| Title: | Visualization Of Differential Expression Analysis |
|---|---|
| Description: | This package contains utility functions to visualize the output from differential expression analysis. The input can be either a model or a list of top tables or a combination of the two. The model can be output from limma, edgeR or DESeq2. |
| Authors: | Katarzyna Gorczak [aut, cre], Laure Cougnaud [aut] |
| Maintainer: | Katarzyna Gorczak <[email protected]> |
| License: | GPL-3 |
| Version: | 0.99.3 |
| Built: | 2026-06-10 07:13:25 UTC |
| Source: | https://github.com/bioc/daVis |
Add column with hover text to top table
addHoverText(input, columns)addHoverText(input, columns)
input |
top table |
columns |
columns to extract from top table for hover info |
top table with additional 'hoverText' column
Katarzyna Gorczak
Arrange top tables
arrangeTopTables( input, featuresIdVar, logFCrange = NULL, commonFeatures = FALSE, fdr = NULL, dir = NULL, output = c("table", "list") )arrangeTopTables( input, featuresIdVar, logFCrange = NULL, commonFeatures = FALSE, fdr = NULL, dir = NULL, output = c("table", "list") )
input |
list of top tables or long table |
featuresIdVar |
column with feature identifier (must be unique) |
logFCrange |
numeric vector with two values (low- and high-threshold for log-foldchange) |
commonFeatures |
logical whether to keep the same set of features per coefficients |
fdr |
fdr threshold |
dir |
direction for the logFC ("pos" or "neg"; NULL by default). The features with "pos" diraction will be extracted with logFC above 0. The features with "neg" diraction will be extracted with logFC below 0. |
output |
whether to output list of top tables or long table |
arranged list or long data.frame with top tables for coefficients of interest
Katarzyna Gorczak
Calculate correlation
calcCorrelation(input)calcCorrelation(input)
input |
long-format table |
text with information from cor.test
Katarzyna Gorczak
Create ggplot object with barplot
callBarplot( tbl, color, annotCex, addPercentage, title, titleCex, xlab, ylab, axesTitleCex, axesCex, legendPosition, legendTitleCex, legendCex )callBarplot( tbl, color, annotCex, addPercentage, title, titleCex, xlab, ylab, axesTitleCex, axesCex, legendPosition, legendTitleCex, legendCex )
tbl |
combined summaries for all coefficients (i.e.: number of significantly up- and down-regulated genes) |
color |
color palette, must contain two colors. |
annotCex |
cex for the text displayed in the plot (indicating number of genes). |
addPercentage |
logical whether to add percentage |
title |
plot title, NULL by default. |
titleCex |
cex for the plot title. |
xlab |
x-axis title, NULL by default. |
ylab |
y-axis title, NULL by default. |
axesTitleCex |
cex for the axis title. |
axesCex |
cex for the axis text. |
legendPosition |
legend position ("right", "bottom", "none"). If 'none', no legend is shown. |
legendTitleCex |
cex for the legend title. |
legendCex |
cex for the legend text. |
ggplot object
Katarzyna Gorczak
Create ggplot object with heatmap
callHeatmap( topTableOutput, xlab, ylab, title, color, axesCex, featuresColor, axesTitleCex, titleCex, legendTitleCex, legendCex, typePlot, coefColor, colorNA )callHeatmap( topTableOutput, xlab, ylab, title, color, axesCex, featuresColor, axesTitleCex, titleCex, legendTitleCex, legendCex, typePlot, coefColor, colorNA )
topTableOutput |
combined topTables for all coefficients |
xlab |
x-axis title, NULL by default. |
ylab |
y-axis title, NULL by default. |
title |
plot title, NULL by default. |
color |
palette for gradient to fill the heatmap. |
axesCex |
cex for the axis text. |
featuresColor |
character vector specifying colors to use for
the feature label text. Either length 1 or the same length as
|
axesTitleCex |
cex for the axis title. |
titleCex |
cex for the plot title. |
legendTitleCex |
cex for the legend title. |
legendCex |
cex for the legend text. |
typePlot |
plot type, can be one of "static" or "interactive". |
coefColor |
color palette for coef labels. |
colorNA |
color for missing values. |
ggplot object
Katarzyna Gorczak
Create ggplot object with log ratio plot
callLogRatioPlot( topTableOutput, xlab, xexpand, axesCex, axesTitleCex, title, titleCex, facetNCol, facetCex, facetColor, color, textVar = NULL, textVarCex, featuresColor, typePlot, multiplePlot )callLogRatioPlot( topTableOutput, xlab, xexpand, axesCex, axesTitleCex, title, titleCex, facetNCol, facetCex, facetColor, color, textVar = NULL, textVarCex, featuresColor, typePlot, multiplePlot )
topTableOutput |
combined topTables for all coefficients |
xlab |
x-axis title, or |
xexpand |
expansion factor for the x-axis
(see |
axesCex |
relative size for the label of the axes. |
axesTitleCex |
relative size for the title of the axes. |
title |
plot title, NULL by default. |
titleCex |
relative size for the title of the plot. |
facetNCol |
number of columns in facets, by default the function
|
facetCex |
cex for the facets if multiple plot are used. |
facetColor |
color for the text of the facets |
color |
string with colors for the bars, one per coefficient. |
textVar |
(optional) String with name of a column to display as text. |
textVarCex |
cex for the text next to bars |
featuresColor |
character vector specifying colors to use for
the feature label text. Either length 1 or the same length as
|
typePlot |
plot type, can be one of "static" or "interactive". |
multiplePlot |
whether to use facet_wrap on coefficients |
ggplot object
Laure Cougnaud, Kirsten Van Hoorde, Katarzyna Gorczak
Create ggplot object with MA plot
callMAplot( topTableOutput, topTableOutputTopGenes, includeTableGenesOfInterest, title, topTableOutputGenesOfInterest, xlab, ylab, axesCex, axesTitleCex, titleCex, facetCex, facetColor, facetNCol, genesToHighlightCex, topGenesCex, color, direction, alpha, sizeVar, size, legendPosition, legendTitleCex, legendCex, typePlot, multiplePlot, ... )callMAplot( topTableOutput, topTableOutputTopGenes, includeTableGenesOfInterest, title, topTableOutputGenesOfInterest, xlab, ylab, axesCex, axesTitleCex, titleCex, facetCex, facetColor, facetNCol, genesToHighlightCex, topGenesCex, color, direction, alpha, sizeVar, size, legendPosition, legendTitleCex, legendCex, typePlot, multiplePlot, ... )
topTableOutput |
combined topTables for all coefficients |
topTableOutputTopGenes |
data.frame with top genes |
includeTableGenesOfInterest |
whether to label |
title |
plot title, NULL by default. |
topTableOutputGenesOfInterest |
data.frame with |
xlab |
x-axis title, NULL by default. |
ylab |
y-axis title, NULL by default. |
axesCex |
cex for the axis text. |
axesTitleCex |
cex for the axis title. |
titleCex |
cex for the plot title. |
facetCex |
cex for the facets if multiple plot are used. |
facetColor |
color for the text of the facets |
facetNCol |
number of columns in facets, by default the function
|
genesToHighlightCex |
cex for genesToHighlight |
topGenesCex |
cex for topGenes labels |
color |
colors for points indicating direction (should be three colors: significant up- and down-regulated and non-significant) |
direction |
logical whether to color significant up- and down-regulated genes |
alpha |
transparency level for the points, 0.4 by default. |
sizeVar |
column name used for the size, empty by default. |
size |
character or factor with specified size(s) (cex) for the points, replicated if needed. This is used only if sizeVar is empty. By default: '2.5' if sizeVar is not specified and default ggplot size(s) otherwise |
legendPosition |
legend position ("right", "bottom", "none"). If 'none', no legend is shown. |
legendTitleCex |
cex for the legend title. |
legendCex |
cex for the legend text. |
typePlot |
plot type can be one of "static" or "interactive". |
multiplePlot |
whether to use facet_wrap on coefficients |
... |
Extra parameters passed to |
ggplot object
Katarzyna Gorczak
Create ggplot object with scatter plot
callScatterPlot( topTableOutput, topTableOutputTopGenes, includeTableGenesOfInterest, coef, topTableOutputGenesOfInterest, facetNCol, xlab, ylab, title, fdr, color, axesCex, axesTitleCex, titleCex, legendPosition, legendTitleCex, legendCex, facetCex, facetColor, topGenesCex, genesToHighlightCex, alpha, pointSize, typePlot, multiplePlot, correlation, correlationCex, ... )callScatterPlot( topTableOutput, topTableOutputTopGenes, includeTableGenesOfInterest, coef, topTableOutputGenesOfInterest, facetNCol, xlab, ylab, title, fdr, color, axesCex, axesTitleCex, titleCex, legendPosition, legendTitleCex, legendCex, facetCex, facetColor, topGenesCex, genesToHighlightCex, alpha, pointSize, typePlot, multiplePlot, correlation, correlationCex, ... )
topTableOutput |
combined topTables for all coefficients |
topTableOutputTopGenes |
data.frame with top genes |
includeTableGenesOfInterest |
whether to label |
coef |
character, coefficient names. |
topTableOutputGenesOfInterest |
data.frame with |
facetNCol |
number of columns in facets, by default the function
|
xlab |
x-axis title, NULL by default. |
ylab |
y-axis title, NULL by default. |
title |
plot title, NULL by default. |
fdr |
threshold considered for significance, NULL by default. |
color |
color palette to distinguish significance groups. Four colors must be specified. |
axesCex |
cex for the axis text. |
axesTitleCex |
cex for the axis title. |
titleCex |
cex for the plot title. |
legendPosition |
legend position ("right", "bottom", "none"). If 'none', no legend is shown. |
legendTitleCex |
cex for the legend title. |
legendCex |
cex for the legend text. |
facetCex |
cex for the facets if multiple plot are used. |
facetColor |
color for the text of the facets |
topGenesCex |
cex for topGenes labels |
genesToHighlightCex |
cex for genesToHighlight |
alpha |
transparency level for the points, 0.4 by default. |
pointSize |
point size, 2 by default. |
typePlot |
plot type, can be one of "static" or "interactive" |
multiplePlot |
logical whether to use facet on coefficients |
correlation |
logical whether to calculate the correlation and add text to the plot. |
correlationCex |
cex for the text with correlation value |
... |
Extra parameters passed to |
ggplot object
Katarzyna Gorczak
Create ggplot object with volcano plot
callVolcanoPlot( topTableOutput, topTableOutputTopGenes, includeTableGenesOfInterest, topTableOutputGenesOfInterest, xlab, ylab, title, fdr, colorVar, color, shapeVar, shape, alphaVar, alpha, alphaRange, sizeVar, size, sizeRange, axesCex, axesTitleCex, titleCex, legendPosition, legendTitleCex, legendCex, facetCex, facetColor, facetNCol, topGenesCex, genesToHighlightCex, typePlot, multiplePlot, ... )callVolcanoPlot( topTableOutput, topTableOutputTopGenes, includeTableGenesOfInterest, topTableOutputGenesOfInterest, xlab, ylab, title, fdr, colorVar, color, shapeVar, shape, alphaVar, alpha, alphaRange, sizeVar, size, sizeRange, axesCex, axesTitleCex, titleCex, legendPosition, legendTitleCex, legendCex, facetCex, facetColor, facetNCol, topGenesCex, genesToHighlightCex, typePlot, multiplePlot, ... )
topTableOutput |
combined topTables for all coefficients |
topTableOutputTopGenes |
data.frame with top genes |
includeTableGenesOfInterest |
whether to label |
topTableOutputGenesOfInterest |
data.frame with |
xlab |
x-axis title, NULL by default. |
ylab |
y-axis title, NULL by default. |
title |
plot title, NULL by default. |
fdr |
threshold considered for significance, NULL by default. |
colorVar |
(optional) string with column name containing variable used for coloring. If not specified, coloring is based on the adjusted p-values. |
color |
color palette, only used if |
shapeVar |
(optional) string with column name containing variable used for shaping. If not specified, only points are used. |
shape |
shape palette, only used if |
alphaVar |
column name used for the transparency, empty by default. |
alpha |
character or factor with specified transparency(s) for the points, replicated if needed. By default: '1' if alphaVar is not specified. |
alphaRange |
transparency (alpha) range used in the plot, possible only if the alphaVar is 'numeric' or 'integer'. |
sizeVar |
column name used for the size, empty by default. |
size |
character or factor with specified size(s) (cex) for the points, replicated if needed. This is used only if sizeVar is empty. By default: '2.5' if sizeVar is not specified and default ggplot size(s) otherwise |
sizeRange |
size (cex) range used in the plot, possible only if the sizeVar is 'numeric' or 'integer' |
axesCex |
cex for the axis text. |
axesTitleCex |
cex for the axis title. |
titleCex |
cex for the plot title. |
legendPosition |
legend position ("right", "bottom", "none"). If 'none', no legend is shown. |
legendTitleCex |
cex for the legend title. |
legendCex |
cex for the legend text. |
facetCex |
cex for the facets if multiple plot are used. |
facetColor |
color for the text of the facets |
facetNCol |
number of columns in facets, by default the function
|
topGenesCex |
cex for topGenes labels |
genesToHighlightCex |
cex for genesToHighlight |
typePlot |
plot type can be one of "static" or "interactive". |
multiplePlot |
whether to use facet_wrap on coefficients |
... |
Extra parameters passed to |
ggplot object
Laure Cougnaud, Kirsten Van Hoorde, Katarzyna Gorczak
Create ggplot object with waterfall plot
callWaterfallPlot( topTableOutput, featuresColor, xlab, ylab, axesCex, axesTitleCex, title, titleCex, facetNCol, fillVar, fill, colorVar, color, alphaVar, alpha, alphaRange, legendPosition, legendCex, legendTitleCex, facetCex, facetColor, typePlot, multiplePlot )callWaterfallPlot( topTableOutput, featuresColor, xlab, ylab, axesCex, axesTitleCex, title, titleCex, facetNCol, fillVar, fill, colorVar, color, alphaVar, alpha, alphaRange, legendPosition, legendCex, legendTitleCex, facetCex, facetColor, typePlot, multiplePlot )
topTableOutput |
combined top tables for all coefficients |
featuresColor |
character vector specifying colors to use for
the feature label text. Either length 1 or the same length as
|
xlab |
x-axis title, 'logFC' by default. |
ylab |
y-axis title, NULL by default. |
axesCex |
cex for the axis text. |
axesTitleCex |
cex for the axis title. |
title |
plot title, NULL by default. |
titleCex |
cex for the plot title. |
facetNCol |
number of columns in facets, by default the function
|
fillVar |
name of variable (in 'input') used for filling, empty by default |
fill |
character or factor with specified color(s) for the boxplot inside, replicated if needed. By default: 'skyblue2' if fillVar is not specified and default ggplot palette otherwise. |
colorVar |
name of variable (in 'input') used for coloring, empty by default |
color |
character or factor with specified color(s) for the bar border, replicated if needed. By default: 'skyblue2' if colorVar is not specified and default ggplot palette otherwise. |
alphaVar |
column name used for the transparency, empty by default. |
alpha |
character or factor with specified transparency(s) for the bars, replicated if needed. By default: '1' if alphaVar is not specified. |
alphaRange |
transparency (alpha) range used in the plot, possible only if the alphaVar is 'numeric' or 'integer'. |
legendPosition |
legend position ("right", "bottom", "none"). If 'none', no legend is shown. |
legendCex |
cex for the legend text. |
legendTitleCex |
cex for the legend title. |
facetCex |
cex for the facets if multiple plot are used. |
facetColor |
color for the text of the facets |
typePlot |
plot type, can be one of "static" or "interactive". |
multiplePlot |
logical whether to use facet_wrap on coefficients |
ggplot object
Katarzyna Gorczak
Combine data.frames by columns allowing for different number of rows.
cbindFill(list, featuresIdVar, sort = FALSE)cbindFill(list, featuresIdVar, sort = FALSE)
list |
list with data frames which should be combined by columns |
featuresIdVar |
columns with feature ids by which data frames should be combined |
sort |
logical, whether the rows should be sorted |
data.frame
Katarzyna Gorczak
Check coefficients
checkCoef(input, coef = NULL)checkCoef(input, coef = NULL)
input |
model or a list with top tables named with coefficients. |
coef |
character vector with coefficients |
no returned value, error if any of the coefficients not present
Check input fields
checkColumns(input, featuresIdVar, coef, cols, error = TRUE)checkColumns(input, featuresIdVar, coef, cols, error = TRUE)
input |
model or a list with top tables named with coefficients. |
featuresIdVar |
column name with unique feature identifiers |
coef |
coefficient name |
cols |
columns with feature annotation |
error |
Logical, if TRUE (by default) an error is returned if required columns are not present |
(invisibly) columns not available in the input and
error if required columns not present (if
error is TRUE)
Katarzyna Gorczak
Check input
checkInput(input, error = TRUE)checkInput(input, error = TRUE)
input |
model or a list with top tables named with coefficients. |
error |
logical whether to return error message |
(invisible) logical indicating if input passes the check,
and error if input is not of the desired class (if error is TRUE).
Color-blind palette
colorBlindPalette(n, grey = TRUE)colorBlindPalette(n, grey = TRUE)
n |
number of colors to return |
grey |
whether '#999999' color should be included |
n colors
Concatenate feature variables
concatenateVars(tbl, vars, nChar = NULL)concatenateVars(tbl, vars, nChar = NULL)
tbl |
long-form top table for all coefficients |
vars |
column names to concatenate |
nChar |
maximum number of characters to truncate the feature labels to |
character vector with concatenated with ' | ' columns
Katarzyna Gorczak
Create table for barplot
createDataBarplot( input, coef, coefLabel = coef, fdr, logFCrange = NULL, addPercentage = FALSE )createDataBarplot( input, coef, coefLabel = coef, fdr, logFCrange = NULL, addPercentage = FALSE )
input |
model or a list with top tables named with coefficients. |
coef |
character vector with coefficients |
coefLabel |
character vector with coefficient labels or list with such vector (in case of multiple labels) |
fdr |
threshold considered for significance, NULL by default. |
logFCrange |
numeric, two values (upper and lower bounds for logFC). |
addPercentage |
logical whether to add percentage |
data.frame
Katarzyna Gorczak
Create table for heatmap
createDataHeatmap( input, coef, coefLabel = NULL, features = NULL, featuresIdVar, featuresVar, featuresMaxNChar, typePlot )createDataHeatmap( input, coef, coefLabel = NULL, features = NULL, featuresIdVar, featuresVar, featuresMaxNChar, typePlot )
input |
model or a list with top tables named with coefficients. |
coef |
character, coefficient names. |
coefLabel |
character vector of labels or a function to transform the existing labels. |
features |
IDs of features to show. If NULL (by default), show top 20 features. |
featuresIdVar |
column name with feature ids. |
featuresVar |
column name with feature ids to label, the same as
|
featuresMaxNChar |
maximum number of characters to truncate the feature labels to. |
typePlot |
plot type, can be one of "static" or "interactive". |
data.frame
Laure Cougnaud, Katarzyna Gorczak
Create table for log ratio plot
createDataLogRatioPlot( input, coef, coefLabel = NULL, text = NULL, features = NULL, featuresIdVar, featuresVar, featuresMaxNChar, order, featuresOrder, errorBars, typePlot )createDataLogRatioPlot( input, coef, coefLabel = NULL, text = NULL, features = NULL, featuresIdVar, featuresVar, featuresMaxNChar, order, featuresOrder, errorBars, typePlot )
input |
model or a list with top tables named with coefficients. |
coef |
character vector with coefficients |
coefLabel |
character vector with coefficient labels or list with such vector (in case of multiple labels) |
text |
(optional) String with name of a column, or function to extract
a text from the columns of a top table, to display as text. |
features |
character vector with features to subset |
featuresIdVar |
column name with unique feature ids (empty by default) |
featuresVar |
column name with feature ids to label. |
featuresMaxNChar |
maximum number of characters to truncate the feature labels to. |
order |
logical whether to order features |
featuresOrder |
if not NULL, features in the graph are re-ordered, either based on similarity ('similarity') or significance ('significance') of the statistics. See section 'Feature ordering'. |
errorBars |
logical whether to add standard errors.
Allowed only for |
typePlot |
plot type, can be one of "static" or "interactive". |
data.frame
Create table for MA plot
createDataMAplot( input, coef, coefLabel = NULL, featuresIdVar, featuresVar, topGenes, topGenesVar, genesToHighlight, genesToHighlightVar, logFCrange, direction, sizeVar, fdr, typePlot )createDataMAplot( input, coef, coefLabel = NULL, featuresIdVar, featuresVar, topGenes, topGenesVar, genesToHighlight, genesToHighlightVar, logFCrange, direction, sizeVar, fdr, typePlot )
input |
model or a list with top tables named with coefficients. |
coef |
character vector with coefficients |
coefLabel |
character vector with coefficient labels or list with such vector (in case of multiple labels) |
featuresIdVar |
column name with unique feature ids (empty by default) |
featuresVar |
column name with feature ids to label. |
topGenes |
numeric, number of top genes with highest logFC or p-value to highlight in the plot for each considered coefficient, 0 by default. |
topGenesVar |
column name with feature identifier to label topGenes |
genesToHighlight |
string with identifiers of the genes to highlight,
NULL by default. The gene identifiers should correspond to the variable
specified in |
genesToHighlightVar |
column name for the labels of genesToHighlight.
Same as |
logFCrange |
numeric, two values (upper and lower bounds for logFC). |
direction |
logical whether to color significant up- and down-regulated genes |
sizeVar |
column name used for the size, empty by default. |
fdr |
threshold considered for direction (up- or down- significant features), 0.05 by default. |
typePlot |
plot type can be one of "static" or "interactive". |
data.frame
Katarzyna Gorczak
Create table for the scatter plot
createDataScatterPlot( input, coef, coefLabel = NULL, featuresIdVar, featuresVar, fdr, topGenes, topGenesVar, genesToHighlight, genesToHighlightVar, genesToHighlightThresholdPValue, genesToHighlightThresholdLogFC, logFCrange, typePlot )createDataScatterPlot( input, coef, coefLabel = NULL, featuresIdVar, featuresVar, fdr, topGenes, topGenesVar, genesToHighlight, genesToHighlightVar, genesToHighlightThresholdPValue, genesToHighlightThresholdLogFC, logFCrange, typePlot )
input |
model or a list with top tables named with coefficients. |
coef |
character vector with coefficients |
coefLabel |
character vector with coefficient labels or list with such vector (in case of multiple labels) |
featuresIdVar |
column name with unique feature ids (empty by default) |
featuresVar |
column name with feature ids to label, the same as 'featuresIdVar' by default. |
fdr |
threshold considered for significance, NULL by default. |
topGenes |
numeric, number of top genes with highest logFC or p-value to highlight in the plot for each considered coefficient, 0 by default. |
topGenesVar |
column name with feature identifier to label topGenes |
genesToHighlight |
string with identifiers of the genes to highlight,
NULL by default. The gene identifiers should correspond to the variable
specified in |
genesToHighlightVar |
column name with the genes to highlight.
Same as |
genesToHighlightThresholdPValue |
numeric, if specified (1 by default) keep among the genes to highlight, the genes which have a raw p-value lower (strict) than this threshold for at least one of the coefficient considered. |
genesToHighlightThresholdLogFC |
numeric, if specified (NULL by default) keep among the genes to highlight, the genes which have an absolute log FC higher (strict) than this threshold for at least one of the coefficient considered. |
logFCrange |
numeric, two values (upper and lower bounds for logFC). |
typePlot |
plot type, can be one of "static" or "interactive" |
data.frame
Katarzyna Gorczak
Create data for upset plot
createDataUpsetPlot( input, coef, coefLabel = NULL, featuresIdVar = character(), fdr, dir )createDataUpsetPlot( input, coef, coefLabel = NULL, featuresIdVar = character(), fdr, dir )
input |
model or a list with top tables named with coefficients. |
coef |
character vector with coefficients |
coefLabel |
character vector with coefficient labels or list with such vector (in case of multiple labels) |
featuresIdVar |
column name with unique feature ids (empty by default) |
fdr |
threshold considered for significance, 0.05 by default. |
dir |
direction for feature regulation ('up' to select up-regulated features; 'down' to select down-regulated features) |
data.frame compatible with UpSetR
Kirsten Van Hoorde, Katarzyna Gorczak
Create table for the volcano plot
createDataVolcanoPlot( input, coef, coefLabel = NULL, featuresIdVar, featuresVar, colorVar, shapeVar, alphaVar, sizeVar, fdr, additionalThresholdsAdjPValue, topGenes, topGenesVar, genesToHighlight, genesToHighlightVar, genesToHighlightThresholdPValue, genesToHighlightThresholdLogFC, logFCrange, typePlot )createDataVolcanoPlot( input, coef, coefLabel = NULL, featuresIdVar, featuresVar, colorVar, shapeVar, alphaVar, sizeVar, fdr, additionalThresholdsAdjPValue, topGenes, topGenesVar, genesToHighlight, genesToHighlightVar, genesToHighlightThresholdPValue, genesToHighlightThresholdLogFC, logFCrange, typePlot )
input |
model or a list with top tables named with coefficients. |
coef |
character vector with coefficients |
coefLabel |
character vector with coefficient labels or list with such vector (in case of multiple labels) |
featuresIdVar |
column name with unique feature ids (empty by default) |
featuresVar |
column name with feature ids to label; used for extra
labels if |
colorVar |
(optional) string with column name containing variable used for coloring. If not specified, coloring is based on the adjusted p-values. |
shapeVar |
(optional) string with column name containing variable used for shaping. If not specified, only points are used. |
alphaVar |
column name used for the transparency, empty by default. |
sizeVar |
column name used for the size, empty by default. |
fdr |
threshold considered for significance, NULL by default. |
additionalThresholdsAdjPValue |
numeric, additional adjusted p-values thresholds to use for the coloring of the points, and indicated in the legend. |
topGenes |
numeric, number of top genes with highest logFC or p-value to highlight in the plot for each considered coefficient, 0 by default. |
topGenesVar |
column name with feature identifier to label topGenes |
genesToHighlight |
string with identifiers of the genes to highlight,
NULL by default. The gene identifiers should correspond to the variable
specified in |
genesToHighlightVar |
column name with the genes to highlight.
Same as |
genesToHighlightThresholdPValue |
numeric, if specified (1 by default) keep among the genes to highlight, the genes which have a raw p-value lower (strict) than this threshold for at least one of the coefficient considered. |
genesToHighlightThresholdLogFC |
numeric, if specified (NULL by default) keep among the genes to highlight, the genes which have an absolute log FC higher (strict) than this threshold for at least one of the coefficient considered. |
logFCrange |
numeric, two values (upper and lower bounds for logFC). |
typePlot |
plot type can be one of "static" or "interactive". |
data.frame
Laure Cougnaud, Kirsten Van Hoorde, Katarzyna Gorczak, Michela Pasetto
Create data for waterfall plot
createDataWaterfallPlot( input, coef, coefLabel = NULL, features = NULL, featuresIdVar, featuresVar, featuresMaxNChar, fillVar, colorVar, alphaVar, fdr, logFCrange, typePlot )createDataWaterfallPlot( input, coef, coefLabel = NULL, features = NULL, featuresIdVar, featuresVar, featuresMaxNChar, fillVar, colorVar, alphaVar, fdr, logFCrange, typePlot )
input |
model or a list with top tables named with coefficients. |
coef |
character vector with coefficients |
coefLabel |
character vector with coefficient labels or list with such vector (in case of multiple labels) |
features |
character vector with features to subset |
featuresIdVar |
column name with unique feature ids (empty by default) |
featuresVar |
column name with feature ids to label, the same as 'featuresIdVar' by default. |
featuresMaxNChar |
numeric, maximum number of characters to truncate the feature labels to. |
fillVar |
name of variable (in 'input') used for filling, empty by default |
colorVar |
name of variable (in 'input') used for coloring, empty by default |
alphaVar |
column name used for the transparency, empty by default. |
fdr |
threshold considered for significance, NULL by default. |
logFCrange |
numeric, two values (upper and lower bounds for logFC). |
typePlot |
plot type, can be one of "static" or "interactive". |
data.frame
Katarzyna Gorczak
Create example limma-, edgeR-object or a list of top tables.
createExampleData( path = ".", output = c("limma", "topTable", "edgeR", "deseq2"), quiet = TRUE )createExampleData( path = ".", output = c("limma", "topTable", "edgeR", "deseq2"), quiet = TRUE )
path |
absolute path where data should be downloaded to |
output |
which object should be created. Possible options: limma, edgeR, topTableList, DESeq2 |
quiet |
if TRUE, suppress status messages (if any), and the progress bar
(see |
list with specific objects (output from limma, edgeR,
topTable and/or DESeq2)
tmpDir <- tempfile(); dir.create(tmpDir) getData <- createExampleData(path = tmpDir, output = "limma")tmpDir <- tempfile(); dir.create(tmpDir) getData <- createExampleData(path = tmpDir, output = "limma")
Create ExpressionSet object from example data
createExpressionSet(path)createExpressionSet(path)
path |
absolute path where data should be downloaded to |
ExpressionSet object
Katarzyna Gorczak
Create pairwise data
createPairData(tbl, featuresIdVar, fdr, typePlot, columns)createPairData(tbl, featuresIdVar, fdr, typePlot, columns)
tbl |
list of top tables |
featuresIdVar |
column name with feature ids, empty by default. If specified and input is a model, featuresIdVar should be a column name in 'genes' slot. |
fdr |
threshold considered for significance, NULL by default. |
typePlot |
plot type, can be one of "static" or "interactive" |
columns |
columns of interest from top table |
data.frame with reference and compared contrasts
Katarzyna Gorczak
Create sample annotation from example data
createSampleAnnotation(path)createSampleAnnotation(path)
path |
absolute path where data should be downloaded to |
data.frame with sample annotation
Katarzyna Gorczak
Create table for genes of interest
createTopTableGenesOfInterest( input, featuresIdVar = character(), genesToHighlight, genesToHighlightVar, genesToHighlightThresholdPValue = 1, genesToHighlightThresholdLogFC = NULL )createTopTableGenesOfInterest( input, featuresIdVar = character(), genesToHighlight, genesToHighlightVar, genesToHighlightThresholdPValue = 1, genesToHighlightThresholdLogFC = NULL )
input |
long-format table with top tables for coefficients of interest |
featuresIdVar |
column name with feature ids, 'ENTREZID' by default. |
genesToHighlight |
string with identifiers of the genes to highlight.
The gene identifiers should correspond to the variable specified in
|
genesToHighlightVar |
column of the data to which the genes to highlight should be map |
genesToHighlightThresholdPValue |
numeric, if specified (1 by default) keep among the genes to highlight, the genes which have a raw p-value lower (strict) than this threshold for at least one of the coefficient considered |
genesToHighlightThresholdLogFC |
numeric, if specified (NULL by default) keep among the genes to highlight, the genes which have an absolute log FC higher (strict) than this threshold for at least one of the coefficient considered |
long-format table
Laure Cougnaud, Kirsten Van Hoorde
This is a function to create a heatmap that represents logFC values for several coefficients.
daHeatmapLogFC( input, coef = NULL, coefLabel = NULL, features = NULL, featuresIdVar = character(), featuresVar = featuresIdVar, featuresColor = "black", featuresMaxNChar = 50, xlab = NULL, ylab = NULL, axesCex = 1, axesTitleCex = 1, title = NULL, titleCex = 1, legendTitleCex = 1, legendCex = 0.8, coefColor = "black", color = c("#0072B2", "white", "#D55E00"), colorNA = "grey", typePlot = c("static", "interactive") )daHeatmapLogFC( input, coef = NULL, coefLabel = NULL, features = NULL, featuresIdVar = character(), featuresVar = featuresIdVar, featuresColor = "black", featuresMaxNChar = 50, xlab = NULL, ylab = NULL, axesCex = 1, axesTitleCex = 1, title = NULL, titleCex = 1, legendTitleCex = 1, legendCex = 0.8, coefColor = "black", color = c("#0072B2", "white", "#D55E00"), colorNA = "grey", typePlot = c("static", "interactive") )
input |
model or a list with top tables named with coefficients. |
coef |
character, coefficient names. |
coefLabel |
character vector of labels or a function to transform the existing labels. |
features |
IDs of features to show. If NULL (by default), show top 20 features. |
featuresIdVar |
column name with feature ids. |
featuresVar |
column name with feature ids to label, the same as
|
featuresColor |
character vector specifying colors to use for
the feature label text. Either length 1 or the same length as
|
featuresMaxNChar |
maximum number of characters to truncate the feature labels to. |
xlab |
x-axis title, NULL by default. |
ylab |
y-axis title, NULL by default. |
axesCex |
cex for the axis text. |
axesTitleCex |
cex for the axis title. |
title |
plot title, NULL by default. |
titleCex |
cex for the plot title. |
legendTitleCex |
cex for the legend title. |
legendCex |
cex for the legend text. |
coefColor |
color palette for coef labels. |
color |
palette for gradient to fill the heatmap. |
colorNA |
color for missing values. |
typePlot |
plot type, can be one of "static" or "interactive". |
ggplot object
Kirsten Van Hoorde, Laure Cougnaud and Katarzyna Gorczak
exampleData <- createExampleData(path = ".", output = c("limma", "topTable")) model <- exampleData$limma topTableList <- exampleData$topTable coefs <- c("B.LvsP", "L.LvsP", "B.PvsV", "L.PvsV") # Simple heatmap daHeatmapLogFC(input = model, coef = coefs) # Specify feature annotation daHeatmapLogFC(input = model, coef = coefs, featuresIdVar = "ENTREZID", featuresVar = c("SYMBOL", "GENENAME"), featuresMaxNChar = 35) # Color coefficient labels daHeatmapLogFC(input = model, coef = coefs, coefLabel = c("A", "B", "C", "D"), coefColor = c("blue", "red", "blue", "red")) # Specify different set of features coefs <- c("B.LvsP", "L.LvsP", "B.PvsV", "L.PvsV", "A") daHeatmapLogFC( input = list(model, A = topTableList[["B.LvsP"]][c(seq_len(6), 9, 10), ]), coef = coefs) # see vignette for other examplesexampleData <- createExampleData(path = ".", output = c("limma", "topTable")) model <- exampleData$limma topTableList <- exampleData$topTable coefs <- c("B.LvsP", "L.LvsP", "B.PvsV", "L.PvsV") # Simple heatmap daHeatmapLogFC(input = model, coef = coefs) # Specify feature annotation daHeatmapLogFC(input = model, coef = coefs, featuresIdVar = "ENTREZID", featuresVar = c("SYMBOL", "GENENAME"), featuresMaxNChar = 35) # Color coefficient labels daHeatmapLogFC(input = model, coef = coefs, coefLabel = c("A", "B", "C", "D"), coefColor = c("blue", "red", "blue", "red")) # Specify different set of features coefs <- c("B.LvsP", "L.LvsP", "B.PvsV", "L.PvsV", "A") daHeatmapLogFC( input = list(model, A = topTableList[["B.LvsP"]][c(seq_len(6), 9, 10), ]), coef = coefs) # see vignette for other examples
This is a function to create barplot with log-foldchanges.
daLogRatioPlot( input, coef = NULL, coefLabel = NULL, features = NULL, featuresIdVar = character(), featuresVar = featuresIdVar, featuresOrder = NULL, featuresMaxNChar = 50, featuresColor = "black", color = character(), text = NULL, textCex = 4, xlab, xexpand = if (!is.null(text)) { ggplot2::expansion(mult = 0.2) }, axesCex = 1, axesTitleCex = 1, title = NULL, titleCex = 1, facetCex = 1, facetColor = "black", facetNCol = NULL, errorBars = TRUE, typePlot = c("static", "interactive") )daLogRatioPlot( input, coef = NULL, coefLabel = NULL, features = NULL, featuresIdVar = character(), featuresVar = featuresIdVar, featuresOrder = NULL, featuresMaxNChar = 50, featuresColor = "black", color = character(), text = NULL, textCex = 4, xlab, xexpand = if (!is.null(text)) { ggplot2::expansion(mult = 0.2) }, axesCex = 1, axesTitleCex = 1, title = NULL, titleCex = 1, facetCex = 1, facetColor = "black", facetNCol = NULL, errorBars = TRUE, typePlot = c("static", "interactive") )
input |
model or a list with top tables named with coefficients. |
coef |
character, coefficient names. |
coefLabel |
character vector of labels or function to transform
the existing labels, or list with such labels.
|
features |
feature ids or numeric. If NULL (by default),
show top 20 features according to the first |
featuresIdVar |
column name with feature ids. |
featuresVar |
column name with feature ids to label. |
featuresOrder |
if not NULL, features in the graph are re-ordered, either based on similarity ('similarity') or significance ('significance') of the statistics. See section 'Feature ordering'. |
featuresMaxNChar |
maximum number of characters to truncate the feature labels to. |
featuresColor |
character vector specifying colors to use for
the feature label text. Either length 1 or the same length as
|
color |
string with colors for the bars, one per coefficient. |
text |
(optional) String with name of a column, or function to extract
a text from the columns of a top table, to display as text. |
textCex |
cex for the text next to bars |
xlab |
x-axis title, or |
xexpand |
expansion factor for the x-axis
(see |
axesCex |
relative size for the label of the axes. |
axesTitleCex |
relative size for the title of the axes. |
title |
plot title, NULL by default. |
titleCex |
relative size for the title of the plot. |
facetCex |
cex for the facets if multiple plot are used. |
facetColor |
color for the text of the facets |
facetNCol |
number of columns in facets, by default the function
|
errorBars |
logical whether to add error bars to the plot (+/- one
standard error). If
|
typePlot |
plot type, can be one of "static" or "interactive". |
ggplot object
The features are ordered based on:
'similarity': a hierarchical clustering of the (Euclidean) distances between the statistics. The statistics are for:
limma: the t-statistic of each coefficient
edgeR: the (overall) F-statistic is considered
'significance': decreasing average (-log10) p-values across coefficients. The p-values are for:
limma: the significance of each coefficient
edgeR: the (overall) p-value of the model
Laure Cougnaud, Katarzyna Gorczak, Heather Turner, Aditya Bhagwat, Kirsten Van Hoorde
exampleData <- createExampleData(path = ".", output = c("limma", "topTable")) model <- exampleData$limma topTableList <- exampleData$topTable coefs <- c("B.LvsP", "L.LvsP", "B.PvsV", "L.PvsV") # Simple log-ratio plot daLogRatioPlot(input = model, coef = coefs, facetNCol = 4) # Specify features and annotation features <- daVis:::getTopFeatures(input = model, coef = "B.LvsP", featuresIdVar = "ENTREZID", n = 20) daLogRatioPlot(input = model, coef = coefs, features = features, featuresIdVar = "ENTREZID", featuresVar = c("SYMBOL", "GENENAME"), featuresMaxNChar = 35, facetNCol = 4) # Specify different set of features coefs <- c("B.LvsP", "L.LvsP", "B.PvsV", "L.PvsV", "A") set.seed(123) features <- sample(features, 20) daLogRatioPlot( input = list(model, A = topTableList[["B.LvsP"]][c(seq_len(6), 9, 10), ]), featuresIdVar = "ENTREZID", features = features, coef = coefs, facetNCol = 5, errorBars = TRUE) # Sort features as specified coefs <- c("B.LvsP", "L.LvsP", "B.PvsV", "L.PvsV") daLogRatioPlot(input = model, featuresIdVar = "ENTREZID", features = features, coef = coefs, facetNCol = 4, errorBars = TRUE) # Sort features based on similarity daLogRatioPlot(input = model, featuresIdVar = "ENTREZID", features = features, coef = coefs, facetNCol = 4, errorBars = TRUE, featuresOrder = "similarity") # see vignette for other examplesexampleData <- createExampleData(path = ".", output = c("limma", "topTable")) model <- exampleData$limma topTableList <- exampleData$topTable coefs <- c("B.LvsP", "L.LvsP", "B.PvsV", "L.PvsV") # Simple log-ratio plot daLogRatioPlot(input = model, coef = coefs, facetNCol = 4) # Specify features and annotation features <- daVis:::getTopFeatures(input = model, coef = "B.LvsP", featuresIdVar = "ENTREZID", n = 20) daLogRatioPlot(input = model, coef = coefs, features = features, featuresIdVar = "ENTREZID", featuresVar = c("SYMBOL", "GENENAME"), featuresMaxNChar = 35, facetNCol = 4) # Specify different set of features coefs <- c("B.LvsP", "L.LvsP", "B.PvsV", "L.PvsV", "A") set.seed(123) features <- sample(features, 20) daLogRatioPlot( input = list(model, A = topTableList[["B.LvsP"]][c(seq_len(6), 9, 10), ]), featuresIdVar = "ENTREZID", features = features, coef = coefs, facetNCol = 5, errorBars = TRUE) # Sort features as specified coefs <- c("B.LvsP", "L.LvsP", "B.PvsV", "L.PvsV") daLogRatioPlot(input = model, featuresIdVar = "ENTREZID", features = features, coef = coefs, facetNCol = 4, errorBars = TRUE) # Sort features based on similarity daLogRatioPlot(input = model, featuresIdVar = "ENTREZID", features = features, coef = coefs, facetNCol = 4, errorBars = TRUE, featuresOrder = "similarity") # see vignette for other examples
This is a function to create a MA plot. When several coefficients are used, multiple plots side by side are returned. MAplot visualizes mean expression versus logFC for each gene.
daMAplot( input, coef = NULL, coefLabel = NULL, featuresIdVar = character(), featuresVar = featuresIdVar, logFCrange = NULL, xlab = "log2 mean expression", ylab = "logFC", axesCex = 1, axesTitleCex = 1, title = NULL, titleCex = 1, legendPosition = c("right", "bottom", "none"), legendTitleCex = 1, legendCex = 0.8, facetCex = 1.1, facetColor = "black", facetNCol = grDevices::n2mfrow(length(coef))[2], topGenes = 0, topGenesVar = featuresIdVar, topGenesCex = 2.5, returnTopGenes = FALSE, genesToHighlight = NULL, genesToHighlightVar = featuresIdVar, genesToHighlightCex = 2.5, direction = FALSE, fdr = 0.05, color = if (direction) character() else "grey", alpha = 0.5, sizeVar = character(), size = if (length(sizeVar) > 0) numeric() else 2, typePlot = c("static", "interactive"), ... )daMAplot( input, coef = NULL, coefLabel = NULL, featuresIdVar = character(), featuresVar = featuresIdVar, logFCrange = NULL, xlab = "log2 mean expression", ylab = "logFC", axesCex = 1, axesTitleCex = 1, title = NULL, titleCex = 1, legendPosition = c("right", "bottom", "none"), legendTitleCex = 1, legendCex = 0.8, facetCex = 1.1, facetColor = "black", facetNCol = grDevices::n2mfrow(length(coef))[2], topGenes = 0, topGenesVar = featuresIdVar, topGenesCex = 2.5, returnTopGenes = FALSE, genesToHighlight = NULL, genesToHighlightVar = featuresIdVar, genesToHighlightCex = 2.5, direction = FALSE, fdr = 0.05, color = if (direction) character() else "grey", alpha = 0.5, sizeVar = character(), size = if (length(sizeVar) > 0) numeric() else 2, typePlot = c("static", "interactive"), ... )
input |
model or a list with top tables named with coefficients. |
coef |
character, coefficient names. |
coefLabel |
character vector of labels or a function to transform existing labels. |
featuresIdVar |
column name with feature ids. |
featuresVar |
column name with feature ids to label. |
logFCrange |
numeric, two values (upper and lower bounds for logFC). |
xlab |
x-axis title, NULL by default. |
ylab |
y-axis title, NULL by default. |
axesCex |
cex for the axis text. |
axesTitleCex |
cex for the axis title. |
title |
plot title, NULL by default. |
titleCex |
cex for the plot title. |
legendPosition |
legend position ("right", "bottom", "none"). If 'none', no legend is shown. |
legendTitleCex |
cex for the legend title. |
legendCex |
cex for the legend text. |
facetCex |
cex for the facets if multiple plot are used. |
facetColor |
color for the text of the facets |
facetNCol |
number of columns in facets, by default the function
|
topGenes |
numeric, number of top genes with highest logFC or p-value to highlight in the plot for each considered coefficient, 0 by default. |
topGenesVar |
column name with feature identifier to label topGenes |
topGenesCex |
cex for topGenes labels |
returnTopGenes |
logical, if TRUE (FALSE by default), return a list with the top genes highlighted in the plot. |
genesToHighlight |
string with identifiers of the genes to highlight,
NULL by default. The gene identifiers should correspond to the variable
specified in |
genesToHighlightVar |
column name for the labels of genesToHighlight.
Same as |
genesToHighlightCex |
cex for genesToHighlight |
direction |
logical whether to color significant up- and down-regulated genes |
fdr |
threshold considered for direction (up- or down- significant features), 0.05 by default. |
color |
colors for points indicating direction (should be three colors: significant up- and down-regulated and non-significant) |
alpha |
transparency level for the points, 0.4 by default. |
sizeVar |
column name used for the size, empty by default. |
size |
character or factor with specified size(s) (cex) for the points, replicated if needed. This is used only if sizeVar is empty. By default: '2.5' if sizeVar is not specified and default ggplot size(s) otherwise |
typePlot |
plot type can be one of "static" or "interactive". |
... |
Extra parameters passed to |
ggplot object or a list with ggplot object and top genes highlighted
in the MA plot (top 10 genes with highest significance and/or highest logFC)
featuresVar with names featuresIdVar
(if returnTopGenes is set to TRUE)
Katarzyna Gorczak
exampleData <- createExampleData(path = ".", output = "limma") model <- exampleData$limma coefs <- c("B.LvsP", "L.LvsP") # Simple MA plot daMAplot(input = model, coef = coefs[1]) # Color by significant direction daMAplot(input = model, coef = coefs, coefLabel = c("A", "B"), direction = TRUE, color = c("steelblue", "firebrick", "grey"), facetNCol = 2) # see vignette for other examplesexampleData <- createExampleData(path = ".", output = "limma") model <- exampleData$limma coefs <- c("B.LvsP", "L.LvsP") # Simple MA plot daMAplot(input = model, coef = coefs[1]) # Color by significant direction daMAplot(input = model, coef = coefs, coefLabel = c("A", "B"), direction = TRUE, color = c("steelblue", "firebrick", "grey"), facetNCol = 2) # see vignette for other examples
This is a function to create a scatter plot. When several coefficients are used, multiple plots side by side are returned.
daScatterPlot( input, coef = NULL, coefLabel = coef, featuresIdVar = character(), featuresVar = featuresIdVar, fdr = 0.05, logFCrange = NULL, xlab = NULL, ylab = NULL, axesCex = 1, axesTitleCex = 1, title = NULL, titleCex = 1, legendPosition = c("right", "bottom", "none"), legendTitleCex = 1, legendCex = 0.8, facetCex = 1, facetColor = "black", facetNCol = grDevices::n2mfrow(length(coef))[2], color = c("gray90", "darkgoldenrod1", "dodgerblue", "darkgreen"), topGenes = 0, topGenesVar = featuresIdVar, topGenesCex = 2.5, returnTopGenes = FALSE, genesToHighlight = NULL, genesToHighlightVar = featuresIdVar, genesToHighlightCex = 2.5, genesToHighlightThresholdPValue = 1, genesToHighlightThresholdLogFC = NULL, alpha = 0.4, pointSize = 2, correlation = FALSE, correlationCex = 3, typePlot = c("static", "interactive"), ... )daScatterPlot( input, coef = NULL, coefLabel = coef, featuresIdVar = character(), featuresVar = featuresIdVar, fdr = 0.05, logFCrange = NULL, xlab = NULL, ylab = NULL, axesCex = 1, axesTitleCex = 1, title = NULL, titleCex = 1, legendPosition = c("right", "bottom", "none"), legendTitleCex = 1, legendCex = 0.8, facetCex = 1, facetColor = "black", facetNCol = grDevices::n2mfrow(length(coef))[2], color = c("gray90", "darkgoldenrod1", "dodgerblue", "darkgreen"), topGenes = 0, topGenesVar = featuresIdVar, topGenesCex = 2.5, returnTopGenes = FALSE, genesToHighlight = NULL, genesToHighlightVar = featuresIdVar, genesToHighlightCex = 2.5, genesToHighlightThresholdPValue = 1, genesToHighlightThresholdLogFC = NULL, alpha = 0.4, pointSize = 2, correlation = FALSE, correlationCex = 3, typePlot = c("static", "interactive"), ... )
input |
model or a list with top tables named with coefficients. |
coef |
character, coefficient names. |
coefLabel |
character vector of labels or a function to transform existing labels. |
featuresIdVar |
column name with feature ids, empty by default. If specified and input is a model, featuresIdVar should be a column name in 'genes' slot. |
featuresVar |
column name with feature ids to label, the same as 'featuresIdVar' by default. |
fdr |
threshold considered for significance, NULL by default. |
logFCrange |
numeric, two values (upper and lower bounds for logFC). |
xlab |
x-axis title, NULL by default. |
ylab |
y-axis title, NULL by default. |
axesCex |
cex for the axis text. |
axesTitleCex |
cex for the axis title. |
title |
plot title, NULL by default. |
titleCex |
cex for the plot title. |
legendPosition |
legend position ("right", "bottom", "none"). If 'none', no legend is shown. |
legendTitleCex |
cex for the legend title. |
legendCex |
cex for the legend text. |
facetCex |
cex for the facets if multiple plot are used. |
facetColor |
color for the text of the facets |
facetNCol |
number of columns in facets, by default the function
|
color |
color palette to distinguish significance groups. Four colors must be specified. |
topGenes |
numeric, number of top genes with highest logFC or p-value to highlight in the plot for each considered coefficient, 0 by default. |
topGenesVar |
column name with feature identifier to label topGenes |
topGenesCex |
cex for topGenes labels |
returnTopGenes |
logical, if TRUE (FALSE by default), return a list with the top genes highlighted in the plot. |
genesToHighlight |
string with identifiers of the genes to highlight,
NULL by default. The gene identifiers should correspond to the variable
specified in |
genesToHighlightVar |
column name with the genes to highlight.
Same as |
genesToHighlightCex |
cex for genesToHighlight |
genesToHighlightThresholdPValue |
numeric, if specified (1 by default) keep among the genes to highlight, the genes which have a raw p-value lower (strict) than this threshold for at least one of the coefficient considered. |
genesToHighlightThresholdLogFC |
numeric, if specified (NULL by default) keep among the genes to highlight, the genes which have an absolute log FC higher (strict) than this threshold for at least one of the coefficient considered. |
alpha |
transparency level for the points, 0.4 by default. |
pointSize |
point size, 2 by default. |
correlation |
logical whether to calculate the correlation and add text to the plot. |
correlationCex |
cex for the text with correlation value |
typePlot |
plot type, can be one of "static" or "interactive" |
... |
Extra parameters passed to |
ggplot object or a list with ggplot object and top genes
highlighted in the scatter plot
(top 10 genes with highest significance and/or highest logFC)
featuresVar with names featuresIdVar
(if returnTopGenes is set to TRUE)
Katarzyna Gorczak
exampleData <- createExampleData(path = ".", output = "limma") model <- exampleData$limma coefs <- c("B.LvsP", "L.LvsP", "B.PvsV", "L.PvsV") # Simple scatter plot daScatterPlot(input = model, coef = coefs[c(1,2)]) # More coefficients daScatterPlot(input = model, coef = coefs, coefLabel = c("A", "B", "C", "D"), facetNCol = 3) # LogFC range daScatterPlot(input = model, coef = coefs, coefLabel = c("A", "B", "C", "D"), facetNCol = 3, logFCrange = c(-2, 2)) # see vignette for other examplesexampleData <- createExampleData(path = ".", output = "limma") model <- exampleData$limma coefs <- c("B.LvsP", "L.LvsP", "B.PvsV", "L.PvsV") # Simple scatter plot daScatterPlot(input = model, coef = coefs[c(1,2)]) # More coefficients daScatterPlot(input = model, coef = coefs, coefLabel = c("A", "B", "C", "D"), facetNCol = 3) # LogFC range daScatterPlot(input = model, coef = coefs, coefLabel = c("A", "B", "C", "D"), facetNCol = 3, logFCrange = c(-2, 2)) # see vignette for other examples
This is a function to create a barplot indicating the number of significant genes in each coefficient. The number of up- and down-regulated genes is shown.
daSignificantGenesBarplot( input, coef, coefLabel = coef, fdr = 0.05, logFCrange = NULL, xlab = NULL, ylab = NULL, axesCex = 1, axesTitleCex = 1.1, title = NULL, titleCex = 1.1, legendPosition = c("right", "bottom", "none"), legendTitleCex = 1, legendCex = 0.8, color = c("#32a6d3", "#e52323"), annotCex = 3.5, addPercentage = FALSE )daSignificantGenesBarplot( input, coef, coefLabel = coef, fdr = 0.05, logFCrange = NULL, xlab = NULL, ylab = NULL, axesCex = 1, axesTitleCex = 1.1, title = NULL, titleCex = 1.1, legendPosition = c("right", "bottom", "none"), legendTitleCex = 1, legendCex = 0.8, color = c("#32a6d3", "#e52323"), annotCex = 3.5, addPercentage = FALSE )
input |
model or a list with top tables named with coefficients. |
coef |
character, coefficient names. |
coefLabel |
character vector of labels or a function to transform the existing labels. |
fdr |
threshold considered for significance, NULL by default. |
logFCrange |
numeric, two values (upper and lower bounds for logFC). |
xlab |
x-axis title, NULL by default. |
ylab |
y-axis title, NULL by default. |
axesCex |
cex for the axis text. |
axesTitleCex |
cex for the axis title. |
title |
plot title, NULL by default. |
titleCex |
cex for the plot title. |
legendPosition |
legend position ("right", "bottom", "none"). If 'none', no legend is shown. |
legendTitleCex |
cex for the legend title. |
legendCex |
cex for the legend text. |
color |
color palette, must contain two colors. |
annotCex |
cex for the text displayed in the plot (indicating number of genes). |
addPercentage |
logical whether to add percentage |
ggplot object
Katarzyna Gorczak
exampleData <- createExampleData(path = ".", output = "limma") model <- exampleData$limma coefs <- c("B.LvsP", "L.LvsP", "B.PvsV", "L.PvsV") # Simple barplot daSignificantGenesBarplot(input = model, coef = coefs) # Add percentage of genes daSignificantGenesBarplot(input = model, coef = coefs, coefLabel = c("A", "B", "C", "D"), addPercentage = TRUE) # see vignette for other examplesexampleData <- createExampleData(path = ".", output = "limma") model <- exampleData$limma coefs <- c("B.LvsP", "L.LvsP", "B.PvsV", "L.PvsV") # Simple barplot daSignificantGenesBarplot(input = model, coef = coefs) # Add percentage of genes daSignificantGenesBarplot(input = model, coef = coefs, coefLabel = c("A", "B", "C", "D"), addPercentage = TRUE) # see vignette for other examples
This is a function to create a customized upset plot for up- or down-regulated genes.
daUpset( input, coef = NULL, coefLabel = coef, featuresIdVar = character(), fdr = 0.05, dir = c("up", "down"), ylab = "Intersection Size", xlab = "Set Size", axesCex = c(1.2, 1.7), axesTitleCex = c(1.4, 1.9), barsCex = 1.5, returnAnalysis = FALSE )daUpset( input, coef = NULL, coefLabel = coef, featuresIdVar = character(), fdr = 0.05, dir = c("up", "down"), ylab = "Intersection Size", xlab = "Set Size", axesCex = c(1.2, 1.7), axesTitleCex = c(1.4, 1.9), barsCex = 1.5, returnAnalysis = FALSE )
input |
model or a list with top tables named with coefficients. |
coef |
character, coefficient names. |
coefLabel |
character vector of labels or a function to transform existing labels. |
featuresIdVar |
column name with unique feature ids, empty by default. |
fdr |
threshold considered for significance, 0.05 by default. |
dir |
direction for feature regulation ('up' to select up-regulated features; 'down' to select down-regulated features) |
ylab |
y-axis title |
xlab |
x-axis title |
axesCex |
cex for the axis text. If two numbers provided, the first one is used for the x-axis and the second one for y-axis. |
axesTitleCex |
cex for the axis title. If two numbers provided, the first one is used for the x-axis and the second one for y-axis. |
barsCex |
cex for the counts above the bars. |
returnAnalysis |
logical, if TRUE (FALSE by default), return also the
output of the analysis (list with all overlapping sets based on
|
uspet plot; if returnAnalysis is TRUE, return a list with
overlapping sets and plot
Kirsten Van Hoorde, Katarzyna Gorczak, Michela Pasetto
exampleData <- createExampleData(path = ".", output = "limma") model <- exampleData$limma coefs <- c("B.LvsP", "L.LvsP", "B.PvsV", "L.PvsV") # Significantly up-regulated genes daUpset(input = model, coef = coefs, fdr = 0.05, dir = "up") # see vignette for other examplesexampleData <- createExampleData(path = ".", output = "limma") model <- exampleData$limma coefs <- c("B.LvsP", "L.LvsP", "B.PvsV", "L.PvsV") # Significantly up-regulated genes daUpset(input = model, coef = coefs, fdr = 0.05, dir = "up") # see vignette for other examples
daVis package.Common parameters for the functions of the daVis package.
input |
model or a list with top tables named with coefficients. |
No returned value
daVis package.Common documentation for the daVis package.
No returned value
The top table extracted from a specified model, or specified by the user contains at least the columns:
'logFC': log fold change
'AveExpr': average expression
'P.Value' and 'adj.PVal': raw and (multiplicity correction) adjusted p-values
For edgeR model: this is extracted from the columns:
'PValue' and 'FDR' respectively.
feature identifier as specified by: featuresIdVar
This is a function to create a volcano plot. When several coefficients are used, multiple plots side by side are returned.
daVolcanoPlot( input, coef = NULL, coefLabel = NULL, featuresIdVar = character(), featuresVar = featuresIdVar, fdr = 0.05, logFCrange = NULL, xlab = "logFC", ylab = "-log10(p-value)", axesCex = 1, axesTitleCex = 1.1, title = NULL, titleCex = 1.1, legendPosition = c("right", "bottom", "none"), legendTitleCex = 1, legendCex = 0.8, facetCex = 1, facetColor = "black", facetNCol = grDevices::n2mfrow(length(coef))[2], topGenes = 0, topGenesVar = featuresIdVar, topGenesCex = 2.5, returnTopGenes = FALSE, genesToHighlight = NULL, genesToHighlightVar = featuresIdVar, genesToHighlightCex = 2.5, genesToHighlightThresholdPValue = 1, genesToHighlightThresholdLogFC = NULL, colorVar = character(), color = if (length(colorVar) > 0) character() else "black", shapeVar = character(), shape = if (length(shapeVar) > 0) numeric() else 19, alphaVar = character(), alpha = if (length(alphaVar) > 0) numeric() else 0.4, alphaRange = numeric(), sizeVar = character(), size = if (length(sizeVar) > 0) numeric() else 2, sizeRange = numeric(), additionalThresholdsAdjPValue = NULL, typePlot = c("static", "interactive"), ... )daVolcanoPlot( input, coef = NULL, coefLabel = NULL, featuresIdVar = character(), featuresVar = featuresIdVar, fdr = 0.05, logFCrange = NULL, xlab = "logFC", ylab = "-log10(p-value)", axesCex = 1, axesTitleCex = 1.1, title = NULL, titleCex = 1.1, legendPosition = c("right", "bottom", "none"), legendTitleCex = 1, legendCex = 0.8, facetCex = 1, facetColor = "black", facetNCol = grDevices::n2mfrow(length(coef))[2], topGenes = 0, topGenesVar = featuresIdVar, topGenesCex = 2.5, returnTopGenes = FALSE, genesToHighlight = NULL, genesToHighlightVar = featuresIdVar, genesToHighlightCex = 2.5, genesToHighlightThresholdPValue = 1, genesToHighlightThresholdLogFC = NULL, colorVar = character(), color = if (length(colorVar) > 0) character() else "black", shapeVar = character(), shape = if (length(shapeVar) > 0) numeric() else 19, alphaVar = character(), alpha = if (length(alphaVar) > 0) numeric() else 0.4, alphaRange = numeric(), sizeVar = character(), size = if (length(sizeVar) > 0) numeric() else 2, sizeRange = numeric(), additionalThresholdsAdjPValue = NULL, typePlot = c("static", "interactive"), ... )
input |
model or a list with top tables named with coefficients. |
coef |
character, coefficient names. |
coefLabel |
character vector of labels or a function to transform existing labels. |
featuresIdVar |
column name with feature ids (should be unique).
If not specified, row names are used. |
featuresVar |
column name with feature ids to label; used for extra
labels if |
fdr |
threshold considered for significance, NULL by default. |
logFCrange |
numeric, two values (upper and lower bounds for logFC). |
xlab |
x-axis title, NULL by default. |
ylab |
y-axis title, NULL by default. |
axesCex |
cex for the axis text. |
axesTitleCex |
cex for the axis title. |
title |
plot title, NULL by default. |
titleCex |
cex for the plot title. |
legendPosition |
legend position ("right", "bottom", "none"). If 'none', no legend is shown. |
legendTitleCex |
cex for the legend title. |
legendCex |
cex for the legend text. |
facetCex |
cex for the facets if multiple plot are used. |
facetColor |
color for the text of the facets |
facetNCol |
number of columns in facets, by default the function
|
topGenes |
numeric, number of top genes with highest logFC or p-value to highlight in the plot for each considered coefficient, 0 by default. |
topGenesVar |
column name with feature identifier to label topGenes |
topGenesCex |
cex for topGenes labels |
returnTopGenes |
logical, if TRUE (FALSE by default), return a list with the top genes highlighted in the plot. |
genesToHighlight |
string with identifiers of the genes to highlight,
NULL by default. The gene identifiers should correspond to the variable
specified in |
genesToHighlightVar |
column name with the genes to highlight.
Same as |
genesToHighlightCex |
cex for genesToHighlight |
genesToHighlightThresholdPValue |
numeric, if specified (1 by default) keep among the genes to highlight, the genes which have a raw p-value lower (strict) than this threshold for at least one of the coefficient considered. |
genesToHighlightThresholdLogFC |
numeric, if specified (NULL by default) keep among the genes to highlight, the genes which have an absolute log FC higher (strict) than this threshold for at least one of the coefficient considered. |
colorVar |
(optional) string with column name containing variable used for coloring. If not specified, coloring is based on the adjusted p-values. |
color |
color palette, only used if |
shapeVar |
(optional) string with column name containing variable used for shaping. If not specified, only points are used. |
shape |
shape palette, only used if |
alphaVar |
column name used for the transparency, empty by default. |
alpha |
character or factor with specified transparency(s) for the points, replicated if needed. By default: '1' if alphaVar is not specified. |
alphaRange |
transparency (alpha) range used in the plot, possible only if the alphaVar is 'numeric' or 'integer'. |
sizeVar |
column name used for the size, empty by default. |
size |
character or factor with specified size(s) (cex) for the points, replicated if needed. This is used only if sizeVar is empty. By default: '2.5' if sizeVar is not specified and default ggplot size(s) otherwise |
sizeRange |
size (cex) range used in the plot, possible only if the sizeVar is 'numeric' or 'integer' |
additionalThresholdsAdjPValue |
numeric, additional adjusted p-values thresholds to use for the coloring of the points, and indicated in the legend. |
typePlot |
plot type can be one of "static" or "interactive". |
... |
Extra parameters passed to |
ggplot object or list with ggplot object and top genes highlighted
in the volcano plot (top 10 genes with highest significance and/or highest
logFC) topGenesVar with names featuresIdVar
(if returnTopGenes is set to TRUE)
Laure Cougnaud, Kirsten Van Hoorde, Katarzyna Gorczak
exampleData <- createExampleData(path = ".", output = "limma") model <- exampleData$limma # Simple volcano plot daVolcanoPlot(input = model, coef = "B.LvsP") # Specify logFC range daVolcanoPlot(input = model, coef = "B.LvsP", logFCrange = c(-2, 2)) # Customized aesthetics daVolcanoPlot(input = model, coef = c("B.LvsP", "L.LvsP"), coefLabel = c("A", "B"), facetNCol = 2, colorVar = "adj.P.Val") # Customized gene annotation model$genes$group <- rep(c("gr A", "gr B", "gr C"), each = 5323) daVolcanoPlot(input = model, coef = "B.LvsP", colorVar = "group", color = setNames(c("orange", "red", "blue"), c("gr A", "gr B", "gr C"))) # Facet by variable(s) coefs <- c("B.LvsP", "L.LvsP", "B.PvsV", "L.PvsV") coefsLabel <- list( sub("(.+)\\.(.+)", "\\2", coefs), sub("(.+)\\.(.+)", "\\1", coefs) ) daVolcanoPlot(input = model, coef = coefs, coefLabel = coefsLabel, facetNCol = 4, colorVar = "adj.P.Val") # see vignette for other examplesexampleData <- createExampleData(path = ".", output = "limma") model <- exampleData$limma # Simple volcano plot daVolcanoPlot(input = model, coef = "B.LvsP") # Specify logFC range daVolcanoPlot(input = model, coef = "B.LvsP", logFCrange = c(-2, 2)) # Customized aesthetics daVolcanoPlot(input = model, coef = c("B.LvsP", "L.LvsP"), coefLabel = c("A", "B"), facetNCol = 2, colorVar = "adj.P.Val") # Customized gene annotation model$genes$group <- rep(c("gr A", "gr B", "gr C"), each = 5323) daVolcanoPlot(input = model, coef = "B.LvsP", colorVar = "group", color = setNames(c("orange", "red", "blue"), c("gr A", "gr B", "gr C"))) # Facet by variable(s) coefs <- c("B.LvsP", "L.LvsP", "B.PvsV", "L.PvsV") coefsLabel <- list( sub("(.+)\\.(.+)", "\\2", coefs), sub("(.+)\\.(.+)", "\\1", coefs) ) daVolcanoPlot(input = model, coef = coefs, coefLabel = coefsLabel, facetNCol = 4, colorVar = "adj.P.Val") # see vignette for other examples
This is a function to create a waterfall plot. When several coefficients are used, multiple plots side by side are returned.
daWaterfallPlot( input, features = NULL, coef, coefLabel = coef, featuresIdVar = character(), featuresVar = featuresIdVar, featuresColor = "black", featuresMaxNChar = 60, fdr = NULL, logFCrange = NULL, xlab = "logFC", ylab = NULL, axesCex = 1, axesTitleCex = 1.1, title = NULL, titleCex = 1.1, legendPosition = c("right", "bottom", "none"), legendTitleCex = 1, legendCex = 0.8, facetCex = 1, facetColor = "black", facetNCol = grDevices::n2mfrow(length(coef))[2], fillVar = character(), fill = if (length(fillVar) > 0) character() else "skyblue2", colorVar = character(), color = if (length(colorVar) > 0) character() else "white", alphaVar = character(), alpha = if (length(alphaVar) > 0) 1 else numeric(), alphaRange = numeric(), typePlot = c("static", "interactive") )daWaterfallPlot( input, features = NULL, coef, coefLabel = coef, featuresIdVar = character(), featuresVar = featuresIdVar, featuresColor = "black", featuresMaxNChar = 60, fdr = NULL, logFCrange = NULL, xlab = "logFC", ylab = NULL, axesCex = 1, axesTitleCex = 1.1, title = NULL, titleCex = 1.1, legendPosition = c("right", "bottom", "none"), legendTitleCex = 1, legendCex = 0.8, facetCex = 1, facetColor = "black", facetNCol = grDevices::n2mfrow(length(coef))[2], fillVar = character(), fill = if (length(fillVar) > 0) character() else "skyblue2", colorVar = character(), color = if (length(colorVar) > 0) character() else "white", alphaVar = character(), alpha = if (length(alphaVar) > 0) 1 else numeric(), alphaRange = numeric(), typePlot = c("static", "interactive") )
input |
model or a list with top tables named with coefficients. |
features |
IDs of features to show. If NULL (by default), show top 20 features. |
coef |
character, coefficient names. |
coefLabel |
character vector of labels or a function to transform existing labels. |
featuresIdVar |
column name with feature ids, empty by default. If specified and input is a model, featuresIdVar should be a column name in 'genes' slot. |
featuresVar |
column name with feature ids to label, the same as 'featuresIdVar' by default. |
featuresColor |
character vector specifying colors to use for
the feature label text. Either length 1 or the same length as
|
featuresMaxNChar |
numeric, maximum number of characters to truncate the feature labels to. |
fdr |
threshold considered for significance, NULL by default. |
logFCrange |
numeric, two values (upper and lower bounds for logFC). |
xlab |
x-axis title, 'logFC' by default. |
ylab |
y-axis title, NULL by default. |
axesCex |
cex for the axis text. |
axesTitleCex |
cex for the axis title. |
title |
plot title, NULL by default. |
titleCex |
cex for the plot title. |
legendPosition |
legend position ("right", "bottom", "none"). If 'none', no legend is shown. |
legendTitleCex |
cex for the legend title. |
legendCex |
cex for the legend text. |
facetCex |
cex for the facets if multiple plot are used. |
facetColor |
color for the text of the facets |
facetNCol |
number of columns in facets, by default the function
|
fillVar |
name of variable (in 'input') used for filling, empty by default |
fill |
character or factor with specified color(s) for the boxplot inside, replicated if needed. By default: 'skyblue2' if fillVar is not specified and default ggplot palette otherwise. |
colorVar |
name of variable (in 'input') used for coloring, empty by default |
color |
character or factor with specified color(s) for the bar border, replicated if needed. By default: 'skyblue2' if colorVar is not specified and default ggplot palette otherwise. |
alphaVar |
column name used for the transparency, empty by default. |
alpha |
character or factor with specified transparency(s) for the bars, replicated if needed. By default: '1' if alphaVar is not specified. |
alphaRange |
transparency (alpha) range used in the plot, possible only if the alphaVar is 'numeric' or 'integer'. |
typePlot |
plot type, can be one of "static" or "interactive". |
ggplot object
Katarzyna Gorczak
exampleData <- createExampleData(path = ".", output = "limma") model <- exampleData$limma # Simple waterfall plot daWaterfallPlot(input = model, coef = "B.LvsP") # see vignette for other examplesexampleData <- createExampleData(path = ".", output = "limma") model <- exampleData$limma # Simple waterfall plot daWaterfallPlot(input = model, coef = "B.LvsP") # see vignette for other examples
Download example data from https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE60450
downloadData(path, quiet)downloadData(path, quiet)
path |
absolute path where data should be downloaded to |
quiet |
if TRUE, suppress status messages (if any), and the progress bar
(see |
(invisibly) downloads files at specific location ('path')
Katarzyna Gorczak
Extract columns of interest from top table
extractColsOfInterest( mean, topTableCoef, columns, stat, errorBars, coefI, text )extractColsOfInterest( mean, topTableCoef, columns, stat, errorBars, coefI, text )
mean |
logical (FALSE by default) whether to extract column with
averaged expression or logCPM ("AveExpr" or "logCPM") depending on the
|
topTableCoef |
top table for one coefficient |
columns |
columns to extract |
stat |
logical (FALSE by default) whether to extract column with statistic: 't' or 'F' |
errorBars |
logical whether to add standard errors.
Allowed only for |
coefI |
coefficient of interest |
text |
(optional) String with name of a column, or function to extract
a text from the columns of a top table, to display as text. |
character vector
Laure Cougnaud, Katarzyna Gorczak
Extract feature ids for each overlapping set
extractFeatures(data)extractFeatures(data)
data |
data.frame with 0s and 1s; the number of columns corresponds to the number of coefs and the number of rows corresponds to the number of unique feature ids each column is filled with 0s or 1s depending on the overlapping set |
list with features in overlapping sets
Katarzyna Gorczak
Get top table per pair (ref and comp coef) for scatter plot
extractPairs( decideTestsOutput, refCoef, iCoef, multiplePlot, compCoef, tbl, typePlot, columns )extractPairs( decideTestsOutput, refCoef, iCoef, multiplePlot, compCoef, tbl, typePlot, columns )
decideTestsOutput |
output of |
refCoef |
coefficient on the x-axis |
iCoef |
coefficients on the y-axis |
multiplePlot |
whether to facet on coefs to compare |
compCoef |
coefficients to compare with |
tbl |
list of top tables |
typePlot |
plot type, can be one of "static" or "interactive" |
columns |
columns of interest from top table |
ggplot object
Katarzyna Gorczak
Extract query list
extractQueryList(dataFrameSG)extractQueryList(dataFrameSG)
dataFrameSG |
data.frame compatible with UpSetR |
a list with queries for upset plot
Extract top tables for coefficients of interest
extractTopTables( input, coef, coefLabel = NULL, features = NULL, n = 20, columns, errorBars = FALSE, mean = FALSE, stat = FALSE, featuresIdVar = character(), hoverText = FALSE, text = NULL, output = c("table", "list") )extractTopTables( input, coef, coefLabel = NULL, features = NULL, n = 20, columns, errorBars = FALSE, mean = FALSE, stat = FALSE, featuresIdVar = character(), hoverText = FALSE, text = NULL, output = c("table", "list") )
input |
model or a list with top tables named with coefficients. |
coef |
character vector with coefficients |
coefLabel |
character vector with coefficient labels or list with such vector (in case of multiple labels) |
features |
character vector with features to subset |
n |
top features to extract. If set to Inf, all features are extracted |
columns |
columns to extract |
errorBars |
logical whether to add standard errors.
Allowed only for |
mean |
logical (FALSE by default) whether to extract column with
averaged expression or logCPM ("AveExpr" or "logCPM") depending on the
|
stat |
logical (FALSE by default) whether to extract column with statistic: 't' or 'F' |
featuresIdVar |
column name with unique feature ids (empty by default) |
hoverText |
logical whether to add point label for interactive plot (FALSE by default) |
text |
(optional) String with name of a column, or function to extract
a text from the columns of a top table, to display as text. |
output |
output can be one of "table" or "list" |
data.frame with top tables or list of those
(if output is 'list') for coefficients of interest
Katarzyna Gorczak, Laure Cougnaud
Extract top table for a specific coef
extractTTcoef(input, coefI, errorBars, text)extractTTcoef(input, coefI, errorBars, text)
input |
model or a list with top tables named with coefficients. |
coefI |
coefficient of interest |
errorBars |
logical whether to add standard errors.
Allowed only for |
text |
(optional) String with name of a column, or function to extract
a text from the columns of a top table, to display as text. |
data.frame with statistics for a specific coef
Laure Cougnaud, Katarzyna Gorczak
Add facets to ggplot object
facet(g, topTableOutput, facetNCol, scales = "fixed")facet(g, topTableOutput, facetNCol, scales = "fixed")
g |
ggplot object |
topTableOutput |
combined topTables for all coefficients |
facetNCol |
number of columns in facets, by default the function
|
scales |
see |
ggplot object
Laure Cougnaud, Katarzyna Gorczak
Filter top tables based on common features across all tables
filterCommonFeatures(input, featuresIdVar)filterCommonFeatures(input, featuresIdVar)
input |
list of top tables |
featuresIdVar |
column with feature identifier (must be unique) |
list of top tables with common features
Katarzyna Gorczak
Filter genes of interest
filterGenesOfInterest( input, featuresIdVar = character(), genesToHighlightVar, genesToHighlightThresholdPValue = 1, genesToHighlightThresholdLogFC = NULL )filterGenesOfInterest( input, featuresIdVar = character(), genesToHighlightVar, genesToHighlightThresholdPValue = 1, genesToHighlightThresholdLogFC = NULL )
input |
long-format table |
featuresIdVar |
column name with feature ids, 'ENTREZID' by default. |
genesToHighlightVar |
column name with genes to highlight |
genesToHighlightThresholdPValue |
numeric, if specified keep among the genes to highlight, the genes which have a raw p-value lower (strict) than this threshold for at least one of the coefficient considered |
genesToHighlightThresholdLogFC |
numeric, if specified keep among the genes to highlight, the genes which have an absolute log FC higher (strict) than this threshold for at least one of the coefficient considered |
long-format table
Laure Cougnaud, Kirsten Van Hoorde, Katarzyna Gorczak
Filter top tables based on logFC threshold
filterLogFC(input, logFCrange)filterLogFC(input, logFCrange)
input |
list of top tables |
logFCrange |
numeric vector with two values (low- and high-threshold for log-foldchange) |
list of top tables with subset of features based on logFC range
Katarzyna Gorczak
Format aesthetics for the main plot with MA plot
formatAesMA(g, topTableOutput, direction, color, sizeVar, size)formatAesMA(g, topTableOutput, direction, color, sizeVar, size)
g |
ggplot object with MA plot |
topTableOutput |
combined topTables for all coefficients |
direction |
logical whether to color significant up- and down-regulated genes |
color |
colors for points indicating direction (should be three colors: significant up- and down-regulated and non-significant) |
sizeVar |
column name used for the size, empty by default. |
size |
character or factor with specified size(s) (cex) for the points, replicated if needed. This is used only if sizeVar is empty. By default: '2.5' if sizeVar is not specified and default ggplot size(s) otherwise |
ggplot object
Katarzyna Gorczak
Format aesthetics for the main plot with volcano plot
formatAesVP( g, topTableOutput, colorVar, color, shapeVar, shape, alphaVar, alpha, alphaRange, sizeVar, size, sizeRange )formatAesVP( g, topTableOutput, colorVar, color, shapeVar, shape, alphaVar, alpha, alphaRange, sizeVar, size, sizeRange )
g |
ggplot object with volcano plot |
topTableOutput |
combined topTables for all coefficients |
colorVar |
(optional) string with column name containing variable used for coloring. If not specified, coloring is based on the adjusted p-values. |
color |
color palette, only used if |
shapeVar |
(optional) string with column name containing variable used for shaping. If not specified, only points are used. |
shape |
shape palette, only used if |
alphaVar |
column name used for the transparency, empty by default. |
alpha |
character or factor with specified transparency(s) for the points, replicated if needed. By default: '1' if alphaVar is not specified. |
alphaRange |
transparency (alpha) range used in the plot, possible only if the alphaVar is 'numeric' or 'integer'. |
sizeVar |
column name used for the size, empty by default. |
size |
character or factor with specified size(s) (cex) for the points, replicated if needed. This is used only if sizeVar is empty. By default: '2.5' if sizeVar is not specified and default ggplot size(s) otherwise |
sizeRange |
size (cex) range used in the plot, possible only if the sizeVar is 'numeric' or 'integer' |
ggplot object
Laure Cougnaud, Kirsten Van Hoorde, Katarzyna Gorczak
Format aesthetics for the main plot with waterfall plot
formatAesWP( topTableOutput, colorVar, color, g, fillVar, fill, alphaVar, alpha, alphaRange )formatAesWP( topTableOutput, colorVar, color, g, fillVar, fill, alphaVar, alpha, alphaRange )
topTableOutput |
combined topTables for all coefficients |
colorVar |
name of variable (in 'input') used for coloring, empty by default |
color |
character or factor with specified color(s) for the bar border, replicated if needed. By default: 'skyblue2' if colorVar is not specified and default ggplot palette otherwise. |
g |
ggplot object with volcano plot |
fillVar |
name of variable (in 'input') used for filling, empty by default |
fill |
character or factor with specified color(s) for the boxplot inside, replicated if needed. By default: 'skyblue2' if fillVar is not specified and default ggplot palette otherwise. |
alphaVar |
column name used for the transparency, empty by default. |
alpha |
character or factor with specified transparency(s) for the bars, replicated if needed. By default: '1' if alphaVar is not specified. |
alphaRange |
transparency (alpha) range used in the plot, possible only if the alphaVar is 'numeric' or 'integer'. |
ggplot object
Katarzyna Gorczak
extend manual scale values if required
formatManualScale(x, valVar, nameVar)formatManualScale(x, valVar, nameVar)
x |
data.frame with |
valVar |
fixed value of variable of aesthetic |
nameVar |
name of variable for aesthetic |
vector of manual scales
Laure Cougnaud
Format top table for a specific coefficient
formatTTcoef( topTableCoef, columns, mean, coefI, coef, featuresIdVar, hoverText )formatTTcoef( topTableCoef, columns, mean, coefI, coef, featuresIdVar, hoverText )
topTableCoef |
top table for one coefficient |
columns |
columns to extract |
mean |
logical (FALSE by default) whether to extract column with
averaged expression or logCPM ("AveExpr" or "logCPM") depending on the
|
coefI |
coefficient of interest |
coef |
character vector with coefficients |
featuresIdVar |
column name with unique feature ids (empty by default) |
hoverText |
logical whether to add point label for interactive plot (FALSE by default) |
data.frame
Laure Cougnaud, Katarzyna Gorczak
shorten string length
formatVariableLength(x, var, length)formatVariableLength(x, var, length)
x |
data.frame with |
var |
column name |
length |
max number of characters in each string |
string of length length
Character vector with shortened variable.
Katarzyna Gorczak
remove space in variable name
formatVariableSpace(var)formatVariableSpace(var)
var |
string |
String with formatted variable
Create colors for coefficient labels
getCoefColor(coef, coefLabel, coefColor)getCoefColor(coef, coefLabel, coefColor)
coef |
character, coefficient names. |
coefLabel |
character vector of labels or a function to transform the existing labels. |
coefColor |
color palette for coef labels. |
named character (named color palette)
Katarzyna Gorczak
Get coefficient label
getCoefLabel(coef, coefLabel)getCoefLabel(coef, coefLabel)
coef |
character vector with coefficients |
coefLabel |
character vector of coefficient labels or a function to transform the coefficients |
labels of the coefficients; either a vector or a list with nested labels
Create colors for feature labels
getFeatureColor( topTableOutput, features, featuresIdVar, featuresColor, order = FALSE, plot = c("logRatio", "heatmap", "waterfall") )getFeatureColor( topTableOutput, features, featuresIdVar, featuresColor, order = FALSE, plot = c("logRatio", "heatmap", "waterfall") )
topTableOutput |
combined topTables for all coefficients |
features |
feature ids or numeric. If NULL (by default),
show top 20 features according to the first |
featuresIdVar |
column name with feature ids. |
featuresColor |
character vector specifying colors to use for
the feature label text. Either length 1 or the same length as
|
order |
logical whether to order features (used only when plot is 'logRatio') |
plot |
plot |
named color palette for feature labels
Katarzyna Gorczak
Get features from input
getFeatures(input, featuresIdVar = character())getFeatures(input, featuresIdVar = character())
input |
model or a list with top tables named with coefficients. |
featuresIdVar |
column with unique feature identifiers |
character with all features from input
Katarzyna Gorczak
list matching to the specific coefficientGet ID of the input list matching to the specific coefficient
getInputIdCoef(input, coef)getInputIdCoef(input, coef)
input |
input list |
coef |
character vector of length 1 with coefficient |
numeric of length 1 specifying which element of the list contains the coefficient
Get coefficients in input
getModelCoefs(input, coef = NULL)getModelCoefs(input, coef = NULL)
input |
object of class |
coef |
(optional) coefficient of interest |
Character vector with coefficients
Get number of significant genes for coefficient
getNumberOfRegulatedGenes( input, fdr = 0.05, logFCrange = NULL, dir = c("up", "down") )getNumberOfRegulatedGenes( input, fdr = 0.05, logFCrange = NULL, dir = c("up", "down") )
input |
a list of top tables. |
fdr |
threshold for adjusted p-value. |
logFCrange |
numeric, upper and lower bound for logFC |
dir |
direction, select either up- or down-regulated genes ('up' - genes with logFC larger than 0, 'down' - genes with logFC smaller than 0). |
named numeric vector with number of up- or down-regulated genes
Katarzyna Gorczak
Get number of significant genes for coefficient
getNumberOfSignificantGenes(input, logFCrange = NULL, fdr = 0.05)getNumberOfSignificantGenes(input, logFCrange = NULL, fdr = 0.05)
input |
a list of top tables. |
logFCrange |
numeric, upper and lower bound for logFC |
fdr |
threshold for adjusted p-value. |
named numeric vector with number of significant genes
Katarzyna Gorczak
The standard errors are computed, according to BioC support question 70175, as:
with:
stdev.unscaled: unscaled standard deviations
s2.post: posterior values for sigma^2
getSEModel(input, coef)getSEModel(input, coef)
input |
object of class |
coef |
character vector with one single coefficient |
If input is MArrayLM, a numeric vector with SE named
by feature ID, NA otherwise.
Laure Cougnaud
exampleData <- createExampleData(path = ".", output = "limma") model <- exampleData$limma getSEModel(input = model, coef = "B.LvsP")exampleData <- createExampleData(path = ".", output = "limma") model <- exampleData$limma getSEModel(input = model, coef = "B.LvsP")
Get top features
getTopFeatures(input, coef, featuresIdVar = character(), n = 10)getTopFeatures(input, coef, featuresIdVar = character(), n = 10)
input |
model or a list with top tables named with coefficients. |
coef |
character vector with coefficient |
featuresIdVar |
column with unique feature identifiers |
n |
numeric, number of top features |
top n features based on their adjusted p-value
Laure Cougnaud, Katarzyna Gorczak
Get top genes with highest significance and top genes with highest logFC
getTopGenes(input, topGenes, featuresIdVar = character())getTopGenes(input, topGenes, featuresIdVar = character())
input |
long-format table |
topGenes |
number of top genes to extract |
featuresIdVar |
column with feature identifier (must be unique) |
data.frame with top genes
Laure Cougnaud, Katarzyna Gorczak
Extract top tables from a model
getTopTableFromModel(input, coef, se = FALSE)getTopTableFromModel(input, coef, se = FALSE)
input |
object of class |
coef |
coefficient of interest |
se |
Logical (FALSE by default), should standard errors be extracted? |
data.frame with top tables for coefficient of interest.
The features are ordered in original (unsorted) order in the model.
Standard errors, if requested are in the column: 'SE'.
Katarzyna Gorczak
Customized ggplot theme
ggPlotTheme( ggObject, panelBackground = NULL, panelBorder = "black", title = NULL, titleSize = 2, xTextSize = 1.2, xTextColor = "black", xTitle = NULL, xTitleSize = 1.4, xTextAngle = 0, xTextHjust = 0.5, yTextSize = 1.2, yTextColor = "black", yTitle = NULL, yTitleSize = 1.4, facetLabelSize = 1.5, facetLabelColor = "black", gridMajor = TRUE, gridMinor = TRUE, yTicks = TRUE, xTicks = TRUE, legendPosition = "right", legendTitleSize = 1.3, legendTextSize = 1.1 )ggPlotTheme( ggObject, panelBackground = NULL, panelBorder = "black", title = NULL, titleSize = 2, xTextSize = 1.2, xTextColor = "black", xTitle = NULL, xTitleSize = 1.4, xTextAngle = 0, xTextHjust = 0.5, yTextSize = 1.2, yTextColor = "black", yTitle = NULL, yTitleSize = 1.4, facetLabelSize = 1.5, facetLabelColor = "black", gridMajor = TRUE, gridMinor = TRUE, yTicks = TRUE, xTicks = TRUE, legendPosition = "right", legendTitleSize = 1.3, legendTextSize = 1.1 )
ggObject |
ggplot object |
panelBackground |
panel background of the entire plot |
panelBorder |
border around plotting area, by default black |
title |
plot title |
titleSize |
cex for plot title, relative to the parent |
xTextSize |
cex for axis text |
xTextColor |
color font on the x axis, by default black |
xTitle |
title of the x axis |
xTitleSize |
cex for the x axis title |
xTextAngle |
angle of the x axis labels |
xTextHjust |
numeric for horizontal adjustment, 0.5 by default |
yTextSize |
cex for axis text |
yTextColor |
color font on the y axis, by default black |
yTitle |
title of the y axis, NULL by default |
yTitleSize |
cex for the y axis title |
facetLabelSize |
cex for the facet labels |
facetLabelColor |
color of the text in facets |
gridMajor |
logical whether to show the major grid lines |
gridMinor |
logical whether to show the minor grid lines |
yTicks |
logical whether to show ticks on the y axis |
xTicks |
logical whether to show ticks on the x axis |
legendPosition |
legend position, by default right |
legendTitleSize |
cex for the legend title |
legendTextSize |
cex for the legend |
ggplot object with customized theme
Check if input is a model
isModel(input)isModel(input)
input |
object of class |
logical
Check if input is a topTable containing colums with logFC, p-value and adjusted p-value
isTopTable(input)isTopTable(input)
input |
a top table (see |
logical
Add label with correlation to scatter plot
labelCorr(topTableOutput, g, correlationCex)labelCorr(topTableOutput, g, correlationCex)
topTableOutput |
combined topTables for all coefficients |
g |
ggplot object |
correlationCex |
cex for the text with correlation value |
ggplot object
Katarzyna Gorczak
Add layer with genes of interest to ggplot object
labelGenesOfInterest( g, topTableOutputGenesOfInterest, typePlot, color = "red", sizeVar = character(), size = if (length(sizeVar) > 0) numeric() else 2, genesToHighlightCex, ... )labelGenesOfInterest( g, topTableOutputGenesOfInterest, typePlot, color = "red", sizeVar = character(), size = if (length(sizeVar) > 0) numeric() else 2, genesToHighlightCex, ... )
g |
ggplot object |
topTableOutputGenesOfInterest |
data.frame with |
typePlot |
plot type can be one of "static" or "interactive". |
color |
color palette, only used if |
sizeVar |
column name used for the size, empty by default. |
size |
character or factor with specified size(s) (cex) for the points, replicated if needed. This is used only if sizeVar is empty. By default: '2.5' if sizeVar is not specified and default ggplot size(s) otherwise |
genesToHighlightCex |
cex for genesToHighlight |
... |
Extra parameters passed to |
ggplot object
Laure Cougnaud, Katarzyna Gorczak
Add optional text to the log-ratio plot
labelTextLRP(topTableOutput, errorBars, g, textVar, textVarCex)labelTextLRP(topTableOutput, errorBars, g, textVar, textVarCex)
topTableOutput |
combined topTables for all coefficients |
errorBars |
logical whether to add error bars to the plot (+/- one
standard error). If
|
g |
ggplot object with volcano plot |
textVar |
(optional) String with name of a column to display as text. |
textVarCex |
cex for the text next to bars |
ggplot object
Laure Cougnaud, Kirsten Van Hoorde, Katarzyna Gorczak
Add point labels to ggplot object
labelTopGenes( g, includeTableGenesOfInterest, topTableOutputTopGenes, topTableOutputGenesOfInterest, colorVar, topGenesCex, ... )labelTopGenes( g, includeTableGenesOfInterest, topTableOutputTopGenes, topTableOutputGenesOfInterest, colorVar, topGenesCex, ... )
g |
ggplot object |
includeTableGenesOfInterest |
whether to label |
topTableOutputTopGenes |
data.frame with top genes |
topTableOutputGenesOfInterest |
data.frame with |
colorVar |
string with column name containing variable used for coloring |
topGenesCex |
cex for topGenes labels |
... |
Extra parameters passed to |
ggplot object
Laure Cougnaud, Katarzyna Gorczak
Create main plot object with heatmap
mainH(typePlot, topTableOutput, color, colorNA)mainH(typePlot, topTableOutput, color, colorNA)
typePlot |
plot type, can be one of "static" or "interactive". |
topTableOutput |
combined topTables for all coefficients |
color |
palette for gradient to fill the heatmap. |
colorNA |
color for missing values. |
ggplot object
Katarzyna Gorczak
Create main plot object with log-ratio plot
mainLRP(typePlot, color, topTableOutput, errorBars)mainLRP(typePlot, color, topTableOutput, errorBars)
typePlot |
plot type, can be one of "static" or "interactive". |
color |
string with colors for the bars, one per coefficient. |
topTableOutput |
combined topTables for all coefficients |
errorBars |
logical whether to add error bars to the plot (+/- one
standard error). If
|
ggplot object
Laure Cougnaud, Kirsten Van Hoorde, Katarzyna Gorczak
Create main plot object with MA plot
mainMA(typePlot, direction, color, sizeVar, topTableOutput, alpha, size)mainMA(typePlot, direction, color, sizeVar, topTableOutput, alpha, size)
typePlot |
plot type can be one of "static" or "interactive". |
direction |
logical whether to color significant up- and down-regulated genes |
color |
colors for points indicating direction (should be three colors: significant up- and down-regulated and non-significant) |
sizeVar |
column name used for the size, empty by default. |
topTableOutput |
combined topTables for all coefficients |
alpha |
transparency level for the points, 0.4 by default. |
size |
character or factor with specified size(s) (cex) for the points, replicated if needed. This is used only if sizeVar is empty. By default: '2.5' if sizeVar is not specified and default ggplot size(s) otherwise |
ggplot object
Katarzyna Gorczak
Create main plot object with scatter plot
mainSP( typePlot, color, topTableOutput, xlab, coef, ylab, multiplePlot, pointSize, alpha )mainSP( typePlot, color, topTableOutput, xlab, coef, ylab, multiplePlot, pointSize, alpha )
typePlot |
plot type, can be one of "static" or "interactive" |
color |
color palette to distinguish significance groups. Four colors must be specified. |
topTableOutput |
combined topTables for all coefficients |
xlab |
x-axis title, NULL by default. |
coef |
character, coefficient names. |
ylab |
y-axis title, NULL by default. |
multiplePlot |
logical whether to use facet on coefficients |
pointSize |
point size, 2 by default. |
alpha |
transparency level for the points, 0.4 by default. |
ggplot object
Katarzyna Gorczak
Create main plot object with volcano plot
mainVP( colorVar, shapeVar, alphaVar, sizeVar, topTableOutput, typePlot, color, shape, alpha, size )mainVP( colorVar, shapeVar, alphaVar, sizeVar, topTableOutput, typePlot, color, shape, alpha, size )
colorVar |
(optional) string with column name containing variable used for coloring. If not specified, coloring is based on the adjusted p-values. |
shapeVar |
(optional) string with column name containing variable used for shaping. If not specified, only points are used. |
alphaVar |
column name used for the transparency, empty by default. |
sizeVar |
column name used for the size, empty by default. |
topTableOutput |
combined topTables for all coefficients |
typePlot |
plot type can be one of "static" or "interactive". |
color |
color palette, only used if |
shape |
shape palette, only used if |
alpha |
character or factor with specified transparency(s) for the points, replicated if needed. By default: '1' if alphaVar is not specified. |
size |
character or factor with specified size(s) (cex) for the points, replicated if needed. This is used only if sizeVar is empty. By default: '2.5' if sizeVar is not specified and default ggplot size(s) otherwise |
ggplot object
Laure Cougnaud, Kirsten Van Hoorde, Katarzyna Gorczak
Create main plot object with waterfall plot
mainWP( typePlot, colorVar, fillVar, alphaVar, topTableOutput, fill, color, alpha )mainWP( typePlot, colorVar, fillVar, alphaVar, topTableOutput, fill, color, alpha )
typePlot |
plot type, can be one of "static" or "interactive". |
colorVar |
name of variable (in 'input') used for coloring, empty by default |
fillVar |
name of variable (in 'input') used for filling, empty by default |
alphaVar |
column name used for the transparency, empty by default. |
topTableOutput |
combined topTables for all coefficients |
fill |
character or factor with specified color(s) for the boxplot inside, replicated if needed. By default: 'skyblue2' if fillVar is not specified and default ggplot palette otherwise. |
color |
character or factor with specified color(s) for the bar border, replicated if needed. By default: 'skyblue2' if colorVar is not specified and default ggplot palette otherwise. |
alpha |
character or factor with specified transparency(s) for the bars, replicated if needed. By default: '1' if alphaVar is not specified. |
ggplot object
Katarzyna Gorczak
Make elements unique
makeElementsUnique(x)makeElementsUnique(x)
x |
vector with values |
vector with unique values, for duplicated values a suffix '_' is added
Normalize data, create model matrix and contrasts
modelExampleData(eset)modelExampleData(eset)
eset |
ExpressionSet |
a list with data, model, design matrix and contrast matrix
Katarzyna Gorczak
Sort the comparison columns according to the coefficients
orderComparison(df, coefLabel)orderComparison(df, coefLabel)
df |
top table for one coefficient |
coefLabel |
character vector with coefficient labels or list with such vector (in case of multiple labels) |
data.frame
Laure Cougnaud, Katarzyna Gorczak
Order features based on similarity or significance
orderFeatures(input, featuresOrder, featuresIdVar)orderFeatures(input, featuresOrder, featuresIdVar)
input |
list of top tables |
featuresOrder |
if not NULL, features in the graph are re-ordered, either based on similarity ('similarity') or significance ('significance') of the statistics. See section 'Feature ordering'. |
featuresIdVar |
column name with feature ids. |
ordered features
The features are ordered based on:
'similarity': a hierarchical clustering of the (Euclidean) distances between the statistics. The statistics are for:
limma: the t-statistic of each coefficient
edgeR: the (overall) F-statistic is considered
'significance': decreasing average (-log10) p-values across coefficients. The p-values are for:
limma: the significance of each coefficient
edgeR: the (overall) p-value of the model
Some can be missing across different coefficients (either different subsets of to tables are provided or provided 'features' are not present in each top table). The table will be processed for each coefficient:
1: missing features will be added (if a missing feature in coef X is present in coef Y, this label will be used)
2: feature missing in all coefs will obviously not be plotted
3: the same 'featuresVar' can happen for different 'featuresIdVar' - add index to duplicates as 'featuresVar' is used for plotting
processFeatures(tbl, coef, featuresIdVar, order)processFeatures(tbl, coef, featuresIdVar, order)
tbl |
logn-form table (top tables for specified coefs) |
coef |
character, coefficient names. |
featuresIdVar |
column name with feature ids. |
order |
character vector with feature ids in a specific order |
data.frame with processed features
Katarzyna Gorczak
Reshape table
reshapeTable(table, timevar)reshapeTable(table, timevar)
table |
long- or wide-format table |
timevar |
column name in long-format table that differentiates multiple records from the same group |
a list of tables
Katarzyna Gorczak
Fit the model with DESeq2
runDESeq2(input)runDESeq2(input)
input |
list of objects (data, contrasts and model matrix) |
DESeq2 output from results() function
Katarzyna Gorczak
Fit the model with edgeR
runEdgeR(input)runEdgeR(input)
input |
list of objects (data, contrasts and model matrix) |
edgeR output from glmQLFTest() function
Katarzyna Gorczak
Fit the model with limma
runLimma(input)runLimma(input)
input |
list of objects (data, contrasts and model matrix) |
limma outpur from eBayes() function
Katarzyna Gorczak
Fit the model and return top table
runTopTable(input)runTopTable(input)
input |
list of objects (data, contrasts and model matrix) |
a list of top tables
Katarzyna Gorczak
check if variable is not numeric (or integer)
setCategoricalElement(x, typeVar)setCategoricalElement(x, typeVar)
x |
data.frame |
typeVar |
column name in |
Logical, TRUE if the variable is not a numeric or integer
check if the aesthetic is fixed (e.g. color, shape, size 'palette')
setFixElement(typeVar, valVar)setFixElement(typeVar, valVar)
typeVar |
name of variable for aesthetic |
valVar |
fixed value of variable of aesthetic |
logical, if TRUE the element is fixed
Laure Cougnaud
This is the case only if typeVar and valVar are specified,
and if the variable is numeric or integer
setGradientScale(x, typeVar, valVar)setGradientScale(x, typeVar, valVar)
x |
data.frame with |
typeVar |
name of variable for aesthetic |
valVar |
fixed value of variable of aesthetic |
logical, if TRUE the manual scale should be set
Katarzyna Gorczak
This is the case only if typeVar and valVar are specified,
and if the variable is not numeric or integer (doesn't work with ggplot2)
setManualScale(x, typeVar, valVar)setManualScale(x, typeVar, valVar)
x |
data.frame with |
typeVar |
name of variable for aesthetic |
valVar |
fixed value of variable of aesthetic |
logical, if TRUE the manual scale should be set
Laure Cougnaud
Subset input with specific features
subsetFeatures(input, features, featuresIdVar = character())subsetFeatures(input, features, featuresIdVar = character())
input |
model or a list with top tables named with coefficients. |
features |
features of interest |
featuresIdVar |
column with unique feature identifiers |
input with subset of features
Katarzyna Gorczak
DESeq2 outputExtract top features from DESeq2 output
topFeaturesDeseq(input, n, featuresIdVar)topFeaturesDeseq(input, n, featuresIdVar)
input |
model or a list with top tables named with coefficients. |
n |
numeric, number of top features |
featuresIdVar |
column with unique feature identifiers |
character vector with feature names
Katarzyna Gorczak
edgeR outputExtract top features from edgeR output
topFeaturesEdger(input, n, featuresIdVar)topFeaturesEdger(input, n, featuresIdVar)
input |
model or a list with top tables named with coefficients. |
n |
numeric, number of top features |
featuresIdVar |
column with unique feature identifiers |
character vector with feature names
Katarzyna Gorczak
limma outputExtract top features from limma output
topFeaturesLimma(input, coef, n, featuresIdVar)topFeaturesLimma(input, coef, n, featuresIdVar)
input |
model or a list with top tables named with coefficients. |
coef |
character vector with coefficient |
n |
numeric, number of top features |
featuresIdVar |
column with unique feature identifiers |
character vector with feature names
Katarzyna Gorczak