## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

## ----volcano, eval = FALSE----------------------------------------------------
# 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)

## ----heatmap, eval = FALSE----------------------------------------------------
# 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")

## ----dotplot, eval = FALSE----------------------------------------------------
# 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")

## ----embedding, eval = FALSE--------------------------------------------------
# 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)

## ----shiny, eval = FALSE------------------------------------------------------
# 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)