BMC Bioinformatics · 2022 · 25 citations · 12 references
Life SciencesBioinformaticsBiologyComputational BiologyGene ExpressionTranscriptomicsOmics Data IntegrationSingle-cell AnalysisSingle-cell GenomicsSingle-cell Multi-omics IntegrationSingle-cell Rna Sequencing AnalysisSingle-cell TranscriptomicsData Transformation PipelinesDevelopmental Cell StatesIntegration Of Single-cell DatasetsRna Sequencing (Rna-seq)Single-cell Data PreprocessingSingle-cell Omics AnalysisSingle-cell Omics IntegrationSingle-cell Rna Sequencing
BACKGROUND: Single-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. RESULTS: We 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. CONCLUSIONS: scDIOR 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 .
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Integrated analysis of multimodal single-cell data
Yuhan Hao, Stephanie Hao, Erica Andersen‐Nissen et al. · Cell · 2021 · 16K citations · Full text
The single-cell transcriptional landscape of mammalian organogenesis
Junyue Cao, Malte Spielmann, Xiaojie Qiu et al. · Nature · 2019 · 4.8K citations