(参考文献:A statistical approach for identifying differential distributions in single-cell RNA-seq experiments.) 6、SigEMD:结合数据填补方法、逻辑回归模型和非参数方法,精确有效地识别scRNA-seq数据中的DE基因, (参考文献:SigEMD: A powerful method for differential gene expression analysis in single-cell RNA...
(参考文献:A statistical approach for identifying differential distributions in single-cell RNA-seq experiments.) 6、SigEMD:结合数据填补方法、逻辑回归模型和非参数方法,精确有效地识别scRNA-seq数据中的DE基因, (参考文献:SigEMD: A powerful method for differential gene expression analysis in single-cell RNA...
差异表达分析(Differential expression analysis) 细胞很多,每个细胞的基因也有很多,那么那些基因才是有意义的呢?需要一些统计手段来把这些基因识别出来,这就是差异表达分析,针对单细胞测序(特别是scRNA-seq)数据的特点,已经开发的算法和软件见下图: 差异分析常用工具 mark基因识别 通过差异分析来识别每个cluster下的标记基...
利用Human ESC scRNA-seq dataset for differential expression analysis 二次采样出三个较小的子集,绘制了数据的分布以及插补值之后的表达值均值(mean) 与标准偏差 (SD) (参见下图6)。所有这些结果表明,scIGANs对于由很少的基因 (约占检测到的基因的5%) 组成的小型数据集具有很强的表达力或细胞间差异,对于其他...
We benchmark the proposed methods with known DEG analysis tools using two real public datasets. The results show that the two procedures can be very competitive with existing methods (scVI, SCDE, MAST, and DEseq) in identifying relevant putative biomarkers. In terms of scalability and correctness...
利用Human ESC scRNA-seq dataset for differential expression analysis 二次采样出三个较小的子集,绘制了数据的分布以及插补值之后的表达值均值(mean) 与标准偏差 (SD) (参见下图6)。所有这些结果表明,scIGANs对于由很少的基因 (约占检测到的基因的5%) 组成的小型数据集具有很强的表达力或细胞间差异,对于其他...
A discriminative learning approach to differential expression analysis for single-cell RNA-seq 目前的方法用于scRNA数据分析遇到的问题是细胞数量众多。而且目前的分析方法大多基于gene counts的量化,因此不能分析individual isoforms. Background Isoform switch: ...
2021). ST-seq data from 8 wild-type mice were integrated by canonical correlation analysis (CCA) and 6 clusters were obtained by unsupervised clustering. Following examination of each cluster by differential gene expression analysis (DGEA), scRNA-seq data were mapped back to ST-seq tissues using...
scRNA-seq differential expression analysis and cluster marker detection The R package MAST was used to perform all single-cell differential gene expression analyses and Likelihood ratio tests was performed to identify DEGs between two conditions. Benjamini–Hochberg multiple testing correction was used to...
Differential expression (DE) analysis and gene set enrichment (GSE) analysis are commonly applied in single cell RNA sequencing (scRNA-seq) studies. Here, we develop an integrative and scalable computational method, iDEA, to perform joint DE and GSE analysis through a hierarchical Bayesian framework...