# GenePlot is typically used to visualize gene-gene relationships, but can How do you feel about the quality of the cells at this initial QC step?.Can you detect the potential outliers in each plot?.What is the difference between nGenes and nUMIs?.If your mitochondrial genes are named differently, then you will need to adjust this pattern accordingly (e.g. We do this using a regular expression as in “ mito.genes <- grep ( pattern = "^MT-".
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Note: In order to detect mitochondrial genes, we need to tell Seurat how to distinguish these genes. VlnPlot(object = pbmc, ot = c("nGene", "nUMI", "percent.mito"), nCol = 3) Pbmc <- AddMetaData(object = pbmc, metadata = percent.mito, col.name = "percent.mito") Percent.mito <- AddMetaData adds columns to and is a great place to Mito.genes <- grep(pattern = "^MT-", x = rownames(x = value = TRUE) NOTE: You must have the Matrix package loaded to # non-log-normalized counts The % of UMI mapping to MT-genes is a common # We use since this represents non-transformed and # mitochondrial genes here and store it in percent.mito using AddMetaData. # the non-normalized values within a cell We calculate the percentage of For non-UMI data, nUMI represents the sum of # The number of genes and UMIs (nGene and nUMI) are automatically calculated We also filter cells based on the percentage of mitochondrial genes present.
Of course this is not a guaranteed method to exclude cell doublets, but we include this as an example of filtering user-defined outlier cells. 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. Seurat allows you to easily explore QC metrics and filter cells based on any user-defined criteria. 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. JBrowse: Visualizing Data Quickly & Easily.Loading your own data in Seurat & Reanalyze a different dataset.Seurat part 3 – Data normalization and PCA.Exercise part4 – Alternative approach in R to plot and visualize the data.Deeptools2 computeMatrix and plotHeatmap using BioSAILs.Prerequisites, data summary and availability.
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