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.
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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.
SummarizedExperiment objects using sechm().rowData and colData (e.g., top_annotation, left_annotation).SummarizedExperiment objects with aligned rows and consistent scales using crossHm().mark argument.SummarizedExperiment container, use ComplexHeatmap::Heatmap or pheatmap directly.SummarizedExperiment object containing at least one assay (e.g., "logcpm", "logFC").rowData(SE) and column annotations stored in colData(SE)."logFC").FALSE): Logical indicating whether to scale rows.colData to display as top annotations.rowData to display as left annotations.TRUE): Controls symmetric scaling and quantile capping (e.g., 0.985 for 98.5% quantile capping).colData to introduce gaps between columns.FALSE): Logical indicating whether to enforce a unique color scale across datasets in crossHm().metadata(SE)$default_view to simplify repetitive plotting.breaks=0.985) when plotting fold-changes to prevent extreme outlier values from dominating the color scale.metadata(SE)$anno_colors to ensure they are automatically applied across all heatmaps generated from that object.breaks to a quantile value less than 1 (e.g., breaks=0.985) to cap the color scale.+: Combining raw sechm objects can lead to conflicts. Fix: Use crossHm() to safely plot multiple SummarizedExperiment objects side-by-side.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.まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
The package contains functions for exploratory oligonucleotide array analysis. The dependence on tkWidgets only concerns few convenience functions. 'affy' is fully functional without it.
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This tool uses the label-tree function from HyPhy to annotate a phylogenetic tree. It allows users to select a subset of leaves using either a regular expression or a list of sequence names, and then apply a specified label to these selecte
日本語の概要は準備中です。原文の説明を表示しています。
This package provides a client for the Bioconductor AnnotationHub web resource. The AnnotationHub web resource provides a central location where genomic files (e.g., VCF, bed, wig) and other resources from standard locations (e.g., UCSC, En
日本語の概要は準備中です。原文の説明を表示しています。
apeglm provides Bayesian shrinkage estimators for effect sizes for a variety of GLM models, using approximation of the posterior for individual coefficients.
日本語の概要は準備中です。原文の説明を表示しています。
ASURAT is a software for single-cell data analysis. Using ASURAT, one can simultaneously perform unsupervised clustering and biological interpretation in terms of cell type, disease, biological process, and signaling pathway activity. Inputting a single-cell RNA-seq data and knowledge-based databases, such as Cell Ontology, Gene Ontology, KEGG, etc., ASURAT transforms gene expression tables into original multivariate tables, termed sign-by-sample matrices (SSMs).
日本語の概要は準備中です。原文の説明を表示しています。
AUCell allows to identify cells with active gene sets (e.g. signatures, gene modules...) in single-cell RNA-seq data. AUCell uses the "Area Under the Curve" (AUC) to calculate whether a critical subset of the input gene set is enriched with
日本語の概要は準備中です。原文の説明を表示しています。