Lamian Helper Package

This package provides a convenient interface to use Lamian, a tool designed to compare expression changes over pseudotime across different conditions. For detailed information on Lamian, please refer to Professor Wenpin Hou's original repository:
https://github.com/Winnie09/Lamian

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Features

Starting from a Seurat object containing sample and gene cluster (meta.data) information, this package enables the following workflow:

1. Harmony integration & normalization
2. Subsetting & renaming samples or metadata
3. Slingshot trajectory inference analysis
4. Filtering of trajectory and features
5. Lamian parameter configuration
6. Visualization options:
   • Heatmap
   • Population plot
   • FeaturePlot split by sample

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Important Fixes & Modifications

We have incorporated fixes and improvements in the Lamian codebase to ensure robustness and usability:

1. Fixing matrix non-invertibility issues
2. Adjusting some default parameters and visualization settings for better performance and clarity

These fixes are essential to prevent errors during analysis.

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Installation

Follow these steps to set up the Lamian Helper Package in your R environment:

1. Create a new R project and activate the renv environment. (This step is typically done within your R IDE, such as RStudio.)

2. Clone the repository into your project directory. Open a terminal in your project's root directory and run:

   git clone git@github.com:Ye1203/Lamian_shinyapp.git

3. Restore the package environment using the provided renv.lock file. In your R console, run the following commands:

   # Install the 'renv' package if you haven't already
   if (!require("renv")) install.packages("renv")
   
   # Initialize and restore the project environment from the lockfile
   renv::init()
   renv::restore()

4. Configure the application path. In the app.R file, change the path_to_shiny variable to the correct path where the Lamian_shinyapp folder are located.

5. Launch the Shiny application. In your R console, run:

   # Run the application
   shiny::runApp("Lamian_shinyapp")

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Example Usage

The typical workflow includes loading your Seurat object, running normalization, harmony, slingshot, and then using Lamian for comparative pseudotime analysis with the enhanced visualization options provided.

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Contact

For any questions or issues, please feel free to open an issue in this repository or contact Bingtian Ye (btye@bu.edu or biangtian@icloud.com).

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