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Updated: Oct 7, 2025

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Published on: August 4, 2016

scDIOR: single cell RNA-seq data IO software.

Huijian Feng^1,2, Lihui Lin³, Jiekai Chen^4,5,6,7

¹Center for Cell Lineage and Atlas, Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, 510005, People's Republic of China.

BMC Bioinformatics

|January 7, 2022

Summary

This summary is machine-generated.

scDIOR enables rapid and stable transformation of single-cell omics data between R and Python platforms. This user-friendly tool streamlines data sharing and analysis across R packages (Seurat, SingleCellExperiment, Monocle) and Python (Scanpy).

Keywords:

Data IO HDF5 Single-cell

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Area of Science:

Computational Biology
Bioinformatics
Genomics

Background:

Single-cell RNA sequencing (scRNA-seq) generates complex data for cell state identification and developmental trajectory reconstruction.
R and Python are dominant programming languages in single-cell data analysis, but lack seamless interoperability.
Existing tools struggle with efficient data transformation between R (Seurat, SingleCellExperiment, Monocle) and Python (Scanpy), hindering research efficiency.

Purpose of the Study:

To develop an efficient and user-friendly software for seamless single-cell omics data transformation between R and Python.
To address the data Input/Output (IO) challenges faced by researchers working across different single-cell analysis platforms.

Main Methods:

Developed scDIOR, a data transformation tool based on Hierarchical Data Format Version 5 (HDF5).
Created a data IO ecosystem connecting R packages (Seurat, SingleCellExperiment, Monocle) with Python's Scanpy.
Implemented modules 'dior' in R and 'diopy' in Python for cross-platform compatibility.

Main Results:

scDIOR facilitates ultrafast data transformation for various single-cell omics data types, including scRNA-seq and spatial transcriptomics.
The tool supports partial data loading for large datasets, optimizing memory usage.
Enables efficient comparison of algorithms across R and Python platforms.

Conclusions:

scDIOR is a versatile and stable tool for rapid single-cell data transformation between R and Python.
The software enhances research efficiency by simplifying data sharing and analysis across platforms.
scDIOR is freely available on GitHub, promoting accessibility for the scientific community.