Sechm

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)

# Load example data
data("Chen2017", package="sechm")
SE <- Chen2017

# 1. Basic scaled heatmap of top variable genes
g <- c("Egr1", "Nr4a1", "Fos", "Egr2", "Sgk1", "Arc", "Dusp1", "Fosb", "Sik1")
sechm(SE, features=g, do.scale=TRUE)

# 2. Heatmap of Log2 Fold Changes with quantile capping (breaks)
sechm(SE, features=g, assayName="logFC", breaks=0.985)

# 3. Customizing colors using global package options
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

• Homepage: bioconductor.org/packages/sechm
• Vignette: https://bioconductor.org/packages/release/bioc/vignettes/sechm/inst/doc/sechm.html