, glimmaVolcano.MArrayLM, glimmaVolcano.DGEExact, glimmaVolcano.DGELRT

Examples

dge <- readRDS(
  system.file("RNAseq123/dge.rds", package = "Glimma"))

dds <- DESeq2::DESeqDataSetFromMatrix(
  countData = dge$counts,
  colData = dge$samples,
  rowData = dge$genes,
  design = ~group
)

dds <- DESeq2::DESeq(dds, quiet=TRUE)
glimmaVolcano(dds)

Glimma Volcano Plot

Description

Draws a two-panel interactive volcano plot from an DGEExact object. This is a special case of the glimmaXY plot.

Usage

## S3 method for class 'DGEExact'
glimmaVolcano(
  x,
  dge = NULL,
  counts = dge$counts,
  groups = dge$samples$group,
  status = edgeR::decideTestsDGE(x),
  anno = x$genes,
  display.columns = NULL,
  status.cols = c("#1052bd", "silver", "#cc212f"),
  sample.cols = NULL,
  p.adj.method = "BH",
  transform.counts = c("logcpm", "cpm", "rpkm", "none"),
  main = paste(x$comparison[2], "vs", x$comparison[1]),
  xlab = "logFC",
  ylab = "negLog10PValue",
  html = NULL,
  width = 920,
  height = 920,
  ...
)

Arguments

x

DGEExact object from which summary statistics are extracted from to create summary (left) plot.
dge

DGEList object with nrow(x) rows from which expression values are extracted from to create expression (right) plot. Gene counts are taken from dge$counts and sample groups from dge$samples$group. By default raw counts are transformed to log-cpm values (see more in the transform.counts argument).
counts

numeric matrix with nrow(x) rows containing gene expression values. This can be used to replace the gene counts from dge$counts, i.e. you may have log-rpkm values stored in a different object that you wish to use.
groups

vector of length ncol(dge) representing categorisation of samples in expression plot.
status

vector of length nrow(x) indicating the status of each gene. By default genes in the summary plot are coloured based on its differential expression status using an adjusted p-value cutoff of 0.05 by calling the edgeR::decideTestsDGE() function, where the value of -1 marks down-regulated genes, 0 marks genes with no expression difference, and 1 marks up-regulated genes.
anno

dataframe with nrow(x) rows containing gene annotations.
display.columns

character vector containing names of columns from anno from which to display in mouseover tooltips and table.
status.cols

vector of length 3 containing valid CSS strings for colours associated with status in the order of -1, 0 and 1.
sample.cols

character vector of length ncol(counts) containing valid CSS strings for colours associated with each sample to be displayed on the expression plot. If left unspecified, samples will be coloured according to groups.
p.adj.method

character string specifying p-value adjustment method.
transform.counts

the type of transformation used on the counts - "logcpm" for using edgeR::cpm(counts, log=TRUE); "cpm" for edgeR::cpm(counts); "rpkm" for edgeR::rpkm(counts); "logrpkm" for edgeR::rpkm(counts, log=TRUE); and "none" for no transformation). Defaults to "logcpm".
main

character string for the main title of summary plot.
xlab

character string for the x-axis label of summary plot.
ylab

character string for the y-axis label of summary plot.
html

character string for naming HTML file for exportation of widget. The extension should be included in the file name e.g. "file.html".
width

numeric value indicating width of widget in pixels.
height

numeric value indicating width of height in pixels.
...

additional unused arguments.

Details

The summary plot on the left represents gene-wise log-fold-change (logFC) on the x-axis versus -log10(pvalue). The expression plot on the right displays sample expression values for a single gene. Interactions with the htmlwidget include clicking on genes (points) in the summary plot to bring up associated sample expression values in the expression plot, as well as the summary statistics in the table below. Alternatively, users can interact with the table by clicking on genes (rows) to highlight genes in the summary plot, as well as bring up associated sample expression values in the expression plot. Briefly, other interactive features include a search box for the table, buttons to save plots and data (summary statistics and expression values), additional pop-up information when hovering on points in plots, and rescaling of the y-axis in the expression plot.

Value

htmlwidget object or NULL if html argument is specified.

Author(s)

Hasaru Kariyawasam, Shian Su and Oliver Voogd

See Also

glimmaVolcano, glimmaVolcano.MArrayLM, glimmaVolcano.DGELRT, glimmaVolcano.DESeqDataSet

Examples

dge <- readRDS(
  system.file("RNAseq123/dge.rds", package = "Glimma"))
design <- readRDS(
  system.file("RNAseq123/design.rds", package = "Glimma"))
contr.matrix <- readRDS(
  system.file("RNAseq123/contr.matrix.rds", package = "Glimma"))

dge <- edgeR::estimateDisp(dge, design)
gfit <- edgeR::glmFit(dge, design)
glrt <- edgeR::glmLRT(gfit, design, contrast = contr.matrix)

glimmaVolcano(glrt, dge = dge)

Glimma Volcano Plot

Description

Draws a two-panel interactive volcano plot from an DGELRT object. This is a special case of the glimmaXY plot.

Usage

## S3 method for class 'DGELRT'
glimmaVolcano(
  x,
  dge = NULL,
  counts = dge$counts,
  groups = dge$samples$group,
  status = edgeR::decideTestsDGE(x),
  anno = x$genes,
  display.columns = NULL,
  status.cols = c("#1052bd", "silver", "#cc212f"),
  sample.cols = NULL,
  p.adj.method = "BH",
  transform.counts = c("logcpm", "cpm", "rpkm", "none"),
  main = paste(x$comparison[2], "vs", x$comparison[1]),
  xlab = "logFC",
  ylab = "negLog10PValue",
  html = NULL,
  width = 920,
  height = 920,
  ...
)

Arguments

x

DGELRT object from which summary statistics are extracted from to create summary (left) plot.
dge

DGEList object with nrow(x) rows from which expression values are extracted from to create expression (right) plot. Gene counts are taken from dge$counts and sample groups from dge$samples$group. By default raw counts are transformed to log-cpm values (see more in the transform.counts argument).
counts

numeric matrix with nrow(x) rows containing gene expression values. This can be used to replace the gene counts from dge$counts, i.e. you may have log-rpkm values stored in a different object that you wish to use.
groups

vector of length ncol(dge) representing categorisation of samples in expression plot.
status

vector of length nrow(x) indicating the status of each gene. By default genes in the summary plot are coloured based on its differential expression status using an adjusted p-value cutoff of 0.05 by calling the edgeR::decideTestsDGE() function, where the value of -1 marks down-regulated genes, 0 marks genes with no expression difference, and 1 marks up-regulated genes.
anno

dataframe with nrow(x) rows containing gene annotations.
display.columns

character vector containing names of columns from anno from which to display in mouseover tooltips and table.
status.cols

vector of length 3 containing valid CSS strings for colours associated with status in the order of -1, 0 and 1.
sample.cols

character vector of length ncol(counts) containing valid CSS strings for colours associated with each sample to be displayed on the expression plot. If left unspecified, samples will be coloured according to groups.
p.adj.method

character string specifying p-value adjustment method.
transform.counts

the type of transformation used on the counts - "logcpm" for using edgeR::cpm(counts, log=TRUE); "cpm" for edgeR::cpm(counts); "rpkm" for edgeR::rpkm(counts); "logrpkm" for edgeR::rpkm(counts, log=TRUE); and "none" for no transformation). Defaults to "logcpm".
main

character string for the main title of summary plot.
xlab

character string for the x-axis label of summary plot.
ylab

character string for the y-axis label of summary plot.
html

character string for naming HTML file for exportation of widget. The extension should be included in the file name e.g. "file.html".
width

numeric value indicating width of widget in pixels.
height

numeric value indicating width of height in pixels.
...

additional unused arguments.

Details

The summary plot on the left represents gene-wise log-fold-change (logFC) on the x-axis versus -log10(pvalue). The expression plot on the right displays sample expression values for a single gene. Interactions with the htmlwidget include clicking on genes (points) in the summary plot to bring up associated sample expression values in the expression plot, as well as the summary statistics in the table below. Alternatively, users can interact with the table by clicking on genes (rows) to highlight genes in the summary plot, as well as bring up associated sample expression values in the expression plot. Briefly, other interactive features include a search box for the table, buttons to save plots and data (summary statistics and expression values), additional pop-up information when hovering on points in plots, and rescaling of the y-axis in the expression plot.

Value

htmlwidget object or NULL if html argument is specified.

Author(s)

Hasaru Kariyawasam, Shian Su and Oliver Voogd

See Also

glimmaVolcano, glimmaVolcano.MArrayLM, glimmaVolcano.DGEExact, glimmaVolcano.DESeqDataSet

Glimma Volcano Plot

Description

Draws a two-panel interactive volcano plot from an MArrayLM object. This is a special case of the glimmaXY plot.

Usage

## S3 method for class 'MArrayLM'
glimmaVolcano(
  x,
  dge = NULL,
  counts = dge$counts,
  groups = dge$samples$group,
  coef = ncol(x$coefficients),
  status = limma::decideTests(x),
  anno = x$genes,
  display.columns = NULL,
  status.cols = c("#1052bd", "silver", "#cc212f"),
  sample.cols = NULL,
  p.adj.method = "BH",
  transform.counts = c("logcpm", "cpm", "rpkm", "none"),
  main = colnames(x)[coef],
  xlab = "logFC",
  ylab = "negLog10PValue",
  html = NULL,
  width = 920,
  height = 920,
  ...
)

Arguments

x

MArrayLM object from which summary statistics are extracted from to create summary (left) plot.
dge

DGEList object with nrow(x) rows from which expression values are extracted from to create expression (right) plot. Gene counts are taken from dge$counts and sample groups from dge$samples$group. By default raw counts are transformed to log-cpm values (see more in the transform.counts argument).
counts

numeric matrix with nrow(x) rows containing gene expression values. This can be used to replace the gene counts from dge$counts, i.e. you may have log-rpkm values stored in a different object that you wish to use.
groups

vector of length ncol(dge) representing categorisation of samples in expression plot.
coef

integer indicating the column in x from the summary plot is created.
status

vector of length nrow(x) indicating the status of each gene. By default genes in the summary plot are coloured based on its differential expression status using an adjusted p-value cutoff of 5% by calling the limma::decideTests function, where the value of -1 marks down-regulated genes, 0 marks genes with no expression difference, and 1 marks up-regulated genes.
anno

dataframe with nrow(x) rows containing gene annotations.
display.columns

character vector containing names of columns from anno from which to display in mouseover tooltips and table.
status.cols

vector of length 3 containing valid CSS strings for colours associated with status in the order of -1, 0 and 1.
sample.cols

character vector of length ncol(counts) containing valid CSS strings for colours associated with each sample to be displayed on the expression plot. If left unspecified, samples will be coloured according to groups.
p.adj.method

character string specifying p-value adjustment method.
transform.counts

the type of transformation used on the counts - "logcpm" for using edgeR::cpm(counts, log=TRUE); "cpm" for edgeR::cpm(counts); "rpkm" for edgeR::rpkm(counts); "logrpkm" for edgeR::rpkm(counts, log=TRUE); and "none" for no transformation). Defaults to "logcpm".
main

character string for the main title of summary plot.
xlab

character string for the x-axis label of summary plot.
ylab

character string for the y-axis label of summary plot.
html

character string for naming HTML file for exportation of widget. The extension should be included in the file name e.g. "file.html".
width

numeric value indicating width of widget in pixels.
height

numeric value indicating width of height in pixels.
...

additional unused arguments.

Details

The summary plot on the left represents gene-wise log-fold-change (logFC) on the x-axis versus -log10(pvalue). The expression plot on the right displays sample expression values for a single gene. Interactions with the htmlwidget include clicking on genes (points) in the summary plot to bring up associated sample expression values in the expression plot, as well as the summary statistics in the table below. Alternatively, users can interact with the table by clicking on genes (rows) to highlight genes in the summary plot, as well as bring up associated sample expression values in the expression plot. Briefly, other interactive features include a search box for the table, buttons to save plots and data (summary statistics and expression values), additional pop-up information when hovering on points in plots, and rescaling of the y-axis in the expression plot.

Value

htmlwidget object or NULL if html argument is specified.

Author(s)

Hasaru Kariyawasam, Shian Su and Oliver Voogd

See Also

glimmaVolcano, glimmaVolcano.DGEExact, glimmaVolcano.DGELRT, glimmaVolcano.DESeqDataSet

Glimma XY Plot

Description

Draws a two-panel interactive XY scatter plot.

Usage

glimmaXY(
  x,
  y,
  xlab = "x",
  ylab = "y",
  dge = NULL,
  counts = dge$counts,
  groups = dge$samples$group,
  status = rep(0, length(x)),
  anno = NULL,
  display.columns = NULL,
  status.cols = c("#1052bd", "silver", "#cc212f"),
  sample.cols = NULL,
  transform.counts = c("logcpm", "cpm", "rpkm", "none"),
  main = "XY Plot",
  html = NULL,
  width = 920,
  height = 920
)

Arguments

x

numeric vector of values to plot on the x-axis of the summary plot.
y

numeric vector of values to plot on the y-axis of the summary plot.
xlab

character string for the x-axis label of summary plot.
ylab

character string for the y-axis label of summary plot.
dge

DGEList object with length(x) rows from which expression values are extracted from to create expression (right) plot. Gene counts are taken from dge$counts and sample groups from dge$samples$group.
counts

numeric matrix with length(x) rows containing gene expression values. This can be used to replace raw gene counts from dge$counts with transformed counts e.g. logCPM or logRPKM values.
groups

vector of length ncol(counts) representing categorisation of samples in expression plot.
status

vector of length length(x) indicating the status of each gene. A value of -1 marks a down-regulated gene, 0 marks a gene with no expression difference, and 1 marks an up-regulated gene.
anno

dataframe with length(x) rows containing gene annotations.
display.columns

character vector containing names of columns from anno from which to display in mouseover tooltips and table.
status.cols

vector of length 3 containing valid CSS strings for colours associated with status in the order of -1, 0 and 1.
sample.cols

character vector of length ncol(counts) containing valid CSS strings for colours associated with each sample to be displayed on the expression plot. If left unspecified, samples will be coloured according to groups.
transform.counts

the type of transformation used on the counts - "logcpm" for using edgeR::cpm(counts, log=TRUE); "cpm" for edgeR::cpm(counts); "rpkm" for edgeR::rpkm(counts); "logrpkm" for edgeR::rpkm(counts, log=TRUE); and "none" for no transformation). Defaults to "logcpm".
main

character string for the main title of summary plot.
html

character string for naming HTML file for exportation of widget. The extension should be included in the file name e.g. "file.html".
width

numeric value indicating width of widget in pixels.
height

numeric value indicating width of height in pixels.

Details

The summary plot on the left displays the x and y values specified. The expression plot on the right displays sample expression values for a single gene. Interactions with the htmlwidget include clicking on genes (points) in the summary plot to bring up associated sample expression values in the expression plot, as well as the summary statistics in the table below. Alternatively, users can interact with the table by clicking on genes (rows) to highlight genes in the summary plot, as well as bring up associated sample expression values in the expression plot. Briefly, other interactive features include a search box for the table, buttons to save plots and data (summary statistics and expression values), additional pop-up information when hovering on points in plots, and rescaling of the y-axis in the expression plot.

Value

htmlwidget object or NULL if html argument is specified.

Author(s)

Hasaru Kariyawasam, Shian Su and Oliver Voogd

Examples

dge <- readRDS(
  system.file("RNAseq123/dge.rds", package = "Glimma"))
design <- readRDS(
  system.file("RNAseq123/design.rds", package = "Glimma"))
contr.matrix <- readRDS(
  system.file("RNAseq123/contr.matrix.rds", package = "Glimma"))

v <- limma::voom(dge, design)
vfit <- limma::lmFit(v, design)
vfit <- limma::contrasts.fit(vfit, contrasts = contr.matrix)
efit <- limma::eBayes(vfit)

glimmaXY(efit$Amean, efit$coefficients)

Glimma MD Plot

Description

Draw an interactive MD plot

Usage

glMDPlot(x, ...)

Arguments

x

the DE object to plot.
...

additional arguments affecting the plots produced. See specific methods for detailed arguments.

Value

Draws a two-panel interactive MD plot in an html page. The left plot shows the log-fold-change vs average expression. The right plot shows the expression levels of a particular gene of each sample. Hovering over points on left plot will plot expression level for corresponding gene, clicking on points will fix the expression plot to gene. Clicking on rows on the table has the same effect as clicking on the corresponding gene in the plot.

Author(s)

Shian Su

See Also

glMDPlot.default, glMDPlot.DGELRT, glMDPlot.DGEExact, glMDPlot.MArrayLM, glMDPlot.DESeqDataSet

Glimma MD Plot

Description

Draw an interactive MD plot from a data.frame

Usage

## Default S3 method:
glMDPlot(
  x,
  xval,
  yval,
  counts = NULL,
  anno = NULL,
  groups = NULL,
  samples = NULL,
  status = rep(0, nrow(x)),
  transform = FALSE,
  main = "",
  xlab = xval,
  ylab = yval,
  side.main = "GeneID",
  side.xlab = "Group",
  side.ylab = "Expression",
  side.log = FALSE,
  side.gridstep = ifelse(!transform || side.log, FALSE, 0.5),
  jitter = 30,
  display.columns = side.main,
  cols = c("#00bfff", "#858585", "#ff3030"),
  sample.cols = rep("#1f77b4", ncol(counts)),
  path = getwd(),
  folder = "glimma-plots",
  html = "MD-Plot",
  launch = TRUE,
  ...
)

Arguments

x

the data.frame object containing expression and fold change values.
xval

the column to plot on x axis of left plot.
yval

the column to plot on y axis of left plot.
counts

the matrix of expression values, with samples in columns.
anno

the data.frame containing gene annotations.
groups

the factor containing experimental groups of the samples.
samples

the names of the samples.
status

vector giving the control status of data point, of same length as the number of rows of object. If NULL, then all points are plotted in the default colour.
transform

TRUE if counts should be log-cpm transformed.
main

the title for the left plot.
xlab

the label on the x axis for the left plot.
ylab

the label on the y axis for the left plot.
side.main

the column containing mains for right plot.
side.xlab

label for x axis on right plot.
side.ylab

label for y axis on right plot.
side.log

TRUE to plot expression on the right plot on log scale.
side.gridstep

intervals along which to place grid lines on y axis. Currently only available for linear scale.
jitter

the amount of jitter to apply to the samples in the expressions plot.
display.columns

character vector containing names of columns to display in mouseover tooltips and table.
cols

vector of strings denoting colours corresponding to control status -1, 0 and 1. (may be R named colours or Hex values)
sample.cols

vector of strings denoting colours for each sample point on the expression plot.
path

the path in which the folder will be created.
folder

the name of the fold to save html file to.
html

the name of the html file to save plots to.
launch

TRUE to launch plot after call.
...

additional arguments to be passed onto the MD plot. (main, xlab, ylab can be set for the left plot)

Value

Draws a two-panel interactive MD plot in an html page. The left plot shows the log-fold-change vs average expression. The right plot shows the expression levels of a particular gene of each sample. Hovering over points on left plot will plot expression level for corresponding gene, clicking on points will fix the expression plot to gene. Clicking on rows on the table has the same effect as clicking on the corresponding gene in the plot.

Author(s)

Shian Su

Glimma MD Plot

Description

Draw an interactive MD plot from a DESeqDataSet object

Usage

## S3 method for class 'DESeqDataSet'
glMDPlot(
  x,
  counts = NULL,
  anno,
  groups,
  samples = NULL,
  status = rep(0, nrow(x)),
  transform = FALSE,
  main = "",
  xlab = "Mean Expression",
  ylab = "log-fold-change",
  side.xlab = "Group",
  side.ylab = "logMean",
  side.log = FALSE,
  side.gridstep = ifelse(!transform || side.log, FALSE, 0.5),
  jitter = 30,
  side.main = "GeneID",
  display.columns = NULL,
  cols = c("#00bfff", "#858585", "#ff3030"),
  sample.cols = rep("#1f77b4", ncol(x)),
  path = getwd(),
  folder = "glimma-plots",
  html = "MD-Plot",
  launch = TRUE,
  ...
)

Arguments

x

the DESeqDataSet object.
counts

the matrix of expression values, with samples in columns.
anno

the data.frame containing gene annotations.
groups

the factor containing experimental groups of the samples.
samples

the names of the samples.
status

vector giving the control status of data point, of same length as the number of rows of object. If NULL, then all points are plotted in the default colour.
transform

TRUE if counts should be log-cpm transformed.
main

the title for the left plot.
xlab

label for x axis on left plot.
ylab

label for y axis on left plot.
side.xlab

label for x axis on right plot.
side.ylab

label for y axis on right plot.
side.log

TRUE to plot expression on the right plot on log scale.
side.gridstep

intervals along which to place grid lines on y axis. Currently only available for linear scale.
jitter

the amount of jitter to apply to the samples in the expressions plot.
side.main

the column containing mains for right plot.
display.columns

character vector containing names of columns to display in mouseover tooltips and table.
cols

vector of strings denoting colours corresponding to control status -1, 0 and 1. (may be R named colours or Hex values)
sample.cols

vector of strings denoting colours for each sample point on the expression plot.
path

the path in which the folder will be created.
folder

the name of the fold to save html file to.
html

the name of the html file to save plots to.
launch

TRUE to launch plot after call.
...

additional arguments to be passed onto the MD plot. (main, xlab, ylab can be set for the left plot)

Value

Draws a two-panel interactive MD plot in an html page. The left plot shows the log-fold-change vs average expression. The right plot shows the expression levels of a particular gene of each sample. Hovering over points on left plot will plot expression level for corresponding gene, clicking on points will fix the expression plot to gene. Clicking on rows on the table has the same effect as clicking on the corresponding gene in the plot.

Author(s)

Shian Su

Glimma MD Plot

Description

Draw an interactive MD plot from a DESeqResults object

Usage

## S3 method for class 'DESeqResults'
glMDPlot(
  x,
  counts = NULL,
  anno,
  groups,
  samples = NULL,
  status = rep(0, nrow(x)),
  transform = FALSE,
  main = "",
  xlab = "Mean Expression",
  ylab = "log-fold-change",
  side.xlab = "Group",
  side.ylab = "Expression",
  side.log = FALSE,
  side.gridstep = ifelse(!transform || side.log, FALSE, 0.5),
  jitter = 30,
  side.main = "GeneID",
  display.columns = NULL,
  cols = c("#00bfff", "#858585", "#ff3030"),
  sample.cols = rep("#1f77b4", ncol(counts)),
  path = getwd(),
  folder = "glimma-plots",
  html = "MD-Plot",
  launch = TRUE,
  ...
)

Arguments

x

the DESeqResults object.
counts

the matrix of expression values, with samples in columns.
anno

the data.frame containing gene annotations.
groups

the factor containing experimental groups of the samples.
samples

the names of the samples.
status

vector giving the control status of data point, of same length as the number of rows of object. If NULL, then all points are plotted in the default colour.
transform

TRUE if counts should be log-cpm transformed.
main

the title for the left plot.
xlab

label for x axis on left plot.
ylab

label for y axis on left plot.
side.xlab

label for x axis on right plot.
side.ylab

label for y axis on right plot.
side.log

TRUE to plot expression on the right plot on log scale.
side.gridstep

intervals along which to place grid lines on y axis. Currently only available for linear scale.
jitter

the amount of jitter to apply to the samples in the expressions plot.
side.main

the column containing mains for right plot.
display.columns

character vector containing names of columns to display in mouseover tooltips and table.
cols

vector of strings denoting colours corresponding to control status -1, 0 and 1. (may be R named colours or Hex values)
sample.cols

vector of strings denoting colours for each sample point on the expression plot.
path

the path in which the folder will be created.
folder

the name of the fold to save html file to.
html

the name of the html file to save plots to.
launch

TRUE to launch plot after call.
...

additional arguments to be passed onto the MD plot. (main, xlab, ylab can be set for the left plot)

Value

Draws a two-panel interactive MD plot in an html page. The left plot shows the log-fold-change vs average expression. The right plot shows the expression levels of a particular gene of each sample. Hovering over points on left plot will plot expression level for corresponding gene, clicking on points will fix the expression plot to gene. Clicking on rows on the table has the same effect as clicking on the corresponding gene in the plot.

Author(s)

Shian Su

Glimma MD Plot

Description

Draw an interactive MD plot from a DGELRT objet

Usage

## S3 method for class 'DGEExact'
glMDPlot(
  x,
  counts = NULL,
  anno = NULL,
  groups = NULL,
  samples = NULL,
  status = rep(0, nrow(x)),
  transform = FALSE,
  main = "",
  xlab = "Average log CPM",
  ylab = "log-fold-change",
  side.xlab = "Group",
  side.ylab = "Expression",
  side.log = FALSE,
  side.gridstep = ifelse(!transform || side.log, FALSE, 0.5),
  p.adj.method = "BH",
  jitter = 30,
  side.main = "GeneID",
  display.columns = NULL,
  cols = c("#00bfff", "#858585", "#ff3030"),
  sample.cols = rep("#1f77b4", ncol(counts)),
  path = getwd(),
  folder = "glimma-plots",
  html = "MD-Plot",
  launch = TRUE,
  ...
)

Arguments

x

the DGEExact object.
counts

the matrix of expression values, with samples in columns.
anno

the data.frame containing gene annotations.
groups

the factor containing experimental groups of the samples.
samples

the names of the samples.
status

vector giving the control status of data point, of same length as the number of rows of object. If NULL, then all points are plotted in the default colour.
transform

TRUE if counts should be log-cpm transformed.
main

the title for the left plot.
xlab

label for x axis on left plot.
ylab

label for y axis on left plot.
side.xlab

label for x axis on right plot.
side.ylab

label for y axis on right plot.
side.log

TRUE to plot expression on the right plot on log scale.
side.gridstep

intervals along which to place grid lines on y axis. Currently only available for linear scale.
p.adj.method

character vector indicating multiple testing correction method. See p.adjust for available methods. (defaults to "BH")
jitter

the amount of jitter to apply to the samples in the expressions plot.
side.main

the column containing mains for right plot.
display.columns

character vector containing names of columns to display in mouseover tooltips and table.
cols

vector of strings denoting colours corresponding to control status -1, 0 and 1. (may be R named colours or Hex values)
sample.cols

vector of strings denoting colours for each sample point on the expression plot.
path

the path in which the folder will be created.
folder

the name of the fold to save html file to.
html

the name of the html file to save plots to.
launch

TRUE to launch plot after call.
...

additional arguments to be passed onto the MD plot. (main, xlab, ylab can be set for the left plot)

Value

Draws a two-panel interactive MD plot in an html page. The left plot shows the log-fold-change vs average expression. The right plot shows the expression levels of a particular gene of each sample. Hovering over points on left plot will plot expression level for corresponding gene, clicking on points will fix the expression plot to gene. Clicking on rows on the table has the same effect as clicking on the corresponding gene in the plot.

Author(s)

Shian Su

Glimma MD Plot

Description

Draw an interactive MD plot from a DGELRT object

Usage

## S3 method for class 'DGELRT'
glMDPlot(
  x,
  counts = NULL,
  anno = NULL,
  groups = NULL,
  samples = NULL,
  status = rep(0, nrow(x)),
  transform = FALSE,
  main = "",
  xlab = "Average log CPM",
  ylab = "log-fold-change",
  side.xlab = "Group",
  side.ylab = "Expression",
  side.log = FALSE,
  side.gridstep = ifelse(!transform || side.log, FALSE, 0.5),
  p.adj.method = "BH",
  jitter = 30,
  side.main = "GeneID",
  display.columns = NULL,
  cols = c("#00bfff", "#858585", "#ff3030"),
  sample.cols = rep("#1f77b4", ncol(counts)),
  path = getwd(),
  folder = "glimma-plots",
  html = "MD-Plot",
  launch = TRUE,
  ...
)

Arguments

x

the DGELRT object.
counts

the matrix of expression values, with samples in columns.
anno

the data.frame containing gene annotations.
groups

the factor containing experimental groups of the samples.
samples

the names of the samples.
status

vector giving the control status of data point, of same length as the number of rows of object. If NULL, then all points are plotted in the default colour.
transform

TRUE if counts should be log-cpm transformed.
main

the title for the left plot.
xlab

label for x axis on left plot.
ylab

label for y axis on left plot.
side.xlab

label for x axis on right plot.
side.ylab

label for y axis on right plot.
side.log

TRUE to plot expression on the right plot on log scale.
side.gridstep

intervals along which to place grid lines on y axis. Currently only available for linear scale.
p.adj.method

character vector indicating multiple testing correction method. See p.adjust for available methods. (defaults to "BH")
jitter

the amount of jitter to apply to the samples in the expressions plot.
side.main

the column containing mains for right plot.
display.columns

character vector containing names of columns to display in mouseover tooltips and table.
cols

vector of strings denoting colours corresponding to control status -1, 0 and 1. (may be R named colours or Hex values)
sample.cols

vector of strings denoting colours for each sample point on the expression plot.
path

the path in which the folder will be created.
folder

the name of the fold to save html file to.
html

the name of the html file to save plots to.
launch

TRUE to launch plot after call.
...

additional arguments to be passed onto the MD plot. (main, xlab, ylab can be set for the left plot)

Value

Draws a two-panel interactive MD plot in an html page. The left plot shows the log-fold-change vs average expression. The right plot shows the expression levels of a particular gene of each sample. Hovering over points on left plot will plot expression level for corresponding gene, clicking on points will fix the expression plot to gene. Clicking on rows on the table has the same effect as clicking on the corresponding gene in the plot.

Author(s)

Shian Su

Glimma MD Plot

Description

Draw an interactive MD plot from a MArrayLM object

Usage

## S3 method for class 'MArrayLM'
glMDPlot(
  x,
  counts = NULL,
  anno = NULL,
  groups = NULL,
  samples = NULL,
  status = rep(0, nrow(x)),
  transform = FALSE,
  main = "",
  xlab = "Average log CPM",
  ylab = "log-fold-change",
  side.main = "GeneID",
  side.xlab = "Group",
  side.ylab = "Expression",
  side.log = FALSE,
  side.gridstep = ifelse(!transform || side.log, FALSE, 0.5),
  coef = ncol(x$coefficients),
  p.adj.method = "BH",
  jitter = 30,
  display.columns = NULL,
  cols = c("#00bfff", "#858585", "#ff3030"),
  sample.cols = rep("#1f77b4", ncol(counts)),
  path = getwd(),
  folder = "glimma-plots",
  html = "MD-Plot",
  launch = TRUE,
  ...
)

Arguments

x

the MArrayLM object.
counts

the matrix of expression values, with samples in columns.
anno

the data.frame containing gene annotations.
groups

the factor containing experimental groups of the samples.
samples

the names of the samples.
status

vector giving the control status of data point, of same length as the number of rows of object. If NULL, then all points are plotted in the default colour.
transform

TRUE if counts should be log-cpm transformed.
main

the title for the left plot.
xlab

label for x axis on left plot.
ylab

label for y axis on left plot.
side.main

the column containing mains for right plot.
side.xlab

label for x axis on right plot.
side.ylab

label for y axis on right plot.
side.log

TRUE to plot expression on the right plot on log scale.
side.gridstep

intervals along which to place grid lines on y axis. Currently only available for linear scale.
coef

integer or character index vector indicating which column of object to plot.
p.adj.method

character vector indicating multiple testing correction method. See p.adjust for available methods. (defaults to "BH")
jitter

the amount of jitter to apply to the samples in the expressions plot.
display.columns

character vector containing names of columns to display in mouseover tooltips and table.
cols

vector of strings denoting colours corresponding to control status -1, 0 and 1. (may be R named colours or Hex values)
sample.cols

vector of strings denoting colours for each sample point on the expression plot.
path

the path in which the folder will be created.
folder

the name of the fold to save html file to.
html

the name of the html file to save plots to.
launch

TRUE to launch plot after call.
...

additional arguments to be passed onto the MD plot. (main, xlab, ylab can be set for the left plot)

Value

Draws a two-panel interactive MD plot in an html page. The left plot shows the log-fold-change vs average expression. The right plot shows the expression levels of a particular gene of each sample. Hovering over points on left plot will plot expression level for corresponding gene, clicking on points will fix the expression plot to gene. Clicking on rows on the table has the same effect as clicking on the corresponding gene in the plot.

Author(s)

Shian Su

glMDPlot Rmarkdown link and instructions

Description

When run inside of a text-block of Rmarkdown document using 'r ...' this produces a link and instructions about the usage of the interactive plots.

Usage

glMDRmd(html = "MD-Plot")

Arguments

html

name of the HTML page containing plots from glMDPlot.

Value

None

See Also

glMDPlot

Examples

glMDRmd()

Glimma MDS Plot

Description

Draw an interactive MD plot from a DGEList object with distances calculated from most variable genes.

Usage

glMDSPlot(x, ...)

Arguments

x

the matrix containing the gene expressions.
...

additional arguments.

Value

Draws a two-panel interactive MDS plot in an html page. The left panel contains the plot between two MDS dimensions, with annotations displayed on hover. The right panel contains a bar plot of the eigenvalues of each dimension, clicking on any of the bars will plot the corresponding dimension against the next dimension.

Author(s)

Shian Su, Gordon Smyth

See Also

glMDSPlot.default, glMDSPlot.DGEList

Glimma MDS Plot

Description

Draw an interactive MD plot from a DGEList object with distances calculated from most variable genes.

Usage

## Default S3 method:
glMDSPlot(
  x,
  top = 500,
  labels = seq_cols(x),
  groups = rep(1, ncol(x)),
  gene.selection = c("pairwise", "common"),
  main = "MDS Plot",
  path = getwd(),
  folder = "glimma-plots",
  html = "MDS-Plot",
  launch = TRUE,
  ...
)

Arguments

x

the matrix containing the gene expressions.
top

the number of top most variable genes to use.
labels

the labels for each sample.
groups

the experimental group to which samples belong.
gene.selection

"pairwise" if most variable genes are to be chosen for each pair of samples or "common" to select the same genes for all comparisons.
main

the title of the plot.
path

the path in which the folder will be created.
folder

the name of the fold to save html file to.
html

the name of the html file to save plots to.
launch

TRUE to launch plot after call.
...

additional arguments.

Value

Draws a two-panel interactive MDS plot in an html page. The left panel contains the plot between two MDS dimensions, with annotations displayed on hover. The right panel contains a bar plot of the eigenvalues of each dimension, clicking on any of the bars will plot the corresponding dimension against the next dimension.

Author(s)

Shian Su, Gordon Smyth

Glimma MDS Plot

Description

Draw an interactive MD plot from a DGEList object with distances calculated from most variable genes.

Usage

## S3 method for class 'DESeqDataSet'
glMDSPlot(
  x,
  top = 500,
  labels = NULL,
  groups = NULL,
  gene.selection = c("pairwise", "common"),
  prior.count = 0.25,
  main = "MDS Plot",
  path = getwd(),
  folder = "glimma-plots",
  html = "MDS-Plot",
  launch = TRUE,
  ...
)

Arguments

x

the DESeqDataSet containing the gene expressions.
top

the number of top most variable genes to use.
labels

the labels for each sample.
groups

the experimental group to which samples belong.
gene.selection

"pairwise" if most variable genes are to be chosen for each pair of samples or "common" to select the same genes for all comparisons.
prior.count

average count to be added to each observation to avoid taking log of zero. Used only if log=TRUE.
main

the title of the plot.
path

the path in which the folder will be created.
folder

the name of the fold to save html file to.
html

the name of the html file to save plots to.
launch

TRUE to launch plot after call.
...

additional arguments.

Value

Draws a two-panel interactive MDS plot in an html page. The left panel contains the plot between two MDS dimensions, with annotations displayed on hover. The right panel contains a bar plot of the eigenvalues of each dimension, clicking on any of the bars will plot the corresponding dimension against the next dimension.

Author(s)

Shian Su, Gordon Smyth

Glimma MDS Plot

Description

Draw an interactive MD plot from a DGEList object with distances calculated from most variable genes.

Usage

## S3 method for class 'DGEList'
glMDSPlot(
  x,
  top = 500,
  labels = NULL,
  groups = rep(1, ncol(x)),
  gene.selection = c("pairwise", "common"),
  prior.count = 2,
  main = "MDS Plot",
  path = getwd(),
  folder = "glimma-plots",
  html = "MDS-Plot",
  launch = TRUE,
  ...
)

Arguments

x

the DGEList containing the gene expressions.
top

the number of top most variable genes to use.
labels

the labels for each sample.
groups

the experimental group to which samples belong.
gene.selection

"pairwise" if most variable genes are to be chosen for each pair of samples or "common" to select the same genes for all comparisons.
prior.count

average count to be added to each observation to avoid taking log of zero. Used only if log=TRUE.
main

the title of the plot.
path

the path in which the folder will be created.
folder

the name of the fold to save html file to.
html

the name of the html file to save plots to.
launch

TRUE to launch plot after call.
...

additional arguments.

Value

Draws a two-panel interactive MDS plot in an html page. The left panel contains the plot between two MDS dimensions, with annotations displayed on hover. The right panel contains a bar plot of the eigenvalues of each dimension, clicking on any of the bars will plot the corresponding dimension against the next dimension.

Author(s)

Shian Su, Gordon Smyth

Glimma XY Plot

Description

Draw an interactive XY plot with multiple panels

Usage

glXYPlot(
  x,
  y,
  counts = NULL,
  groups = NULL,
  samples = NULL,
  status = rep(0, nrow(data)),
  anno = NULL,
  display.columns = NULL,
  xlab = "x",
  ylab = "y",
  side.main = "GeneID",
  side.xlab = "Group",
  side.ylab = "Expression",
  sample.cols = rep("#1f77b4", length(groups)),
  cols = c("#00bfff", "#858585", "#ff3030"),
  jitter = 30,
  path = getwd(),
  folder = "glimma-plots",
  html = "XY-Plot",
  launch = TRUE,
  ...
)

Arguments

x

a numeric vector of values to plot on the x-axis of the summary plot.
y

a numeric vector of values to plot on the y-axis of the summary plot.
counts

the matrix containing all counts, the column order should correspond to the order of the x and y vectors.
groups

the factor containing experimental groups of the samples.
samples

the names of the samples.
status

vector giving the control status of data point, of same length as the number of rows of object. If NULL, then all points are plotted in the default colour
anno

the data.frame containing gene annotations.
display.columns

character vector containing names of columns to display in mouseover tooltips and table.
xlab

the label on the x axis for the left plot.
ylab

the label on the y axis for the left plot.
side.main

the column containing mains for right plot.
side.xlab

the label on the x axis for the right plot.
side.ylab

the label on the y axis for the right plot.
sample.cols

vector of strings denoting colours for each sample point on the expression plot.
cols

vector of strings denoting colours corresponding to control status -1, 0 and 1. (may be R named colours or Hex values)
jitter

the amount of jitter to apply to the samples in the expressions plot.
path

the path in which the folder will be created.
folder

the name of the fold to save html file to.
html

the name of the html file to save plots to.
launch

TRUE to launch plot after call.
...

additional arguments to be passed onto the MD plot. (main, etc. can be set for the left plot)

Value

Draws a two-panel interactive XY scatter plot in an html page. The left plot shows the x and y values specified. The right plot shows the expression levels of a particular gene in each sample. Hovering over points on left plot will plot expression level for the corresponding gene, clicking on points will fix the expression plot to that gene. Clicking on rows on the table has the same effect as clicking on the corresponding gene in the plot. This function generates a display that is similar in style to glMDPlot, except that it provides more flexibility in what the user can provide.

Author(s)

Charity Law and Shian Su

Examples

data(iris)

glXYPlot(iris$Sepal.Width, iris$Sepal.Length, 
         xlab="Sepal.Width", ylab="Sepal.Length", side.main="PlantID")

Hexcode colours

Description

Check if string(s) are valid hex colour representation

Usage

is.hex(x)

Arguments

x

the colour value(s) to check.

Value

Logical vector indicating if strings(s) are valid hex representations

Mouse based RNAseq data for study of smchd1 gene.

Description

Mouse based RNAseq data for study of smchd1 gene.

Author(s)

Ruijie Liu, Kelan Chen, Natasha Jansz, Marnie E. Blewitt, Matthew E. Ritchie

References

http://www.sciencedirect.com/science/article/pii/S2213596015301306