What can Seurat be used for in QC?
Seurat allows you to easily explore QC metrics and filter cells based on any user-defined criteria. In the example below, we visualize gene and molecule counts, plot their relationship, and exclude cells with a clear outlier number of genes detected as potential multiplets.
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 read a single sample in Seurat?
Read10X (): This function is from the Seurat package and will use the Cell Ranger output directory as input. In this way individual files do not need to be loaded in, instead the function will load and combine them into a sparse matrix for you. We will be using this function to load in our data! Reading in a single sample (read10X ())
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.
Do you have to filter out cells in Seurat?
Now that we have loaded our data in seurat (using the CreateSeuratObject), we want to perform some initial QC on our cells. While the CreateSeuratObject imposes a basic minimum gene-cutoff, you may want to filter out cells at this stage based on technical or biological parameters.
How to calculate Umi content in Seurat Part 2?
Since there is a rare subset of cells # with an outlier level of high mitochondrial percentage and also low UMI # content, we filter these as well par (mfrow = c (1, 2)) GenePlot (object = pbmc, gene1 = “nUMI”, gene2 = “percent.mito”) GenePlot (object = pbmc, gene1 = “nUMI”, gene2 = “nGene”)
Which is the best way to load data into Seurat?
We can first load the data individually by reading directly from HDF5 file format (.h5). We can now load the expression matricies into objects and then merge them into a single merged object. Each analysis workflow (Seurat, Scater, Scranpy, etc) has its own way of storing data.