ScDIOR: single cell RNA-seq data IO software

Huijian Feng,Jiekai Chen,Lihui Lin

doi:10.1186/s12859-021-04528-3

Abstract

BackgroundSingle-cell RNA sequencing is becoming a powerful tool to identify cell states, reconstruct developmental trajectories, and deconvolute spatial expression. The rapid development of computational methods promotes the insight of heterogeneous single-cell data. An increasing number of tools have been provided for biological analysts, of which two programming languages- R and Python are widely used among researchers. R and Python are complementary, as many methods are implemented specifically in R or Python. However, the different platforms immediately caused the data sharing and transformation problem, especially for Scanpy, Seurat, and SingleCellExperiemnt. Currently, there is no efficient and user-friendly software to perform data transformation of single-cell omics between platforms, which makes users spend unbearable time on data Input and Output (IO), significantly reducing the efficiency of data analysis.ResultsWe developed scDIOR for single-cell data transformation between platforms of R and Python based on Hierarchical Data Format Version 5 (HDF5). We have created a data IO ecosystem between three R packages (Seurat, SingleCellExperiment, Monocle) and a Python package (Scanpy). Importantly, scDIOR accommodates a variety of data types across programming languages and platforms in an ultrafast way, including single-cell RNA-seq and spatial resolved transcriptomics data, using only a few codes in IDE or command line interface. For large scale datasets, users can partially load the needed information, e.g., cell annotation without the gene expression matrices. scDIOR connects the analytical tasks of different platforms, which makes it easy to compare the performance of algorithms between them.ConclusionsscDIOR contains two modules, dior in R and diopy in Python. scDIOR is a versatile and user-friendly tool that implements single-cell data transformation between R and Python rapidly and stably. The software is freely accessible at https://github.com/JiekaiLab/scDIOR.

Highlights

Single-cell RNA sequencing is becoming a powerful tool to identify cell states, reconstruct developmental trajectories, and deconvolute spatial expression
Seurat v3 is the recommended method for batch integration [11]; Scran is widely known for normalization of the non-full-length dataset [12]; Monocle is expert at the reconstruction of the cell fate trajectory [3, 9]; Scanpy is scalable in dealing with large datasets of more than one million cells and flexible with strong functional expansion [10]
We developed scDIOR for single-cell data Input and Output (IO) between R and Python, which contained two modules, dior and diopy. scDIOR implements the unification of data format of different single-cell analytics platforms

Summary

Results

We developed scDIOR for single-cell data IO between R and Python, which contained two modules, dior and diopy. scDIOR implements the unification of data format of different single-cell analytics platforms. One can perform trajectory analysis using Monocle3 [3] in R, transform the single-cell data to Scanpy in Python using scDIOR, such as expression profiles of spliced and unspliced, as well as low dimensional layout. ScDIOR provides easy functions for partial information extraction, by which users can load the data partially instead of the whole dataset, e.g., loading the cell annotation data frame regardless of the gene expression matrix with great size. This character of scDIOR helps save the memory and accelerate the file reading (Fig. 4). ScDIOR is widely compatible with multiple versions of SingleCellExperiment (≥ 1.8.0), Seurat (≥ v3) and Scanpy (≥ 1.4). scDIOR is an effective and user-friendly tool that will improve the utilization of advantages of different analytics platforms

Background

Discussion

Conclusion