bio-experimental-design-batch-design

Batch Design and Mitigation

Core Principle

Batch effects are unavoidable. Good design makes them correctable.

Design Rules

1. Never confound batch with condition - Each batch must contain all conditions
2. Balance samples across batches - Equal numbers per condition per batch
3. Randomize within constraints - Avoid systematic patterns
4. Include controls - Same samples across batches if possible

Balanced Design Example

# BAD: Confounded design
# Batch 1: All treated samples
# Batch 2: All control samples
# -> Cannot separate batch from treatment

# GOOD: Balanced design
# Batch 1: 3 treated, 3 control
# Batch 2: 3 treated, 3 control
# -> Batch effect can be estimated and removed

Sample Assignment

library(designit)

# Create balanced assignment
samples <- data.frame(
  sample_id = paste0('S', 1:24),
  condition = rep(c('ctrl', 'treat'), each = 12),
  sex = rep(c('M', 'F'), 12)
)

# Optimize batch assignment
batch_design <- osat(samples, batch_size = 8,
                     balance_cols = c('condition', 'sex'))

Detecting Batch Effects

library(sva)

# From count matrix
mod <- model.matrix(~condition, colData)
mod0 <- model.matrix(~1, colData)

# Estimate number of surrogate variables (hidden batches)
n_sv <- num.sv(counts_normalized, mod)

# Estimate surrogate variables
svobj <- sva(counts_normalized, mod, mod0, n.sv = n_sv)

Correction Methods

Documenting Design

Always record:

• Date of sample processing
• Reagent lot numbers
• Operator
• Equipment/​lane assignments
• Any deviations from protocol

Related Skills

• experimental-design/​power-analysis - Account for batch in power calculations
• differential-expression/​batch-correction - Correcting batch effects in analysis
• single-cell/​batch-integration - scRNA-seq batch correction