What is Seurat analysis?
Seurat is an R package designed for QC, analysis, and exploration of single-cell RNA-seq data. Seurat aims to enable users to identify and interpret sources of heterogeneity from single-cell transcriptomic measurements, and to integrate diverse types of single-cell data.
What is slide seq?
Slide-seq provides researchers with a new way to study the spatial organization of tissue, allowing them to visualize how different cell types are distributed throughout the tissue and how tissue organization is disrupted by disease.
How to test for differential expression in Seurat?
As a default, Seurat performs differential expression based on the non-parameteric Wilcoxon rank sum test. This replaces the previous default test (‘bimod’). To test for differential expression between two specific groups of cells, specify the ident.1 and ident.2 parameters.
How are gene expression measurements normalized in Seurat?
After removing unwanted cells from the dataset, the next step is to normalize the data. By default, we employ a global-scaling normalization method “LogNormalize” that normalizes the gene expression measurements for each cell by the total expression, multiplies this by a scale factor (10,000 by default), and log-transforms the result.
How does Seurat work for single cell RNA-Seq?
To overcome the extensive technical noise in any single gene for scRNA-seq data, Seurat clusters cells based on their PCA scores, with each PC essentially representing a ‘metagene’ that combines information across a correlated gene set. Determining how many PCs to include downstream is therefore an important step.
Which is the normalization method used in Seurat?
By default, we employ a global-scaling normalization method “LogNormalize” that normalizes the gene expression measurements for each cell by the total expression, multiplies this by a scale factor (10,000 by default), and log-transforms the result. Seurat calculates highly variable genes and focuses on these for downstream analysis.