plot(gene_relevance, 'Gene') plots the differential map of this/these gene(s), plot(gene_relevance) a relevance map of a selection of genes. Alternatively, you can use plot_differential_map or plot_gene_relevance on a GeneRelevance or DiffusionMap object, or with two matrices.

plot_differential_map(coords, exprs, ..., genes, dims = 1:2,
  pal = hcl.colors, faceter = facet_wrap(~Gene))

# S4 method for matrix,matrix
plot_differential_map(coords, exprs, ..., genes,
  dims = 1:2, pal = hcl.colors, faceter = facet_wrap(~Gene))

# S4 method for DiffusionMap,missing
plot_differential_map(coords, exprs, ...,
  genes, dims = 1:2, pal = hcl.colors, faceter = facet_wrap(~Gene))

# S4 method for GeneRelevance,missing
plot_differential_map(coords, exprs, ...,
  genes, dims = 1:2, pal = hcl.colors, faceter = facet_wrap(~Gene))

plot_gene_relevance(coords, exprs, ..., iter_smooth = 2L, n_top = 10L,
  genes = NULL, dims = 1:2, pal = palette(), col_na = "grey",
  limit = TRUE)

# S4 method for matrix,matrix
plot_gene_relevance(coords, exprs, ...,
  iter_smooth = 2L, n_top = 10L, genes = NULL, dims = 1:2,
  pal = palette(), col_na = "grey", limit = TRUE)

# S4 method for DiffusionMap,missing
plot_gene_relevance(coords, exprs, ...,
  iter_smooth = 2L, n_top = 10L, genes = NULL, dims = 1:2,
  pal = palette(), col_na = "grey", limit = TRUE)

# S4 method for GeneRelevance,missing
plot_gene_relevance(coords, exprs, ...,
  iter_smooth = 2L, n_top = 10L, genes = NULL, dims = 1:2,
  pal = palette(), col_na = "grey", limit = TRUE)

plot_gene_relevance_rank(coords, exprs, ..., genes, dims = 1:2,
  n_top = 10L, pal = c("#3B99B1", "#F5191C"), bins = 10L,
  faceter = facet_wrap(~Gene))

# S4 method for matrix,matrix
plot_gene_relevance_rank(coords, exprs, ...,
  genes, dims = 1:2, n_top = 10L, pal = c("#3B99B1", "#F5191C"),
  bins = 10L, faceter = facet_wrap(~Gene))

# S4 method for DiffusionMap,missing
plot_gene_relevance_rank(coords, exprs,
  ..., genes, dims = 1:2, n_top = 10L, pal = c("#3B99B1", "#F5191C"),
  bins = 10L, faceter = facet_wrap(~Gene))

# S4 method for GeneRelevance,missing
plot_gene_relevance_rank(coords, exprs,
  ..., genes, dims = 1:2, n_top = 10L, pal = c("#3B99B1", "#F5191C"),
  bins = 10L, faceter = facet_wrap(~Gene))

# S4 method for GeneRelevance,character
plot(x, y, ...)

# S4 method for GeneRelevance,numeric
plot(x, y, ...)

# S4 method for GeneRelevance,missing
plot(x, y, ...)

Arguments

coords

A DiffusionMap/GeneRelevance object or a cells \(\times\) dims matrix.

exprs

An cells \(\times\) genes matrix. Only provide if coords is a matrix.

...

Passed to plot_differential_map/plot_gene_relevance.

genes

Genes to base relevance map on (vector of strings). You can also pass an index into the gene names (vector of numbers or logicals with length > 1). The default NULL means all genes.

dims

Names or indices of dimensions to plot. When not plotting a GeneRelevance object, the relevance for the dimensions 1:max(dims) will be calculated.

pal

Palette. Either A colormap function or a list of colors.

faceter

A ggplot faceter like facet_wrap(~ Gene).

iter_smooth

Number of label smoothing iterations to perform on relevance map. The higher the more homogenous and the less local structure.

n_top

Number the top n genes per cell count towards the score defining which genes to return and plot in the relevance map.

col_na

Color for cells that end up with no most relevant gene.

limit

Limit the amount of displayed gene labels to the amount of available colors in pal?

bins

Number of hexagonal bins for plot_gene_relevance_rank.

x

GeneRelevance object.

y

Gene name(s) or index/indices to create differential map for. (integer or character)

Value

ggplot2 plot, when plotting a relevance map with a list member $ids containing the gene IDs used.

See also

Examples

data(guo_norm) dm <- DiffusionMap(guo_norm) gr <- gene_relevance(dm) plot(gr) # or plot_gene_relevance(dm)
plot(gr, 'Fgf4') # or plot_differential_map(dm, 'Fgf4')
guo_norm_mat <- t(Biobase::exprs(guo_norm)) pca <- prcomp(guo_norm_mat)$x plot_gene_relevance(pca, guo_norm_mat, dims = 2:3)
plot_differential_map(pca, guo_norm_mat, genes = c('Fgf4', 'Nanog'))