annotation_group | R Documentation |
'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.
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")
)
.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) |
A tidyHeatmap object with group annotation strips added
[Mangiola and Papenfuss., 2020](https://joss.theoj.org/papers/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.
# 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
)
)
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