PDF(Pubmed)
Abstract：
BACKGROUND: In the past decade, single-cell RNA sequencing (scRNA-seq) has emerged as a pivotal method for transcriptomic profiling in biomedical research. Precise cell-type identification is crucial for subsequent analysis of single-cell data. And the integration and refinement of annotated data are essential for building comprehensive databases. However, prevailing annotation techniques often overlook the hierarchical organization of cell types, resulting in inconsistent annotations. Meanwhile, most existing integration approaches fail to integrate datasets with different annotation depths and none of them can enhance the labels of outdated data with lower annotation resolutions using more intricately annotated datasets or novel biological findings.
RESULTS: Here, we introduce scPLAN, a hierarchical computational framework designed for scRNA-seq data analysis. scPLAN excels in annotating unlabeled scRNA-seq data using a reference dataset structured along a hierarchical cell-type tree. It identifies potential novel cell types in a systematic, layer-by-layer manner. Additionally, scPLAN effectively integrates annotated scRNA-seq datasets with varying levels of annotation depth, ensuring consistent refinement of cell-type labels across datasets with lower resolutions. Through extensive annotation and novel cell detection experiments, scPLAN has demonstrated its efficacy. Two case studies have been conducted to showcase how scPLAN integrates datasets with diverse cell-type label resolutions and refine their cell-type labels.
BACKGROUND: https://github.com/michaelGuo1204/scPLAN.
RESULTS: Here, we introduce scPLAN, a hierarchical computational framework designed for scRNA-seq data analysis. scPLAN excels in annotating unlabeled scRNA-seq data using a reference dataset structured along a hierarchical cell-type tree. It identifies potential novel cell types in a systematic, layer-by-layer manner. Additionally, scPLAN effectively integrates annotated scRNA-seq datasets with varying levels of annotation depth, ensuring consistent refinement of cell-type labels across datasets with lower resolutions. Through extensive annotation and novel cell detection experiments, scPLAN has demonstrated its efficacy. Two case studies have been conducted to showcase how scPLAN integrates datasets with diverse cell-type label resolutions and refine their cell-type labels.
BACKGROUND: https://github.com/michaelGuo1204/scPLAN.
摘要：
背景：在过去的十年中，单细胞RNA测序（scRNA-seq）已成为生物医学研究中转录组学分析的关键方法。精确的细胞类型识别对于随后的单细胞数据分析至关重要。注释数据的集成和细化对于构建全面的数据库至关重要。然而,流行的注释技术通常忽略了细胞类型的分层组织，导致注释不一致。同时,大多数现有的集成方法无法集成具有不同注释深度的数据集，并且它们都无法使用更复杂的注释数据集或新颖的生物学发现来增强具有较低注释分辨率的过时数据的标签。
结果：这里，我们介绍SCPLAN,为scRNA-seq数据分析设计的分层计算框架。scPLAN擅长使用沿分层细胞类型树构造的参考数据集注释未标记的scRNA-seq数据。它在系统中识别出潜在的新型细胞类型，逐层方式。此外,scPLAN有效地整合了具有不同注释深度水平的带注释的scRNA-seq数据集，确保在分辨率较低的数据集之间一致地细化细胞类型标签。通过广泛的注释和新颖的细胞检测实验，scPLAN已经证明了它的功效。已经进行了两个案例研究，以展示scPLAN如何整合具有不同细胞类型标签分辨率的数据集并完善其细胞类型标签。
背景：https://github.com/michaelGuo1204/scPLAN。
结果：这里，我们介绍SCPLAN,为scRNA-seq数据分析设计的分层计算框架。scPLAN擅长使用沿分层细胞类型树构造的参考数据集注释未标记的scRNA-seq数据。它在系统中识别出潜在的新型细胞类型，逐层方式。此外,scPLAN有效地整合了具有不同注释深度水平的带注释的scRNA-seq数据集，确保在分辨率较低的数据集之间一致地细化细胞类型标签。通过广泛的注释和新颖的细胞检测实验，scPLAN已经证明了它的功效。已经进行了两个案例研究，以展示scPLAN如何整合具有不同细胞类型标签分辨率的数据集并完善其细胞类型标签。
背景：https://github.com/michaelGuo1204/scPLAN。
