SCHdeepinsight 0.2.13

A tool for processing and hierarchically annotating immune scRNA-seq data with DeepInsight and CNN.

SCHdeepinsight

SCHdeepinsight is a Python package designed for processing and annotating single-cell RNA sequencing (scRNA-seq) data, specifically for immune cells. It leverages DeepInsight and Convolutional Neural Networks (CNN) to develop an automated model for annotating immune cells. By conducting an in-depth analysis of the hierarchical structure of immune cells, the model achieves highly efficient and accurate cell type annotation for single-cell RNA sequencing (scRNA-seq) data. The model is particularly effective in handling immune cells, demonstrating exceptional accuracy in identifying both common and potential rare cell types.

Features

• Preprocessing: Normalizes and logarithmically transforms scRNA-seq data stored in .h5ad files.
• Image Transformation: Converts processed scRNA-seq data into images suitable for deep learning model input.
• Cell Type Prediction: Uses a pre-trained deep learning model to predict cell types, including base and detailed types.
• Rare Cell Identification: Identifies potential rare cell types by analyzing prediction probabilities.

Installation

You can install SCHdeepinsight using pip. Note that the package has a dependency on pyDeepInsight, which is installed from GitHub.

python3 -m pip -q install git+https://github.com/alok-ai-lab/pyDeepInsight.git#egg=pyDeepInsight

R Dependencies

Before running the batch correction process, ensure that the following R packages are installed. Some of these packages need to be installed directly from GitHub. The installation instructions include commands to install both CRAN packages and GitHub packages using the remotes package.

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

# Load the remotes package
library(remotes)

# Install necessary packages from GitHub
remotes::install_github("carmonalab/STACAS")
remotes::install_github("carmonalab/ProjecTILs")
remotes::install_github("mojaveazure/seurat-disk")

# Install other required packages from CRAN
install.packages(c("Seurat", "Matrix", "sctransform"))

Notes

• Ensure you have an active internet connection to download the packages.
• If you encounter any installation issues, ensure you have the necessary development tools for your operating system, as some packages may require compilation.

Usage

Here's a brief overview of how to use SCHdeepinsight in your workflow:

1. Preprocess the Data: Use the preprocess method to normalize and log-transform your scRNA-seq data. This step prepares the data for further analysis.
2. Batch Correction: Perform batch correction using the batch_correction method if you need to correct for batch effects. This method uses an R script to project the query dataset onto a reference, ensuring that technical differences between batches do not interfere with downstream analysis. If batch correction is not required, you can skip this step.
3. Image Transformation: Convert the processed data into images using the image_transform method. This step is crucial for transforming the gene expression data into a format suitable for input into the deep learning model.
4. Prediction: Use the predict method to classify cell types. This step includes both base type and detailed subtype classification and identifies potential rare cell types based on probability thresholds.

Example

Here’s an example of how to use the Immune class to preprocess, batch correct, transform images, and predict:

# Import the Immune class
from immune import Immune

# Set the output prefix path
output_prefix = "output_directory"

# Create an instance of the Immune class
immune = Immune(output_prefix=output_prefix)

# Option 1: Batch correction (Recommended)
ref_file = "reference.h5ad"  # Path to the reference data file
batch_corrected_path = immune.batch_correction(input_file="input_query.h5ad", ref_file=ref_file)
print(f"Batch-corrected file saved at: {batch_corrected_path}")

# Option 2: Preprocess the data (Use this if batch correction is not needed)
# query_path = "input_query.h5ad"  # Path to the input data file
# preprocessed_path = immune.preprocess(query_path)
# print(f"Preprocessed file saved at: {preprocessed_path}")

# Image transformation
# By default, use the batch-corrected path if batch correction was performed
# If preprocessing was used instead, pass the preprocessed path to image_transform
image_path = immune.image_transform(query_path=batch_corrected_path)
print(f"Image data saved at: {image_path}")

# Prediction
predictions = immune.predict(batch_size=128, rare_base_threshold=60, rare_detailed_threshold=10)
print("Prediction results:")
print(predictions)

Explanation

1. Create an Immune Instance:

   • Use the output_prefix parameter to specify the directory for output files.

2. Option 1: Batch Correction (Recommended):

   • Perform batch correction on the input data using the batch_correction method. This method corrects the input data based on a reference dataset and saves the corrected data. The batch-corrected file is recommended for further analysis.

3. Option 2: Data Preprocessing:

   • If batch correction is not needed, you can use the preprocess method to normalize and log-transform the input .h5ad file. Only use this if batch correction is unnecessary.

4. Image Transformation:

   • This step converts the batch-corrected data into image format for prediction. By default, it uses the batch-corrected path. If you used preprocessing instead, replace batch_corrected_path with preprocessed_path in this step.

5. Prediction:

   • Use the predict method to make predictions on the transformed image data, outputting the prediction results, including cell types and potential rare cell markers.