Full Workflow: Estimate Gene Expression Entropy

Aryan Kamal, Charles Dussiau, Judith Zaugg

30 April 2025

This vignette demonstrates a complete example of how to use the Entropy package to compute entropy values for cells in a Seurat object.

📦 Load libraries and data 

## Loading required package: SeuratObject
## Loading required package: sp
## 
## Attaching package: 'SeuratObject'
## The following objects are masked from 'package:base':
## 
##     intersect, t
data("pbmc_small")
DefaultAssay(pbmc_small) <- "RNA"
result <- run_entropy(
  seu = pbmc_small,
  assay = "RNA",
  nn_list = NULL,           # Automatically computes neighbors
  output_path = NULL,       # Don’t save the full matrix for now
  add_assay = TRUE          # Add entropy matrix as a new assay
)
## INFO [2025-04-30 12:02:05] Starting entropy estimation for assay: RNA
## INFO [2025-04-30 12:02:05] Input Seurat object contains 80 cells and 230 features
## INFO [2025-04-30 12:02:05] Assay 'RNA' found and set as default assay.
## INFO [2025-04-30 12:02:05] No neighbor list provided. Computing neighbors using PCA reduction.
## Computing nearest neighbors
## Only one graph name supplied, storing nearest-neighbor graph only
## INFO [2025-04-30 12:02:05] Reading the precomputed table
## INFO [2025-04-30 12:02:10] Entropy matrix added to Seurat object as 'RNA_entropy'.
# Check the added assay
result[["RNA_entropy"]]
## Assay data with 230 features for 80 cells
## First 10 features:
##  MS4A1, CD79B, CD79A, HLA-DRA, TCL1A, HLA-DQB1, HVCN1, HLA-DMB, LTB,
## LINC00926
# Visualize mean entropy per cell
means <- colMeans(GetAssayData(result[["RNA_entropy"]]))
hist(means, main = "Entropy distribution", xlab = "Mean entropy", col = "steelblue")