Simulated log-expression for 36 genes across 18 normal and tumour samples, with three pathway-level response patterns.
One row represents one gene measured in one sample.
Tessera · 收录于 2026-09-02
| gene | sample | expression | pathway | type |
|---|---|---|---|---|
| CC_G01 | Sample01 | 6.19 | Cell cycle | Normal |
| CC_G01 | Sample02 | NA | Cell cycle | Normal |
| CC_G01 | Sample03 | 5.28 | Cell cycle | Normal |
| CC_G01 | Sample04 | 6.71 | Cell cycle | Normal |
| CC_G01 | Sample05 | 6.55 | Cell cycle | Normal |
| CC_G01 | Sample06 | 6.27 | Cell cycle | Normal |
| # | 变量 | 类型 | 缺失 | 统计 |
|---|---|---|---|---|
| 1 | geneSynthetic gene identifier; prefixes identify the simulated pathway family. | 字符型 | — | 不同值 36CC_G01 · CC_G02 · CC_G03 · CC_G04 · CC_G05 · CC_G06 · CC_G07 · CC_G08 |
| 2 | sampleSynthetic sample identifier. | 字符型 | — | 不同值 18Sample01 · Sample02 · Sample03 · Sample04 · Sample05 · Sample06 · Sample07 · Sample08 |
| 3 | expressionkeySimulated log-expression value. Six fixed cells are missing. | 双精度 | 6 | min 2.46q1 —中位 6.38均值 6.31q3 —max 10.1 |
| 4 | pathwaySimulated pathway assigned to the gene. | 字符型 | — | 不同值 3Cell cycle · Immune response · Lipid metabolism |
| 5 | typeSample type; Normal or Tumor. | 字符型 | — | 不同值 2Normal · Tumor |
library(readr)
expression_heatmap <- read_csv(
"https://assets.evanzhou.org/tessera/csv/expression_heatmap.csv",
col_types = cols(
gene = col_character(),
sample = col_character(),
expression = col_double(),
pathway = col_character(),
type = col_character()
)
)https://assets.evanzhou.org/tessera/csv/expression_heatmap.csv为表达矩阵热图生成的模拟数据。36 个模拟基因在 18 个样本中形成 648 行长表；Normal 与 Tumor 各 9 个样本。基因分属 Cell cycle、Immune response 和 Lipid metabolism 三条模拟通路。
数据包含基因基线、样本偏移和观测噪声。Tumor 样本中的 Cell cycle 整体升高、Immune response 中度升高、Lipid metabolism 整体降低，同时保留基因和样本之间的差异。所有数值均为模拟值，不对应真实基因或生物学分析。
六个固定位置设为缺失，用于演示热图中的 NA。绘图时可将长表转换为 gene × sample 矩阵，并按基因进行 z-score；原始 CSV 保留模拟的 log-expression。
# Generate the expression-matrix toy data used by heatmap recipes.
script_dir <- local({
arg <- grep("^--file=", commandArgs(trailingOnly = FALSE), value = TRUE)
path <- if (length(arg)) sub("^--file=", "", arg[[1L]]) else sys.frame(1)$ofile
dirname(normalizePath(path, mustWork = TRUE))
})
out_csv <- file.path(script_dir, "..", "csv", "expression_heatmap.csv")
dir.create(dirname(out_csv), recursive = TRUE, showWarnings = FALSE)
set.seed(20260902)
pathways <- data.frame(
prefix = c("CC", "IR", "LM"),
pathway = c("Cell cycle", "Immune response", "Lipid metabolism"),
tumor_effect = c(1.60, 0.90, -1.30)
)
genes <- do.call(rbind, lapply(seq_len(nrow(pathways)), function(i) {
data.frame(
gene = sprintf("%s_G%02d", pathways$prefix[i], seq_len(12L)),
pathway = pathways$pathway[i],
gene_baseline = rnorm(12L, mean = 6, sd = 0.65),
tumor_effect = pathways$tumor_effect[i] + rnorm(12L, 0, 0.18)
)
}))
samples <- data.frame(
sample = sprintf("Sample%02d", seq_len(18L)),
type = rep(c("Normal", "Tumor"), each = 9L),
sample_offset = rnorm(18L, mean = 0, sd = 0.28)
)
expression_heatmap <- merge(
genes[, c("gene", "pathway", "gene_baseline", "tumor_effect")],
samples,
by = NULL
)
# Three coherent pathway blocks, with gene-, sample-, and observation-level
# variation. The values are synthetic log-expression, not biological results.
expression_heatmap$expression <- with(
expression_heatmap,
gene_baseline + sample_offset +
ifelse(type == "Tumor", tumor_effect, 0) +
rnorm(nrow(expression_heatmap), mean = 0, sd = 0.45)
)
# Fixed missing cells make NA rendering reproducible without weakening any
# entire gene or sample enough to prevent clustering.
missing_cells <- c(37L, 146L, 255L, 364L, 473L, 582L)
expression_heatmap$expression[missing_cells] <- NA_real_
expression_heatmap <- expression_heatmap[, c(
"gene", "sample", "expression", "pathway", "type"
)]
expression_heatmap <- expression_heatmap[order(
match(expression_heatmap$gene, genes$gene),
match(expression_heatmap$sample, samples$sample)
), ]
expression_heatmap$expression <- round(expression_heatmap$expression, 4)
rownames(expression_heatmap) <- NULL
write.csv(expression_heatmap, out_csv, row.names = FALSE, na = "NA")表中统计由 scripts/profile_dataset.py 于 2026-09-02 数出。