MultiFeaturePlot: Visualize 'features' on a dimensional reduction plot
In lyc-1995/MySeuratWrappers: My extentions to Seurat package

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

MultiFeaturePlot(
  object,
  features,
  dims = c(1, 2),
  cells = NULL,
  cols = c("lightgrey", "blue"),
  pt.size = NULL,
  order = FALSE,
  min.cutoff = NA,
  max.cutoff = NA,
  reduction = NULL,
  split.by = NULL,
  shape.by = NULL,
  slot = "data",
  label = FALSE,
  label.size = 4,
  repel = FALSE,
  ncol = NULL,
  sort.cell = FALSE,
  features.position = c("row", "col"),
  title.size = 12,
  title.face = c("bold", "plain", "italic", "bold.italic"),
  axis.type = c("default", "keep line", "keep title", "no axis"),
  line.size = 0.5,
  legend.title = NULL,
  ...
)

`object`	Seurat object
`features`	Vector of features to plot. Features can come from: An `Assay` feature (e.g. a gene name - "MS4A1") A column name from meta.data (e.g. mitochondrial percentage - "percent.mito") A column name from a `DimReduc` object corresponding to the cell embedding values (e.g. the PC 1 scores - "PC_1")
`dims`	Dimensions to plot, must be a two-length numeric vector specifying x- and y-dimensions
`cells`	Vector of cells to plot (default is all cells)
`cols`	The two colors to form the gradient over. Provide as string vector with the first color corresponding to low values, the second to high. Also accepts a Brewer color scale or vector of colors. Note: this will bin the data into number of colors provided.
`pt.size`	Adjust point size for plotting
`order`	Boolean determining whether to plot cells in order of expression. Can be useful if cells expressing given feature are getting buried.
`min.cutoff, max.cutoff`	Vector of minimum and maximum cutoff values for each feature, may specify quantile in the form of 'q##' where '##' is the quantile (eg, 'q1', 'q10')
`reduction`	Which dimensionality reduction to use. If not specified, first searches for umap, then tsne, then pca
`split.by`	A factor in object metadata to split the feature plot by, pass 'ident' to split by cell identity'; similar to the old `FeatureHeatmap`
`shape.by`	If NULL, all points are circles (default). You can specify any cell attribute (that can be pulled with FetchData) allowing for both different colors and different shapes on cells
`slot`	Which slot to pull expression data from?
`label`	Whether to label the clusters
`label.size`	Sets size of labels
`repel`	Repel labels
`ncol`	Number of columns for display when combining plots
`sort.cell`	If `TRUE`, the positive cells will overlap the negative cells
`features.position`	Where the feature label should be placed
`title.size, title.face`	Size and face of feature labels
`axis.type`	Select one of four axis types
`line.size`	Size of axis line
`legend.title`	A vector of user-defined legend titles.
`...`	Extra parameters to `LabelClusters`