Volcano plots are a presentation method commonly used to visualize the results of differential expression analysis, and are widely used in biology, genomics, drug development and other fields. With volcano maps, researchers can visualize differences in gene or molecule expression under different conditions and identify significant changes in biological processes, disease development, and more. Here to share with you a powerful and intuitive R suite for drawing volcano maps, EnhancedVolcano.
SNPRelate 1.34.1 is an R package for directly computing distance matrices between samples from VCF files, building kinship trees or performing hierarchical clustering. Its main purpose is to provide convenient and fast functions to help users quickly generate kinship trees or cluster analysis results from sequence data.
Single Cell RNA Sequencing (scRNA-seq) can reveal the heterogeneity of gene expression in single cells. However, this technique faces a common problem, that is, the interference of doublets. Double cells are mixtures of RNA from two or more different cells, the presence of which can lead to inaccurate interpretation of gene expression. Therefore, we can use the DoubletFinder suite to remove these double cells to obtain more accurate and cleaner scRNA-seq data.
When there are tens or hundreds of samples, the data collation at the front end is enough to make people dizzy. In addition, the laboratory has insufficient money to purchase analysis software. Most of the bosses can only sacrifice the eyes of the analysts to complete these things manually. Since the laboratory has no money and does not want to break the eyes, the R language is our best friend. The following is the method of merging all the sequencing files for your reference.
scCATCH is an R suite that automatically annotates cell types, including at least 353 cell types, 686 cell subtypes, and 2096 human and mouse cell type references. This article introduces the usage and operation process of scCATCH in detail.
This article details the R package - Escape used to analyze ssGSEA. Through the kit, users can quickly perform sample pathway analysis, visualization, and significant analysis. In addition, this article also provides sample data for user reference.
Subclustering cell populations is a common step in the study of single-cell data. This article shares analysis steps and methods for specific cell populations and provides example data.
This article will introduce how to use Seurat to analyze single-cell data, including how to extract specific cells, name and calculate percentages, etc., with sample data for reference.
Batch effects are a common problem in the analysis of single-cell data and can lead to misinterpretation of differences between samples. The Seurat Integration method can be used to solve data integration and transfer problems.
