Additional File 9 of Single Cell Eqtl Analysis Identifies Cell Type-Specific Genetic Control of Gene Expression in Fibroblasts and Reprogrammed Induced Pluripotent Stem Cells

Drew Neavin,Quan Nguyen,Maciej Daniszewski,Helena Liang,Han Sheng Chiu,Yong Kiat Wee,Anne Senabouth,Samuel W. Lukowski,Duncan E. Crombie,Grace E. Lidgerwood,Damián Hernández,James C. Vickers,Anthony Cook,Nathan J. Palpant,Alice Pébay,Alex W. Hewitt,Joseph E. Powell

Figshare（2021）

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Abstract

Additional file 9: Table S8. eGenes with independent top eQTLs in different cell types.

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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】：论文通过单细胞eQTL分析识别出成纤维细胞和重编程诱导多能干细胞中细胞类型特异性的基因表达遗传控制，发现了不同细胞类型中独立的顶级eQTLs。

【方法】：采用单细胞eQTL分析技术，研究基因表达与遗传变异之间的关系。

【实验】：实验具体数据在附加文件9中的表S8展示，涉及eGenes在不同细胞类型中独立顶级eQTLs的分析，但未明确提及具体数据集名称及实验结果。