Cell type annotation task Summary 对于cell-type annotation 任务,简单说来就是给定一个细胞的基因表达量,预测该细胞的类别,可以是做是一份分类问题。 Benchmark:scBERT, TOSICA, scGPT Datasets: MS dataset, myeloid dataset, Human pancrease dataset 总的实验设计思路:先找文献,然后关注文献里这些数据集,在这...
OSCA单细胞数据分析笔记11—Cell type annotation 分享是一种态度 对应原版教程第12章http://bioconductor.org/books/release/OSCA/overview.html “物以类聚”的类是什么类?比如将一群水果分为不同的类群,则又红又圆特征的可能是苹果。对于单细胞聚类的结果,类的最直接注释就是细胞类型。本节将学习单细胞数据分析...
Cell Type annotation---Cell-ID篇 Cell-ID发表在NBT上面:Genesignature extraction and cell identity recognition at the single-cell levelwith Cell-ID。 最近我看了很多细胞类型注释的思路。在谈到自动注释时,又基于marker gene的方法,例如scCATCH,SCCA等前面都介绍过了。还有基于相关性也就是elation分析的,例如Si...
# Cell type annotation by GPT-4 res <- gptcelltype(markers, model = 'gpt-4') # Assign cell type annotation back to Seurat object obj@meta.data$celltype <- as.factor(res[as.character(Idents(obj))]) # Visualize cell type annotation on UMAP DimPlot(obj,group.by='celltype') 而且如果...
Supervised cell-type annotation is a popular approach in this field, which utilizes well-annotated single-cell RNA sequencing (scRNA-seq) references. Compared with unsupervised methods, this method saves time and effort, but it often relies on the assumption that the reference da...
scCATCH全称是single cell Cluster-based Annotation Toolkit for Cellular Heterogeneity,是一个用于实现单细胞转录组聚类结果进行注释的工具。软件核心函数是和scCATCH,findmarkergenes则是辅助用于寻找标记。属于marker gene based cell type annotation工具中的一种。但是缺点是目前只支持human和mouse,后台没有其它物种的库...
在这里,我们证明大语言模型 GPT-4 可以在单细胞 RNA 测序分析中使用标记基因信息准确注释细胞类型。 当对数百种组织和细胞类型进行评估时,GPT-4 生成的细胞类型注释与手动注释表现出很强的一致性。 此功能可以大大减少细胞类型注释所需的工作量和专业知识。 此外,我们还为 GPT-4 的自动细胞类型注释开发了 R 软件...
In fact, annotating cell-types in scATAC-seq data is a challenging task since, unlike in scRNA-seq data, we lack knowledge of 'marker regions' which could be used for cell-type annotation. Current annotation methods typically translate accessibility to expression space and rely on gene ...
To addresses this challenge, we developed a pre-trained cell-type annotation method, namely scDeepSort, using a state-of-the-art deep learning algorithm, i.e. a modified graph neural network (GNN) model. In brief, scDeepSort was constructed based on our weighted GNN framework and was then...
Consistent annotation transfer from reference dataset to query dataset is fundamental to the development and reproducibility of single-cell research. Compared with traditional annotation methods, deep learning based methods are faster and more automated.