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Transcript-averaged cell scoring (TACS)

Source: R/TACSPlot.R
TACSPlot.Rd

TACS is a method for plotting a FACS-like plot for two features based on sc-RNA-seq data. For each of two query features, 100 features with similar expression patterns are selected and ranked by their Pearson correlation with the query. In a process akin to compensation, the intersection of the feature lists is removed from each list. The log normalized expression of the resulting features are then averaged within each cell, and the resulting quantities are plotted. This function is based on a simple scheme: choose features similar to the ones specified and average them to reduce the noise.

Usage

TACSPlot(
  srt,
  ref_srt = NULL,
  assay = "RNA",
  layer = "data",
  group.by = NULL,
  feature1,
  feature2,
  cutoffs = NULL,
  density = FALSE,
  palette = "Paired",
  num_features_add = 100,
  features_predetermined = FALSE,
  aggregator = "sum",
  remove_outliers = FALSE,
  aspect.ratio = 1,
  title = NULL,
  subtitle = NULL,
  xlab = NULL,
  ylab = NULL,
  suffix = " expression level",
  legend.position = "right",
  legend.direction = "vertical",
  theme_use = "theme_scop",
  theme_args = list(),
  include_all = FALSE,
  all_color = "grey20",
  quadrants_line_color = "grey30",
  quadrants_line_type = "solid",
  quadrants_line_width = 0.3,
  quadrants_label_size = 3,
  density_alpha = NULL,
  bins = 20,
  h = NULL,
  nrow = NULL,
  ncol = NULL,
  verbose = TRUE,
  ...
)

Arguments

srt

A Seurat object.

ref_srt

A Seurat object. If your dataset is perturbed in a way that would substantially alter feature-feature correlations, for example if different time points are present or certain cell types are mostly depleted, you can feed in a reference srt, and TACS will choose axes based on the reference data. Default is NULL.

assay

Which assay to use. Default is "RNA".

layer

Which layer to use. Default is data.

group.by

Name of one or more meta.data columns to group (color) cells by.

feature1

Horizontal axis on plot mimics this feature. Character, usually length 1 but possibly longer.

feature2

Vertical axis on plot mimics this feature. Character, usually length 1 but possibly longer.

cutoffs

If given, divide plot into four quadrants and annotate with percentages. Can be a numeric vector of length 1 or 2, or a list of two numeric vectors for x and y axes respectively.

density

If TRUE, plot contours instead of points.

palette

Color palette name. Available palettes can be found in thisplot::show_palettes. Default is "Paired".

num_features_add

Each axis shows a simple sum of similar features. This is how many (before removing overlap).

features_predetermined

If FALSE, plot the sum of many features similar to feature1 instead of feature1 alone (same for feature2). See GetSimilarFeatures. If TRUE, plot the sum of only the features given.

aggregator

How to combine correlations when finding similar features. Options: "sum" (default), "min" (for "and"-like filter), "max", or "mean".

remove_outliers

If TRUE, remove outliers from the plot. Default is FALSE.

aspect.ratio

Aspect ratio of the panel. Default is 1.

title

The text for the title. Default is NULL.

subtitle

The text for the subtitle for the plot which will be displayed below the title. Default is NULL.

xlab

The x-axis label of the plot. Default is NULL.

ylab

The y-axis label of the plot. Default is NULL.

suffix

The suffix of the axis labels. Default is " expression level".

legend.position

The position of legends, one of "none", "left", "right", "bottom", "top". Default is "right".

legend.direction

The direction of the legend in the plot. Can be one of "vertical" or "horizontal".

theme_use

Theme used. Can be a character string or a theme function. Default is "theme_scop".

theme_args

Other arguments passed to the theme_use. Default is list().

include_all

If TRUE, include a panel with all cells. Default is FALSE.

all_color

The color of the all cells panel. Default is "grey20".

quadrants_line_color

The color of the quadrants lines. Default is "grey30".

quadrants_line_type

The type of the quadrants lines. Default is "solid".

quadrants_line_width

The width of the quadrants lines. Default is 0.3.

quadrants_label_size

The size of the quadrants labels. Default is 3.

density_alpha

The alpha of the density plot. Default is NULL.

bins

Number of bins for density plot. Default is 20.

h

Bandwidth for density plot. Default is NULL.

nrow

Number of rows in the combined plot. Default is NULL, which means determined automatically based on the number of plots.

ncol

Number of columns in the combined plot. Default is NULL, which means determined automatically based on the number of plots.

verbose

Whether to print the message. Default is TRUE.

...

Additional parameters passed to ggplot2::stat_density2d.

References

Kernfeld et al. paper, Github

Examples

data(pancreas_sub)
pancreas_sub <- standard_scop(pancreas_sub)
#> ℹ [2026-01-27 08:35:40] Start standard scop workflow...
#> ℹ [2026-01-27 08:35:41] Checking a list of <Seurat>...
#> ! [2026-01-27 08:35:41] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-01-27 08:35:41] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on the data 1/1 of the `srt_list`...
#> ℹ [2026-01-27 08:35:43] Perform `Seurat::FindVariableFeatures()` on the data 1/1 of the `srt_list`...
#> ℹ [2026-01-27 08:35:43] Use the separate HVF from srt_list
#> ℹ [2026-01-27 08:35:43] Number of available HVF: 2000
#> ℹ [2026-01-27 08:35:44] Finished check
#> ℹ [2026-01-27 08:35:44] Perform `Seurat::ScaleData()`
#> ℹ [2026-01-27 08:35:44] Perform pca linear dimension reduction
#> ℹ [2026-01-27 08:35:45] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-01-27 08:35:45] Reorder clusters...
#> ℹ [2026-01-27 08:35:45] Perform umap nonlinear dimension reduction
#> ℹ [2026-01-27 08:35:45] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2026-01-27 08:35:50] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ✔ [2026-01-27 08:35:54] Run scop standard workflow completed
TACSPlot(
  pancreas_sub,
  feature1 = "H3f3b",
  feature2 = "Eif1",
  group.by = "CellType"
)


TACSPlot(
  pancreas_sub,
  feature1 = "H3f3b",
  feature2 = "Eif1",
  group.by = "CellType",
  density = TRUE,
  include_all = TRUE,
  cutoffs = c(3, 2.5)
)


TACSPlot(
  pancreas_sub,
  feature1 = "H3f3b",
  feature2 = "Eif1",
  group.by = "CellType",
  density = TRUE,
  cutoffs = list(x = c(2, 3), y = c(2.5))
)


TACSPlot(
  pancreas_sub,
  feature1 = "H3f3b",
  feature2 = "Eif1",
  group.by = "SubCellType",
  density = TRUE
)