| Type: | Package |
| Title: | A Tidy Implementation of Heatmap |
| Version: | 1.12.2 |
| Author: | Stefano Mangiola [aut, cre], Anthony Papenfuss [ctb] |
| Maintainer: | Stefano Mangiola <mangiolastefano@gmail.com> |
| Description: | This is a tidy implementation for heatmap. At the moment it is based on the (great) package 'ComplexHeatmap'. The goal of this package is to interface a tidy data frame with this powerful tool. Some of the advantages are: Row and/or columns colour annotations are easy to integrate just specifying one parameter (column names). Custom grouping of rows is easy to specify providing a grouped tbl. For example: df %>% group_by(...). Labels size adjusted by row and column total number. Default use of Brewer and Viridis palettes. |
| License: | GPL-3 |
| URL: | https://www.r-project.org, https://github.com/stemangiola/tidyHeatmap |
| BugReports: | https://github.com/stemangiola/tidyHeatmap/issues |
| Depends: | R (≥ 4.3.0) |
| Imports: | methods, stats, utils, dplyr (≥ 0.8.5), magrittr (≥ 1.5), tidyr (≥ 1.0.3), rlang (≥ 0.4.5), purrr (≥ 0.3.3), tibble, ComplexHeatmap (≥ 2.2.0), viridis (≥ 0.5.1), circlize (≥ 0.4.8), RColorBrewer (≥ 1.1), grid, grDevices, lifecycle (≥ 0.2.0), dendextend, patchwork |
| Suggests: | spelling, testthat, vdiffr, BiocManager, knitr, rmarkdown, qpdf, covr, roxygen2, forcats, ggplot2 |
| VignetteBuilder: | knitr |
| RdMacros: | lifecycle |
| Biarch: | true |
| biocViews: | AssayDomain, Infrastructure |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.3.2 |
| Language: | en-US |
| NeedsCompilation: | no |
| Packaged: | 2025-07-22 09:18:18 UTC; a1234450 |
| Repository: | CRAN |
| Date/Publication: | 2025-07-22 09:40:02 UTC |
Add two tidyHeatmap objects together
Description
Add two tidyHeatmap objects together
Usage
## S3 method for class 'InputHeatmap'
e1 + e2
Arguments
e1 |
First tidyHeatmap object |
e2 |
Second tidyHeatmap object |
Value
A combined ComplexHeatmap object
Source
[Mangiola and Papenfuss., 2020](https://joss.theoj.org/papers/10.21105/joss.02472)
References
Mangiola, S. and Papenfuss, A.T., 2020. "tidyHeatmap: an R package for modular heatmap production based on tidy principles." Journal of Open Source Software. doi:10.21105/joss.02472.
Example data set N52
Description
Example data set N52
Usage
N52
Format
An object of class tbl_df (inherits from tbl, data.frame) with 520 rows and 15 columns.
Source
[Mangiola and Papenfuss., 2020](https://joss.theoj.org/papers/10.21105/joss.02472)
References
Mangiola, S. and Papenfuss, A.T., 2020. "tidyHeatmap: an R package for modular heatmap production based on tidy principles." Journal of Open Source Software. doi:10.21105/joss.02472.
add_annotation
Description
add_annotation() takes a tbl object and easily produces a ComplexHeatmap plot, with integration with tibble and dplyr frameworks.
Usage
add_annotation(
my_input_heatmap,
annotation,
type = rep("tile", length(quo_names(annotation))),
palette_discrete = list(),
palette_continuous = list(),
size = NULL,
...
)
Arguments
my_input_heatmap |
A 'InputHeatmap' formatted as | <SAMPLE> | <TRANSCRIPT> | <COUNT> | <...> | |
annotation |
Vector of quotes |
type |
A character vector of the set c(\"tile\", \"point\", \"bar\", \"line\") |
palette_discrete |
A list of character vectors. This is the list of palettes that will be used for horizontal and vertical discrete annotations. The discrete classification of annotations depends on the column type of your input tibble (e.g., character and factor). |
palette_continuous |
A list of character vectors. This is the list of palettes that will be used for horizontal and vertical continuous annotations. The continuous classification of annotations depends on the column type of your input tibble (e.g., integer, numerical, double). |
size |
A grid::unit object, e.g. unit(2, "cm"). This is the height or width of the annotation depending on the orientation. |
... |
The arguments that will be passed to top_annotation or left_annotation of the ComplexHeatmap container |
Details
To be added.
Value
A 'ComplexHeatmap' object
Source
[Mangiola and Papenfuss., 2020](https://joss.theoj.org/papers/10.21105/joss.02472)
References
Mangiola, S. and Papenfuss, A.T., 2020. "tidyHeatmap: an R package for modular heatmap production based on tidy principles." Journal of Open Source Software. doi:10.21105/joss.02472.
Add attribute to abject
Description
Add attribute to abject
Usage
add_attr(var, attribute, name)
Arguments
var |
A tibble |
attribute |
An object |
name |
A character name of the attribute |
Value
A tibble with an additional attribute
Add class to abject
Description
Add class to abject
Usage
add_class(var, name)
Arguments
var |
A tibble |
name |
A character name of the attribute |
Value
A tibble with an additional attribute
annot_to_list
Description
annot_to_list
Usage
annot_to_list(.data)
Arguments
.data |
A data frame |
Value
A list
Adds a bar annotation layer to a 'InputHeatmap', that on evaluation creates a 'ComplexHeatmap'
Description
annotation_bar() from a 'InputHeatmap' object, adds a bar annotation layer.
Usage
annotation_bar(.data, .column, palette = NULL, size = NULL, ...)
## S4 method for signature 'InputHeatmap'
annotation_bar(.data, .column, palette = NULL, size = NULL, ...)
Arguments
.data |
a 'InputHeatmap' object created calling 'tidyHeatmap::heatmap()' |
.column |
Vector of quotes |
palette |
A character vector of colors, or a function such as colorRamp2 (see examples). |
size |
A grid::unit object, e.g. unit(2, "cm"). This is the height or width of the annotation depending on the orientation. |
... |
The arguments that will be passed to
|
Details
![[Maturing]](./figures/lifecycle-maturing.svg)
It uses 'ComplexHeatmap' as visualisation tool.
Value
A 'InputHeatmap' object that gets evaluated to a 'ComplexHeatmap'
A 'InputHeatmap' object that gets evaluated to a 'ComplexHeatmap'
Source
[Mangiola and Papenfuss., 2020](https://joss.theoj.org/papers/10.21105/joss.02472)
References
Mangiola, S. and Papenfuss, A.T., 2020. "tidyHeatmap: an R package for modular heatmap production based on tidy principles." Journal of Open Source Software. doi:10.21105/joss.02472.
Examples
hm =
tidyHeatmap::N52 |>
tidyHeatmap::heatmap(
.row = symbol_ct,
.column = UBR,
.value = `read count normalised log`
)
hm |> annotation_bar(inflection)
Add group annotation strips to a tidyHeatmap
Description
'annotation_group()' adds group annotation strips to a tidyHeatmap object, allowing you to visually group rows or columns based on categorical variables. This is useful for highlighting biological or experimental groups in your heatmap.
Usage
annotation_group(.data, ...)
## S4 method for signature 'InputHeatmap'
annotation_group(
.data,
...,
palette_grouping = list(),
group_label_fontsize = 8,
show_group_name = TRUE,
group_strip_height = grid::unit(9, "pt")
)
Arguments
.data |
A tidyHeatmap object |
... |
Grouping columns (unquoted, like dplyr::group_by) |
palette_grouping |
List of color vectors for each grouping. Each element should be a vector of colors for the corresponding grouping variable. |
group_label_fontsize |
Font size for group labels |
show_group_name |
Logical, show the group annotation name |
group_strip_height |
Height of group strip as a grid unit (default: 9pt) |
Value
A tidyHeatmap object with group annotation strips added
Source
[Mangiola and Papenfuss., 2020](https://joss.theoj.org/papers/10.21105/joss.02472)
References
Mangiola, S. and Papenfuss, A.T., 2020. "tidyHeatmap: an R package for modular heatmap production based on tidy principles." Journal of Open Source Software. doi:10.21105/joss.02472.
Examples
# Basic usage with row grouping
tidyHeatmap::N52 |>
tidyHeatmap::heatmap(
.row = symbol_ct,
.column = UBR,
.value = `read count normalised log`
) |>
annotation_group(CAPRA_TOTAL)
# With custom colors and formatting
tidyHeatmap::N52 |>
tidyHeatmap::heatmap(
.row = symbol_ct,
.column = UBR,
.value = `read count normalised log`
) |>
annotation_group(
CAPRA_TOTAL,
palette_grouping = list(c("#E64B35", "#4DBBD5")),
group_label_fontsize = 10,
show_group_name = FALSE
)
# Multiple grouping variables
tidyHeatmap::N52 |>
tidyHeatmap::heatmap(
.row = symbol_ct,
.column = UBR,
.value = `read count normalised log`
) |>
annotation_group(
CAPRA_TOTAL,
`Cell type`,
palette_grouping = list(
c("#E64B35", "#4DBBD5"), # colors for CAPRA_TOTAL
c("#00A087", "#F39B7F") # colors for Cell type
)
)
Adds a line annotation layer to a 'InputHeatmap', that on evaluation creates a 'ComplexHeatmap'
Description
annotation_line() from a 'InputHeatmap' object, adds a line annotation layer.
Usage
annotation_line(.data, .column, palette = NULL, size = NULL, ...)
## S4 method for signature 'InputHeatmap'
annotation_line(.data, .column, palette = NULL, size = NULL, ...)
Arguments
.data |
a 'InputHeatmap' object created calling 'tidyHeatmap::heatmap()' |
.column |
Vector of quotes |
palette |
A character vector of colors, or a function such as colorRamp2 (see examples). |
size |
A grid::unit object, e.g. unit(2, "cm"). This is the height or width of the annotation depending on the orientation. |
... |
The arguments that will be passed to
|
Details
It uses 'ComplexHeatmap' as visualisation tool.
Value
A 'InputHeatmap' object that gets evaluated to a 'ComplexHeatmap'
A 'InputHeatmap' object that gets evaluated to a 'ComplexHeatmap'
Source
[Mangiola and Papenfuss., 2020](https://joss.theoj.org/papers/10.21105/joss.02472)
References
Mangiola, S. and Papenfuss, A.T., 2020. "tidyHeatmap: an R package for modular heatmap production based on tidy principles." Journal of Open Source Software. doi:10.21105/joss.02472.
Examples
hm =
tidyHeatmap::N52 |>
tidyHeatmap::heatmap(
.row = symbol_ct,
.column = UBR,
.value = `read count normalised log`
)
hm |> annotation_line(inflection)
Adds a numeric annotation layer to an 'InputHeatmap', that on evaluation creates a 'ComplexHeatmap'
Description
'annotation_numeric()' from an 'InputHeatmap' object adds a numeric annotation layer.
Usage
annotation_numeric(
.data,
.column,
palette = NULL,
size = NULL,
labels_format = function(x) sprintf("%.1f", x),
...
)
## S4 method for signature 'InputHeatmap'
annotation_numeric(
.data,
.column,
palette = NULL,
size = NULL,
labels_format = function(x) sprintf("%.1f", x),
...
)
Arguments
.data |
A 'tbl_df' formatted as | <ELEMENT> | <FEATURE> | <VALUE> | <...> | |
.column |
Vector of quotes |
palette |
A character vector of colours, or a function such as colorRamp2 (see examples). |
size |
A grid::unit object, e.g. unit(2, "cm"). This is the height or width of the annotation depending on the orientation. |
labels_format |
A function to format the numeric labels. By default, it formats numbers to two decimal places using 'sprintf(" supply any function that takes a numeric vector and returns a character vector for customised formatting. |
... |
The arguments that will be passed to
|
Details
It uses 'ComplexHeatmap' as the visualisation tool.
Value
An 'InputHeatmap' object that gets evaluated to a 'ComplexHeatmap'.
An 'InputHeatmap' object that gets evaluated to a 'ComplexHeatmap'.
Source
[Mangiola and Papenfuss, 2020](https://joss.theoj.org/papers/10.21105/joss.02472)
References
Mangiola, S. and Papenfuss, A.T., 2020. "tidyHeatmap: an R package for modular heatmap production based on tidy principles." Journal of Open Source Software. doi:10.21105/joss.02472.
Examples
hm =
tidyHeatmap::N52 |>
tidyHeatmap::heatmap(
.row = symbol_ct,
.column = UBR,
.value = `read count normalised log`
)
hm |> annotation_numeric(inflection)
# Align to the right
hm |> annotation_numeric(inflection, align_to = "right")
Adds a point annotation layer to a 'InputHeatmap', that on evaluation creates a 'ComplexHeatmap'
Description
annotation_point() from a 'InputHeatmap' object, adds a point annotation layer.
Usage
annotation_point(.data, .column, palette = NULL, size = NULL, ...)
## S4 method for signature 'InputHeatmap'
annotation_point(.data, .column, palette = NULL, size = NULL, ...)
Arguments
.data |
a 'InputHeatmap' object created calling 'tidyHeatmap::heatmap()' |
.column |
Vector of quotes |
palette |
A character vector of colors, or a function such as colorRamp2 (see examples). |
size |
A grid::unit object, e.g. unit(2, "cm"). This is the height or width of the annotation depending on the orientation. |
... |
The arguments that will be passed to
|
Details
It uses 'ComplexHeatmap' as visualisation tool.
Value
A 'InputHeatmap' object that gets evaluated to a 'ComplexHeatmap'
A 'InputHeatmap' object that gets evaluated to a 'ComplexHeatmap'
Source
[Mangiola and Papenfuss., 2020](https://joss.theoj.org/papers/10.21105/joss.02472)
References
Mangiola, S. and Papenfuss, A.T., 2020. "tidyHeatmap: an R package for modular heatmap production based on tidy principles." Journal of Open Source Software. doi:10.21105/joss.02472.
Examples
hm =
tidyHeatmap::N52 |>
tidyHeatmap::heatmap(
.row = symbol_ct,
.column = UBR,
.value = `read count normalised log`
)
hm |> annotation_point(inflection)
Adds a tile annotation layer to a 'InputHeatmap', that on evaluation creates a 'ComplexHeatmap'
Description
annotation_tile() from a 'InputHeatmap' object, adds a tile annotation layer.
Usage
annotation_tile(.data, .column, palette = NULL, size = NULL, ...)
## S4 method for signature 'InputHeatmap'
annotation_tile(.data, .column, palette = NULL, size = NULL, ...)
Arguments
.data |
a 'InputHeatmap' object created calling 'tidyHeatmap::heatmap()' |
.column |
Vector of quotes |
palette |
A character vector of colors, or a function such as colorRamp2 (see examples). |
size |
A grid::unit object, e.g. unit(2, "cm"). This is the height or width of the annotation depending on the orientation. |
... |
The arguments that will be passed to
|
Details
It uses 'ComplexHeatmap' as visualisation tool.
Value
A 'InputHeatmap' object that gets evaluated to a 'ComplexHeatmap'
A 'InputHeatmap' object that gets evaluated to a 'ComplexHeatmap'
Source
[Mangiola and Papenfuss., 2020](https://joss.theoj.org/papers/10.21105/joss.02472)
References
Mangiola, S. and Papenfuss, A.T., 2020. "tidyHeatmap: an R package for modular heatmap production based on tidy principles." Journal of Open Source Software. doi:10.21105/joss.02472.
Examples
hm =
tidyHeatmap::N52 |>
tidyHeatmap::heatmap(
.row = symbol_ct,
.column = UBR,
.value = `read count normalised log`
)
hm |> annotation_tile(CAPRA_TOTAL)
hm |>
annotation_tile(
inflection,
palette = circlize::colorRamp2(c(0, 3,10), c("white", "green", "red"))
)
Creates a 'ComplexHeatmap' object for less standard plot manipulation (e.g. changing legend position)
Description
as_ComplexHeatmap() takes a 'InputHeatmap' object and produces a 'Heatmap' object
Usage
as_ComplexHeatmap(tidyHeatmap)
## S4 method for signature 'InputHeatmap'
as_ComplexHeatmap(tidyHeatmap)
Arguments
tidyHeatmap |
A 'InputHeatmap' object from tidyHeatmap::heatmap() call |
Details
Value
A 'ComplexHeatmap'
Source
[Mangiola and Papenfuss., 2020](https://joss.theoj.org/papers/10.21105/joss.02472)
[Mangiola and Papenfuss., 2020](https://joss.theoj.org/papers/10.21105/joss.02472)
References
Mangiola, S. and Papenfuss, A.T., 2020. "tidyHeatmap: an R package for modular heatmap production based on tidy principles." Journal of Open Source Software. doi:10.21105/joss.02472.
Mangiola, S. and Papenfuss, A.T., 2020. "tidyHeatmap: an R package for modular heatmap production based on tidy principles." Journal of Open Source Software. doi:10.21105/joss.02472.
Examples
tidyHeatmap::N52 |>
tidyHeatmap::heatmap(
.row = symbol_ct,
.column = UBR,
.value = `read count normalised log`,
) |>
as_ComplexHeatmap()
Get matrix from tibble
Description
Get matrix from tibble
Usage
as_matrix(tbl, rownames = NULL, do_check = TRUE)
Arguments
tbl |
A tibble |
rownames |
A character string of the rownames |
do_check |
A boolean |
Value
A matrix
Check whether there are NA counts
Description
Check whether there are NA counts
Usage
check_if_counts_is_na(.data, .abundance)
Arguments
.data |
A tibble of read counts |
.abundance |
A character name of the read count column |
Value
A tbl
Check whether there are duplicated genes/transcripts
Description
Check whether there are duplicated genes/transcripts
Usage
check_if_duplicated_genes(.data, .sample, .transcript, .abundance)
Arguments
.data |
A tibble of read counts |
.sample |
A character name of the sample column |
.transcript |
A character name of the transcript/gene column |
.abundance |
A character name of the read count column |
Value
A tbl
Check whether there are NA counts
Description
Check whether there are NA counts
Usage
check_if_wrong_input(.data, list_input, expected_type)
Arguments
.data |
A tibble of read counts |
list_input |
A list |
expected_type |
A character string |
Value
A tbl
Remove class to abject
Description
Remove class to abject
Usage
drop_class(var, name)
Arguments
var |
A tibble |
name |
A character name of the class |
Value
A tibble with an additional attribute
Check whether a numeric vector has been log transformed
Description
Check whether a numeric vector has been log transformed
Usage
error_if_log_transformed(x, .abundance)
Arguments
x |
A numeric vector |
.abundance |
A character name of the transcript/gene abundance column |
Value
NA
Get column names either from user or from attributes
Description
Get column names either from user or from attributes
Usage
get_abundance_norm_if_exists(.data, .abundance)
Arguments
.data |
A tibble |
.abundance |
A character name of the abundance column |
Value
A list of column enquo or error
Get column names either from user or from attributes
Description
Get column names either from user or from attributes
Usage
get_elements(.data, .element, of_samples = TRUE)
Arguments
.data |
A tibble |
.element |
A character name of the sample column |
of_samples |
A boolean |
Value
A list of column enquo or error
Get column names either from user or from attributes
Description
Get column names either from user or from attributes
Usage
get_elements_features(.data, .element, .feature, of_samples = TRUE)
Arguments
.data |
A tibble |
.element |
A character name of the sample column |
.feature |
A character name of the transcript/gene column |
of_samples |
A boolean |
Value
A list of column enquo or error
Get column names either from user or from attributes
Description
Get column names either from user or from attributes
Usage
get_elements_features_abundance(
.data,
.element,
.feature,
.abundance,
of_samples = TRUE
)
Arguments
.data |
A tibble |
.element |
A character name of the sample column |
.feature |
A character name of the transcript/gene column |
.abundance |
A character name of the read count column |
of_samples |
A boolean |
Value
A list of column enquo or error
Retrieve heatmap data and dendrograms as plotted
Description
get_heatmap_data() extracts the heatmap matrix as it appears in the plot along with the row and column dendrograms, all with consistent naming.
Usage
get_heatmap_data(.data)
## S4 method for signature 'InputHeatmap'
get_heatmap_data(.data)
Arguments
.data |
A 'InputHeatmap' object from tidyHeatmap::heatmap() |
Details
This function converts the InputHeatmap to ComplexHeatmap, draws it to perform clustering, then extracts the ordered matrix and dendrograms exactly as they appear in the heatmap plot.
Value
A list containing:
matrix: The abundance matrix with rows and columns ordered as in the heatmap
row_dend: The row dendrogram object
column_dend: The column dendrogram object
A list containing the ordered matrix, row dendrogram, and column dendrogram
Source
[Mangiola and Papenfuss., 2020](https://joss.theoj.org/papers/10.21105/joss.02472)
References
Mangiola, S. and Papenfuss, A.T., 2020. "tidyHeatmap: an R package for modular heatmap production based on tidy principles." Journal of Open Source Software. doi:10.21105/joss.02472.
Examples
hm <- tidyHeatmap::N52 |>
tidyHeatmap::heatmap(
.row = symbol_ct,
.column = UBR,
.value = `read count normalised log`
) |>
annotation_group(
CAPRA_TOTAL,
palette_grouping = list(c("#E64B35", "#4DBBD5")),
group_label_fontsize = 10,
show_group_name = FALSE
)
# Multiple grouping variables
tidyHeatmap::N52 |>
tidyHeatmap::heatmap(
.row = symbol_ct,
.column = UBR,
.value = `read count normalised log`
) |>
annotation_group(
CAPRA_TOTAL,
`Cell type`,
palette_grouping = list(
c("#E64B35", "#4DBBD5"), # colors for CAPRA_TOTAL
c("#00A087", "#F39B7F") # colors for Cell type
)
)
# Get heatmap data as plotted
result <- hm |> get_heatmap_data()
ordered_matrix <- result$matrix
row_dendrogram <- result$row_dend
column_dendrogram <- result$column_dend
Get column names either from user or from attributes
Description
Get column names either from user or from attributes
Usage
get_sample_counts(.data, .sample, .abundance)
Arguments
.data |
A tibble |
.sample |
A character name of the sample column |
.abundance |
A character name of the read count column |
Value
A list of column enquo or error
Get column names either from user or from attributes
Description
Get column names either from user or from attributes
Usage
get_sample_transcript(.data, .sample, .transcript)
Arguments
.data |
A tibble |
.sample |
A character name of the sample column |
.transcript |
A character name of the transcript/gene column |
Value
A list of column enquo or error
Get column names either from user or from attributes
Description
Get column names either from user or from attributes
Usage
get_sample_transcript_counts(.data, .sample, .transcript, .abundance)
Arguments
.data |
A tibble |
.sample |
A character name of the sample column |
.transcript |
A character name of the transcript/gene column |
.abundance |
A character name of the read count column |
Value
A list of column enquo or error
get_x_y_annotation_columns
Description
get_x_y_annotation_columns
Usage
get_x_y_annotation_columns(.data, .column, .row, .abundance)
Arguments
.data |
A 'tbl' formatted as | <SAMPLE> | <TRANSCRIPT> | <COUNT> | <...> | |
.column |
The name of the column horizontally presented in the heatmap |
.row |
The name of the column vertically presented in the heatmap |
.abundance |
The name of the transcript/gene abundance column |
Value
A list
Creates a 'InputHeatmap' object from 'tbl_df' on evaluation creates a 'ComplexHeatmap'
Description
heatmap() takes a tbl object and easily produces a ComplexHeatmap plot, with integration with tibble and dplyr frameworks.
Usage
heatmap(
.data,
.row,
.column,
.value,
transform = NULL,
scale = "none",
palette_value = c("#440154FF", "#21908CFF", "#fefada"),
palette_grouping = list(),
.scale = NULL,
...
)
heatmap_(
.data,
.row,
.column,
.value,
transform = NULL,
scale = "none",
palette_value = c("#440154FF", "#21908CFF", "#fefada"),
palette_grouping = list(),
.scale = NULL,
...
)
## S4 method for signature 'tbl'
heatmap(
.data,
.row,
.column,
.value,
transform = NULL,
scale = "none",
palette_value = c("#440154FF", "#21908CFF", "#fefada"),
palette_grouping = list(),
.scale = NULL,
...
)
## S4 method for signature 'tbl_df'
heatmap(
.data,
.row,
.column,
.value,
transform = NULL,
scale = "none",
palette_value = c("#440154FF", "#21908CFF", "#fefada"),
palette_grouping = list(),
.scale = NULL,
...
)
Arguments
.data |
A 'tbl_df' formatted as | <ELEMENT> | <FEATURE> | <VALUE> | <...> | |
.row |
The name of the column to use as rows |
.column |
The name of the column to use as columns |
.value |
The name of the column to use as values |
transform |
A function to transform the data (optional) |
scale |
One of "none", "row", "column", or "both" for scaling |
palette_value |
A character vector of colors or a function for value colors |
palette_grouping |
A list of color palettes for grouping annotations |
.scale |
DEPRECATED: Use scale instead |
... |
Additional arguments passed to ComplexHeatmap |
Details
This function takes a tbl as an input and creates a 'ComplexHeatmap' plot. The information is stored in a 'InputHeatmap' object that is updated along the pipe statement, for example adding annotation layers.
Value
An 'InputHeatmap' object that gets evaluated to a 'ComplexHeatmap'
A 'InputHeatmap' object
A 'InputHeatmap' object
A 'InputHeatmap' object
Source
[Mangiola and Papenfuss., 2020](https://joss.theoj.org/papers/10.21105/joss.02472)
References
Mangiola, S. and Papenfuss, A.T., 2020. "tidyHeatmap: an R package for modular heatmap production based on tidy principles." Journal of Open Source Software. doi:10.21105/joss.02472.
Examples
tidyHeatmap::N52 |>
dplyr::group_by( `Cell type`) |>
tidyHeatmap::heatmap(
.row = symbol_ct,
.column = UBR,
.value = `read count normalised log`,
)
This is a generalisation of ifelse that accepts an object and return an objects
Description
This is a generalisation of ifelse that accepts an object and return an objects
Usage
ifelse2_pipe(.x, .p1, .p2, .f1, .f2, .f3 = NULL)
Arguments
.x |
A tibble |
.p1 |
A boolean |
.p2 |
ELSE IF condition |
.f1 |
A function |
.f2 |
A function |
.f3 |
A function |
Value
A tibble
This is a generalisation of ifelse that accepts an object and return an objects
Description
This is a generalisation of ifelse that accepts an object and return an objects
Usage
ifelse_pipe(.x, .p, .f1, .f2 = NULL)
Arguments
.x |
A tibble |
.p |
A boolean |
.f1 |
A function |
.f2 |
A function |
Value
A tibble
input_heatmap
Description
input_heatmap() takes a tbl object and easily produces a ComplexHeatmap plot, with integration with tibble and dplyr frameworks.
Usage
input_heatmap(
.data,
.horizontal,
.vertical,
.abundance,
transform = NULL,
scale = "none",
palette_value = c("#440154FF", "#21908CFF", "#fefada"),
palette_grouping = list(),
...
)
Arguments
.data |
A 'tbl' formatted as | <SAMPLE> | <TRANSCRIPT> | <COUNT> | <...> | |
.horizontal |
The name of the column horizontally presented in the heatmap |
.vertical |
The name of the column vertically presented in the heatmap |
.abundance |
The name of the transcript/gene abundance column |
transform |
A function, used to transform .value, for example log1p |
scale |
A character string. Possible values are c(\"none\", \"row\", \"column\", \"both\") |
palette_value |
A character vector, or a function for higher customisation (colorRamp2). This is the palette that will be used as gradient for abundance. If palette_value is a vector of hexadecimal colours, it should have 3 values. If you want more customisation, you can pass to palette_value a function, that is derived as for example 'colorRamp2(c(-2, 0, 2), palette_value)' |
palette_grouping |
A list of character vectors. This is the list of palettes that will be used for grouping |
... |
Further arguments to be passed to ComplexHeatmap::Heatmap |
Details
To be added.
Value
A 'ComplexHeatmap' object
Source
[Mangiola and Papenfuss., 2020](https://joss.theoj.org/papers/10.21105/joss.02472)
References
Mangiola, S. and Papenfuss, A.T., 2020. "tidyHeatmap: an R package for modular heatmap production based on tidy principles." Journal of Open Source Software. doi:10.21105/joss.02472.
Adds a layers of symbols above the heatmap tiles to a 'InputHeatmap', that on evaluation creates a 'ComplexHeatmap'
Description
layer_arrow_down() from a 'InputHeatmap' object, adds a bar annotation layer.
Usage
layer_arrow_down(.data, ..., .size = NULL)
## S4 method for signature 'InputHeatmap'
layer_arrow_down(.data, ..., .size = NULL)
Arguments
.data |
A 'InputHeatmap' |
... |
Expressions that return a logical value, and are defined in terms of the variables in .data. If multiple expressions are included, they are combined with the & operator. Only rows for which all conditions evaluate to TRUE are kept. |
.size |
A column name or a double. The size of the elements of the layer. |
Details
It uses 'ComplexHeatmap' as visualisation tool.
Value
A 'InputHeatmap' object that gets evaluated to a 'ComplexHeatmap'
A 'InputHeatmap' object that gets evaluated to a 'ComplexHeatmap'
Source
[Mangiola and Papenfuss., 2020](https://joss.theoj.org/papers/10.21105/joss.02472)
References
Mangiola, S. and Papenfuss, A.T., 2020. "tidyHeatmap: an R package for modular heatmap production based on tidy principles." Journal of Open Source Software. doi:10.21105/joss.02472.
Examples
hm =
tidyHeatmap::N52 |>
tidyHeatmap::heatmap(
.row = symbol_ct,
.column = UBR,
.value = `read count normalised log`
)
hm |> layer_arrow_down()
Adds a layers of symbols above the heatmap tiles to a 'InputHeatmap', that on evaluation creates a 'ComplexHeatmap'
Description
layer_arrow_up() from a 'InputHeatmap' object, adds a bar annotation layer.
Usage
layer_arrow_up(.data, ..., .size = NULL)
## S4 method for signature 'InputHeatmap'
layer_arrow_up(.data, ..., .size = NULL)
Arguments
.data |
A 'InputHeatmap' |
... |
Expressions that return a logical value, and are defined in terms of the variables in .data. If multiple expressions are included, they are combined with the & operator. Only rows for which all conditions evaluate to TRUE are kept. |
.size |
A column name or a double. The size of the elements of the layer. |
Details
It uses 'ComplexHeatmap' as visualisation tool.
Value
A 'InputHeatmap' object that gets evaluated to a 'ComplexHeatmap'
A 'InputHeatmap' object that gets evaluated to a 'ComplexHeatmap'
Source
[Mangiola and Papenfuss., 2020](https://joss.theoj.org/papers/10.21105/joss.02472)
References
Mangiola, S. and Papenfuss, A.T., 2020. "tidyHeatmap: an R package for modular heatmap production based on tidy principles." Journal of Open Source Software. doi:10.21105/joss.02472.
Examples
hm =
tidyHeatmap::N52 |>
tidyHeatmap::heatmap(
.row = symbol_ct,
.column = UBR,
.value = `read count normalised log`
)
hm |> layer_arrow_up()
Adds a layer of symbols above the heatmap tiles to a 'InputHeatmap', that on evaluation creates a 'ComplexHeatmap'
Description
layer_asterisk() from a 'InputHeatmap' object, adds a symbol annotation layer over the heatmap tiles.
Usage
layer_asterisk(.data, ..., .size = NULL)
## S4 method for signature 'InputHeatmap'
layer_asterisk(.data, ..., .size = NULL)
Arguments
.data |
A 'InputHeatmap' |
... |
Expressions that return a logical value, and are defined in terms of the variables in .data. If multiple expressions are included, they are combined with the & operator. Only rows for which all conditions evaluate to TRUE are kept. |
.size |
A column name or a double. The size of the elements of the layer. |
Details
It uses 'ComplexHeatmap' as visualisation tool.
Value
A 'InputHeatmap' object that gets evaluated to a 'ComplexHeatmap'
A 'InputHeatmap' object that gets evaluated to a 'ComplexHeatmap'
Source
[Mangiola and Papenfuss., 2020](https://joss.theoj.org/papers/10.21105/joss.02472)
References
Mangiola, S. and Papenfuss, A.T., 2020. "tidyHeatmap: an R package for modular heatmap production based on tidy principles." Journal of Open Source Software. doi:10.21105/joss.02472.
Examples
hm =
tidyHeatmap::N52 |>
tidyHeatmap::heatmap(
.row = symbol_ct,
.column = UBR,
.value = `read count normalised log`
)
hm |> layer_asterisk()
Adds a layers of symbols above the heatmap tiles to a 'InputHeatmap', that on evaluation creates a 'ComplexHeatmap'
Description
layer_diamond() from a 'InputHeatmap' object, adds a bar annotation layer.
Usage
layer_diamond(.data, ..., .size = NULL)
## S4 method for signature 'InputHeatmap'
layer_diamond(.data, ..., .size = NULL)
Arguments
.data |
A 'InputHeatmap' |
... |
Expressions that return a logical value, and are defined in terms of the variables in .data. If multiple expressions are included, they are combined with the & operator. Only rows for which all conditions evaluate to TRUE are kept. |
.size |
A column name or a double. The size of the elements of the layer. |
Details
It uses 'ComplexHeatmap' as visualisation tool.
Value
A 'InputHeatmap' object that gets evaluated to a 'ComplexHeatmap'
A 'InputHeatmap' object that gets evaluated to a 'ComplexHeatmap'
Source
[Mangiola and Papenfuss., 2020](https://joss.theoj.org/papers/10.21105/joss.02472)
References
Mangiola, S. and Papenfuss, A.T., 2020. "tidyHeatmap: an R package for modular heatmap production based on tidy principles." Journal of Open Source Software. doi:10.21105/joss.02472.
Examples
hm =
tidyHeatmap::N52 |>
tidyHeatmap::heatmap(
.row = symbol_ct,
.column = UBR,
.value = `read count normalised log`
)
hm |> layer_diamond()
Adds a layers of symbols above the heatmap tiles to a 'InputHeatmap', that on evaluation creates a 'ComplexHeatmap'
Description
layer_point() from a 'InputHeatmap' object, adds a bar annotation layer.
Usage
layer_point(.data, ..., .size = NULL)
## S4 method for signature 'InputHeatmap'
layer_point(.data, ..., .size = NULL)
Arguments
.data |
A 'InputHeatmap' |
... |
Expressions that return a logical value, and are defined in terms of the variables in .data. If multiple expressions are included, they are combined with the & operator. Only rows for which all conditions evaluate to TRUE are kept. |
.size |
A column name or a double. The size of the elements of the layer. |
Details
It uses 'ComplexHeatmap' as visualisation tool.
Value
A 'InputHeatmap' object that gets evaluated to a 'ComplexHeatmap'
A 'InputHeatmap' object that gets evaluated to a 'ComplexHeatmap'
Source
[Mangiola and Papenfuss., 2020](https://joss.theoj.org/papers/10.21105/joss.02472)
References
Mangiola, S. and Papenfuss, A.T., 2020. "tidyHeatmap: an R package for modular heatmap production based on tidy principles." Journal of Open Source Software. doi:10.21105/joss.02472.
Examples
hm =
tidyHeatmap::N52 |>
tidyHeatmap::heatmap(
.row = symbol_ct,
.column = UBR,
.value = `read count normalised log`
)
hm |> layer_point()
Adds a layers of symbols above the heatmap tiles to a 'InputHeatmap', that on evaluation creates a 'ComplexHeatmap'
Description
layer_square() from a 'InputHeatmap' object, adds a bar annotation layer.
Usage
layer_square(.data, ..., .size = NULL)
## S4 method for signature 'InputHeatmap'
layer_square(.data, ..., .size = NULL)
Arguments
.data |
A 'InputHeatmap' |
... |
Expressions that return a logical value, and are defined in terms of the variables in .data. If multiple expressions are included, they are combined with the & operator. Only rows for which all conditions evaluate to TRUE are kept. |
.size |
A column name or a double. The size of the elements of the layer. |
Details
It uses 'ComplexHeatmap' as visualisation tool.
Value
A 'InputHeatmap' object that gets evaluated to a 'ComplexHeatmap'
A 'InputHeatmap' object that gets evaluated to a 'ComplexHeatmap'
Source
[Mangiola and Papenfuss., 2020](https://joss.theoj.org/papers/10.21105/joss.02472)
References
Mangiola, S. and Papenfuss, A.T., 2020. "tidyHeatmap: an R package for modular heatmap production based on tidy principles." Journal of Open Source Software. doi:10.21105/joss.02472.
Examples
hm =
tidyHeatmap::N52 |>
tidyHeatmap::heatmap(
.row = symbol_ct,
.column = UBR,
.value = `read count normalised log`
)
hm |> layer_square()
Adds a layer of symbols above the heatmap tiles to a 'InputHeatmap', that on evaluation creates a 'ComplexHeatmap'
Description
layer_star() from a 'InputHeatmap' object, adds a symbol annotation layer over the heatmap tiles.
Usage
layer_star(.data, ..., .size = NULL)
## S4 method for signature 'InputHeatmap'
layer_star(.data, ..., .size = NULL)
Arguments
.data |
A 'InputHeatmap' |
... |
Expressions that return a logical value, and are defined in terms of the variables in .data. If multiple expressions are included, they are combined with the & operator. Only rows for which all conditions evaluate to TRUE are kept. |
.size |
A column name or a double. The size of the elements of the layer. |
Details
It uses 'ComplexHeatmap' as visualisation tool.
Value
A 'InputHeatmap' object that gets evaluated to a 'ComplexHeatmap'
A 'InputHeatmap' object that gets evaluated to a 'ComplexHeatmap'
Source
[Mangiola and Papenfuss., 2020](https://joss.theoj.org/papers/10.21105/joss.02472)
References
Mangiola, S. and Papenfuss, A.T., 2020. "tidyHeatmap: an R package for modular heatmap production based on tidy principles." Journal of Open Source Software. doi:10.21105/joss.02472.
Examples
hm =
tidyHeatmap::N52 |>
tidyHeatmap::heatmap(
.row = symbol_ct,
.column = UBR,
.value = `read count normalised log`
)
hm |> layer_star()
Adds a layers of texts above the heatmap tiles to a 'InputHeatmap', that on evaluation creates a 'ComplexHeatmap'
Description
layer_text() from a 'InputHeatmap' object, adds a text annotation layer.
Usage
layer_text(.data, ..., .value, .size = NULL)
## S4 method for signature 'InputHeatmap'
layer_text(.data, ..., .value, .size = NULL)
Arguments
.data |
A 'InputHeatmap' |
... |
Expressions that return a logical value, and are defined in terms of the variables in .data. If multiple expressions are included, they are combined with the & operator. Only rows for which all conditions evaluate to TRUE are kept. |
.value |
A column name or character string. |
.size |
A column name or a double. The size of the elements of the layer. |
Details
It uses 'ComplexHeatmap' as visualisation tool.
Value
A 'InputHeatmap' object that gets evaluated to a 'ComplexHeatmap'
A 'InputHeatmap' object that gets evaluated to a 'ComplexHeatmap'
Source
[Mangiola and Papenfuss., 2020](https://joss.theoj.org/papers/10.21105/joss.02472)
References
Mangiola, S. and Papenfuss, A.T., 2020. "tidyHeatmap: an R package for modular heatmap production based on tidy principles." Journal of Open Source Software. doi:10.21105/joss.02472.
Examples
library(dplyr)
hm =
tidyHeatmap::N52 |>
mutate(my_text = "t") |>
tidyHeatmap::heatmap(
.row = symbol_ct,
.column = UBR,
.value = `read count normalised log`
)
hm |> layer_text(.value = "a")
hm |> layer_text(.value = my_text)
.formula parser
Description
.formula parser
Usage
parse_formula(fm)
Arguments
fm |
a formula |
Value
A character vector
Example data set Pasilla
Description
Example data set Pasilla
Usage
pasilla
Format
An object of class tbl_df (inherits from tbl, data.frame) with 504 rows and 8 columns.
Source
[Mangiola and Papenfuss., 2020](https://joss.theoj.org/papers/10.21105/joss.02472)
References
Mangiola, S. and Papenfuss, A.T., 2020. "tidyHeatmap: an R package for modular heatmap production based on tidy principles." Journal of Open Source Software. doi:10.21105/joss.02472.
From rlang deprecated
Description
From rlang deprecated
Usage
prepend(x, values, before = 1)
Arguments
x |
An array |
values |
An array |
before |
A boolean |
Value
An array
Convert array of quosure (e.g. c(col_a, col_b)) into character vector
Description
Convert array of quosure (e.g. c(col_a, col_b)) into character vector
Usage
quo_names(v)
Arguments
v |
A array of quosures (e.g. c(col_a, col_b)) |
Value
A character vector
Save plot on PDF file
Description
save_pdf() takes as input a Heatmap from ComplexHeatmap and save it to PDF file
Usage
save_pdf(
.heatmap,
filename,
width = NULL,
height = NULL,
units = c("in", "cm", "mm")
)
Arguments
.heatmap |
A 'Heatmap' |
filename |
A character string. The name of the output file/path |
width |
A 'double'. Plot width |
height |
A 'double'. Plot height |
units |
A character string. units ("in", "cm", or "mm") |
Details
It simply save an 'Heatmap' to a PDF file use pdf() function in the back end
Value
NA
Source
[Mangiola and Papenfuss., 2020](https://joss.theoj.org/papers/10.21105/joss.02472)
References
Mangiola, S. and Papenfuss, A.T., 2020. "tidyHeatmap: an R package for modular heatmap production based on tidy principles." Journal of Open Source Software. doi:10.21105/joss.02472.
Examples
tidyHeatmap::heatmap(
dplyr::group_by(tidyHeatmap::pasilla, location, type),
.column = sample,
.row = symbol,
.value = `count normalised adjusted`,
) |>
save_pdf(tempfile())
save_pdf
Description
save_pdf
Usage
## S4 method for signature 'Heatmap'
save_pdf(
.heatmap,
filename,
width = NULL,
height = NULL,
units = c("in", "cm", "mm")
)
Arguments
.heatmap |
A 'Heatmap' |
filename |
A character string. The name of the output file/path |
width |
A 'double'. Plot width |
height |
A 'double'. Plot height |
units |
A character string. units ("in", "cm", or "mm") |
save_pdf
Description
save_pdf
Usage
## S4 method for signature 'InputHeatmap'
save_pdf(
.heatmap,
filename,
width = NULL,
height = NULL,
units = c("in", "cm", "mm")
)
Arguments
.heatmap |
A 'Heatmap' |
filename |
A character string. The name of the output file/path |
width |
A 'double'. Plot width |
height |
A 'double'. Plot height |
units |
A character string. units ("in", "cm", or "mm") |
Scale counts in a robust way against sd == 0
Description
Scale counts in a robust way against sd == 0
Usage
scale_robust(y)
Arguments
y |
A numerical array |
Value
A scaled and centred numerical array
Source
[Mangiola and Papenfuss., 2020](https://joss.theoj.org/papers/10.21105/joss.02472)
References
Mangiola, S. and Papenfuss, A.T., 2020. "tidyHeatmap: an R package for modular heatmap production based on tidy principles." Journal of Open Source Software. doi:10.21105/joss.02472.
Sub function of remove_redundancy_elements_though_reduced_dimensions
Description
Sub function of remove_redundancy_elements_though_reduced_dimensions
Usage
select_closest_pairs(df)
Arguments
df |
A tibble |
Value
A tibble with pairs to drop
Split the heatmap row-wise depending on the biggest branches in the cladogram.
Description
split_rows() from a 'InputHeatmap' object, split the row cladogram.
split_columns() from a 'InputHeatmap' object, split the column cladogram.
Usage
split_rows(.data, number_of_groups)
## S4 method for signature 'InputHeatmap'
split_rows(.data, number_of_groups)
split_columns(.data, number_of_groups)
## S4 method for signature 'InputHeatmap'
split_columns(.data, number_of_groups)
Arguments
.data |
A 'InputHeatmap' |
number_of_groups |
An integer. The number of groups to split the cladogram into. |
Details
It uses 'ComplexHeatmap' as visualisation tool.
It uses 'ComplexHeatmap' as visualisation tool.
Value
A 'InputHeatmap' object that gets evaluated to a 'ComplexHeatmap'
A 'InputHeatmap' object that gets evaluated to a 'ComplexHeatmap'
A 'InputHeatmap' object that gets evaluated to a 'ComplexHeatmap'
A 'InputHeatmap' object that gets evaluated to a 'ComplexHeatmap'
Source
[Mangiola and Papenfuss., 2020](https://joss.theoj.org/papers/10.21105/joss.02472)
[Mangiola and Papenfuss., 2020](https://joss.theoj.org/papers/10.21105/joss.02472)
References
Mangiola, S. and Papenfuss, A.T., 2020. "tidyHeatmap: an R package for modular heatmap production based on tidy principles." Journal of Open Source Software. doi:10.21105/joss.02472.
Mangiola, S. and Papenfuss, A.T., 2020. "tidyHeatmap: an R package for modular heatmap production based on tidy principles." Journal of Open Source Software. doi:10.21105/joss.02472.
Examples
hm =
tidyHeatmap::N52 |>
tidyHeatmap::heatmap(
.row = symbol_ct,
.column = UBR,
.value = `read count normalised log`
)
hm |> split_rows(2)
hm =
tidyHeatmap::N52 |>
tidyHeatmap::heatmap(
.row = symbol_ct,
.column = UBR,
.value = `read count normalised log`
)
hm |> split_columns(2)
Wrap tidyHeatmap (ComplexHeatmap) in a patchwork-compliant patch
Description
In order to add tidyHeatmap (ComplexHeatmap) element to a patchwork they can be converted to a compliant representation using the 'wrap_heatmap()' function. This allows you to position either grobs, ggplot objects, patchwork objects, or even base graphics (if passed as a formula) in either the full area, the full plotting area (anything between and including the axis label), or the panel area (only the actual area where data is drawn).
Usage
wrap_heatmap(
panel = NULL,
plot = NULL,
full = NULL,
clip = TRUE,
ignore_tag = FALSE,
padding = NULL
)
## S4 method for signature 'InputHeatmap'
wrap_heatmap(
panel = NULL,
plot = NULL,
full = NULL,
clip = TRUE,
ignore_tag = FALSE,
padding = NULL
)
Arguments
panel, plot, full |
A grob, ggplot, patchwork, formula, raster, or nativeRaster object to add to the respective area. |
clip |
Should the grobs be clipped if expanding outside its area |
ignore_tag |
Should tags be ignored for this patch. This is relevant when using automatic tagging of plots and the content of the patch does not qualify for a tag. |
padding |
A grid::unit object. It defined the padding distance for the plot. It is helpful when the heatmap is assembled with other ggplots through patchwork. |
Value
A wrapped_patch object
A wrapped_patch object
Source
[Mangiola and Papenfuss., 2020](https://joss.theoj.org/papers/10.21105/joss.02472)
References
Mangiola, S. and Papenfuss, A.T., 2020. "tidyHeatmap: an R package for modular heatmap production based on tidy principles." Journal of Open Source Software. doi:10.21105/joss.02472.
Examples
tidyHeatmap::N52 |>
tidyHeatmap::heatmap(
.row = symbol_ct,
.column = UBR,
.value = `read count normalised log`,
) |>
wrap_heatmap()