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📖 科研基础 -- 读文献、看教材

文献--肝癌空间转录组数据分析

Comprehensive analysis of spatial architecture in primary liver cancer Sci Adv. 2021 Dec 17 Doi: 10.1126/sciadv.abg3750. 简单摘要: 这是一篇资源型的文章,应该是第一篇较大规模的肝癌空间转录组测序文献。基于ST的所包含的空间信息,本文进行多种新颖的分析思路,例如leading-edge section区域的特征,空间相邻的细胞通讯分析,肿瘤中心与四周的通路表达差异等。文章分析的角度涉及很多方面,但具体每个角度来说大多以数据分析结果描述为主。 ...

Create:&nbsp;<span title='2022-10-08 00:00:00 +0000 UTC'>2022-10-08</span>&nbsp;|&nbsp;Update:&nbsp;2022-10-08&nbsp;|&nbsp;Words:&nbsp;2425&nbsp;|&nbsp;5 min&nbsp;|&nbsp;Lishensuo

文献--生信套路之肿瘤预后

【01】ccRCC与免疫浸润 标题:Landscape of immune cell infiltration in clear cell renal cell carcinoma to aid immunotherapy 期刊|日期:Cancer Science | 13 March 2021 1 第一次分亚群 TCGA 525样本(TPM) 29个 imunne signature:16 免疫细胞与13个免疫相关功能 使用ssGSEA方法计算每个样本的signature score 基于signature score,使用ConsensuClusterPlus包分得5个亚群 PCA可视化轮廓 PD-1与PD-L1表达差异 C1+C4与C2+C3+C5间生存差异 2 第二次分亚群 两大组之间(C1+C4与C2+C3+C5)进行差异分析,得到658个差异基因 基于上述差异基因,再次使用ConsensuClusterPlus包重新分成3个亚群 PCA可视化轮廓 PD-1与PD-L1表达差异 三组间生存分析 29种signature score在三组间的分布差异 immune score与stromal score在三组间的分布差异(ESTIMATE) 根据与分组的相关性,将上述差异基因分成signature A/B 通路富集分析 3 TII分数及衍生分析 分别计算signatue A/B的第一主成分之和,然后相减作为每个样本的TII score 根据survmier包计算最佳阈值划分成high与low score两组,并进行一系列后续分析: (1)生存分析 ...

Create:&nbsp;<span title='2023-04-17 00:00:00 +0000 UTC'>2023-04-17</span>&nbsp;|&nbsp;Update:&nbsp;2024-04-17&nbsp;|&nbsp;Words:&nbsp;2251&nbsp;|&nbsp;5 min&nbsp;|&nbsp;Lishensuo

文献--ccRCC单细胞转录组

题目:Single-cell analyses of renal cell cancers reveal insights into tumor microenvironment, cell of origin, and therapy response 期刊 | 日期:PNAS | May 5, 2021 DOI:https://doi.org/10.1073/pnas.2103240118 ...

Create:&nbsp;<span title='2023-04-17 00:00:00 +0000 UTC'>2023-04-17</span>&nbsp;|&nbsp;Update:&nbsp;2024-04-17&nbsp;|&nbsp;Words:&nbsp;986&nbsp;|&nbsp;2 min&nbsp;|&nbsp;Lishensuo

文献--髓鞘脱失与再生模型小鼠snRNAseq数据分析

题目:Transcriptomic atlas and interaction networks of brain cells in mouse CNS demyelination and remyelination 期刊|日期:Cell Report | April 2023 DOI:https://doi.org/10.1016/j.celrep.2023.112293 ...

Create:&nbsp;<span title='2023-04-17 00:00:00 +0000 UTC'>2023-04-17</span>&nbsp;|&nbsp;Update:&nbsp;2023-04-17&nbsp;|&nbsp;Words:&nbsp;1115&nbsp;|&nbsp;3 min&nbsp;|&nbsp;Lishensuo

文献--基于泛癌scRNAseq的T细胞图谱整合分析

题目:Pan-cancer T cell atlas links a cellular stress response state to immunotherapy resistance 期刊|日期:nature medicine | 26 April 2023 DOI:https://doi.org/10.1038/s41591-023-02371-y ...

Create:&nbsp;<span title='2023-06-18 00:00:00 +0000 UTC'>2023-06-18</span>&nbsp;|&nbsp;Update:&nbsp;2023-06-18&nbsp;|&nbsp;Words:&nbsp;2200&nbsp;|&nbsp;5 min&nbsp;|&nbsp;Lishensuo

文献--浆细胞(Plasma cells)在膀胱癌(Bladder cancer)中的预后分析

题目:The potential crosstalk between tumor and plasma cells and its association with clinical outcome and immunotherapy response in bladder cancer 期刊|日期:Journal of Translational Medicine | 03 May 2023 DOI:https://doi.org/10.1186/s12967-023-04151-1 这篇文章讨论了浆细胞在膀胱癌免疫微环境的所扮演的作用。相比于传统肿瘤预后类文章,觉得有如下几方面新意: ...

Create:&nbsp;<span title='2023-06-23 00:00:00 +0000 UTC'>2023-06-23</span>&nbsp;|&nbsp;Update:&nbsp;2023-06-23&nbsp;|&nbsp;Words:&nbsp;1402&nbsp;|&nbsp;3 min&nbsp;|&nbsp;Lishensuo

文献--机器学习模型预测药物肾毒性-1

题目:In Silico Prediction and Insights Into the Structural Basis of Drug Induced Nephrotoxicity 期刊 | 日期:05 January 2022 DOI: 10.3389/fphar.2021.793332 简括:建立机器学习模型的经典流程,值得注意的是包括化合物特征提取,模型建立/评价都是在online chemical database and modeling environment (OCHEM)平台完成的。文章建立的模型也上传到该平台中:https://ochem.eu/article/140251 ...

Create:&nbsp;<span title='2023-02-10 00:00:00 +0000 UTC'>2023-02-10</span>&nbsp;|&nbsp;Update:&nbsp;2022-02-10&nbsp;|&nbsp;Words:&nbsp;737&nbsp;|&nbsp;2 min&nbsp;|&nbsp;Lishensuo

文献--机器学习模型预测药物肾毒性-2

题目:In silico prediction of potential drug-induced nephrotoxicity with machine learning methods 期刊 | 日期 : Journal of Applied Toxicology | 11 April 2022 简介:标准的机器学习模型分析流程,从数据收集到数据整理,从训练模型到评价模型。 1、数据收集 1.1 标签数据 (1)从SIDER、DrugBank、ChEMBL以及TCM@taiwan等4个数据库收集了1366个标签化合物 ...

Create:&nbsp;<span title='2023-02-10 00:00:00 +0000 UTC'>2023-02-10</span>&nbsp;|&nbsp;Update:&nbsp;2022-02-10&nbsp;|&nbsp;Words:&nbsp;1073&nbsp;|&nbsp;3 min&nbsp;|&nbsp;Lishensuo

文献--单细胞组学大模型之scGPT

文献: scGPT: toward building a foundation model for single-cell multi-omics using generative AI 时间:2024 Feb. (Published) 期刊:Nature Method DOI:https://doi.org/10.1038/s41592-024-02201-0 ...

Create:&nbsp;<span title='2024-09-29 00:00:00 +0000 UTC'>2024-09-29</span>&nbsp;|&nbsp;Update:&nbsp;2024-09-29&nbsp;|&nbsp;Words:&nbsp;6652&nbsp;|&nbsp;14 min&nbsp;|&nbsp;Lishensuo

文献--单细胞组学大模型之scBERT

标题: scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data 期刊|日期:nature machine intelligence, 2022/09 DOI:https://doi.org/10.1038/s42256-022-00534-z ...

Create:&nbsp;<span title='2024-11-09 00:00:00 +0000 UTC'>2024-11-09</span>&nbsp;|&nbsp;Update:&nbsp;2024-11-09&nbsp;|&nbsp;Words:&nbsp;1150&nbsp;|&nbsp;3 min&nbsp;|&nbsp;Lishensuo
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