| name | bioconductor-sechm |
| description | sechm provides a simple interface between SummarizedExperiment objects and the ComplexHeatmap package. It enables plotting annotated heatmaps from SE objects, with easy access to rowData and colData columns, and implements a number of features to make the generation of heatmaps easier and more flexible. These functionalities used to be part of the SEtools package. |
sechm
Workflows
Standard Workflow
sechm provides a simple interface between SummarizedExperiment objects and the ComplexHeatmap package. It enables plotting annotated heatmaps from SE objects, with easy access to rowData and colData columns, and implements a number of features to make the generation of heatmaps easier and more flexible.
library(SummarizedExperiment)
library(sechm)
data("Chen2017", package="sechm")
SE <- Chen2017
g <- c("Egr1", "Nr4a1", "Fos", "Egr2", "Sgk1", "Arc", "Dusp1", "Fosb", "Sik1")
sechm(SE, features=g, do.scale=TRUE)
sechm(SE, features=g, assayName="logFC", breaks=0.985)
setSechmOption("hmcols", value=c("white","grey","black"))
sechm(SE, features=g, do.scale=TRUE)
resetAllSechmOptions()
Input: A SummarizedExperiment object and a vector of feature names. Output: A Heatmap object from ComplexHeatmap.
When to Use
- To plot highly customizable, annotated heatmaps directly from
SummarizedExperiment objects using sechm().
- To display row and column annotations automatically extracted from
rowData and colData (e.g., top_annotation, left_annotation).
- To combine multiple heatmaps from different
SummarizedExperiment objects with aligned rows and consistent scales using crossHm().
- To highlight specific genes of interest on a large heatmap using the
mark argument.
When NOT to Use
- For plotting heatmaps from raw matrices or data frames without a
SummarizedExperiment container, use ComplexHeatmap::Heatmap or pheatmap directly.
Data Requirements
- A
SummarizedExperiment object containing at least one assay (e.g., "logcpm", "logFC").
- Row annotations stored in
rowData(SE) and column annotations stored in colData(SE).
Key Parameters
- features: A vector of row/feature names to plot.
- assayName: Name of the assay to extract data from (e.g.,
"logFC").
- do.scale (
FALSE): Logical indicating whether to scale rows.
- top_annotation: Column names from
colData to display as top annotations.
- left_annotation: Column names from
rowData to display as left annotations.
- breaks (
TRUE): Controls symmetric scaling and quantile capping (e.g., 0.985 for 98.5% quantile capping).
- gaps_at: Column name from
colData to introduce gaps between columns.
- uniqueScale (
FALSE): Logical indicating whether to enforce a unique color scale across datasets in crossHm().
Best Practices
- Store default visualization settings (like default assay and annotations) in the object's metadata under
metadata(SE)$default_view to simplify repetitive plotting.
- Use quantile capping (e.g.,
breaks=0.985) when plotting fold-changes to prevent extreme outlier values from dominating the color scale.
- Define custom annotation colors in
metadata(SE)$anno_colors to ensure they are automatically applied across all heatmaps generated from that object.
Common Pitfalls
- Extreme values compressing the color scale: Outliers in fold-change data can make normal variations invisible. Fix: Set
breaks to a quantile value less than 1 (e.g., breaks=0.985) to cap the color scale.
- Duplicate heatmap or annotation names when combining heatmaps with
+: Combining raw sechm objects can lead to conflicts. Fix: Use crossHm() to safely plot multiple SummarizedExperiment objects side-by-side.
Alternatives
ComplexHeatmap: The underlying engine, but requires manual extraction of assays and annotations from SummarizedExperiment.pheatmap: A classic heatmap package, but lacks native integration with SummarizedExperiment metadata.
Citations
- Chen et al., 2017 (vignette reference for example data).
References
