Create 2D feature plot

Create a scatter plot with cells colored based on the provided feature. This can be used to create dimensional reduction plots (e.g. UMAP, tSNE, PCA) or to compare different cell attributes (e.g. CD4 vs CD8 expression).

plot_vdj_feature() allows per-chain V(D)J data to be summarized and plotted for each cell.

Usage

plot_features(input, ...)

# S3 method for default
plot_features(
  input,
  feature = NULL,
  x = "UMAP_1",
  y = "UMAP_2",
  group_col = NULL,
  plot_colors = NULL,
  plot_lvls = names(plot_colors),
  trans = "identity",
  min_q = NULL,
  max_q = NULL,
  panel_nrow = NULL,
  panel_scales = "fixed",
  na_color = "grey80",
  ...
)

# S3 method for Seurat
plot_features(
  input,
  feature = NULL,
  x = "UMAP_1",
  y = "UMAP_2",
  group_col = NULL,
  data_slot = "data",
  plot_colors = NULL,
  plot_lvls = names(plot_colors),
  trans = "identity",
  min_q = NULL,
  max_q = NULL,
  panel_nrow = NULL,
  panel_scales = "fixed",
  na_color = "grey80",
  ...
)

plot_vdj_feature(input, ...)

# S3 method for default
plot_vdj_feature(
  input,
  data_col,
  x = "UMAP_1",
  y = "UMAP_2",
  group_col = NULL,
  summary_fn = NULL,
  chain = NULL,
  plot_colors = NULL,
  plot_lvls = names(plot_colors),
  trans = "identity",
  min_q = NULL,
  max_q = NULL,
  panel_nrow = NULL,
  panel_scales = "fixed",
  na_color = "grey80",
  chain_col = "chains",
  sep = ";",
  ...
)

# S3 method for Seurat
plot_vdj_feature(
  input,
  data_col,
  x = "UMAP_1",
  y = "UMAP_2",
  group_col = NULL,
  data_slot = "data",
  summary_fn = NULL,
  chain = NULL,
  plot_colors = NULL,
  plot_lvls = names(plot_colors),
  trans = "identity",
  min_q = NULL,
  max_q = NULL,
  panel_nrow = NULL,
  panel_scales = "fixed",
  na_color = "grey80",
  chain_col = "chains",
  sep = ";",
  ...
)

Arguments

input

Single cell object or data.frame containing V(D)J data. If a data.frame is provided, the cell barcodes should be stored as row names.

...

Additional arguments to pass to ggplot2, e.g. color, fill, size, linetype, etc.

feature

Variable to use for coloring points

x

Variable to plot on x-axis

y

Variable to plot on y-axis

group_col

meta.data column to use for splitting plot into panels

plot_colors

Vector of colors to use for plotting

plot_lvls

Levels to use for ordering feature

trans

Transformation to use when coloring cells by a continuous variable, e.g. 'log10'. By default values are not transformed, refer to ggplot2::continuous_scale() for more options.

min_q

Minimum quantile cutoff for color scale.

max_q

Maximum quantile cutoff for color scale.

panel_nrow

The number of rows to use for arranging plot panels

panel_scales

Should scales for plot panels be fixed or free. This passes a scales specification to ggplot2::facet_wrap, can be 'fixed', 'free', 'free_x', or 'free_y'. 'fixed' will cause panels to share the same scales.

na_color

Color to use for missing values

data_slot

Slot in the Seurat object to pull data

data_col

meta.data column containing V(D)J data to use for coloring cells

summary_fn

Function to use for summarizing values for each cell, possible values can be either a function, e.g. mean, or a purrr-style lambda, e.g. ~ mean(.x, na.rm = TRUE) where ".x" refers to the column. If NULL, the mean will be calculated for numeric values, non-numeric columns will be combined into a single string.

chain

Chain(s) to use for filtering data before plotting. If NULL data will not be filtered based on chain.

chain_col

meta.data column containing chains for each cell

sep

Separator used for storing per-chain V(D)J data for each cell

Value

ggplot object

See also

summarize_vdj() for more examples on how per-chain data can be summarized for each cell

Examples

# Create UMAP with cells colored based on sample name
plot_features(
  tiny_so,
  feature = "orig.ident"
)


# Compare UMI counts for each cell with number of genes detected
plot_features(
  tiny_sce,
  feature = "orig.ident",
  x = "nFeature_RNA",
  y = "nCount_RNA"
)


# Plot average CDR3 length for each cell for light chains
plot_vdj_feature(
  vdj_so,
  data_col = "cdr3_length",
  summary_fn = mean,
  chain = c("IGK", "IGL")
)


# Plot median number of insertions for each cell
plot_vdj_feature(
  vdj_sce,
  data_col = "all_ins",
  summary_fn = stats::median
)


# Using a lambda function to summarize values
# use ".x" to refer to values in the column
# this calculates the mean number of deletions for each cell and
# log10-transforms the result
plot_vdj_feature(
  vdj_so,
  data_col = "all_del",
  summary_fn = ~ log10(mean(.x) + 1)
)