What is Seurat used for?
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What is Seurat used for?
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.
How does Seurat cluster cells?
Cluster the cells To do this, Seurat uses a graph-based clustering approach, which embeds cells in a graph structure, using a K-nearest neighbor (KNN) graph (by default), with edges drawn between cells with similar gene expression patterns.
What are counts in Seurat?
nCount_RNA is the total number of molecules detected within a cell.
How do you integrate a Seurat object?
Tips for integrating large datasets
- Create a list of Seurat objects to integrate.
- Perform normalization, feature selection, and scaling separately for each dataset.
- Run PCA on each object in the list.
- Integrate datasets, and proceed with joint analysis.
Does Seurat do heatmap?
Draws a heatmap of single cell feature expression….Arguments.
| object | Seurat object |
|---|---|
| slot | Data slot to use, choose from ‘raw.data’, ‘data’, or ‘scale.data’ |
| assay | Assay to pull from |
| label | Label the cell identies above the color bar |
| size | Size of text above color bar |
How do you get gene names from Seurat object?
We can find the gene names as the rownames of the @assays$RNA@counts slot of the Seurat object and we identify the mitochondrial genes by their names starting with “MT-”.
Can you save a Seurat object?
Saving a Seurat object to an h5Seurat file is a fairly painless process. All assays, dimensional reductions, spatial images, and nearest-neighbor graphs are automatically saved as well as extra metadata such as miscellaneous data, command logs, or cell identity classes from a Seurat object.
What is integration in Seurat?
Cell 2019, Seurat v3 introduces new methods for the integration of multiple single-cell datasets. These methods aim to identify shared cell states that are present across different datasets, even if they were collected from different individuals, experimental conditions, technologies, or even species.
How does Seurat normalize data?
By default, Seurat implements 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.
What are the slots in a Seurat object?
Slots
| Slot | Function |
|---|---|
| active.ident | Identity classes for the current object |
| graphs | A list of nearest neighbor graphs |
| reductions | A list of DimReduc objects |
| project.name | User-defined project name (optional) |
What is the difference between merge and integrate in Seurat?
You should only use merge for technical replicates, and in theory for a group of samples with a low batch effect. Integration in Seurat (and related) was developed because there tends to be a relatively strong batch in the manifolds.
How do you know if a Seurat object is normalized?
method = “LogNormalize”, scale. factor = 10000, margin = 1, verbose = TRUE, ) # S3 method for Seurat NormalizeData( object, assay = NULL, normalization. method = “LogNormalize”, scale….Arguments.
| object | An object |
|---|---|
| scale.factor | Sets the scale factor for cell-level normalization |
What heatmap means?
A heat map is a two-dimensional representation of data in which values are represented by colors. A simple heat map provides an immediate visual summary of information. More elaborate heat maps allow the viewer to understand complex data sets.
Why is it called a heat map?
Software designer Cormac Kinney trademarked the term ‘heat map’ in 1991 to describe a 2D display depicting financial market information. The company that acquired Kinney’s invention in 2003 unintentionally allowed the trademark to lapse.
