README.md

README for scFPCDE

Functional PCA-Based Differential Expression for Single-Cell Trajectories

The scFPCDE package provides statistical tools to test for differential gene expression along pseudotime inferred from single-cell RNA-seq data using Functional Principal Component Analysis (FPCA).

Installation

Install the package directly from GitHub:

devtools::install_github("LopezRicardo1/scFPCDE", build_vignettes = TRUE)

Example

library(scFPCDE)

# Load the built-in simulated dataset
data(scFPCDE_simdata)
yt <- scale(scFPCDE_simdata$yt)  # Standardize gene expression
tt <- scFPCDE_simdata$tt

# Run the differential expression pipeline
res <- scFPCDE_run(yt, tt)

# Plot distribution of p-values from D-statistic test
hist(res$D_test$pval, breaks = 40,
     main = "P-value Distribution (D-test)",
     xlab = "P-value", col = "gray")

# Plot the smoothed and observed gene expression trajectories
scFPCDE_gene_curves(
  tt = tt,
  yt = yt,
  yt_fit = res$fpca_result$xt_hat,
  cell_cluster = scFPCDE_simdata$clusters,
  subset = 1:12
)

License

This package is released under the MIT License.