genes_example: Example Gene Expression Data for Bioinformatics

In corrselect: Correlation-Based and Model-Based Predictor Pruning

Example Gene Expression Data for Bioinformatics

Description

A simulated gene expression dataset with 200 genes measured across 100 samples, organized into co-expression modules with a binary disease outcome.

Usage

genes_example

Format

A data frame with 100 rows and 202 variables:

sample_id

Character. Unique sample identifier

disease_status

Factor. Disease status (Healthy, Disease)

GENE001, GENE002, GENE003, GENE004, GENE005, GENE006, GENE007, GENE008, GENE009, GENE010, GENE011, GENE012, GENE013, GENE014, GENE015, GENE016, GENE017, GENE018, GENE019, GENE020, GENE021, GENE022, GENE023, GENE024, GENE025, GENE026, GENE027, GENE028, GENE029, GENE030, GENE031, GENE032, GENE033, GENE034, GENE035, GENE036, GENE037, GENE038, GENE039, GENE040, GENE041, GENE042, GENE043, GENE044, GENE045, GENE046, GENE047, GENE048, GENE049, GENE050, GENE051, GENE052, GENE053, GENE054, GENE055, GENE056, GENE057, GENE058, GENE059, GENE060, GENE061, GENE062, GENE063, GENE064, GENE065, GENE066, GENE067, GENE068, GENE069, GENE070, GENE071, GENE072, GENE073, GENE074, GENE075, GENE076, GENE077, GENE078, GENE079, GENE080, GENE081, GENE082, GENE083, GENE084, GENE085, GENE086, GENE087, GENE088, GENE089, GENE090, GENE091, GENE092, GENE093, GENE094, GENE095, GENE096, GENE097, GENE098, GENE099, GENE100, GENE101, GENE102, GENE103, GENE104, GENE105, GENE106, GENE107, GENE108, GENE109, GENE110, GENE111, GENE112, GENE113, GENE114, GENE115, GENE116, GENE117, GENE118, GENE119, GENE120, GENE121, GENE122, GENE123, GENE124, GENE125, GENE126, GENE127, GENE128, GENE129, GENE130, GENE131, GENE132, GENE133, GENE134, GENE135, GENE136, GENE137, GENE138, GENE139, GENE140, GENE141, GENE142, GENE143, GENE144, GENE145, GENE146, GENE147, GENE148, GENE149, GENE150, GENE151, GENE152, GENE153, GENE154, GENE155, GENE156, GENE157, GENE158, GENE159, GENE160, GENE161, GENE162, GENE163, GENE164, GENE165, GENE166, GENE167, GENE168, GENE169, GENE170, GENE171, GENE172, GENE173, GENE174, GENE175, GENE176, GENE177, GENE178, GENE179, GENE180, GENE181, GENE182, GENE183, GENE184, GENE185, GENE186, GENE187, GENE188, GENE189, GENE190, GENE191, GENE192, GENE193, GENE194, GENE195, GENE196, GENE197, GENE198, GENE199, GENE200

Numeric. Gene expression values (log-transformed)

Details

This dataset simulates a high-dimensional, low-sample scenario common in genomics. Genes are organized into four co-expression modules:

• Module 1 (GENE001-GENE050): Highly correlated (r ~= 0.80), disease-associated

• Module 2 (GENE051-GENE100): Moderately correlated (r ~= 0.60)

• Module 3 (GENE101-GENE150): Weakly correlated (r ~= 0.40)

• Module 4 (GENE151-GENE200): Independent (r ~= 0)

Disease outcome depends on a subset of genes from Module 1.

Use case: Demonstrating corrPrune() with mode = "greedy" for handling high-dimensional data efficiently.

Source

Simulated data based on typical gene expression microarray structures

See Also

corrPrune()

Examples

data(genes_example)

# Greedy pruning for high-dimensional data
gene_data <- genes_example[, -(1:2)]  # Exclude ID and outcome
pruned <- corrPrune(gene_data, threshold = 0.8, mode = "greedy")
ncol(pruned)  # Reduced from 200 to 177 genes

# Use pruned genes for classification
pruned_with_outcome <- data.frame(
  disease_status = genes_example$disease_status,
  pruned
)

corrselect documentation built on July 19, 2026, 1:06 a.m.