一张图总结一下CellMarker 2.0数据库概况:👇 3CellMarker 2.0 更新亮点 本次更新的亮点如下:👇 新增36300个tissue-cell type-marker条目、474个组织、1901个细胞类型和4566个marker; 新增48种测序技术分类的细胞marker,包括10×Chromium、Smart-seq2和Drop-seq等; 新增29种细胞Marker,包括蛋白编码基因,lncRNA和p...
大家可以通过选择物种、组织和输入基因来定义细胞类型,这里我们以人类的血液为例,heatmap显示输入的基因出现在哪些细胞类型的marker中,以及这些细胞类型的得分。🥳 2️⃣ 这个Score的计算是这样得来的:👇 CellTypeScorei=AB Note !其中A代表输入基因与细胞类型i中Marker基因的交集数,B代表细胞类型i中Marker基因...
第一种方法是基于Marker基因的细胞自动注释,依据一些特定基因来对细胞进行标记,判定细胞类型,选定的Marker基因或基因集应当具有细胞类型特异性并且能够稳定表达的。因此,该方法非常依赖Marker的准确性。获取Marker基因的方法有很多种,通常可以从一些公共数据库或者文献中得到。CellMarker[2]和PanglaoDB[3]是较为常见的存储...
We annotated cell clusters based on the expression of curated known cell markers. The above cell markers with cell types were selected from Cell Markers and CellTypist database83,84 and these selected marker genes in the annotated clusters have log2...
The built-in cell type marker database was used as the reference for all ScType annotations. Manually annotated cell types were treated as cell clusters and given as inputs to ScType. ScType directly generates cluster-level cell type annotations. CellMarker2.0 CellMarker2.0 (ref. 13) only ...
http://localhost:17435/notebooks/shared_dataset/zhixin/2023_Hickey_Snyder_human_Intestine_Nature/data_preparation.ipynb#cell-type-find-mapping 我称之为fine mapping 用一个旧model(旧marker)来预测一个新数据,大概率会有错误预测的部分。 我们可以根据预测来重新鉴定新marker,基于此marker来用新数据预测新数据...
We curated a comprehensive cell marker database named scMayoMapDatabase and developed a companion R package scMayoMap, an easy-to-use single-cell annotation tool, to provide fast and accurate cell type annotation. The effectiveness of scMayoMap was demonstrated in 48 independent scRNA-seq dataset...
Predict the celltype of a cluster from Seurat FindMarker data scrna-seq marker-genes celltype seurat Updated Sep 24, 2024 Python proteomicsyates / pctsea-parent Star 0 Code Issues Pull requests PCTSEA (Proteomics Cell Type Enrichment Analysis) is a tool designed to statistically determine...
rcellmarkerprovides method to identify cell type based on single cell sequencing data. Since most methods try to annotate cell types manually after clustering the single-cell RNA-seq data. Such methods are labor-intensive and heavily rely on user expertise, which may lead to inconsistent results....
为了鉴别EC的异质性,研究团队首先通过血管床中在组织间保存或以组织特异性方式表达的marker,将每个组织的所有簇分成4中EC表型(图4B): (1)动脉ECs(包含大动脉和动脉EC子簇);(2)毛细血管ECs(所有毛细血管EC子簇);(3)静脉ECs(大静脉和静脉EC子簇);(4)淋巴ECs(所有淋巴EC子簇)。