SUMMARY: In single-cell transcriptomics, inconsistent cell type annotations due to varied naming conventions and hierarchical granularity impede data integration, machine learning applications, and meaningful evaluations. To address this challenge, we developed the unified Hierarchical Annotation Framework (uHAF), which includes organ-specific hierarchical cell type trees (uHAF-T) and a mapping tool (uHAF-Agent) based on large language models. uHAF-T provides standardized hierarchical references for 38 organs, allowing for consistent label unification and analysis at different levels of granularity. uHAF-Agent leverages GPT-4 to accurately map diverse and informal cell type labels onto uHAF-T nodes, streamlining the harmonization process. By simplifying label unification, uHAF enhances data integration, supports machine learning applications, and enables biologically meaningful evaluations of annotation methods. Our framework serves as an essential resource for standardizing cell type annotations and fostering collaborative refinement in the single-cell research community. AVAILABILITY AND IMPLEMENTATION: uHAF is publicly available at: https://uhaf.unifiedcellatlas.org and https://github.com/SuperBianC/uhaf.

Text, visual, and acoustic are usually complementary in the Multimodal Sentiment Analysis (MSA) task. However, current methods primarily concern shared representations while neglecting the critical private aspects of data within individual modalities. In this work, we propose shared-private memory networks based on the recent advances in the attention mechanism, called SPMN, to decouple multimodal representation from shared and private perspectives. It contains three components: a) a shared memory to learn the shared representations of multimodal data; b) three private memories to learn the private representations of individual modalities, respectively; c) and adaptive fusion gates to fuse multimodal private and shared representations. To evaluate the effectiveness of SPMN, we integrate it into different pre-trained language representation models, such as BERT and XLNET, and conduct experiments on two public datasets, CMU-MOSI and CMU-MOSEI. Experimental results indicate that the performances of pre-trained language representation models are significantly improved because of SPMN and demonstrate the superiority of our model compared to the state-of-the-art methods. SPMN's source code is publicly available at:https://github.com/xiaobaicaihhh/SPMN.