单细胞的故事从给分群结果命名开始，也就是每个细胞群做细胞类型鉴定。尽管我们已经介绍过许多种方法了，但是还是有很多的方法啊。
clustifyr classifies cells and clusters in single-cell RNA sequencing experiments using reference bulk RNA-seq data sets, sorted microarray expression data, single-cell gene signatures, or lists of marker genes.
Here we introduce the usage of celltype package. The main function uses a dictionary of immune cell markershat consist on expression level measurements of genes in different immune cell types. We use this dictionary to predict the likely cell type of a experimental dataset of single cell transcriptome measurements. The assignment is made using correlation between the expression levels of the markers in the dictionary and the same genes in the cell
scRNA-seq analysis packages allow to perform clustering and get biomarkers for inferred cluster in order to explore cell types in a population of cells. However, manual curation of cell types can be long and tidious. The goal of this repo is to provide biologist with a script to automate cell type annotation of clusters in data analyzed with Scanpy, byusing their own list of markers and curated cell types. The formating of this table is left to the user, but is ideally a CSV with each row being a cell type, followed by its associated markers.
An R-package for identifying disease-associated cell types using cell type-specific interactomes. This package provides 2 methods for identifying disease-cell type associations: gene set compactness (GSC) and gene set overexpression (GSO). Also provided are functions for recreating the results from Cornish et al.
scTGIF is developed to reduce this trial-and-error cycle; This tool directly connects the unannotated cells and related gene function. Since this tool does not use reference DB, marker gene list, and cluster label can be used in any situation without expert knowledge and is not influenced by the change of cellular label.
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