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, ..., gene, dims = 1:2,
  pal = hcl.colors, faceter = facet_wrap(~Gene))

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

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

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

plot_gene_relevance(coords, exprs, ..., iter_smooth = 2L, n_top = 10L,
  genes = 5L, dims = 1:2, pal = palette())

# S4 method for matrix,matrix
plot_gene_relevance(coords, exprs, ...,
  iter_smooth = 2L, n_top = 10L, genes = 5L, dims = 1:2,
  pal = palette())

# S4 method for DiffusionMap,missing
plot_gene_relevance(coords, exprs, ...,
  iter_smooth = 2L, n_top = 10L, genes = 5L, dims = 1:2,
  pal = palette())

# S4 method for GeneRelevance,missing
plot_gene_relevance(coords, exprs, ...,
  iter_smooth = 2L, n_top = 10L, genes = 5L, dims = 1:2,
  pal = palette())

# 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.

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.

genes

Genes to based relevance map on or number of genes to use. (vector of strings or one number) You can also pass an index into the gene names. (vector of numbers or logicals with length > 1)

x

GeneRelevance object.

y, gene

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)
#> Warning: Removed 227 rows containing missing values (geom_point).
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)
#> Warning: Removed 134 rows containing missing values (geom_point).
plot_differential_map(pca, guo_norm_mat, gene = c('Fgf4', 'Nanog'))