What kind of plotting function does Seurat use?
With Seurat, all plotting functions return ggplot2-based plots by default, allowing one to easily capture and manipulate plots just like any other ggplot2-based plot. Seurat utilizes R’s plotly graphing library to create interactive plots. This interactive plotting feature works with any ggplot2-based scatter plots (requires a geom_point layer).
How to set doheatmap to false in Seurat?
Set this to FALSE if you are encountering that issue (note that plots may take longer to produce/render). Integer number to adjust the width of the separating white lines. Corresponds to the number of “cells” between each group. Combine plots into a single patchworked ggplot object.
How to calculate the color of a dotplot in Seurat?
The color represents the average expression level DotPlot (pbmc3k.final, features = features) + RotatedAxis () # Calculate feature-specific contrast levels based on quantiles of non-zero expression.
How to draw a heatmap of a feature expression?
Draws a heatmap of single cell feature expression. A vector of features to plot, defaults to VariableFeatures (object = object) A vector of variables to group cells by; pass ‘ident’ to group by cell identity classes Maximum display value (all values above are clipped); defaults to 2.5 if slot is ‘scale.data’, 6 otherwise
How to create a scatter plot of two features?
Creates a scatter plot of two features (typically feature expression), across a set of single cells. Cells are colored by their identity class. Pearson correlation between the two features is displayed above the plot.
How to visualize single cell expression distributions in ridgeplot?
Visualize single cell expression distributions in each cluster RidgePlot (pbmc3k.final, features = features, ncol = 2) # Dot plots – the size of the dot corresponds to the percentage of cells expressing the # feature in each cluster.