feature.plot: Visualize 'features' on a dimensional reduction plot
In paodan/studySeu: Seurat : R toolkit for single cell genomics

Description Usage Arguments Details Value

Colors single cells on a dimensional reduction plot according to a 'feature' (i.e. gene expression, PC scores, number of genes detected, etc.)

feature.plot(object, features.plot, dim.1 = 1, dim.2 = 2,
  cells.use = NULL, pt.size = 1, cols.use = heat.colors(10),
  pch.use = 16, reduction.use = "tsne", use.imputed = FALSE,
  nCol = NULL, ...)

`object`	Seurat object
`features.plot`	Vector of features to plot
`dim.1`	Dimension for x-axis (default 1)
`dim.2`	Dimension for y-axis (default 2)
`cells.use`	Vector of cells to plot (default is all cells)
`pt.size`	Adjust point size for plotting
`cols.use`	Ordered vector of colors to use for plotting. Default is heat.colors(10).
`pch.use`	Pch for plotting
`reduction.use`	Which dimensionality reduction to use. Default is "tsne", can also be "pca", or "ica", assuming these are precomputed.
`use.imputed`	Use imputed values for gene expression (default is FALSE)
`nCol`	Number of columns to use when plotting multiple features.

To determine the color, the feature values across all cells are placed into discrete bins, and then assigned a color based on cols.use. The number of bins is determined by the number of colors in cols.use

No return value, only a graphical output

paodan/studySeu documentation built on May 23, 2019, 3:06 p.m.