Implementation of MaSigPro for scRNA-Seq Data.

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Introduction

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.

Installation

Bioconductor and Dependencies

# Install Dependencies
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install(version = "3.14")

BiocManager::install(c('SingleCellExperiment', 'maSigPro', 'MatrixGenerics', 'S4Vectors'))

scMaSigPro latest version

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

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

# Install scMaSigPro
devtools::install_github("BioBam/scMaSigPro",
                         ref = "main",
                         build_vignettes = FALSE,
                         build_manual = TRUE,
                         upgrade = "never",
                         force = TRUE,
                         quiet = TRUE)

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Basic Usage

The basic workflow of scMaSigPro involves the following steps:

1. Load Package and Dataset

set.seed(123)
library(scMaSigPro)
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 <- sc.t.fit(scmp.ob)

7. Gene selection with $R^2$

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.

• Basic Workflow
• maSigPro Analysis
• scMaSigPro Class

Contributing

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

License

This project is licensed under GPL>=2 - see the LICENSE.md 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.

Citation

If you use scMaSigPro in your research, please cite:

Priyansh Srivastava, Marta Benegas Coll, Stefan Götz, María José Nueda, Ana Conesa, "scMaSigPro: differential expression analysis along single-cell trajectories"", Bioinformatics, Volume 40, Issue 7, July 2024, btae443, https://doi.org/10.1093/bioinformatics/btae443

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