pseudobulkproR

pseudobulkproR is an R-based tool designed for analyzing single-cell pseudobulk data. It aims to provide a more robust and reliable method for downstream analysis by aggregating single-cell RNA-seq data into pseudobulk samples, thereby enhancing the reliability of statistical analysis and the biological interpretability of the results.

Features

• Data Aggregation: Supports aggregating single-cell data into pseudobulk samples to reduce the impact of intra-cell heterogeneity on analysis results.
• Simplified Workflow: Provides one-click functionality for common downstream analyses such as differential expression and clustering, simplifying complex data processing workflows.
• Flexibility: Allows users to customize aggregation strategies to meet different research needs.
• Compatibility: Seamlessly integrates with popular single-cell analysis frameworks like Seurat and SingleCellExperiment, supporting a wide range of input data formats.

Installation

You can install pseudobulkproR from GitHub:

# Install devtools package (if not already installed)
install.packages("devtools")

# Install pseudobulkproR from GitHub
devtools::install_github("username/pseudobulkproR")

Usage

Quick Start

Here is a simple example of using pseudobulkproR:

library(pseudobulkproR)

# Load single-cell dataset (assuming a Seurat object)
seurat_object <- readRDS("path/to/your/seurat_object.rds")

# Generate pseudobulk data
pseudobulk_data <- create_pseudobulk(seurat_object, group_by = "sample_id")

# Differential expression analysis
dea_results <- differential_expression(pseudobulk_data)

Functional Modules

• create_pseudobulk(): Aggregates single-cell data into pseudobulk samples based on user-defined grouping strategies.
• differential_expression(): Performs differential expression analysis on pseudobulk data.
• visualize_pseudobulk(): Provides visualization tools to check the quality and aggregation effect of pseudobulk data.

Use Cases

• Population-Level Analysis: By using pseudobulk data, reduce individual cell variability and enhance the identification of population-level features.
• Differential Expression: Provides a more robust data foundation for differential expression analysis, reducing false positive rates in single-cell analyses.

Contribution

We welcome issues and code contributions. Please refer to CONTRIBUTING.md for details on how to contribute.

License

This project is open-sourced under the MIT License. For more details, see LICENSE.