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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.

Usage

TACSPlot(
  srt,
  ref_srt = NULL,
  assay = "RNA",
  layer = "data",
  group.by = NULL,
  feature1,
  feature2,
  cutoffs = NULL,
  density = FALSE,
  palette = "Chinese",
  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.

assay

Which assay to use. Default is "RNA".

layer

Assay layer to use.

group.by

Metadata column(s) used to color cells.

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.

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

Panel aspect ratio.

title, subtitle, xlab, ylab

Plot labels.

suffix

The suffix of the axis labels.

legend.position

Legend placement ("none", "left", "right", "bottom", "top"), direction, and title. legend.title = NULL uses the group name.

legend.direction

Legend direction: "horizontal" or "vertical".

theme_use, theme_args

Theme name or function, plus extra theme arguments.

include_all

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

all_color

The color of the all cells panel.

quadrants_line_color

The color of the quadrants lines.

quadrants_line_type

The type of the quadrants lines.

quadrants_line_width

The width of the quadrants lines.

quadrants_label_size

The size of the quadrants labels.

density_alpha

The alpha of the density plot.

bins

Number of bins for density plot.

h

Bandwidth for density plot.

nrow

Combine plots with patchwork. combine = FALSE returns a list of ggplots.

ncol

Number of columns of the combined plot.

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 <- RunStandardWorkflow(pancreas_sub)
#> ℹ [2026-08-30 05:56:05] Start standard processing workflow...
#> ℹ [2026-08-30 05:56:05] Checking a list of <Seurat>...
#> ! [2026-08-30 05:56:05] Data 1/1 of the `srt_list` is "unknown"
#> Warning: Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-08-30 05:56:05] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 1/1 of `srt_list`...
#> ℹ [2026-08-30 05:56:05] Perform `FindVariableFeatures()` on 1/1 of `srt_list`...
#> ℹ [2026-08-30 05:56:05] Use the separate HVF from `srt_list`
#> ℹ [2026-08-30 05:56:06] Number of available HVF: 2000
#> ℹ [2026-08-30 05:56:06] Finished check
#> ℹ [2026-08-30 05:56:06] Perform `ScaleData()`
#> ℹ [2026-08-30 05:56:06] Perform pca linear dimension reduction
#> ℹ [2026-08-30 05:56:06] Use stored estimated dimensions 1:23 for Standardpca
#> ℹ [2026-08-30 05:56:06] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-08-30 05:56:06] Reorder clusters...
#> ℹ [2026-08-30 05:56:06] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-08-30 05:56:07] Perform umap nonlinear dimension reduction
#> ✔ [2026-08-30 05:56:16] Standard processing 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
)