heatmap_se: Heatmap for SummarizedExperiment data
In jmw86069/jamses: Jam SummarizedExperiment Stats
heatmap_se R Documentation
Heatmap for SummarizedExperiment data
Description
Heatmap for SummarizedExperiment data
Usage
heatmap_se(
se,
sestats = NULL,
hm_name = NULL,
hm_title = NULL,
rows = NULL,
row_type = "rows",
column_type = "samples",
data_type = "expression",
correlation = FALSE,
assay_name = NULL,
contrast_names = NULL,
contrast_suffix = "",
cutoff_name = NULL,
alt_sestats = NULL,
alt_assay_name = assay_name,
alt_contrast_names = NULL,
alt_contrast_suffix = "",
alt_cutoff_name = NULL,
isamples = colnames(se),
normgroup_colname = NULL,
centerby_colnames = NULL,
controlSamples = NULL,
control_label = "",
controlFloor = NA,
naControlAction = c("na", "row", "floor", "min"),
naControlFloor = 0,
top_colnames = NULL,
top_annotation = NULL,
top_annotation_name_gp = grid::gpar(),
rowData_colnames = NULL,
left_annotation = NULL,
left_annotation_name_gp = grid::gpar(),
left_annotation_name_rot = 90,
right_annotation = NULL,
simple_anno_size = grid::unit(8, "mm"),
legend_title_gp = grid::gpar(fontsize = 10),
legend_labels_gp = grid::gpar(fontsize = 10),
legend_grid_cex = 1,
row_names_gp = NULL,
row_split = NULL,
row_subcluster = NULL,
row_title_rot = 0,
sample_color_list = NULL,
legend_at = NULL,
legend_labels = NULL,
subset_legend_colors = TRUE,
row_cex = 0.8,
column_cex = 1,
row_anno_fontsize = 11,
useMedian = FALSE,
show_row_names = NULL,
show_row_dend = length(rows) < 2000,
mark_rows = NULL,
mark_labels_gp = grid::gpar(),
column_title = character(0),
apply_hm_column_title = FALSE,
hm_title_buffer = 0,
show_heatmap_legend = TRUE,
show_top_legend = TRUE,
show_left_legend = TRUE,
legend_border_color = "black",
show_top_annotation_name = TRUE,
show_left_annotation_name = TRUE,
row_label_colname = NULL,
cluster_columns = FALSE,
cluster_column_slices = FALSE,
cluster_rows = function(x, ...) {
amap::hcluster(jamba::rmNA(naValue = 0, x), ...,
method = "euclidean", link = "ward")
},
cluster_row_slices = FALSE,
column_names_gp = NULL,
column_split = NULL,
column_split_sep = ",",
color_max = 3,
color_floor = 0,
lens = 2,
rename_contrasts = TRUE,
rename_alt_contrasts = TRUE,
use_raster = TRUE,
verbose = FALSE,
debug = FALSE,
...
)
Arguments
se
SummarizedExperiment by default, or one of the following:
-
SummarizedExperiment with accessor functions
rowData(), colData(), and assays(). It will use
values(rowRanges()) if no slot rowData exists.
-
SingleCellExperiment with accessor functions
rowData(), colData(), and assays(). It will use
values(rowRanges()) if no slot rowData exists.
-
Seurat object, which is coerced to SingleCellExperiment and
handled accordingly
-
ExpressionSet or compatible object with accessor functions
featureData(), phenoData(), and assayData().
sestats
one of the following types of data:
-
list output from se_contrast_stats(), which
specifically contains hit_array as a 3-dimensional array of hits
with dimensions "Cutoffs", "Contrasts", "Signal".
-
numeric matrix intended to represent an incidence matrix,
where a value 0 indicates absence, and non-zero indicates presence.
This format is useful for supplying any incidence matrix, such as
gene-by-pathway (for example Github package "jmw86069/multienrichjam"
provides mem$memIM with gene-by-pathway matrix),
or gene-by-class (see Github package "jmw86069/pajam"
for examples using ProteinAtlas protein classification, including
membrane-bound, secreted, transcription factors, etc.), or any
incidence matrix defined by Github "jmw86069/venndir" function
list2im_value() or list2im() which converts input to a Venn diagram
into an incidence matrix.
When sestats is supplied, data is converted to incidence matrix,
then columns are matched with contrast_names. All rows with non-zero
entry in those columns are included in the heatmap.
When rows is also supplied, then the intersection of incidence
matrix rows and rows is displayed in the heatmap.
Note that alt_sestats does not subset rows displayed in the
heatmap.
hm_name
character string, or NULL (default) which uses the
data_type value. Note that the legend title uses the data_type,
and is also used for hm_name when hm_name=NULL.
The hm_name is most useful to customize because this string is used
as the prefix for grid graphical components, for example seen with
ComplexHeatmap::list_components(). When two heatmaps or a
HeatmapList is drawn, the names can be used to define specific
grid regions of each heatmap. If the heatmaps share the same
hm_name then the regions will also have identical name and cannot
be addressed distinctly.
hm_title
character string, or NULL (default) which generates
a heatmap title using the dimensions, assay_name, data_type,
and a string which describes the data centering.
When provided as a character string, it is used as-is.
(In future this value may accept variable names.)
rows
character vector of rownames(se) to define a specific
set of rows to display. When sestats is supplied, then the
intersection of rows with genes defined by sestats is displayed.
Note that rows are required to be in rownames(se), all other rows
are dropped.
row_type
character string used in the title of the heatmap
which indicates how many rows are displayed. For example
"1,234 genes detected above background" or
"1,234 DEGs by limma-voom".
When row_type="" or row_type=NULL this information
is not included in the heatmap title.
column_type
character string used in the title of the heatmap
which indicates how many column are displayed. For example
"12 samples" or "12 biological replicates".
When column_type="" or column_type=NULL this information
is not included in the heatmap title.
data_type
character string used as title of the heatmap
color gradient legend, for example "expression" indicates
the data contains gene expression measurements. Notes:
The prefix "centered" is automatically appended whenever
the data is also centered for the heatmap. Set centerby_colnames=FALSE
to display data that is not centered.
The prefix "correlation of" is automatically appended when
correlation=TRUE which displays correlation of whatever data
is included in the heatmap.
correlation
logical indicating whether to calculate sample
correlation, and plot a sample-by-sample correlation heatmap.
This option is included here since many of the same arguments
are required for data centering, and sample annotations.
Note that color_max is forced to a maximum value of 1.0,
representing the maximum correlation value.
assay_name
character string indicating the name in
assays(se) to use for data to be displayed in the heatmap.
When multiple assay_name values are supplied, the first
assay_name that matches names(assays(se)) will be used in the
heatmap. In this way, multiple assay_names can be supplied to
define statistical hits in sestats, which calls hit_array_to_list()
to combine hits across assay_name entries; but only the first
assay_name found in se is used for the heatmap values.
When there is only one value for assayNames(se), then
assay_name will default to this value, instead of acting like
it couldn't possibly know what was intended. Haha.
Lastly, assay_name can be a numeric index, helpful in case
assays(se) contains no names - not recommended but it can happen.
contrast_names
character vector of contrasts in
sestats$hit_array to use for the heatmap. When contrast_names=NULL
then all contrasts are displayed, which is the default.
contrast_suffix
character string with optional suffix to append
to the end of each contrast name label for sestats hit incidence
matrix beside the heatmap. This suffix may be useful when comparing
two methods for the same set of contrast names, with sestats and
alt_sestats.
cutoff_name
character or integer index used to define the
specific statistical cutoffs to use from sestats$hit_array. This
argument is passed to hit_array_to_list() as cutoff_names.
alt_sestats, alt_assay_name, alt_contrast_names, alt_contrast_suffix
arguments analogous to those described above for sestats which
are used when alt_sestats is supplied.
isamples
character vector of colnames(se) used to visualize a
subset of samples used for the data centering step. Note that
data centering uses all columns supplied in se, and after centering,
the subset of columns defined in isamples is displayed in the heatmap.
This distinction makes it possible to center data by some control group,
then optionally not display the control group data.
normgroup_colname
character vector of colnames in colData(se)
used during data centering. When supplied, samples are centered
independently within each normgroup grouping. These values are
equivalent to using centerby_colnames.
centerby_colnames
either:
-
character vector of colnames in colData(se)
used during data centering. When supplied, samples are centered
independently within each centerby grouping. It is typically used
for things like cell lines, to center each cell line by a time
point control, or untreated control.
-
NULL to perform centering across all columns in se.
-
FALSE to disable centering.
controlSamples
character optional vector of samples to use as the
reference during data centering. Note that samples are still
centered within each normgroup and centerby grouping, and within
that grouping samples are centered to the controlSamples
which are present in that grouping. Any center group for which no
samples are defined in controlSamples will use all samples in that
center group. Typically, controlSamples is used to define a
specific group as the reference for centering, so changes are displayed
relative to that group. Make sure to define control_name to include
an appropriate label in the heatmap title.
control_label
character string used in heatmap title
to describe the control used during data centering, relevant when
controlSamples is also supplied. Recommended format:
"versus Wildtype" or "vs. Wildtype".
The heatmap title will include data centering and control_label
in this format:
"centered within {centerby_colnames}, {control_label}", for example
"centered within Genotype/Time, versus Vehicle".
controlFloor, naControlAction, naControlFloor
passed to
jamma::centerGeneData() to customize data centering.
-
controlFloor imposes an optional noise floor to control group
mean/median values, so the summary value during centering is at
least controlFloor. Useful for defining an effective noise floor
for a platform technology.
-
naControlAction defines the action taken only when values for
all control samples are NA.
-
naControlFloor is a numeric value used when
naControlAction="floor", which causes the group reference value
to use the value provided in naControlFloor.
top_colnames
one of the following types:
-
character vector of colnames to use from
colData(se) as annotations to display in top_annotation above
the heatmap.
-
NULL, will call choose_annotation_colnames() to detect
reasonable colnames: columns with more than one unique value;
columns with at least one duplicated value.
-
FALSE will hide the top_colnames, which also occurs when
colData(se) is empty.
top_annotation
specific heatmap annotation as defined by
ComplexHeatmap::HeatmapAnnotation(). When supplied, the top_colnames
described above is not used.
top_annotation_name_gp
grid::gpar object to customize the
annotation name displayed beside the top annotation.
rowData_colnames
character vector of colnames in rowData(se)
to use for heatmap annotations displayed on the left side of
the heatmap. Specific colors can be included in sample_color_list
as a named list of color vectors or color functions. The names
of this list must match colnames to be displayed, otherwise
ComplexHeatmap::Heatmap() will define its own color function.
left_annotation
specific heatmap annotation as defined by
ComplexHeatmap::rowAnnotation(). When supplied, the rowData_colnames
and sestats row annotations are not displayed. In order to supply
custom row annotations and not lose left_annotation defined above,
supply the row annotations as right_annotation.
left_annotation_name_gp
grid::gpar object to customize the
annotation name displayed beside the left annotation.
left_annotation_name_rot
numeric rotation of left annotation
label, in degrees, where 0 indicates normal text, and 90 is
rotated vertically.
right_annotation
specific heatmap annotation as defined by
ComplexHeatmap::HeatmapAnnotation(). This element is created
automatically when mark_rows is supplied.
simple_anno_size
grid::unit size used to define heatmap
annotation sizes (height or width of each line) for any simple
annotations.
legend_title_gp
grid::gpar to customize the legend title
fonts, applied to each legend: top annotation, left annotation,
main heatmap.
legend_labels_gp
grid::gpar to customize the legend label
fonts, applied to each legend: top annotation, left annotation,
main heatmap.
legend_grid_cex
numeric multiplied to adjust the relative
size of each legend grid unit, applied to each relevant metric.
row_names_gp
gpar to define custom column name settings.
When "fontsize" is not defined, the automatic font size calculation
is added to the row_names_gp supplied.
row_split
is used to define heatmap split by row, ultimately
passed to ComplexHeatmap::Heatmap() argument row_split. However,
the input type can vary:
-
integer number of row splits based upon row clustering. If
row_split is greater than the number of rows, it will be set
to the number of rows.
-
character value or values in colnames of rowData(se) to split
using row annotation in se.
-
data.frame whose rownames() must contain all rows to be
displayed in the heatmap. This argument is passed directly to
ComplexHeatmap::Heatmap() to apply the split appropriately.
-
character or factor vector named by rownames(se) with another
custom row split, passed directly to ComplexHeatmap::Heatmap()
argument row_split, with proper order for rows being displayed
row_subcluster
integer or character vector representing one
or more elements returned by row_split to use as a drill-down
sub-cluster heatmap. This argument is experimental, and is intended
to make it easy to "drill down" into specific row clusters.
The process internally creates a full heatmap using all arguments
as defined, then extracts the jamba::heatmap_row_order() which
contains row split data in a list of rownames vectors. The list
elements that match row_subcluster are extracted and used again
for a subsequent heatmap, and are displayed in the same order
in which they appear in the original full heatmap - which means
cluster_rows=FALSE is defined at this point. However row_split
is retained for this subset of rows, to indicate the original
row split annotation.
Note that row_subcluster must match the names() returned
by jamba::heatmap_row_order() for the full heatmap, or should
include a numeric index for the list element or elements to
use.
In principle this process would be run in two stages: First,
view a heatmap with row_split=6, then re-run the same heatmap
with row_subcluster=4 to see cluster number 4 from the full
heatmap.
row_title_rot
numeric value indicating text rotation in degrees
to use for row titles.
sample_color_list
named list of color vectors or color functions,
where names correspond to colnames in either colData(se) or
rowData(se), and which are passed to corresponding left or top
annotation functions. When colors are not defined,
ComplexHeatmap::Heatmap() will define colors using its own internal
function.
legend_at, legend_labels
numeric and character, respectively,
to define custom values for the heatmap color gradient legend.
When legend_at is supplied, it is used as provided.
When legend_labels is supplied, it is used only when its length
equals length(legend_at), in which case it is used as provided.
When centerby_colnames=FALSE and the matrix data does not contain
negative values, legend_at uses integers from 0 to color_max,
to avoid presenting a color legend with unnecessary negative values.
However, when color_max <= 1 it uses pretty(c(0, color_max)),
removing extraneous values, then ensuring the maximum value is
color_max. For example when color_max=0.85,
the legend_at is likely to be c(0, 0.2, 0.4, 0.6, 0.8, 0.85).
When centerby_colnames is not FALSE, and/or data contains
negative values, the legend_at is symmetric above and below zero.
When color_max <= 1 the label is created using
pretty(c(-color_max, color_max)), as described above, so color_max
is used as the minimum and maximum value.
When color_max > 1 the legend_at uses integer steps.
When color_max <= 1 the legend_labels are presented as-is with
no transformation.
When color_max > 1 the legend_labels are transformed with
exp2signed(x) which is the inverse of log2(1 + x). This inverse
tranform displays normal space values, in the case of centered data,
the values represent normal space fold changes.
For example the legend_at=c(-2, -1, 0, 1, 2) would result in
legend_labels=c("-4", "-2", "1", "2", "4").
When correlation=TRUE the legend_labels by default use legend_at,
following rules for color_max <= 1 above.
Otherwise, legend_labels values inverse transformed from log2(1 + x)
in order to display normal space fold change values,
To override any of this behavior, supply both legend_at
and corresponding legend_labels.
subset_legend_colors
logical indicating whether to subset colors
shown in the color key defined by sample_color_list, which is useful
when the heatmap only represents a subset of categorical color values.
When subset_legend_colors == TRUE, the color key will only
include colors shown in the top_annotation.
When subset_legend_colors == FALSE all colors defined in
sample_color_list will be included for each relevant column.
row_cex, column_cex
numeric values used to adjust the row and
column name font size, relative to the automatic adjustment that
is already done based upon the number of rows and columns being
displayed.
row_anno_fontsize
numeric base font size for row
annotation labels. This value is only used when left_annotation_name_gp
is not supplied. Note these labels appears underneath row annotations,
alongside column labels, and therefore they are also adjusted
by multiplying column_cex so these labels are adjusted together.
useMedian
logical passed to jamma::centerGeneData() during
data centering.
show_row_names, show_row_dend
logical indicating whether to
display row names, and row dendrogram, respectively. With more than
2,000 rows this step can become somewhat slow.
mark_rows
character vector of values in rownames(se) that
should be labeled using ComplexHeatmap::anno_mark() in call-out
style. Usually this argument is used when show_row_names=FALSE,
hiding the row labels, but is not required. Values in mark_rows
are intersected with rows displayed in the heatmap, therefore only
matching entries will be labeled.
mark_labels_gp
grid::gpar to customize the font used by labels
when mark_rows is supplied.
column_title
character optional title to include at the top
of the heatmap. It can include a single value, or multiple values
representing each column_split in the order they appear.
Note: This argument is ignored when apply_hm_column_title=TRUE.
When column_title=character(0) (default) or column_title="",
the ComplexHeatmap::Heatmap() uses its usual default behavior,
which is to assign column_title using column_split values
when they are being used.
apply_hm_column_title
logical (default FALSE) whether to
apply the heatmap title to column_title. This option makes it
convenient to display the title atop the heatmap without additional
effort, however it hides any other column_title created by
using column_split.
When using both column_split and apply_hm_column_title=TRUE
it may be useful to call heatmap_column_group_labels().
hm_title_buffer
numeric number of whitespace lines to add
to the heatmap title (attr(hm, "hm_title") between the title
and the heatmap below it. This whitespace can be useful when also
calling heatmap_column_group_labels(), to provide enough space
to draw the additional annotations.
show_heatmap_legend, show_left_legend, show_top_legend
logical
indicating whether each legend should be displayed. Sometimes there
are too many annotations, and the color legends can overwhelm the
figure. Note that show_left_legend is applied in a specific order,
with these rules:
-
show_left_legend is extended to at least length 2, then values
are used in order for: sestats, rowData_colnames, in order,
using whichever is defined.
If sestats is defined, the first value in show_left_legend
is used for this annotation, then the remaining values are used
for rowData_colnames. Setting the first show_left_legend value
to FALSE will ensure the legend for sestats is not displayed.
If rowData_colnames is defined, then the remaining values in
show_left_legend are recycled for all columns in
rowData_colnames, and applied in order.
In this way, individual columns can have the legend displayed or hidden.
If alt_sestats is defined, the legend is always hidden, in favor
of showing only the legend for sestats without duplicating this legend.
legend_border_color
character color used as border color tofor
be used as a border color for the various legend colors. Note this
argument recognizes only the first color provided, and does not
recycle different colors across the various legend borders.
show_top_annotation_name, show_left_annotation_name
logical
indicating whether to display the annotation name beside the top and
left annotations, respectively.
row_label_colname
character string used as a row label, where
this value is a colname in rowData(se). It is useful when rownames
are some identifier that is not user-friendly, and where another column
in the data may provide a more helpful label, for example "SYMBOL"
to display gene symbol instead of accession number.
cluster_columns, cluster_rows
logical indicating whether
to cluster columns by hierarchical clustering; or function with
a specific function that produces hclust or dendrogram output,
given a numeric matrix. Note that cluster_rows default will replace
NA values with zero 0 to avoid errors with missing data, and
uses amap::hcluster() by default which is a one-step compiled
process to perform distance calculation and hierarchical clustering.
column_names_gp
gpar to define custom column name settings.
When "fontsize" is not defined, the automatic font size calculation
is added to the column_names_gp supplied.
column_split
character or integer vector used to define
heatmap column split.
column_split_sep
character string used as delimited when
column_split defines multiple split levels.
color_max
numeric value passed to colorjam::col_div_xf()
which defines the upper limit of color gradient used in the heatmap.
color_floor
numeric value passed to colorjam::col_div_xf()
argument floor which defines the minimum non-zero numeric value
for a color to be applied. This option is available to prevent coloring
values below the color_floor which can be useful in some circumstances.
lens
numeric value passed to colorjam::col_div_xf() to control
the intensity of color gradient applied to the numeric range.
rename_contrasts, rename_alt_contrasts
logical (default TRUE)
whether to rename long contrast names in sestats and alt_sestats
using contrast2comp().
use_raster
logical passed to ComplexHeatmap::Heatmap() to
determine whether heatmaps should be converted to raster images,
which effectively turns each heatmap panel into a single graphical
object.
Recommend use_raster=TRUE and also installing R package magick
which greatly enhances speed and quality of rasterized heatmap
output. When magick is not available, it may be best to use
use_raster=FALSE.
When use_raster=FALSE each pixel square of a heatmap
is its own graphical object. For heatmaps with very large dimensions,
having each pixel as an object can make the heatmap extremely large
in memory, and sometimes pixels can overlap others because the
minimum pixel size of the output graphics device does not
reflect the actual size of each pixel.
verbose
logical indicating whether to print verbose output.
debug
logical indicating debug mode, data is returned in a list:
-
hm object ComplexHeatmap::Heatmap
-
top_annotation object ComplexHeatmap::HeatmapAnnotation for columns
-
left_annotation object ComplexHeatmap::HeatmapAnnotation for rows
-
hm_title object character string with the heatmap title.
...
additional arguments are passed to supporting functions.
Details
Note: Still a work in progress. This function is the basis
for the majority of heatmaps created for Omics data.
This function is a bold attempt to simplify the intricate task
of creating an expression heatmap, using ComplexHeatmap::Heatmap(),
given a SummarizedExperiment object.
It attempts to enable:
selection of assays(se) to use in the heatmap
use of rowData(se) or colData(se) to produce row and
column annotations, respectively.
re-use of defined colors for annotations, see platjam::design2colors()
define and adjust heatmap color gradient and scale
data centering by row: versus all columns, or specific controls,
optionally within independent centering groups
filtering rows to show only the statistical hits
display annotation of statistical hits beside the heatmap
split rows or columns using rowData(se) and colData(se), respectively
heatmap title to display key options used, for easy reference
Additional Features
data centering can be disabled with centerby_colnames=FALSE.
alternative hits can be displayed using alt_sestats. It does not
subset heatmap rows, it inherits rows from sestats.
display a subset of columns after row centering, useful to hide
the control group for certain figures.
option to display correlation heatmap, using the same data
centering, then calculates Pearson correlation across sample columns.
labels and legend grids can be customized to exact sizes
with grid::gpar() and grid::unit() definitions, for manuscript figures.
mark annotations option to label a subset of rows
row subclusters can be visualized using row_subcluster to drill down
into specific subclusters from hierarchical clustering, k-means clustering,
or any row_split.
Data Centering
The intent is to display expression values from assays(se),
centered across all columns, or with customization defined by
centerby_colnames and normgroup_colnames. The resulting centered
data can be subsetted by argument isamples, which occurs after
centering in order to decouple the centering step from the display
of resulting data. To subset samples involved in centering itself,
either subset the input se data, or supply controlSamples to
define a subset of samples used as the baseline in centering.
See jamma::centerGeneData() for more details.
Paired data, also called repeated measures data, can be visualized
by including the pairing as centerby_colnames so that centering
is calculated within each pairing subgroup. In this case if also using
controlSamples to define a "time zero" or "baseline", then all
baseline samples will have exactly zero, if there is only one replicate
per pairing group at the baseline. In this case, it may be useful
to create the full heatmap once to confirm the centering is performing
as intended, then create a second heatmap using isamples to show only
the non-baseline samples - thus removing the large chunk of values with 0.
Note: data centering can be disabled with centerby_colnames=FALSE.
Heatmap Title
A heatmap title is returned as an attribute attr(hm, "hm_title"),
which describes:
total rows displayed, with row_type indicating the measured entity
(gene, probe, DEGs, etc.)
total columns displayed, with column_type indicating the sampled entity
(samples, total replicates, etc.)
the assay_name for the data being displayed
relevant options for data centering, for example
"global-centered" (by default) or
"Centered within Cell Line, versus Wildtype"
To include the heatmap title:
ComplexHeatmap::draw(hm, column_title=attr(hm, "hm_title))
Top and Left Annotations
The top heatmap annotations use colData(se) with user-supplied
top_colnames or by auto-detecting those colnames that apply
to multiple colnames(se).
Colors can be supplied using argument sample_color_list, as
described below.
The an incidence matrix of statistical hits can be displayed
on the left of the heatmap, using arguments sestats and alt_sestats.
These arguments can accept either the output of se_contrast_stats(),
or they can be a numeric matrix with values c(-1, 0, 1), indicating
statistical hits down, no change, and up, respectively.
The contrasts can optionally be subset with contrast_names,
which corresponds to columns in the matrix if supplied in that format.
When sestats is supplied, it will subset all heatmap rows to include
only rows with at least one non-zero value in the incidence matrix.
If argument rows is supplied, then all rownames(se) matching
rows are displayed, regardless of statistical hits.
For comparison across other sestats results, argument alt_sestats
is treated similar to sestats except that the heatmap is not subset
based upon these values. That means the heatmap will be subset to
match hits defined by sestats but not alt_sestats.
The alt_sestats incidence matrix is displayed to the far left
of the sestats incidence matrix. For clarity, it can be useful to
add alt_sestats_suffix to add a suffix to each contrast label,
for example if sestats represents limma hits, use
sestats_suffix=" limma", and if alt_sestats represents limma-voom
hits, use alt_sestats_suffix=" limmavoom".
Argument rowData_colnames can be supplied, which enables display of
rowData(se) annotations in the left_annotation of the heatmap.
Colors can be supplied using argument sample_color_list.
Argument sample_color_list is a list named by each annotation column
to be displayed as top or left annotation. Each list element is either:
a character vector of R colors named by character value, or
a function defined by circlize::colorRamp2() to be applied
for numeric column values. In this case the breaks used to
define the color function are used to define the color legend.
The function platjam::design2colors() can be used to create
sample_color_list starting with a data.frame of annotations,
and will soon be moved into this package.
A custom left_annotation can be supplied, but this method currently
prevents the other annotations described above from being displayed.
To display automated annotations with rowData_colnames and custom
row annotations, supply custom annotations with right_annotation.
Note that annotations must be supplied in exact row order, which
is usually easiest when supplying rows with specific set of rows.
Compatible Input Formats
Data provided in se is expected to be SummarizedExperiment, however
other Bioconductor data types are accepted that provide
accessor functions: featureData(), phenoData(), and assayData(),
including for example the "MethyLumiSet" class.
Note that matrix input is currently not supported, however it can
be converted to SummarizedExperiment like this:
se <- SummarizedExperiment::SummarizedExperiment(
assays=list(data=matrix),
rowData=data.frame(Gene=rownames(matrix)),
colData=data.frame(Sample=colnames(matrix)))
See Also
Other jamses heatmaps:
detect_heatmap_components(),
heatmap_column_group_labels()
Examples
se <- make_se_test(nrow=1000, ngroups=4, nreps=8)
# optionally define factor levels to force the order of labels
SummarizedExperiment::rowData(se)$Class <- factor(
sample(head(LETTERS, 5), size=nrow(se), replace=TRUE))
# basic heatmap
hm <- heatmap_se(se, rowData_colnames="Class")
# draw by printing hm, or call draw() to add useful options
ComplexHeatmap::draw(hm,
column_title=attr(hm, "hm_title"),
merge_legends=TRUE)
# define specific colors
sample_color_list <- list(
group=colorjam::group2colors(
unique(SummarizedExperiment::colData(se)$group)),
Class=colorjam::group2colors(
unique(SummarizedExperiment::rowData(se)$Class)))
heatmap_se(se,
rowData_colnames="Class",
sample_color_list=sample_color_list)
# split rows by "Class"
heatmap_se(se,
rowData_colnames="Class",
row_split="Class",
sample_color_list=sample_color_list)
# let's have some fun now
hm2 <- heatmap_se(se,
column_split=c("group"),
column_title_rot=90,
row_split=c("Class"),
rowData_colnames=c("Class"),
cluster_row_slices=FALSE,
sample_color_list=sample_color_list)
hm2drawn <- ComplexHeatmap::draw(hm2, merge_legends=TRUE)
# as an example, extract the row order
# technically you should use hm2drawn, but usually hm2 is enough
hro <- jamba::heatmap_row_order(hm2drawn);
jamba::sdim(hro)
lapply(hro, head, 7)
# (the names will differ from values when `row_labels` are customized)
# center by WildType samples
# - controlSamples
# - control_label
hm2 <- heatmap_se(se,
controlSamples=rownames(subset(
SummarizedExperiment::colData(se), group %in% "groupA")),
control_label="vs groupA",
column_split=c("group"),
column_title_rot=90,
row_split=c("Class"),
rowData_colnames=c("Class"),
cluster_row_slices=FALSE,
sample_color_list=sample_color_list)
hm2drawn <- ComplexHeatmap::draw(hm2,
column_title=attr(hm2, "hm_title"),
merge_legends=TRUE)
# add "callout" labels for a subset of rows
mark_rows <- c(sample(jamba::heatmap_row_order(hm2drawn)[[1]], size=5),
sample(jamba::heatmap_row_order(hm2drawn)[[1]], size=3));
# turn off ComplexHeatmap warning when using RStudio
ComplexHeatmap::ht_opt(message=FALSE)
hm3 <- heatmap_se(se,
mark_rows=mark_rows,
controlSamples=rownames(
subset(SummarizedExperiment::colData(se), group %in% "groupA")),
control_label="vs groupA",
column_split=c("group"),
column_title_rot=90,
row_split=c("Class"),
rowData_colnames=c("Class"),
cluster_row_slices=FALSE,
sample_color_list=sample_color_list)
ComplexHeatmap::draw(hm3,
column_title=attr(hm3, "hm_title"),
merge_legends=TRUE)
# sestats can accept list, incidence matrix, hit_array, or sestats
# this example defines random set of hits
sestats_list <- list(
contrast1=setNames(sample(c(1, -1), replace=TRUE, size=50),
sample(rownames(se), size=50)),
contrast2=setNames(sample(c(1, -1), replace=TRUE, size=50),
sample(rownames(se), size=50)))
hm4 <- heatmap_se(se,
controlSamples=rownames(
subset(SummarizedExperiment::colData(se), group %in% "groupA")),
control_label="vs groupA",
sestats=sestats_list,
column_split=c("group"),
row_split=c("Class"),
rowData_colnames=c("Class"),
cluster_row_slices=FALSE,
sample_color_list=sample_color_list)
ComplexHeatmap::draw(hm4,
column_title=attr(hm4, "hm_title"),
merge_legends=TRUE)
# it doesn't take much effort to run stats really quick
sedesign <- groups_to_sedesign(SummarizedExperiment::colData(se)[, "group", drop=FALSE])
contrast_names(sedesign) <- jamba::vigrep("-groupA", contrast_names(sedesign))
sestats <- se_contrast_stats(se=se,
fold_cutoff=4,
sedesign=sedesign, assay_name="counts")
hm4s <- heatmap_se(se,
controlSamples=rownames(
subset(SummarizedExperiment::colData(se), group %in% "groupA")),
control_label="vs groupA",
sestats=sestats,
column_split=c("group"),
row_split=6,
rowData_colnames=c("Class"),
cluster_row_slices=FALSE,
sample_color_list=sample_color_list)
ComplexHeatmap::draw(hm4s,
column_title=attr(hm4s, "hm_title"),
merge_legends=TRUE)
# for fun, "drill down" into cluster 5
hm4s_4 <- heatmap_se(se,
controlSamples=rownames(
subset(SummarizedExperiment::colData(se), group %in% "groupA")),
control_label="vs groupA",
sestats=sestats,
column_split=c("group"),
row_split=6,
row_subcluster=4,
rowData_colnames=c("Class"),
cluster_row_slices=FALSE,
sample_color_list=sample_color_list)
ComplexHeatmap::draw(hm4s_4,
column_title=attr(hm4s_4, "hm_title"),
merge_legends=TRUE)
# sestats can be provided as an incidence matrix
if (jamba::check_pkg_installed("venndir")) {
# convert sestats to list
sestats_hitlist <- hit_array_to_list(sestats)
# convert sestats hitlist to incidence matrix
# - for fun, use only the first two contrasts
sestats_hitim <- venndir::list2im_value(sestats_hitlist[1:2])
print(head(sestats_hitim));
# convert sestats_list to signed incidence matrix
sestats_im <- venndir::list2im_value(sestats_list)
print(head(sestats_im, 10));
# if the list has items (no direction) use venndir::list2im_opt()
hm5 <- heatmap_se(se,
controlSamples=rownames(
subset(SummarizedExperiment::colData(se), group %in% "groupA")),
control_label="vs groupA",
sestats=sestats_hitim,
column_split=c("group"),
rowData_colnames=c("Class"),
cluster_row_slices=FALSE,
sample_color_list=sample_color_list)
ComplexHeatmap::draw(hm5,
column_title=attr(hm5, "hm_title"),
merge_legends=TRUE)
}
# customize column label fonts using column_names_gp
column_bold <- ifelse(
SummarizedExperiment::colData(se)$group %in% "groupA",
2, 1);
hm6 <- heatmap_se(se,
controlSamples=rownames(
subset(SummarizedExperiment::colData(se), group %in% "groupA")),
control_label="vs WildType",
column_names_gp=grid::gpar(col=sample_color_list$group[
as.character(SummarizedExperiment::colData(se)$group)],
font=column_bold),
column_split=c("group"),
row_split=c("Class"),
rowData_colnames=c("Class"),
cluster_row_slices=FALSE,
sample_color_list=sample_color_list)
ComplexHeatmap::draw(hm6,
column_title=attr(hm6, "hm_title"),
merge_legends=TRUE)
# correlation=TRUE, any heatmap becomes a sample correlation heatmap
hm6corr <- heatmap_se(se,
correlation=TRUE,
apply_hm_column_title=TRUE,
controlSamples=rownames(
subset(SummarizedExperiment::colData(se), group %in% "groupA")),
control_label="vs groupA",
column_names_gp=grid::gpar(col=sample_color_list$group[
as.character(SummarizedExperiment::colData(se)$group)],
font=rep(c(1, 2, 1), c(3, 5, 24))),
column_split=c("Group"),
sample_color_list=sample_color_list)
ComplexHeatmap::draw(hm6corr,
merge_legends=TRUE)
## Final heatmap:
# 1. Applies heatmap title automatically.
# 2. Hides the top_colnames
# 3. Adds fancy grouped labels above the heatmap.
#
# apply_hm_column_title=TRUE
# convenient way to define a title,
# but it does not also display column_split labels
#
# hm_title_buffer=4
# convenient way to insert some whitespace lines
#
# heatmap_column_group_labels()
# adds to a drawn heatmap - it must already be drawn
#
SummarizedExperiment::colData(se)$Genotype <- rep(c("WT", "KO"), each=16);
SummarizedExperiment::colData(se)$Treatment <- rep(c("Control", "Dex"), each=8);
hm7 <- heatmap_se(se,
apply_hm_column_title=TRUE,
hm_title_buffer=3,
controlSamples=rownames(
subset(SummarizedExperiment::colData(se), group %in% "groupA")),
control_label="vs groupA",
sestats=sestats_list,
top_colnames=FALSE,
column_split=c("group"),
row_split=c("Class"),
rowData_colnames=c("Class"),
cluster_row_slices=FALSE,
sample_color_list=sample_color_list)
hm7_drawn <- ComplexHeatmap::draw(hm7,
merge_legends=TRUE)
# now add fancy labels
heatmap_column_group_labels(
hm_group_list=c("Treatment", "Genotype"),
se=se,
hm_drawn=hm7_drawn)
# Note: this step does not work consistently inside RStudio plot pane,
# in that case call dev.new() then run the step above to create hm7_drawn,
# then repeat the step below
#
# adjust the height of labels with argument y_offset_lines
# with positive values (upward), or negative values (downward).
jmw86069/jamses documentation built on Nov. 4, 2024, 9:25 p.m.
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| heatmap_se | R Documentation |
Heatmap for SummarizedExperiment data
Description
Heatmap for SummarizedExperiment data
Usage
heatmap_se(
se,
sestats = NULL,
hm_name = NULL,
hm_title = NULL,
rows = NULL,
row_type = "rows",
column_type = "samples",
data_type = "expression",
correlation = FALSE,
assay_name = NULL,
contrast_names = NULL,
contrast_suffix = "",
cutoff_name = NULL,
alt_sestats = NULL,
alt_assay_name = assay_name,
alt_contrast_names = NULL,
alt_contrast_suffix = "",
alt_cutoff_name = NULL,
isamples = colnames(se),
normgroup_colname = NULL,
centerby_colnames = NULL,
controlSamples = NULL,
control_label = "",
controlFloor = NA,
naControlAction = c("na", "row", "floor", "min"),
naControlFloor = 0,
top_colnames = NULL,
top_annotation = NULL,
top_annotation_name_gp = grid::gpar(),
rowData_colnames = NULL,
left_annotation = NULL,
left_annotation_name_gp = grid::gpar(),
left_annotation_name_rot = 90,
right_annotation = NULL,
simple_anno_size = grid::unit(8, "mm"),
legend_title_gp = grid::gpar(fontsize = 10),
legend_labels_gp = grid::gpar(fontsize = 10),
legend_grid_cex = 1,
row_names_gp = NULL,
row_split = NULL,
row_subcluster = NULL,
row_title_rot = 0,
sample_color_list = NULL,
legend_at = NULL,
legend_labels = NULL,
subset_legend_colors = TRUE,
row_cex = 0.8,
column_cex = 1,
row_anno_fontsize = 11,
useMedian = FALSE,
show_row_names = NULL,
show_row_dend = length(rows) < 2000,
mark_rows = NULL,
mark_labels_gp = grid::gpar(),
column_title = character(0),
apply_hm_column_title = FALSE,
hm_title_buffer = 0,
show_heatmap_legend = TRUE,
show_top_legend = TRUE,
show_left_legend = TRUE,
legend_border_color = "black",
show_top_annotation_name = TRUE,
show_left_annotation_name = TRUE,
row_label_colname = NULL,
cluster_columns = FALSE,
cluster_column_slices = FALSE,
cluster_rows = function(x, ...) {
amap::hcluster(jamba::rmNA(naValue = 0, x), ...,
method = "euclidean", link = "ward")
},
cluster_row_slices = FALSE,
column_names_gp = NULL,
column_split = NULL,
column_split_sep = ",",
color_max = 3,
color_floor = 0,
lens = 2,
rename_contrasts = TRUE,
rename_alt_contrasts = TRUE,
use_raster = TRUE,
verbose = FALSE,
debug = FALSE,
...
)
Arguments
se |
|
sestats |
one of the following types of data:
|
hm_name |
|
hm_title |
|
rows |
|
row_type |
|
column_type |
|
data_type |
|
correlation |
|
assay_name |
|
contrast_names |
|
contrast_suffix |
|
cutoff_name |
|
alt_sestats, alt_assay_name, alt_contrast_names, alt_contrast_suffix |
arguments analogous to those described above for |
isamples |
|
normgroup_colname |
|
centerby_colnames |
either:
|
controlSamples |
|
control_label |
|
controlFloor, naControlAction, naControlFloor |
passed to
|
top_colnames |
one of the following types:
|
top_annotation |
specific heatmap annotation as defined by
|
top_annotation_name_gp |
|
rowData_colnames |
|
left_annotation |
specific heatmap annotation as defined by
|
left_annotation_name_gp |
|
left_annotation_name_rot |
|
right_annotation |
specific heatmap annotation as defined by
|
simple_anno_size |
|
legend_title_gp |
|
legend_labels_gp |
|
legend_grid_cex |
|
row_names_gp |
|
row_split |
is used to define heatmap split by row, ultimately
passed to
|
row_subcluster |
|
row_title_rot |
|
sample_color_list |
named |
legend_at, legend_labels |
|
subset_legend_colors |
|
row_cex, column_cex |
|
row_anno_fontsize |
|
useMedian |
|
show_row_names, show_row_dend |
|
mark_rows |
|
mark_labels_gp |
|
column_title |
|
apply_hm_column_title |
|
hm_title_buffer |
|
show_heatmap_legend, show_left_legend, show_top_legend |
|
legend_border_color |
|
show_top_annotation_name, show_left_annotation_name |
|
row_label_colname |
|
cluster_columns, cluster_rows |
|
column_names_gp |
|
column_split |
|
column_split_sep |
|
color_max |
|
color_floor |
|
lens |
|
rename_contrasts, rename_alt_contrasts |
|
use_raster |
|
verbose |
|
debug |
|
... |
additional arguments are passed to supporting functions. |
Details
Note: Still a work in progress. This function is the basis for the majority of heatmaps created for Omics data.
This function is a bold attempt to simplify the intricate task
of creating an expression heatmap, using ComplexHeatmap::Heatmap(),
given a SummarizedExperiment object.
It attempts to enable:
selection of
assays(se)to use in the heatmapuse of
rowData(se)orcolData(se)to produce row and column annotations, respectively.re-use of defined colors for annotations, see
platjam::design2colors()define and adjust heatmap color gradient and scale
data centering by row: versus all columns, or specific controls, optionally within independent centering groups
filtering rows to show only the statistical hits
display annotation of statistical hits beside the heatmap
split rows or columns using
rowData(se)andcolData(se), respectivelyheatmap title to display key options used, for easy reference
Additional Features
data centering can be disabled with
centerby_colnames=FALSE.alternative hits can be displayed using
alt_sestats. It does not subset heatmap rows, it inherits rows fromsestats.display a subset of columns after row centering, useful to hide the control group for certain figures.
option to display correlation heatmap, using the same data centering, then calculates Pearson correlation across sample columns.
labels and legend grids can be customized to exact sizes with
grid::gpar()andgrid::unit()definitions, for manuscript figures.mark annotations option to label a subset of rows
row subclusters can be visualized using
row_subclusterto drill down into specific subclusters from hierarchical clustering, k-means clustering, or anyrow_split.
Data Centering
The intent is to display expression values from assays(se),
centered across all columns, or with customization defined by
centerby_colnames and normgroup_colnames. The resulting centered
data can be subsetted by argument isamples, which occurs after
centering in order to decouple the centering step from the display
of resulting data. To subset samples involved in centering itself,
either subset the input se data, or supply controlSamples to
define a subset of samples used as the baseline in centering.
See jamma::centerGeneData() for more details.
Paired data, also called repeated measures data, can be visualized
by including the pairing as centerby_colnames so that centering
is calculated within each pairing subgroup. In this case if also using
controlSamples to define a "time zero" or "baseline", then all
baseline samples will have exactly zero, if there is only one replicate
per pairing group at the baseline. In this case, it may be useful
to create the full heatmap once to confirm the centering is performing
as intended, then create a second heatmap using isamples to show only
the non-baseline samples - thus removing the large chunk of values with 0.
Note: data centering can be disabled with centerby_colnames=FALSE.
Heatmap Title
A heatmap title is returned as an attribute attr(hm, "hm_title"),
which describes:
total rows displayed, with
row_typeindicating the measured entity (gene, probe, DEGs, etc.)total columns displayed, with
column_typeindicating the sampled entity (samples, total replicates, etc.)the
assay_namefor the data being displayedrelevant options for data centering, for example
"global-centered"(by default) or"Centered within Cell Line, versus Wildtype"
To include the heatmap title:
ComplexHeatmap::draw(hm, column_title=attr(hm, "hm_title))
Top and Left Annotations
The top heatmap annotations use colData(se) with user-supplied
top_colnames or by auto-detecting those colnames that apply
to multiple colnames(se).
Colors can be supplied using argument sample_color_list, as
described below.
The an incidence matrix of statistical hits can be displayed
on the left of the heatmap, using arguments sestats and alt_sestats.
These arguments can accept either the output of se_contrast_stats(),
or they can be a numeric matrix with values c(-1, 0, 1), indicating
statistical hits down, no change, and up, respectively.
The contrasts can optionally be subset with contrast_names,
which corresponds to columns in the matrix if supplied in that format.
When sestats is supplied, it will subset all heatmap rows to include
only rows with at least one non-zero value in the incidence matrix.
If argument rows is supplied, then all rownames(se) matching
rows are displayed, regardless of statistical hits.
For comparison across other sestats results, argument alt_sestats
is treated similar to sestats except that the heatmap is not subset
based upon these values. That means the heatmap will be subset to
match hits defined by sestats but not alt_sestats.
The alt_sestats incidence matrix is displayed to the far left
of the sestats incidence matrix. For clarity, it can be useful to
add alt_sestats_suffix to add a suffix to each contrast label,
for example if sestats represents limma hits, use
sestats_suffix=" limma", and if alt_sestats represents limma-voom
hits, use alt_sestats_suffix=" limmavoom".
Argument rowData_colnames can be supplied, which enables display of
rowData(se) annotations in the left_annotation of the heatmap.
Colors can be supplied using argument sample_color_list.
Argument sample_color_list is a list named by each annotation column
to be displayed as top or left annotation. Each list element is either:
a
charactervector of R colors named bycharactervalue, ora
functiondefined bycirclize::colorRamp2()to be applied fornumericcolumn values. In this case thebreaksused to define the color function are used to define the color legend.
The function platjam::design2colors() can be used to create
sample_color_list starting with a data.frame of annotations,
and will soon be moved into this package.
A custom left_annotation can be supplied, but this method currently
prevents the other annotations described above from being displayed.
To display automated annotations with rowData_colnames and custom
row annotations, supply custom annotations with right_annotation.
Note that annotations must be supplied in exact row order, which
is usually easiest when supplying rows with specific set of rows.
Compatible Input Formats
Data provided in se is expected to be SummarizedExperiment, however
other Bioconductor data types are accepted that provide
accessor functions: featureData(), phenoData(), and assayData(),
including for example the "MethyLumiSet" class.
Note that matrix input is currently not supported, however it can
be converted to SummarizedExperiment like this:
se <- SummarizedExperiment::SummarizedExperiment( assays=list(data=matrix), rowData=data.frame(Gene=rownames(matrix)), colData=data.frame(Sample=colnames(matrix)))
See Also
Other jamses heatmaps:
detect_heatmap_components(),
heatmap_column_group_labels()
Examples
se <- make_se_test(nrow=1000, ngroups=4, nreps=8)
# optionally define factor levels to force the order of labels
SummarizedExperiment::rowData(se)$Class <- factor(
sample(head(LETTERS, 5), size=nrow(se), replace=TRUE))
# basic heatmap
hm <- heatmap_se(se, rowData_colnames="Class")
# draw by printing hm, or call draw() to add useful options
ComplexHeatmap::draw(hm,
column_title=attr(hm, "hm_title"),
merge_legends=TRUE)
# define specific colors
sample_color_list <- list(
group=colorjam::group2colors(
unique(SummarizedExperiment::colData(se)$group)),
Class=colorjam::group2colors(
unique(SummarizedExperiment::rowData(se)$Class)))
heatmap_se(se,
rowData_colnames="Class",
sample_color_list=sample_color_list)
# split rows by "Class"
heatmap_se(se,
rowData_colnames="Class",
row_split="Class",
sample_color_list=sample_color_list)
# let's have some fun now
hm2 <- heatmap_se(se,
column_split=c("group"),
column_title_rot=90,
row_split=c("Class"),
rowData_colnames=c("Class"),
cluster_row_slices=FALSE,
sample_color_list=sample_color_list)
hm2drawn <- ComplexHeatmap::draw(hm2, merge_legends=TRUE)
# as an example, extract the row order
# technically you should use hm2drawn, but usually hm2 is enough
hro <- jamba::heatmap_row_order(hm2drawn);
jamba::sdim(hro)
lapply(hro, head, 7)
# (the names will differ from values when `row_labels` are customized)
# center by WildType samples
# - controlSamples
# - control_label
hm2 <- heatmap_se(se,
controlSamples=rownames(subset(
SummarizedExperiment::colData(se), group %in% "groupA")),
control_label="vs groupA",
column_split=c("group"),
column_title_rot=90,
row_split=c("Class"),
rowData_colnames=c("Class"),
cluster_row_slices=FALSE,
sample_color_list=sample_color_list)
hm2drawn <- ComplexHeatmap::draw(hm2,
column_title=attr(hm2, "hm_title"),
merge_legends=TRUE)
# add "callout" labels for a subset of rows
mark_rows <- c(sample(jamba::heatmap_row_order(hm2drawn)[[1]], size=5),
sample(jamba::heatmap_row_order(hm2drawn)[[1]], size=3));
# turn off ComplexHeatmap warning when using RStudio
ComplexHeatmap::ht_opt(message=FALSE)
hm3 <- heatmap_se(se,
mark_rows=mark_rows,
controlSamples=rownames(
subset(SummarizedExperiment::colData(se), group %in% "groupA")),
control_label="vs groupA",
column_split=c("group"),
column_title_rot=90,
row_split=c("Class"),
rowData_colnames=c("Class"),
cluster_row_slices=FALSE,
sample_color_list=sample_color_list)
ComplexHeatmap::draw(hm3,
column_title=attr(hm3, "hm_title"),
merge_legends=TRUE)
# sestats can accept list, incidence matrix, hit_array, or sestats
# this example defines random set of hits
sestats_list <- list(
contrast1=setNames(sample(c(1, -1), replace=TRUE, size=50),
sample(rownames(se), size=50)),
contrast2=setNames(sample(c(1, -1), replace=TRUE, size=50),
sample(rownames(se), size=50)))
hm4 <- heatmap_se(se,
controlSamples=rownames(
subset(SummarizedExperiment::colData(se), group %in% "groupA")),
control_label="vs groupA",
sestats=sestats_list,
column_split=c("group"),
row_split=c("Class"),
rowData_colnames=c("Class"),
cluster_row_slices=FALSE,
sample_color_list=sample_color_list)
ComplexHeatmap::draw(hm4,
column_title=attr(hm4, "hm_title"),
merge_legends=TRUE)
# it doesn't take much effort to run stats really quick
sedesign <- groups_to_sedesign(SummarizedExperiment::colData(se)[, "group", drop=FALSE])
contrast_names(sedesign) <- jamba::vigrep("-groupA", contrast_names(sedesign))
sestats <- se_contrast_stats(se=se,
fold_cutoff=4,
sedesign=sedesign, assay_name="counts")
hm4s <- heatmap_se(se,
controlSamples=rownames(
subset(SummarizedExperiment::colData(se), group %in% "groupA")),
control_label="vs groupA",
sestats=sestats,
column_split=c("group"),
row_split=6,
rowData_colnames=c("Class"),
cluster_row_slices=FALSE,
sample_color_list=sample_color_list)
ComplexHeatmap::draw(hm4s,
column_title=attr(hm4s, "hm_title"),
merge_legends=TRUE)
# for fun, "drill down" into cluster 5
hm4s_4 <- heatmap_se(se,
controlSamples=rownames(
subset(SummarizedExperiment::colData(se), group %in% "groupA")),
control_label="vs groupA",
sestats=sestats,
column_split=c("group"),
row_split=6,
row_subcluster=4,
rowData_colnames=c("Class"),
cluster_row_slices=FALSE,
sample_color_list=sample_color_list)
ComplexHeatmap::draw(hm4s_4,
column_title=attr(hm4s_4, "hm_title"),
merge_legends=TRUE)
# sestats can be provided as an incidence matrix
if (jamba::check_pkg_installed("venndir")) {
# convert sestats to list
sestats_hitlist <- hit_array_to_list(sestats)
# convert sestats hitlist to incidence matrix
# - for fun, use only the first two contrasts
sestats_hitim <- venndir::list2im_value(sestats_hitlist[1:2])
print(head(sestats_hitim));
# convert sestats_list to signed incidence matrix
sestats_im <- venndir::list2im_value(sestats_list)
print(head(sestats_im, 10));
# if the list has items (no direction) use venndir::list2im_opt()
hm5 <- heatmap_se(se,
controlSamples=rownames(
subset(SummarizedExperiment::colData(se), group %in% "groupA")),
control_label="vs groupA",
sestats=sestats_hitim,
column_split=c("group"),
rowData_colnames=c("Class"),
cluster_row_slices=FALSE,
sample_color_list=sample_color_list)
ComplexHeatmap::draw(hm5,
column_title=attr(hm5, "hm_title"),
merge_legends=TRUE)
}
# customize column label fonts using column_names_gp
column_bold <- ifelse(
SummarizedExperiment::colData(se)$group %in% "groupA",
2, 1);
hm6 <- heatmap_se(se,
controlSamples=rownames(
subset(SummarizedExperiment::colData(se), group %in% "groupA")),
control_label="vs WildType",
column_names_gp=grid::gpar(col=sample_color_list$group[
as.character(SummarizedExperiment::colData(se)$group)],
font=column_bold),
column_split=c("group"),
row_split=c("Class"),
rowData_colnames=c("Class"),
cluster_row_slices=FALSE,
sample_color_list=sample_color_list)
ComplexHeatmap::draw(hm6,
column_title=attr(hm6, "hm_title"),
merge_legends=TRUE)
# correlation=TRUE, any heatmap becomes a sample correlation heatmap
hm6corr <- heatmap_se(se,
correlation=TRUE,
apply_hm_column_title=TRUE,
controlSamples=rownames(
subset(SummarizedExperiment::colData(se), group %in% "groupA")),
control_label="vs groupA",
column_names_gp=grid::gpar(col=sample_color_list$group[
as.character(SummarizedExperiment::colData(se)$group)],
font=rep(c(1, 2, 1), c(3, 5, 24))),
column_split=c("Group"),
sample_color_list=sample_color_list)
ComplexHeatmap::draw(hm6corr,
merge_legends=TRUE)
## Final heatmap:
# 1. Applies heatmap title automatically.
# 2. Hides the top_colnames
# 3. Adds fancy grouped labels above the heatmap.
#
# apply_hm_column_title=TRUE
# convenient way to define a title,
# but it does not also display column_split labels
#
# hm_title_buffer=4
# convenient way to insert some whitespace lines
#
# heatmap_column_group_labels()
# adds to a drawn heatmap - it must already be drawn
#
SummarizedExperiment::colData(se)$Genotype <- rep(c("WT", "KO"), each=16);
SummarizedExperiment::colData(se)$Treatment <- rep(c("Control", "Dex"), each=8);
hm7 <- heatmap_se(se,
apply_hm_column_title=TRUE,
hm_title_buffer=3,
controlSamples=rownames(
subset(SummarizedExperiment::colData(se), group %in% "groupA")),
control_label="vs groupA",
sestats=sestats_list,
top_colnames=FALSE,
column_split=c("group"),
row_split=c("Class"),
rowData_colnames=c("Class"),
cluster_row_slices=FALSE,
sample_color_list=sample_color_list)
hm7_drawn <- ComplexHeatmap::draw(hm7,
merge_legends=TRUE)
# now add fancy labels
heatmap_column_group_labels(
hm_group_list=c("Treatment", "Genotype"),
se=se,
hm_drawn=hm7_drawn)
# Note: this step does not work consistently inside RStudio plot pane,
# in that case call dev.new() then run the step above to create hm7_drawn,
# then repeat the step below
#
# adjust the height of labels with argument y_offset_lines
# with positive values (upward), or negative values (downward).
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