PlotEigenHeatmap

Generates a pheatmap of the top genes contributing to a specific principal component from a geneda object. Genes are selected by absolute eigenvector loading for the chosen PC, and expression values are Z-score scaled per gene. Optionally annotate columns with metadata variables and add row annotations showing gene-level percent variance.

Usage

PlotEigenHeatmap(
  object,
  pc = "PC1",
  n = 25,
  annotate_by = NULL,
  annotate_colors = NULL,
  return = c("object", "plot")
)

Arguments

object

A geneda object containing normalized expression data and PCA in DimReduction.

pc

Character. Principal component to visualize (e.g., "PC1").

n

Integer. Number of genes to select by absolute loading (default: 25).

annotate_by

Character vector of metadata column names for column annotations (optional).

annotate_colors

Named list of color vectors for metadata columns. Names should match annotate_by.

return

One of c("object","plot"). If "plot", draws the heatmap via grid.

Value

A list with topGenes (data.frame with EigenVector and PctVar), expression (scaled matrix), and heatmap (pheatmap object)

Examples

data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)
obj <- FindVariableFeatures(obj, nfeatures = 1000)
obj <- RunPCA(obj)
#> Calculating principal components from top 1000 HVGs
#> Percent variations:
#> PC1: 88.56 %
#> PC2: 5.9 %
#> PC3: 2.91 %
#> PC4: 1.55 %
#> PC5: 1.08 %
#> PC6: 0 %
res <- PlotEigenHeatmap(obj,
    pc = "PC1", n = 25,
    annotate_by = "Genotype",
    annotate_colors = list(Genotype = c(
        WT = "grey40", Snai1_KO =
            "firebrick3"
    )),
    return = "plot"
)