Figure S6 from T-cell States, Repertoire, and Function in Classical Hodgkin Lymphoma Revealed Through Single-Cell Analyses

Xiufen Chen,Jovian Yu,Girish Venkataraman,Sonali M. Smith,Mengjie Chen,Alan Cooper,Sravya Tumuluru,Joshua D. Brody,James Godfrey,Justin Kline

crossref（2024）

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Abstract

Supplemental Figure S6. Markers differentiated across T cell clusters via multi-spectral flow cytometry from all samples

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B-Cell Receptor Signaling

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

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【要点】：本文通过单细胞分析揭示了经典霍奇金淋巴瘤中的T细胞状态、谱系和功能，特别是利用多光谱流式细胞术区分不同T细胞簇的标记物。

【方法】：研究使用了单细胞分析技术，特别是多光谱流式细胞术来识别和区分T细胞簇。

【实验】：实验涉及对多个样本进行多光谱流式细胞术分析，具体数据集名称未在摘要中提及，但结果显示了不同T细胞簇的明确标记物区分。