Please have a look also to
- nanny for tidy high-level data analysis and manipulation
- tidygate for adding custom gate information to your tibble
- tidySCE for tidy manipulation of Seurat objects
- tidyseurat for tidy manipulation of Seurat objects
- tidybulk for tidy high-level data analysis and manipulation
- tidySE for heatmaps produced with tidy principles
website: stemangiola.github.io/tidyHeatmap/
tidyHeatmap
is a package that introduces tidy principles to the
creation of information-rich heatmaps. This package uses
ComplexHeatmap
as graphical engine.
Advantages:
- Modular annotation with just specifying 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
Function | Description |
---|---|
heatmap |
Plot base heatmap |
add_tile |
Add tile annotation to the heatmap |
add_point |
Add point annotation to the heatmap |
add_bar |
Add bar annotation to the heatmap |
add_line |
Add line annotation to the heatmap |
save_pdf |
Save the PDF of the heatmap |
To install the most up-to-date version
devtools::install_github("stemangiola/tidyHeatmap")
To install the most stable version (however please keep in mind that this package is under a maturing lifecycle stage)
install.packages("tidyHeatmap")
If you want to contribute to the software, report issues or problems with the software or seek support please open an issue here
The heatmaps visualise a multi-element, multi-feature dataset, annotated with independent variables. Each observation is a element-feature pair (e.g., person-physical characteristics).
element | feature | value | independent_variables |
---|---|---|---|
chr or fctr |
chr or fctr |
numeric |
… |
Let’s transform the mtcars dataset into a tidy “element-feature-independent variables” data frame. Where the independent variables in this case are ‘hp’ and ‘vs’.
mtcars_tidy <-
mtcars %>%
as_tibble(rownames="Car name") %>%
# Scale
mutate_at(vars(-`Car name`, -hp, -vs), scale) %>%
# tidyfy
pivot_longer(cols = -c(`Car name`, hp, vs), names_to = "Property", values_to = "Value")
mtcars_tidy
## # A tibble: 288 x 5
## `Car name` hp vs Property Value[,1]
## <chr> <dbl> <dbl> <chr> <dbl>
## 1 Mazda RX4 110 0 mpg 0.151
## 2 Mazda RX4 110 0 cyl -0.105
## 3 Mazda RX4 110 0 disp -0.571
## 4 Mazda RX4 110 0 drat 0.568
## 5 Mazda RX4 110 0 wt -0.610
## 6 Mazda RX4 110 0 qsec -0.777
## 7 Mazda RX4 110 0 am 1.19
## 8 Mazda RX4 110 0 gear 0.424
## 9 Mazda RX4 110 0 carb 0.735
## 10 Mazda RX4 Wag 110 0 mpg 0.151
## # … with 278 more rows
For plotting, you simply pipe the input data frame into heatmap, specifying:
- The rows, cols relative column names (mandatory)
- The value column name (mandatory)
- The annotations column name(s)
mtcars
mtcars_heatmap <-
mtcars_tidy %>%
heatmap(`Car name`, Property, Value ) %>%
add_tile(hp)
mtcars_heatmap
mtcars_heatmap %>% save_pdf("mtcars_heatmap.pdf")
We can easily group the data (one group per dimension maximum, at the moment only the vertical dimension is supported) with dplyr, and the heatmap will be grouped accordingly
mtcars_tidy %>%
group_by(vs) %>%
heatmap(`Car name`, Property, Value ) %>%
add_tile(hp)
We can easily use custom palette, using strings, hexadecimal color character vector,
mtcars_tidy %>%
heatmap(
`Car name`,
Property,
Value,
palette_value = c("red", "white", "blue")
)
Or a grid::colorRamp2 function for higher flexibility
mtcars_tidy %>%
heatmap(
`Car name`,
Property,
Value,
palette_value = circlize::colorRamp2(c(-2, -1, 0, 1, 2), viridis::magma(5))
)
tidyHeatmap::pasilla %>%
group_by(location, type) %>%
heatmap(
.column = sample,
.row = symbol,
.value = `count normalised adjusted`
) %>%
add_tile(condition) %>%
add_tile(activation)
This feature requires >= 0.99.20 version
“tile” (default), “point”, “bar” and “line” are available
# Create some more data points
pasilla_plus <-
tidyHeatmap::pasilla %>%
dplyr::mutate(act = activation) %>%
tidyr::nest(data = -sample) %>%
dplyr::mutate(size = rnorm(n(), 4,0.5)) %>%
dplyr::mutate(age = runif(n(), 50, 200)) %>%
tidyr::unnest(data)
# Plot
pasilla_plus %>%
heatmap(
.column = sample,
.row = symbol,
.value = `count normalised adjusted`
) %>%
add_tile(condition) %>%
add_point(activation) %>%
add_tile(act) %>%
add_bar(size) %>%
add_line(age)