Skip to content

Latest commit

 

History

History
131 lines (94 loc) · 3.94 KB

README.md

File metadata and controls

131 lines (94 loc) · 3.94 KB
Raw

scMaSigPro Logo

Implementation of MaSigPro for scRNA-Seq Data.

R-CMD-Check test-coverage

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)

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.

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