# Run decoupleR with DoRothEA prior knowledge
net <- decoupleR::get_dorothea(organism = "human", levels = c("A", "B", "C"))
activities <- decoupleR::run_wmean(mat = as.matrix(sample[["RNA"]]$data),
net = net)
p <- SCpubr::do_TFActivityHeatmap(sample = sample,
activities = activities)
pdo_TFActivityHeatmap() | Transcription factor activity
TF activity heatmaps visualize DoRothEA transcription factor activity scores from decoupleR. Shows which transcription factors are most active in different cell populations.
Due to dependency problems with OmnipathR, this page will not show plots as it can not compute.
Basic usage
First compute TF activities using decoupleR:
Limit number of TFs
p <- SCpubr::do_TFActivityHeatmap(sample = sample,
activities = activities,
n_tfs = 10)
pSelect specific TFs
p <- SCpubr::do_TFActivityHeatmap(sample = sample,
activities = activities,
tfs.use = c("PAX5", "STAT3", "NF-kB"))
pGroup by custom variable
p <- SCpubr::do_TFActivityHeatmap(sample = sample,
activities = activities,
group.by = "cell_type")
pSplit by condition
p <- SCpubr::do_TFActivityHeatmap(sample = sample,
activities = activities,
split.by = "condition")
pShow values
p <- SCpubr::do_TFActivityHeatmap(sample = sample,
activities = activities,
values.show = TRUE,
values.size = 4)
pParameter reference
Note
For parameters shared across many functions (color palettes, typography, legend styling, grid), see Shared features.
Core parameters
| Parameter | Description | Default |
|---|---|---|
activities |
DecoupleR output with DoRothEA | — |
n_tfs |
Number of top TFs | 25 |
tfs.use |
Specific TFs to include | NULL |
statistic |
Activity statistic | "norm_wmean" |
return_object |
Return data along with plot | FALSE |
Values display
| Parameter | Description | Default |
|---|---|---|
values.show |
Show numeric values | FALSE |
values.size |
Value text size | 3 |
values.threshold |
Threshold on which the text changes color | NULL |
values.round |
Decimal places | 1 |
See also
- Shared features — Common parameters
- do_PathwayActivityHeatmap() — Pathway activities
- do_ActivityHeatmap() — Gene set activities