Statistical Power Analysis for Transcriptomics Experiments

Bulk, Single-Cell, and Spatial RNA-seq

UF Health Cancer Center BCB-SR Bioinformatics Unit

2025-10-10

Overview

Paper: Statistical Power Analysis for Designing Bulk, Single-Cell, and Spatial Transcriptomics Experiments

Authors: Jeon et al., 2023, Biomolecules

Today’s Agenda:

  1. Power analysis fundamentals
  2. Bulk RNA-seq: Considerations & demo
  3. Single-cell RNA-seq: Considerations & demos
  4. Spatial transcriptomics: Considerations & challenges
  5. Discussion questions

Poll: Have You Done Power Analysis for Transcriptomics?

Un-mute for 1, 2, or 3!

  1. I always do power analysis for my transcriptomics experiments
  2. I have done it or heard about it at some point
  3. I have not done or come across power analysis for transcriptomics

The Expensive Problem

Too Few Samples:

  • Waste time and money
  • Miss real biology
  • Underpowered studies

Too Many Samples:

  • Waste money
  • Waste precious samples

How do we know the right number?

What is Statistical Power?

Statistical Power = Probability of detecting a true effect when it exists

\[\text{Power} = P(\text{reject } H_0 | H_0 \text{ is false})\]

Or simply: Your chance of finding real biology

What is Power Analysis?

Power Analysis = Systematic examination of the relationship between power and experimental design parameters

Key parameters that affect power:

  • Sample size (biological replicates)
  • Effect size (how big is the change?)
  • Sequencing depth
  • Significance threshold (α, FDR)
  • Biological variability

Power Analysis Goals

Prospective (before experiment):

  • Determine optimal experimental design
  • Balance cost vs. statistical power
  • Avoid under- or over-sampling

Retrospective (after experiment):

  • Evaluate achieved power
  • Interpret negative results
  • Plan follow-up studies

Power in Transcriptomics: A Key Difference

Classical power analysis:

  • One outcome → One hypothesis test
  • Power = Probability of rejecting H₀ when H₀ is false
  • Example: “80% probability of detecting the treatment effect”

Transcriptomics power analysis:

  • Thousands of genes → Thousands of tests simultaneously
  • Each gene has its own power (based on expression, variability, effect size)
  • Reported power = Average power across all genes

Why This Matters Practically

When a tool reports “80% power”:

Genes you’ll likely detect (>90% power):

  • Highly expressed genes with large fold-changes

Genes you’ll likely miss (<50% power):

  • Lowly expressed genes with small fold-changes

Genes in the middle (50-80% power):

  • This is where your sample size decision matters most

Many tools report stratified power by expression level

Key Concepts: The Glossary

Term Meaning
Statistical Power Your chance of finding real DEGs
Effect Size How big is the fold-change?
FDR Of your hits, how many are false?
Sequencing Depth Reads per sample
Biological Replicates Different individuals (CRITICAL)

Key takeaway: More biological replicates > deeper sequencing

The Three Technologies

Each technology has different:

  • Data characteristics
  • Research questions
  • Cost structures
  • Power considerations

Let’s examine each in detail…

Bulk RNA-seq

Bulk RNA-seq: Technology Overview

Technology: Next-generation sequencing of RNA from cell populations

Data characteristics:

  • Gene expression measured as read counts
  • Highly reproducible (technical replicates unnecessary)
  • Follows negative binomial distribution

Primary research objective:

  • Detect differentially expressed genes (DEGs) between conditions

Bulk RNA-seq: Power Considerations

Key experimental factors affecting power:

  • Number of biological replicates (most influential)
  • Sequencing depth (affects technical precision)
  • Gene-specific parameters: mean expression and dispersion
  • Effect size distribution (expected fold-changes)
  • Target FDR level (typically 0.05 or 0.10)

Critical findings:

  • Biological replicates > sequencing depth for power
  • Gene-specific parameters from pilot data improve accuracy
  • Rule of thumb: 6-8 biological replicates

Bulk RNA-seq: Available Tools

Tool Approach Key Features
ssizeRNA Analytical Fast, FDR control, accurate
RnaSeqSampleSize Analytical Conservative, may overestimate
PROPER Simulation Flexible but slower
RNASeqPower Analytical Single gene focus

Recommended: ssizeRNA

Demo: ssizeRNA for Bulk RNA-seq

Scenario: 10,000 genes, expecting 20% differentially expressed

Parameters:

  • Total genes = 10,000
  • Proportion non-DE = 0.8
  • Average read count = 10
  • Dispersion = 0.1
  • Fold change = 2
  • Target FDR = 0.05
  • Desired power = 0.8
size1 <- ssizeRNA_single(
  nGenes = 10000, 
  pi0 = 0.8, 
  m = 200, 
  mu = 10,
  disp = 0.1, 
  fc = 2, 
  fdr = 0.05,
  power = 0.8, 
  maxN = 20
)

Demo: ssizeRNA Results

Result: “Power = 0.80” means:

  • Expected proportion (assumption): 80% of the 2,000 true DEGs
  • Expected number detected: ~1,600 genes (400 false negatives)
  • Average detection rate: Weighted average across all DE genes

Result: Need n=14 samples per group

  • Of 2,000 true DEGs, expect to detect approximately 1,600
  • 400 false negatives expected
  • Weighted average across all gene characteristics

ssizeRNA: Three Scenarios

Single Parameter Gene-Specific, Fixed FC Fully Gene-Specific
Input One mean, dispersion, FC Gene-specific mean/dispersion + single FC Gene-specific mean/dispersion + FC distribution
Best for Minimal pilot data Pilot data, similar effects Good pilot data, variable effects
Trade-off Very conservative More realistic, still conservative Most realistic and accurate

Bulk RNA-seq: Key Takeaways

  1. Biological replicates matter most
  2. Use pilot data for gene-specific parameters
  3. ssizeRNA is recommended tool
  4. Expect heterogeneity in power across genes
  5. Plan conservatively

Single-Cell RNA-seq

Single-Cell RNA-seq: Technology Overview

Technology: Individual cell-level RNA sequencing

Data characteristics:

  • Cell-level gene expression resolution
  • High proportion of zeros (sparsity)
  • Hierarchical structure (cells within individuals)
  • Greater variability than bulk RNA-seq

Three main research objectives:

  1. Cell subpopulation detection
  2. Differential cell abundance
  3. Differential gene expression

Single-Cell RNA-seq: Power Considerations (1/3)

Three Main Research Objectives:

  1. Cell subpopulation detection
  2. Differential cell abundance
  3. Differential expression

Added complexity relative to bulk RNA-seq

Single-Cell RNA-seq: Power Considerations (2/3)

Added Complexity:

  • Dual sample size problem (cells AND individuals)
  • Greater proportion of zeros and variability
  • Hierarchical structure (cells nested in individuals)
  • Zero-inflation requires specialized models

Single-Cell RNA-seq: Power Considerations (3/3)

Key Experimental Factors:

  • Cell subpopulation proportions
  • Number of cells per sample
  • Number of biological replicates
  • Sequencing depth
  • Batch effects
  • Experimental design

Key finding: More replicates > more cells per sample

The Pseudoreplication Problem

Wrong: Treat all cells as independent samples

Right: Account for individual-level variation

Takeaway: Need biological replicates (individuals), not just technical replicates (cells)

scRNA-seq: Available Tools

Cell subpopulation detection: SCOPIT, howmanycells

Differential cell abundance: scPOST, Sensei

Differential expression (multi-sample): scPower, hierarchicell

Differential expression (single-sample): POWSC, powsimR

Demo: scPOST for Differential Cell Abundance (1/2)

Research Question: Can I detect expansion of HLA-DRA+ fibroblasts in disease vs. healthy?

Required Inputs:

  • Cell state annotations (from clustering)
  • Sample annotations (individual IDs)
  • Batch annotations
  • Principal components from PCA

Demo: scPOST for Differential Cell Abundance (1/2)

Core Functions:

# Step 1: Estimate cell state frequency variation
freqEstimates <- estimateFreqVar(
  meta = data$meta, 
  clusCol = 'celltype',
  sampleCol = 'sample'
)

# Step 2: Estimate gene expression variation
pcEstimates <- estimatePCVar(
  pca = data$pca, 
  npcs = 20,
  meta = data$meta,
  clusCol = 'celltype',
  sampleCol = 'sample', 
  batchCol = 'batch'
)

Demo: scPOST for Differential Cell Abundance (2/2)

Simulate and Test:

# Step 3: Simulate datasets and perform association testing
simDataset.withMASC(
  ncases = 17, nctrls = 3,  # Study design
  ncells = 250,              # Cells per sample
  centroids = pcEstimates$centroids,
  batch_vars = pcEstimates$batch_vars,
  sample_vars = pcEstimates$sample_vars,
  meanFreqs = freqEstimates$meanFreq,
  fc = 5                     # Fold-change to induce
)

# Step 4: Calculate power
getPowerFromRes(resFiles, resTables, threshold = 0.05)

Demo: scPOST Example Results (5-fold expansion, 500 simulations):

STUDY DESIGN CASES CONTROLS POWER
Unbalanced 17 3 12%
Balanced 10 10 60%

Key finding: Balanced designs dramatically increase power

Demo 2: Differential Expression in scRNA-seq

Question: Can I detect DEGs between conditions within a cell type?

Scenario:

  • Cell type: T cells
  • Expected fold-change: 2
  • Cells per sample: 500
  • How many biological replicates?

Tool: scPower

Demo 2: Differential Expression in scRNA-seq with scPower

demo

scRNA-seq: Key Takeaways

  1. Shallow sequencing of many cells > deep sequencing of few cells
  2. Optimal read depth is surprisingly low (~10k reads/cell)
  3. Sample size depends on effect size (large n for small effects)
  4. Biological replicates essential for valid inference
  5. Different applications need different designs (DE vs annotation vs co-expression)
  6. Use well-matched priors when planning studies

Spatial Transcriptomics

Spatial Transcriptomics: Technology Overview

Two main types:

Imaging-based: Single-cell resolution, limited genes

Sequencing-based: Spot-level resolution, genome-wide

Key addition: Spatial coordinates preserved

Spatial Transcriptomics: Research Questions

Three main goals:

  1. Spatially variable genes
  2. Tissue architecture
  3. Cell-cell communication

Spatial Transcriptomics: Power Considerations

Key experimental factors:

  • Number of fields of view
  • Size of fields of view
  • Choice of tissue area
  • Number of cells or spots
  • Spatial structure

Spatial Transcriptomics: Current State

Limited tools available:

  • Baker et al. simulation approach
  • Bost et al. empirical FoV optimization
  • poweREST for DE between two conditions

Key findings:

  • Tissue complexity affects FoV requirements
  • Tumor samples need more FoVs
  • Size and number both matter

Major limitation: Power analysis tools are under-developed

Demo: PoweREST for Spatial Transcriptomics

Question: Power to detect DE genes between two conditions/groups

demo

Spatial Transcriptomics: Key Takeaways

  1. Effect size and gene detection rate critically determine power
  2. Single samples per group only provide adequate power with large fc
  3. More spots per sample helps, but biological replicates needed for inference
  4. Full R package needed, web app is very limited
  5. Tissue-specific power surfaces available (pancreatic, colorectal cancer)

Practical Considerations

The Pilot Data Problem

Where to get parameters?

Option 1: Your own pilot data (most accurate, expensive)

Option 2: Public data (free, may not match perfectly)

Tip: Conservative estimates are okay

Common Mistakes to Avoid

  1. Using n=3 for everything
  2. Treating cells as replicates
  3. Sequencing deeper instead of more samples
  4. Using wrong tool for technology
  5. Ignoring batch effects
  6. Unrealistic effect sizes

When Can You Skip Power Analysis?

Sometimes formal calculation is not feasible:

  • Exploratory studies
  • Constrained samples (rare diseases)
  • Following established protocols
  • Hypothesis-generating studies

But always consider minimum detectable effect size

Practical Workflow

Before starting:

  1. Find similar published data
  2. Run quick power calculation
  3. Get ballpark sample size

During planning:

  1. Budget for replicates FIRST
  2. Balance number vs depth vs cells
  3. Consider batch structure
  4. Plan for failures

Discussion

Discussion Questions

  1. What is your main barrier to using power analysis?
  2. How does pseudoreplication change scRNA-seq design?
  3. How would you plan spatial experiments?
  4. How do you choose realistic effect sizes?
  5. Should power analysis be required for publication?
  6. How do you balance power with sample availability?

Key Takeaways

  1. Power analysis is important
  2. Biological replicates > sequencing depth
  3. Different technologies need different approaches
  4. Tools exist and are usable
  5. Balanced designs matter
  6. Spatial is still developing

Resources

Paper: Jeon et al. 2023, Biomolecules

Software:

  • Bulk: ssizeRNA
  • scRNA-seq: POWSC, scPower, scPOST, SCOPIT
  • Spatial: Limited tools

Key References:

  • Schurch et al. 2016
  • Liu et al. 2014
  • Squair et al. 2021

Thank You!

Questions?

Contact: UFHCC-BCB-SR@ad.ufl.edu