plotArrow: Arrow sample plot
In mixOmicsTeam/mixOmics: Omics Data Integration Project
plotArrow R Documentation
Arrow sample plot
Description
Represents samples from multiple coordinates to assess the alignment in the
latent space.
Usage
plotArrow(
object,
comp = c(1, 2),
ind.names = TRUE,
group = NULL,
col.per.group = NULL,
col = NULL,
ind.names.position = c("start", "end"),
ind.names.size = 2,
pch = NULL,
pch.size = 2,
arrow.alpha = 0.6,
arrow.size = 0.5,
arrow.length = 0.2,
legend = if (is.null(group)) FALSE else TRUE,
legend.title = NULL,
...
)
Arguments
object
object of class inheriting from mixOmics: PLS,
sPLS, rCC, rGCCA, sGCCA, sGCCDA
comp
integer vector of length two (or three to 3d). The components
that will be used on the horizontal and the vertical axis respectively to
project the individuals.
ind.names
either a character vector of names for the individuals to
be plotted, or FALSE for no names. If TRUE, the row names of
the first (or second) data matrix is used as names (see Details).
group
Factor indicating the group membership for each sample.
col.per.group
character (or symbol) color to be used when 'group' is
defined. Vector of the same length as the number of groups.
col
character (or symbol) color to be used, possibly vector.
ind.names.position
One of c('start', 'end') indicating where to show
the ind.names . Not used in block analyses, where centroids are used.
ind.names.size
Numeric, sample name size.
pch
plot character. A character string or a named vector of single
characters or integers whose names match those of object$variates.
pch.size
Numeric, sample point character size.
arrow.alpha
Numeric between 0 and 1 determining the opacity of arrows.
arrow.size
Numeric, variable arrow head size.
arrow.length
Numeric, length of the arrow head in 'cm'.
legend
Logical, whether to show the legend if group != NULL.
legend.title
Character, the legend title if group != NULL.
...
Not currently used.
sample size to display sample names.
Details
Graphical of the samples (individuals) is displayed in a superimposed manner
where each sample will be indicated using an arrow. The start of the arrow
indicates the location of the sample in X in one plot, and the tip the
location of the sample in Y in the other plot. Short arrows indicate a
strong agreement between the matching data sets, long arrows a disagreement
between the matching data sets. The representation space is scaled using the
range of coordinates so minimum and maximum values are equal for all blocks.
Since the algorithm maximises the covariance of these components, the
absolute values do not affect the alignment.
For objects of class "GCCA" and if there are more than 2 blocks, the
start of the arrow indicates the centroid between all data sets for a given
individual and the tips of the arrows the location of that individual in
each block.
Value
A ggplot object
Author(s)
Al J Abadi
References
Lê Cao, K.-A., Martin, P.G.P., Robert-Granie, C. and Besse, P.
(2009). Sparse canonical methods for biological data integration:
application to a cross-platform study. BMC Bioinformatics
10:34.
See Also
arrows, text, points and
http://mixOmics.org/graphics for more details.
Examples
## plot of individuals for objects with two datasets only (X and Y)
# ----------------------------------------------------
data(nutrimouse)
X <- nutrimouse$lipid
Y <- nutrimouse$gene
nutri.res <- rcc(X, Y, ncomp = 3, lambda1 = 0.064, lambda2 = 0.008)
## plot of individuals for objects of class 'pls' or 'spls'
# ----------------------------------------------------
plotArrow(nutri.res)
## customise the ggplot object as you wish
plotArrow(nutri.res) + geom_vline(xintercept = 0, alpha = 0.5) +
geom_hline(yintercept = 0, alpha = 0.5) +
labs(x = 'Dim 1' , y = 'Dim 2', title = 'Nutrimouse') +
theme_minimal()
## individual name position
plotArrow(nutri.res, ind.names.position = 'end')
plotArrow(nutri.res, comp = c(1,3))
## custom pch
plotArrow(nutri.res, pch = 10, pch.size = 3)
plotArrow(nutri.res, pch = c(X = 1, Y = 0))
## custom arrow
plotArrow(nutri.res, arrow.alpha = 0.6, arrow.size = 0.6, arrow.length = 0.15)
## group samples
plotArrow(nutri.res, group = nutrimouse$genotype)
plotArrow(nutri.res, group = nutrimouse$genotype, legend.title = 'Genotype')
## custom ind.names
plotArrow(nutri.res,
ind.names = paste0('ID', rownames(nutrimouse$gene)),
ind.names.size = 3)
## plot of individuals for objects of class 'pls' or 'spls'
# ----------------------------------------------------
data(liver.toxicity)
X <- liver.toxicity$gene
Y <- liver.toxicity$clinic
toxicity.spls <- spls(X, Y, ncomp = 3, keepX = c(50, 50, 50),
keepY = c(10, 10, 10))
# colors indicate time of necropsy, text is the dose, label at start of arrow
plotArrow(toxicity.spls, group = liver.toxicity$treatment[, 'Time.Group'],
ind.names = liver.toxicity$treatment[, 'Dose.Group'],
legend = TRUE, position.names = 'start', legend.title = 'Time.Group')
## individual representation for objects of class 'sgcca' (or 'rgcca')
# ----------------------------------------------------
data(nutrimouse)
Y = unmap(nutrimouse$diet)
data = list(gene = nutrimouse$gene, lipid = nutrimouse$lipid, Y = Y)
design1 = matrix(c(0,1,1,1,0,1,1,1,0), ncol = 3, nrow = 3, byrow = TRUE)
nutrimouse.sgcca <- wrapper.sgcca(X = data,
design = design1,
penalty = c(0.3, 0.5, 1),
ncomp = 3)
plotArrow(nutrimouse.sgcca, group = nutrimouse$genotype, ind.names = TRUE,
legend.title = 'Genotype' )
## custom pch by block
blocks <- names(nutrimouse.sgcca$variates)
pch <- seq_along(blocks)
names(pch) <- blocks
pch
#> gene lipid Y
#> 1 2 3
p <- plotArrow(nutrimouse.sgcca, group = nutrimouse$genotype, ind.names = TRUE,
pch = pch, legend.title = 'Genotype')
p
### further customise the ggplot object
# custom labels
p + labs(x = 'Variate 1',
y = 'Variate 2') +
guides(
shape = guide_legend(title = 'BLOCK')
)
# TODO include these customisations into function args
## custom shapes
p + scale_shape_manual(values = c(
centroid = 1,
gene = 2,
lipid = 3,
Y = 4
))
## individual representation for objects of class 'sgccda'
# ----------------------------------------------------
# Note: the code differs from above as we use a 'supervised' GCCA analysis
data(nutrimouse)
Y = nutrimouse$diet
data = list(gene = nutrimouse$gene, lipid = nutrimouse$lipid)
design1 = matrix(c(0,1,0,1), ncol = 2, nrow = 2, byrow = TRUE)
nutrimouse.sgccda1 <-
wrapper.sgccda(X = data,
Y = Y,
design = design1,
ncomp = 2,
keepX = list(gene = c(10,10), lipid = c(15,15)))
## Default colours correspond to outcome Y
plotArrow(nutrimouse.sgccda1)
mixOmicsTeam/mixOmics documentation built on Nov. 19, 2024, 11:53 a.m.
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| plotArrow | R Documentation |
Arrow sample plot
Description
Represents samples from multiple coordinates to assess the alignment in the latent space.
Usage
plotArrow(
object,
comp = c(1, 2),
ind.names = TRUE,
group = NULL,
col.per.group = NULL,
col = NULL,
ind.names.position = c("start", "end"),
ind.names.size = 2,
pch = NULL,
pch.size = 2,
arrow.alpha = 0.6,
arrow.size = 0.5,
arrow.length = 0.2,
legend = if (is.null(group)) FALSE else TRUE,
legend.title = NULL,
...
)
Arguments
object |
object of class inheriting from mixOmics: |
comp |
integer vector of length two (or three to 3d). The components that will be used on the horizontal and the vertical axis respectively to project the individuals. |
ind.names |
either a character vector of names for the individuals to
be plotted, or |
group |
Factor indicating the group membership for each sample. |
col.per.group |
character (or symbol) color to be used when 'group' is defined. Vector of the same length as the number of groups. |
col |
character (or symbol) color to be used, possibly vector. |
ind.names.position |
One of c('start', 'end') indicating where to show the ind.names . Not used in block analyses, where centroids are used. |
ind.names.size |
Numeric, sample name size. |
pch |
plot character. A character string or a named vector of single
characters or integers whose names match those of |
pch.size |
Numeric, sample point character size. |
arrow.alpha |
Numeric between 0 and 1 determining the opacity of arrows. |
arrow.size |
Numeric, variable arrow head size. |
arrow.length |
Numeric, length of the arrow head in 'cm'. |
legend |
Logical, whether to show the legend if |
legend.title |
Character, the legend title if |
... |
Not currently used. sample size to display sample names. |
Details
Graphical of the samples (individuals) is displayed in a superimposed manner
where each sample will be indicated using an arrow. The start of the arrow
indicates the location of the sample in X in one plot, and the tip the
location of the sample in Y in the other plot. Short arrows indicate a
strong agreement between the matching data sets, long arrows a disagreement
between the matching data sets. The representation space is scaled using the
range of coordinates so minimum and maximum values are equal for all blocks.
Since the algorithm maximises the covariance of these components, the
absolute values do not affect the alignment.
For objects of class "GCCA" and if there are more than 2 blocks, the
start of the arrow indicates the centroid between all data sets for a given
individual and the tips of the arrows the location of that individual in
each block.
Value
A ggplot object
Author(s)
Al J Abadi
References
Lê Cao, K.-A., Martin, P.G.P., Robert-Granie, C. and Besse, P. (2009). Sparse canonical methods for biological data integration: application to a cross-platform study. BMC Bioinformatics 10:34.
See Also
arrows, text, points and
http://mixOmics.org/graphics for more details.
Examples
## plot of individuals for objects with two datasets only (X and Y)
# ----------------------------------------------------
data(nutrimouse)
X <- nutrimouse$lipid
Y <- nutrimouse$gene
nutri.res <- rcc(X, Y, ncomp = 3, lambda1 = 0.064, lambda2 = 0.008)
## plot of individuals for objects of class 'pls' or 'spls'
# ----------------------------------------------------
plotArrow(nutri.res)
## customise the ggplot object as you wish
plotArrow(nutri.res) + geom_vline(xintercept = 0, alpha = 0.5) +
geom_hline(yintercept = 0, alpha = 0.5) +
labs(x = 'Dim 1' , y = 'Dim 2', title = 'Nutrimouse') +
theme_minimal()
## individual name position
plotArrow(nutri.res, ind.names.position = 'end')
plotArrow(nutri.res, comp = c(1,3))
## custom pch
plotArrow(nutri.res, pch = 10, pch.size = 3)
plotArrow(nutri.res, pch = c(X = 1, Y = 0))
## custom arrow
plotArrow(nutri.res, arrow.alpha = 0.6, arrow.size = 0.6, arrow.length = 0.15)
## group samples
plotArrow(nutri.res, group = nutrimouse$genotype)
plotArrow(nutri.res, group = nutrimouse$genotype, legend.title = 'Genotype')
## custom ind.names
plotArrow(nutri.res,
ind.names = paste0('ID', rownames(nutrimouse$gene)),
ind.names.size = 3)
## plot of individuals for objects of class 'pls' or 'spls'
# ----------------------------------------------------
data(liver.toxicity)
X <- liver.toxicity$gene
Y <- liver.toxicity$clinic
toxicity.spls <- spls(X, Y, ncomp = 3, keepX = c(50, 50, 50),
keepY = c(10, 10, 10))
# colors indicate time of necropsy, text is the dose, label at start of arrow
plotArrow(toxicity.spls, group = liver.toxicity$treatment[, 'Time.Group'],
ind.names = liver.toxicity$treatment[, 'Dose.Group'],
legend = TRUE, position.names = 'start', legend.title = 'Time.Group')
## individual representation for objects of class 'sgcca' (or 'rgcca')
# ----------------------------------------------------
data(nutrimouse)
Y = unmap(nutrimouse$diet)
data = list(gene = nutrimouse$gene, lipid = nutrimouse$lipid, Y = Y)
design1 = matrix(c(0,1,1,1,0,1,1,1,0), ncol = 3, nrow = 3, byrow = TRUE)
nutrimouse.sgcca <- wrapper.sgcca(X = data,
design = design1,
penalty = c(0.3, 0.5, 1),
ncomp = 3)
plotArrow(nutrimouse.sgcca, group = nutrimouse$genotype, ind.names = TRUE,
legend.title = 'Genotype' )
## custom pch by block
blocks <- names(nutrimouse.sgcca$variates)
pch <- seq_along(blocks)
names(pch) <- blocks
pch
#> gene lipid Y
#> 1 2 3
p <- plotArrow(nutrimouse.sgcca, group = nutrimouse$genotype, ind.names = TRUE,
pch = pch, legend.title = 'Genotype')
p
### further customise the ggplot object
# custom labels
p + labs(x = 'Variate 1',
y = 'Variate 2') +
guides(
shape = guide_legend(title = 'BLOCK')
)
# TODO include these customisations into function args
## custom shapes
p + scale_shape_manual(values = c(
centroid = 1,
gene = 2,
lipid = 3,
Y = 4
))
## individual representation for objects of class 'sgccda'
# ----------------------------------------------------
# Note: the code differs from above as we use a 'supervised' GCCA analysis
data(nutrimouse)
Y = nutrimouse$diet
data = list(gene = nutrimouse$gene, lipid = nutrimouse$lipid)
design1 = matrix(c(0,1,0,1), ncol = 2, nrow = 2, byrow = TRUE)
nutrimouse.sgccda1 <-
wrapper.sgccda(X = data,
Y = Y,
design = design1,
ncomp = 2,
keepX = list(gene = c(10,10), lipid = c(15,15)))
## Default colours correspond to outcome Y
plotArrow(nutrimouse.sgccda1)
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