生信基础分析
基于R语言的生物信息学分析方法，涵盖R编程基础与常用包、GEO/RNA-seq数据处理与差异富集分析、TCGA多组学分析（包括生存分析与机器学习），以及单细胞转录组分析（含聚类、拟时序与轨迹分析）等，带你快速了解入门生信分析
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空间转录组
空间转录组技术在文章中的应用思路、Seurat与Scanpy分析流程、细胞反卷积与差异基因分析、轨迹与共定位分析、细胞通讯及生态位分析等核心方法
纯生信分析思路: GEO, TCGA+多组学整合分析鉴定新靶点
文献《Identification of potential feature genes in non-alcoholic fatty liver disease using bioinformatics analysis and machine learning strategies》思路解析
纯生信分析思路: TCGA数据库+LASSO模型构建肿瘤预后模型
文献《Development and validation of polyamines metabolism-associated gene signatures to predict prognosis and immunotherapy response in lung adenocarcinoma》思路解析
纯生信分析思路:单细胞+多机器学习寻找hub基因
文献《Integrated Analysis of Single-Cell RNA-Seq and Bulk RNA-Seq Combined with Multiple Machine Learning Identified a Novel Immune Signature in Diabetic Nephropathy》思路解析
纯生信分析思路: 单细胞测序联合WGCNA，全新m7G模型预测葡萄膜黑色素瘤预后
文献《Single cell sequencing analysis constructed the N7- methylguanosine (m7G)-related prognostic signature in uveal melanoma》思路解析
纯生信分析思路:PPI网络 +WGCNA，外加免疫浸润分析筛选关键基因
文献《Identification of essential genes and immune cell infiltration in rheumatoid arthritis by bioinformatics analysis》思路解析
纯生信分析思路: TCGA数据挖掘，Cox回归与LASSO构建子宫内膜癌代谢预后模型
文献《A novel five-gene metabolism-related risk signature for predicting prognosis and immune infiltration in endometrial cancer: A TCGA data mining》思路解析