bdiv_heatmap: Display beta diversities in an all vs all grid.
In cmmr/rbiom: Read/Write, Analyze, and Visualize 'BIOM' Data
bdiv_heatmap R Documentation
Display beta diversities in an all vs all grid.
Description
Display beta diversities in an all vs all grid.
Usage
bdiv_heatmap(
biom,
bdiv = "Bray-Curtis",
weighted = TRUE,
tree = NULL,
grid = "devon",
color.by = NULL,
order.by = NULL,
limit.by = NULL,
label = TRUE,
label_size = NULL,
rescale = "none",
clust = "complete",
trees = TRUE,
asp = 1,
tree_height = 10,
track_height = 10,
legend = "right",
title = TRUE,
xlab.angle = "auto",
...
)
Arguments
biom
An rbiom object, such as from as_rbiom().
Any value accepted by as_rbiom() can also be given here.
bdiv
Beta diversity distance algorithm(s) to use. Options are:
"Bray-Curtis", "Manhattan", "Euclidean",
"Jaccard", and "UniFrac". For "UniFrac", a
phylogenetic tree must be present in biom or explicitly
provided via tree=. Default: "Bray-Curtis"
Multiple/abbreviated values allowed.
weighted
Take relative abundances into account. When
weighted=FALSE, only presence/absence is considered.
Default: TRUE
Multiple values allowed.
tree
A phylo object representing the phylogenetic
relationships of the taxa in biom. Only required when
computing UniFrac distances. Default: biom$tree
grid
Color palette name, or a list with entries for label,
colors, range, bins, na.color, and/or
guide. See the Track Definitions section for details.
Default: "devon"
color.by
Add annotation tracks for these metadata column(s).
See "Annotation Tracks" section below for details.
Default: NULL
order.by
Which metadata column(s) to use for ordering the samples
across the x and y axes. Overrides any clust argument.
See "Ordering and Limiting" section below for details.
Default: NULL
limit.by
Metadata definition(s) to use for sample subsetting prior
to calculations.
See "Ordering and Limiting" section below for details.
Default: NULL
label
Label the matrix rows and columns. You can supply a list
or logical vector of length two to control row labels and column
labels separately, for example
label = c(rows = TRUE, cols = FALSE), or simply
label = c(T, F). Other valid options are "rows",
"cols", "both", "bottom", "right",
and "none".
Default: TRUE
label_size
The font size to use for the row and column labels. You
can supply a numeric vector of length two to control row label sizes
and column label sizes separately, for example
c(rows = 20, cols = 8), or simply c(20, 8).
Default: NULL, which computes:
pmax(8, pmin(20, 100 / dim(mtx)))
rescale
Rescale rows or columns to all have a common min/max.
Options: "none", "rows", or "cols".
Default: "none"
clust
Clustering algorithm for reordering the rows and columns by
similarity. You can supply a list or character vector of length two to
control the row and column clustering separately, for example
clust = c(rows = "complete", cols = NA), or simply
clust = c("complete", NA). Options are:
FALSE or NA - Disable reordering.
An hclust class object E.g. from stats::hclust().
A method name - "ward.D",
"ward.D2", "single", "complete",
"average", "mcquitty", "median", or
"centroid".
Default: "complete"
trees
Draw a dendrogram for rows (left) and columns (top). You can
supply a list or logical vector of length two to control the row tree
and column tree separately, for example
trees = c(rows = T, cols = F), or simply trees = c(T, F).
Other valid options are "rows", "cols", "both",
"left", "top", and "none".
Default: TRUE
asp
Aspect ratio (height/width) for entire grid.
Default: 1 (square)
tree_height, track_height
The height of the dendrogram or annotation
tracks as a percentage of the overall grid size. Use a numeric vector
of length two to assign c(top, left) independently.
Default: 10 (10% of the grid's height)
legend
Where to place the legend. Options are: "right" or
"bottom". Default: "right"
title
Plot title. Set to TRUE for a default title, NULL for
no title, or any character string. Default: TRUE
xlab.angle
Angle of the labels at the bottom of the plot.
Options are "auto", '0', '30', and '90'.
Default: "auto".
...
Additional arguments to pass on to ggplot2::theme().
For example, labs.subtitle = "Plot subtitle".
Value
A ggplot2 plot.
The computed data points and ggplot
command are available as $data and $code,
respectively.
Annotation Tracks
Metadata can be displayed as colored tracks above the heatmap. Common use
cases are provided below, with more thorough documentation available at
https://cmmr.github.io/rbiom .
## Categorical ----------------------------
color.by = "Body Site"
color.by = list('Body Site' = "bright")
color.by = list('Body Site' = c("Stool", "Saliva"), 'colors' = "bright")
color.by = list('Body Site' = c('Stool' = "blue", 'Saliva' = "green"))
## Numeric --------------------------------
color.by = "Age"
color.by = list('Age' = "reds")
color.by = list('Age' = c(20,NA), 'colors' = "reds") # at least 20 years old
color.by = list('Age' = c(20,40)) # between 20 and 40 years old (inclusive)
## Multiple Tracks ------------------------
color.by = c("Body Site", "Age")
color.by = list('Body Site' = "bright", 'Age' = "reds")
color.by = list(
'Body Site' = c('Stool' = "blue", 'Saliva' = "green"),
'Age' = list(range = c(20,40), 'colors' = "reds") )
The following entries in the track definitions are understood:
colors - A pre-defined palette name or custom set of colors to map to.
range - The c(min,max) to use for scale values.
label - Label for this track. Defaults to the name of this list element.
side - Options are "top" (default) or "left".
na.color - The color to use for NA values.
bins - Bin a gradient into this many bins/steps.
guide - A list of arguments for guide_colorbar() or guide_legend().
All built-in color palettes are colorblind-friendly.
Categorical palette names: "okabe", "carto", "r4",
"polychrome", "tol", "bright", "light",
"muted", "vibrant", "tableau", "classic",
"alphabet", "tableau20", "kelly", and "fishy".
Numeric palette names: "reds", "oranges", "greens",
"purples", "grays", "acton", "bamako",
"batlow", "bilbao", "buda", "davos",
"devon", "grayC", "hawaii", "imola",
"lajolla", "lapaz", "nuuk", "oslo",
"tokyo", "turku", "bam", "berlin",
"broc", "cork", "lisbon", "roma",
"tofino", "vanimo", and "vik".
Ordering and Limiting
order.by controls which metadata column(s) are used to arrange
samples on the plot. It also enables subsetting to a particular set or
range of values. Prefix a column name with - to arrange values in
descending order rather than ascending.
## Categorical ----------------------------
order.by = "Body Site"
order.by = list('Body Site' = c("Stool", "Saliva"))
## Numeric --------------------------------
order.by = "-Age"
order.by = list('Age' = c(20,NA)) # at least 20 years old
order.by = list('-Age' = c(20,40)) # between 20 and 40 years old (inclusive)
## Multiple / Mixed -----------------------
order.by = c("-Body Site", "Age")
order.by = list("Body Site", '-Age' = c(20,40))
limit.by is used to specify a subset of samples without any
side-effects on aesthetics. It is especially useful for limiting the data
to a single categorical metadata value. Unlike the other *.by parameters,
limit.by must always be a named list().
## Categorical ----------------------------
limit.by = list('Sex' = "Male")
## Numeric --------------------------------
limit.by = list('Age' = c(20,NA)) # at least 20 years old
limit.by = list('Age' = c(20,40)) # between 20 and 40 years old (inclusive)
## Multiple / Mixed -----------------------
limit.by = list(
'Sex' = "Male",
'Body Site' = c("Stool", "Saliva")
'Age' = c(20,40) )
See Also
Other beta_diversity:
bdiv_boxplot(),
bdiv_corrplot(),
bdiv_ord_plot(),
bdiv_ord_table(),
bdiv_stats(),
bdiv_table(),
distmat_stats()
Other visualization:
adiv_boxplot(),
adiv_corrplot(),
bdiv_boxplot(),
bdiv_corrplot(),
bdiv_ord_plot(),
plot_heatmap(),
rare_corrplot(),
rare_multiplot(),
rare_stacked(),
stats_boxplot(),
stats_corrplot(),
taxa_boxplot(),
taxa_corrplot(),
taxa_heatmap(),
taxa_stacked()
Examples
library(rbiom)
# Keep and rarefy the 10 most deeply sequenced samples.
hmp10 <- rarefy(hmp50, n = 10)
bdiv_heatmap(hmp10, color.by=c("Body Site", "Age"))
bdiv_heatmap(hmp10, bdiv="uni", weighted=c(T,F), color.by="sex")
cmmr/rbiom documentation built on April 28, 2024, 6:38 a.m.
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| bdiv_heatmap | R Documentation |
Display beta diversities in an all vs all grid.
Description
Display beta diversities in an all vs all grid.
Usage
bdiv_heatmap(
biom,
bdiv = "Bray-Curtis",
weighted = TRUE,
tree = NULL,
grid = "devon",
color.by = NULL,
order.by = NULL,
limit.by = NULL,
label = TRUE,
label_size = NULL,
rescale = "none",
clust = "complete",
trees = TRUE,
asp = 1,
tree_height = 10,
track_height = 10,
legend = "right",
title = TRUE,
xlab.angle = "auto",
...
)
Arguments
biom |
An rbiom object, such as from |
bdiv |
Beta diversity distance algorithm(s) to use. Options are:
|
weighted |
Take relative abundances into account. When
|
tree |
A |
grid |
Color palette name, or a list with entries for |
color.by |
Add annotation tracks for these metadata column(s).
See "Annotation Tracks" section below for details.
Default: |
order.by |
Which metadata column(s) to use for ordering the samples
across the x and y axes. Overrides any |
limit.by |
Metadata definition(s) to use for sample subsetting prior
to calculations.
See "Ordering and Limiting" section below for details.
Default: |
label |
Label the matrix rows and columns. You can supply a list
or logical vector of length two to control row labels and column
labels separately, for example
|
label_size |
The font size to use for the row and column labels. You
can supply a numeric vector of length two to control row label sizes
and column label sizes separately, for example
|
rescale |
Rescale rows or columns to all have a common min/max.
Options: |
clust |
Clustering algorithm for reordering the rows and columns by
similarity. You can supply a list or character vector of length two to
control the row and column clustering separately, for example
Default: |
trees |
Draw a dendrogram for rows (left) and columns (top). You can
supply a list or logical vector of length two to control the row tree
and column tree separately, for example
|
asp |
Aspect ratio (height/width) for entire grid.
Default: |
tree_height, track_height |
The height of the dendrogram or annotation
tracks as a percentage of the overall grid size. Use a numeric vector
of length two to assign |
legend |
Where to place the legend. Options are: |
title |
Plot title. Set to |
xlab.angle |
Angle of the labels at the bottom of the plot.
Options are |
... |
Additional arguments to pass on to ggplot2::theme().
For example, |
Value
A ggplot2 plot.
The computed data points and ggplot
command are available as $data and $code,
respectively.
Annotation Tracks
Metadata can be displayed as colored tracks above the heatmap. Common use cases are provided below, with more thorough documentation available at https://cmmr.github.io/rbiom .
## Categorical ----------------------------
color.by = "Body Site"
color.by = list('Body Site' = "bright")
color.by = list('Body Site' = c("Stool", "Saliva"), 'colors' = "bright")
color.by = list('Body Site' = c('Stool' = "blue", 'Saliva' = "green"))
## Numeric --------------------------------
color.by = "Age"
color.by = list('Age' = "reds")
color.by = list('Age' = c(20,NA), 'colors' = "reds") # at least 20 years old
color.by = list('Age' = c(20,40)) # between 20 and 40 years old (inclusive)
## Multiple Tracks ------------------------
color.by = c("Body Site", "Age")
color.by = list('Body Site' = "bright", 'Age' = "reds")
color.by = list(
'Body Site' = c('Stool' = "blue", 'Saliva' = "green"),
'Age' = list(range = c(20,40), 'colors' = "reds") )
The following entries in the track definitions are understood:
colors- A pre-defined palette name or custom set of colors to map to.range- The c(min,max) to use for scale values.label- Label for this track. Defaults to the name of this list element.side- Options are"top"(default) or"left".na.color- The color to use forNAvalues.bins- Bin a gradient into this many bins/steps.guide- A list of arguments for guide_colorbar() or guide_legend().
All built-in color palettes are colorblind-friendly.
Categorical palette names: "okabe", "carto", "r4",
"polychrome", "tol", "bright", "light",
"muted", "vibrant", "tableau", "classic",
"alphabet", "tableau20", "kelly", and "fishy".
Numeric palette names: "reds", "oranges", "greens",
"purples", "grays", "acton", "bamako",
"batlow", "bilbao", "buda", "davos",
"devon", "grayC", "hawaii", "imola",
"lajolla", "lapaz", "nuuk", "oslo",
"tokyo", "turku", "bam", "berlin",
"broc", "cork", "lisbon", "roma",
"tofino", "vanimo", and "vik".
Ordering and Limiting
order.by controls which metadata column(s) are used to arrange
samples on the plot. It also enables subsetting to a particular set or
range of values. Prefix a column name with - to arrange values in
descending order rather than ascending.
## Categorical ----------------------------
order.by = "Body Site"
order.by = list('Body Site' = c("Stool", "Saliva"))
## Numeric --------------------------------
order.by = "-Age"
order.by = list('Age' = c(20,NA)) # at least 20 years old
order.by = list('-Age' = c(20,40)) # between 20 and 40 years old (inclusive)
## Multiple / Mixed -----------------------
order.by = c("-Body Site", "Age")
order.by = list("Body Site", '-Age' = c(20,40))
limit.by is used to specify a subset of samples without any
side-effects on aesthetics. It is especially useful for limiting the data
to a single categorical metadata value. Unlike the other *.by parameters,
limit.by must always be a named list().
## Categorical ----------------------------
limit.by = list('Sex' = "Male")
## Numeric --------------------------------
limit.by = list('Age' = c(20,NA)) # at least 20 years old
limit.by = list('Age' = c(20,40)) # between 20 and 40 years old (inclusive)
## Multiple / Mixed -----------------------
limit.by = list(
'Sex' = "Male",
'Body Site' = c("Stool", "Saliva")
'Age' = c(20,40) )
See Also
Other beta_diversity:
bdiv_boxplot(),
bdiv_corrplot(),
bdiv_ord_plot(),
bdiv_ord_table(),
bdiv_stats(),
bdiv_table(),
distmat_stats()
Other visualization:
adiv_boxplot(),
adiv_corrplot(),
bdiv_boxplot(),
bdiv_corrplot(),
bdiv_ord_plot(),
plot_heatmap(),
rare_corrplot(),
rare_multiplot(),
rare_stacked(),
stats_boxplot(),
stats_corrplot(),
taxa_boxplot(),
taxa_corrplot(),
taxa_heatmap(),
taxa_stacked()
Examples
library(rbiom)
# Keep and rarefy the 10 most deeply sequenced samples.
hmp10 <- rarefy(hmp50, n = 10)
bdiv_heatmap(hmp10, color.by=c("Body Site", "Age"))
bdiv_heatmap(hmp10, bdiv="uni", weighted=c(T,F), color.by="sex")
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