Contents
What is PCA Seurat?
Seurat provides RunPCA (pca), and RunTSNE (tsne), and representing dimensional reduction techniques commonly applied to scRNA-seq data. When using these functions, all slots are filled automatically.
What is Sctransform Seurat?
As described in our paper, sctransform calculates a model of technical noise in scRNA-seq data using ‘regularized negative binomial regression’. The residuals for this model are normalized values, and can be positive or negative. data contains the residuals (normalized values), and is used directly as input to PCA.
How are highly variable genes useful?
With scRNA-seq, highly variable gene (HVG) discovery allows the detection of genes that contribute strongly to cell-to-cell variation within a homogeneous cell population, such as a population of embryonic stem cells. This analysis is implemented in many software packages.
How is the count matrix used in Seurat?
The values in this matrix represent the number of molecules for each feature (i.e. gene; row) that are detected in each cell (column). We next use the count matrix to create a Seurat object. The object serves as a container that contains both data (like the count matrix) and analysis (like PCA, or clustering results) for a single-cell dataset.
How to extaxct normalized expression matrix from Seurat?
Their cheat sheet shows how to access any of the data you’d need. Probably the data slot, maybe from the SCT assay if you’re using SCTransform, for your case. Thanks Jared. You could use either data slot, or scale.data slot after SCTransform.
How to prefilter the data in Seurat Part 1?
At this point, it is a good idea to perform some initial prefiltering of the data. We do this at the gene and cell level by excluding any genes that are not expressed in at least 3 cells, and excluding any genes that do not have a minimum of 200 expressed genes in total.
How does the read10x function in Seurat work?
The Read10X function reads in the output of the cellranger pipeline from 10X, returning a unique molecular identified (UMI) count matrix. The values in this matrix represent the number of molecules for each feature (i.e. gene; row) that are detected in each cell (column). We next use the count matrix to create a Seurat object.