Getting started with plotomics

Volcano plot

A volcano plot shows differential-expression results: log2 fold change on the x-axis, statistical significance on the y-axis.

library(plotomics)

set.seed(42)
de <- data.frame(
  x     = rnorm(5000),
  y     = abs(rnorm(5000)) * 3,
  label = paste0("GENE", seq_len(5000))
)
volcano(de, fc_threshold = 1, label_top_n = 5)

The widget renders all 5 000 points on the GPU, so even with hundreds of thousands of genes the plot stays interactive. Threshold lines and gene labels are vector overlays drawn on top.

Expression heatmap

bioheatmap() displays a numeric matrix as a colormap texture. Row and column labels come from dimnames.

set.seed(1)
mat <- matrix(rnorm(200 * 50), nrow = 200, ncol = 50)
rownames(mat) <- paste0("gene", seq_len(200))
colnames(mat) <- paste0("sample", seq_len(50))

bioheatmap(mat, z_score = TRUE, colormap = "rdbu")

Setting z_score = TRUE normalizes each row before coloring, which is useful when comparing expression levels across genes with different baselines. The "rdbu" colormap gives a red-white-blue diverging scale centered at zero.

Dot plot

A dot plot encodes two values per cell: dot size for the fraction of cells expressing a gene, and dot colour for the expression level.

genes    <- c("CD3D", "CD3E", "CD8A", "MS4A1", "CD79A", "LYZ", "CD14")
clusters <- c("CD8 T", "CD4 T", "B", "Mono")

df <- expand.grid(
  gene    = factor(genes, levels = genes),
  cluster = factor(clusters, levels = clusters),
  stringsAsFactors = FALSE
)
set.seed(7)
df$pct   <- sample(5:95, nrow(df), replace = TRUE)
df$value <- round(runif(nrow(df), 0, 3), 1)

dotplot(df, colormap = "viridis")

Row and column order follows the factor levels of gene and cluster, so you control the layout without sorting the data frame itself.

UMAP / t-SNE embedding

embedding() renders a 2-D scatter of reduced-dimension coordinates. Points are drawn with WebGL, so several hundred thousand cells stay smooth.

set.seed(3)
n <- 2000
emb <- data.frame(
  x     = c(rnorm(n/2, -3), rnorm(n/2, 3)),
  y     = c(rnorm(n/2, 0), rnorm(n/2, 2)),
  color = factor(rep(c("Cluster A", "Cluster B"), each = n/2))
)
embedding(emb, point_size = 4)

When color is a factor, the legend order and colour assignment follow the factor levels. This matches the drop = FALSE convention in ggplot2: unused levels are preserved and the palette stays stable across subsets.

Shiny usage

Every widget comes with a *Output() / render*() pair for Shiny. A minimal app:

library(shiny)
library(plotomics)

ui <- fluidPage(
  volcanoOutput("vol", height = "500px")
)

server <- function(input, output) {
  output$vol <- renderVolcano({
    df <- data.frame(x = rnorm(1000), y = abs(rnorm(1000)) * 3)
    volcano(df)
  })
}

shinyApp(ui, server)

Next steps

All 15 widgets follow the same pattern: pass a data frame (or matrix), set options, get back an htmlwidget. See the function reference for the full list and their parameters.