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Implementation of MaSigPro for scRNA-Seq Data

R-CMD-Check test-coverage


scMaSigPro is an R package designed for analyzing single-cell RNA-seq data over pseudotime. Building on the maSigPro package, it identifies genes with significant expression changes across branching paths in a pseudotime-ordered dataset. This guide provides a step-by-step workflow for ScMaSigPro, making it accessible for users.


To install scMaSigPro from GitHub, use the following R code:

# Install devtools if not already installed
if (!requireNamespace("devtools", quietly = TRUE)) {

# Install scMaSigPro
                         ref = "main",
                         build_vignettes = FALSE,
                         build_manual = TRUE,
                         upgrade = "never",
                         force = TRUE,
                         quiet = TRUE)

Basic Usage

The basic workflow of scMaSigPro involves the following steps:

1. Load Package and Dataset

data("splat.sim", package = "scMaSigPro")

2. Create scMaSigPro Object

# Helper Function to convert annotated SCE object to scmpObject
scmp.ob <- as_scmp(
  object = splat.sim, from = "sce",
  align_pseudotime = FALSE,
  verbose = TRUE,
  additional_params = list(
    labels_exist = TRUE,
    exist_ptime_col = "Step",
    exist_path_col = "Group"

3. Pseudo-bulking with sc.squeeze()

This function discretizes a continuous pseudotime column into bins:

scmp.ob <- sc.squeeze(scmp.ob)

4. Setting up the Polynomial Model

scmp.ob <- sc.set.poly(scmp.ob)

5. Detecting Genes with Non-Flat Profiles

scmp.ob <- sc.p.vector(scmp.ob)

6. Model Refinement

scmp.ob <-

7. Gene selection with R2

scmp.ob <- sc.filter(scmp.ob, vars = "all")

8. Gene Trend Visualization

plotTrend(scmp.ob, "Gene10")

For detailed instructions and additional steps, please refer to the following vignettes.


Contributions, including bug reports, suggestions, and pull requests, are welcome.


This project is licensed under GPL>=2 - see the file for details.

Funding Information

This project has received funding from the European Union’s Framework Programme for Research and Innovation Horizon 2020 (2014-2020) under the Marie Skłodowska-Curie Grant Agreement No 953407.


If you use scMaSigPro in your research, please cite: