The kit for automatic identification of cells can only be used for preliminary identification of cell types. If you want to further confirm the cell type, you need to rely on the knowledge and experience of the researcher for judgment. The following are some commonly used cell marker databases for single-cell data analysis. The methods of use are very simple. You only need to input the name of the characteristic gene, and you can find the possible corresponding cells from the database for your reference.
A tool for automatic annotation of single-cell cell types
Clustree is an R-based suite for visualizing the structure and hierarchy of clustering trees. The suite provides a simple and intuitive way for researchers to visually explore the results of cluster analysis.
Single cell data analysis R suite - Seurat complete teaching!
Today's single-cell sequencing is becoming more and more proficient. From a technology that could only isolate a few or even dozens of single cells, it has evolved into a single experiment that can produce tens of thousands or even hundreds of thousands of single-cell data. It only took a short ten or twenty years.
Data visualization is a very important part of data analysis and research, and the ggplot2 package is a very powerful data visualization package in R language, which is used by almost all R users. In this article, we will briefly introduce the basic operation, download and several common drawing methods of ggplot2.
In the previous R language - system installation (R4.3.0) and basic usage, we have learned how to install R software and RSstudio. Next, we will further learn the basic instructions of R language.
fastreeR (version 1.4.0) is an R language suite for directly computing distance matrices between samples from VCF or FASTA files, building kinship trees or performing hierarchical clustering. Its main goal is to provide convenient and fast functions to help users quickly generate kinship trees or cluster analysis results from sequence data.
R language is a powerful and free programming language widely used in fields such as statistics, data science and machine learning. This article describes the history of R, its advantages and disadvantages, and provides a method for installing R and packages. If you are interested in fields such as statistical analysis, data processing, and machine learning, you might as well learn R language to start your data science journey.
