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Authors: Yunning Yuan, Salman Fawad, Nathan Skene


Vignette updated: May-21-2025

Source: vignettes/power_analysis.Rmd

The poweranalysis R package is designed to run robust power analysis for differential gene expression in scRNA-seq studies and provides tools to estimate the optimal number of samples and cells needed to achieve reliable power levels.

Differential Gene Expression (DGE) Analysis

Import an SCE object and perform differential expression analysis using a pseudobulking approach, enabling the robust identification of differentially expressed genes (DEGs) across conditions or groups from single-cell data.
To run the DGE analysis, first load your own SingleCellExperiment (SCE) object.

# Load your SCE object (replace with actual file path)
library(qs)

SCE <- qs::qread("sce.qs")

To use the DGE_analysis function, specify the formula for comparison along with the pseudobulk ID and celltype ID. The function requires the following key arguments:

· design: A formula that defines the variables included in the model for differential expression analysis. This determines how gene expression is compared across different groups.

· coef: A character string that specifies which group in the design formula you want to investigate for differential expression.

Example Usage:
To validate the differential expression (DEG) analysis approach, you can run a comparison between sexes using the design = ~ sex. This will assess how gene expression differs between male and female groups.

# Run the DGE_analysis function for a sex comparison
DGE_analysis.sex <- DGE_analysis(
  SCE,
  design = ~ sex,
  coef = "M",
  celltypeID="cluster_celltype",
  sampleID = "sample_id",
)

If you want to compare disease and control conditions, specify the disease status in the formula and the disease group of interest in the coef.

# Run the DGE_analysis function for a disease vs. control comparison
DGE_analysis.AD <- DGE_analysis(
  SCE,
  design = ~ pathological_diagnosis,
  coef = "AD",
  sampleID = "sample_id",
  celltypeID = "cluster_celltype"
)

Power Analysis

Perform power analysis to estimate the accuracy and reliability of DEG detection in your scRNA‑seq dataset under different levels of sampling. This evaluates how well DEGs can be recovered at varying numbers of individuals and cells. DEGs identified in each down‑sampled subset are compared to those from the full dataset to compute the percentage of true positives recovered, along with the False Discovery Rate (FDR).

To assess power based on sex‑specific DEGs, use the following function:

# Specify the down-sampling range (or use the default range)
range_ind = c(10,20,30,40)
range_cell = c(10,30,50,70)

# Run the power_analysis function for a sex comparison
power_analysis.sex <- power_analysis(
  SCE,
  range_downsampled_individuals = range_ind,  
  range_downsampled_cells = range_cell,
  design = ~ sex,
  coef = "M",
  sampleID = "sample_id",
  celltypeID = "cluster_celltype",
  Nperms = 3)

The power_analysis function generates several key outputs:

  • QC plots display distributions of effect sizes (log2 fold-change) across detected DEGs and the number of cells per individual in the full dataset.

  • DGE analysis results identify PTP DEGs and non-DEGs using a 0.05 cut-off for both nominal and adjusted p-values over the down-sampling range of datasets.

  • Power plots show the mean percentage of PTP DEGs detected and FDR trends as sample size or number of cells per sample increases.
    Down-sampling individuals:

Down-sampling cells:

  • Effect size-specific detection rates assess the DEG recovery across different absolute log2 fold-change bins.
    Down-sampling individuals:

Down-sampling cells:

  • Correlation plots assess the correlation of effect sizes for the top 500 and 1000 genes across down-sampling levels.
    Down-sampling individuals:

Down-sampling cells: