tTEscanR Visualization Module
1. Overview
tTEscanR includes a dedicated visualization module that provides multiple functions for generating plots based on the output tables produced at each step of the analysis. The primary goal of this module is to facilitate a more intuitive and streamlined interpretation of results, allowing researchers to easily explore and understand their data. Additionally, it helps summarize complex findings in a visually accessible manner, enhancing the overall clarity and impact of the analysis.
To illustrate the usage of each plotting function and demonstrate the flexibility provided by various parameters, we will first run tTEscanR. In this tutorial, we will analyze a single-cell fetal human atlas described in (Cao et al. 2020) and (Domcke et al. 2020), and previously examined by (Gao et al. 2022). A subset of this dataset is included as a default dataset in tTEscanR and can be directly loaded to performm the analysis. A step-by-step explanation of this pipeline is available in the tTEscanR User Guide vignette.
tTEobject <- runPipeline(
mRNA_data = default_tTEscanR_mRNA_data,
tRNA_data = default_tTEscanR_tRNA_data,
metadata = default_tTEscanR_metadata,
species = "hg38", batch = "tissue",
runDESeq = FALSE, verbose = FALSE
)
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The visualization functions in tTEscanR are highly customizable and can be applied to any properly formatted dataset. Depending on the plot type, specific data requirements must be met, which may involve prior data transformation or restructuring.
This guide provides an overview of the available visualization options in tTEscanR, and illustrates key parameter settings and their effects through practical examples.
The visualization functions in tTEscanR are not limited to outputs generated within the package’s pipeline. Users can apply them to external dataset, provided that the required data formatting and structure are met.
| Function | Purpose |
|---|---|
plotProportion() |
Shows features’ frequencies differences within and |
| between conditions | |
plotDistribution() |
Displays features’ distributions across |
| conditions | |
plotTargetComparison() |
Variation of plotDistribution() to |
| compare a target feature against the mean across conditions | |
plotCorrelation() |
Features correlation |
plotPermutation() |
Compares the baseline codon exonic background |
| against the current codon usage | |
plotTEscore() |
Represents the tTE scores obtained from
Compute_tTE() |
2.1. Data transformation
There are helper functions in
tTEscanR designed to properly transform data for
downstream analysis and visualization. the data. One such function is
transformFormat(), which converts a count
matrix, with features as rows and conditions as columns, into a
long-format table. In this format, each row-column
combination from the original matrix becomes an individual row in the
output.
The function also provides an option to normalize
the input data. Parameters such as rownames_to_column,
names_to and values_to allow users to
customize the names of the columns in the resulting long-format
table.
The normalization performed by transformFormat() is done by converting the raw counts into relative abundances (i.e. dividing each column by its column-wise sum).
long_format_codon_usage <- transformFormat(
data = getAssay(tTEobject, "CodonUsage"), normalize = TRUE,
rownames_to_column = "codon", # features (row) of the CodonUsage matrix
names_to = "condition", # conditions (col) of the CodonUsage matrix
values_to = "usage"
) # values of the CodonUsage matrix
# long_format_codon_usage contains 3 columns: codon, condition, usage
# We are going to divide the condition column into tissue and cell_type
long_format_codon_usage <- long_format_codon_usage %>%
tidyr::separate(.data$condition, into = c("tissue", "cell_type"), sep = "-")long_format_AA_demand <- transformFormat(
data = getAssay(tTEobject, "AADemand"), normalize = TRUE,
rownames_to_column = "AA", # features (row) of the AADemand matrix
names_to = "condition", # conditions (col) of the AADemand matrix
values_to = "demand"
) # values of the AADemand matrix
# long_format_AA_demand contains 3 columns: AA, condition, demand
# We are going to divide the condition column into tissue and cell_type
long_format_AA_demand <- long_format_AA_demand %>%
tidyr::separate(.data$condition, into = c("tissue", "cell_type"), sep = "-")2.2. Parameters
To enhance usability, parameters’ names and structures have been kept consistent across functions with most parameters being shared among them. This standardization simplifies customization and ensures an intuitive workflow.
|————————|————————————————-| | data | Properly formatted
dataset | | plot | A character string indicating the type
of plot to generate | | ncols | Numeric; Number of columns
for arranging panels. Defaults to 1 | | x_axis_col | Name
of the column in data to use for the x-axis | | y_axis_col
| Name of the column in data to use for the y-axis | |
condition_col | Name of the column in data to use for
coloring/grouping by condition | | targeted_arg | Optional;
A vector defining key feature clusters to highlight or label. | |
color_palette | Optional; A vector of color codes to
customize plot appearance | | save_format | Optional; A
character string indicating the format for saving the plot. Supported
formats: “png” or “pdf” | | out_name | Optional; Name for
the saved plot (if save_format specified) | |
out_directory | Optional; Path to the directory where the
plot will be saved (if save_format specified) | |
show_legend | A character string specifying the position of
the legend. Supported formats: “none” (default), “top”, “bottom”,
“right” and “left” | | add_titles | Logical; if TRUE,
includes titles in the plot. Defaults to TRUE |
3. Visualization options
The plotDistribution() function
generates jitter plots, barplots or
boxplots to visualize data distributions (e.g. raw or
normalize codon usage) across features (e.g. codons). This function
provides an intuitive representation of how usage patterns vary across
conditions. The type of plot can be specified via the plot
argument, while layout and grouping can be controlled using the
panels parameter.
In the example below, we visualize normalized codon usage across conditions to explore how codon preferences fluctuate. The dataset is a single-cell dataset, where each cell is annotated by its type and tissue of origin. As a general guideline, the jitter plot mode is well-suited for large datasets, as it allows users to observe the distribution of values at a general level and identify patterns across groups.
# Codon usage distribution plot (jitter)
plotDistribution(
data = long_format_codon_usage, plot = "jitter", x_axis_col = "codon",
y_axis_col = "usage", condition_col = "tissue", show_legend = "right",
add_titles = FALSE
)
Alternatively, to reduce dataset complexity and enable more in-depth analysis, we can focus on a subset of target conditions. In this example, we restrict the analysis to spleen-derived cells, specifically selecting those belonging to the lymphoid and myeloid lineages. For this approach we can use a barplot plot mode.
# Data subset: spleen tissue and selected cell types (lymphoid and myeloid)
spleen_indexes <- grep("Spleen", long_format_codon_usage$tissue)
long_format_cu_spleen <- long_format_codon_usage[spleen_indexes, ]
lymphoid_indexes <- grep("Lymphoid", long_format_cu_spleen$cell_type)
myeloid_indexes <- grep("Myeloid", long_format_cu_spleen$cell_type)
cells_indexes <- c(lymphoid_indexes, myeloid_indexes)
long_format_codon_usage_subset <- long_format_cu_spleen[cells_indexes, ]To facilitate visualization, we enable the panels
parameter in plotDistribution, which
arranges each condition in a separate facet. This separation allows for
easier comparison across groups. Additionally, the ncols
parameter controls the number of columns used in the
facet layout, helping optimize the plot’s readability, especially when
working with multiple conditions.
# Codon usage distribution plot (barplot)
plotDistribution(
data = long_format_codon_usage_subset, plot = "barplot", ncols = 1,
facet_col = "cell_type",
x_axis_col = "codon", y_axis_col = "usage", condition_col = "cell_type",
show_legend = "none", add_titles = FALSE
)
If, instead of comparing the distributions of two cell types within the same tissue, we want to explore how distributions vary across two different tissues, we can switch to the boxplot mode. This plot type summarizes the variation and central tendency of the data, making it easier to compare distribution between broader biological groups.
# Data subset: specific codons in spleen and heart tissues
spleen_indexes <- grep("Spleen", long_format_codon_usage$tissue)
heart_indexes <- grep("Heart", long_format_codon_usage$tissue)
tissue_indexes <- c(spleen_indexes, heart_indexes)
long_format_cu_tissues <- long_format_codon_usage[tissue_indexes, ]
selected_codons <- c(
"CAA", "CAC", "CAG", "CAT", "CCA", "CCC", "CCG", "CCT", "CGA", "CGC",
"CGG", "CGT", "CTA", "CTC", "CTG", "CTT", "GAA", "GAC", "GAG", "GAT",
"GCA", "GCC", "GCG", "GCT", "GGA", "GGC", "GGG", "GGT", "GTA", "GTC",
"GTG", "GTT"
)
codons_indexes <- which(long_format_cu_tissues$codon %in% selected_codons)
long_format_cu_tissues <- long_format_cu_tissues[codons_indexes, ]In this example, we introduce a pre-defined
color_palette to explicitly assign specific colors to each
of the tissues being analyzed.
# Codon usage distribution plot (barplot)
plotDistribution(
data = long_format_cu_tissues, plot = "boxplot",
x_axis_col = "codon", y_axis_col = "usage", add_stats = FALSE,
condition_col = "tissue",
color_palette = c(Heart = "#de77ae", Spleen = "#7fbc41"),
show_legend = "bottom", add_titles = FALSE
)
The plotTargetComparison() function
extends the functionality of
plotDistribution() by allowing direct
comparison between a target confition and the overall mean. This
visualization helps identify how codon, anticodon, or amino acid usage
in the selected condition deviates from the average profile.
Function-specific parameters:
mean - A numeric vector containing the mean values of the
codons/anticodon/amino acids present in data.
show_difference - Logical; if TRUE, displays the
differences between the mean and the targeted values.
In the following example, we focus on cells from the cerebellum. To
enable a proper comparison, we first subset the dataset to include only
cerebellum-specific data and then compute the mean codon usage using
computeMeanUsage(). The overall mean codon
usage was previously obtained during the initial execution of
runPipeline().
# Data subset: mean codon usage across cerebellum cell types
codon_usage <- getAssay(tTEobject, "CodonUsage")
cerebellum_indexes <- grep("Cerebellum", colnames(codon_usage))
cerebrum_indexes <- grep("Cerebrum", colnames(codon_usage))
brain_indexes <- c(cerebellum_indexes, cerebrum_indexes)
brain_codon_usage <- codon_usage[, brain_indexes]
# Define the metadata
metadata_brain <- data.frame(
label = colnames(brain_codon_usage), stringsAsFactors = FALSE
)
metadata_brain <- tidyr::separate(
metadata_brain, label,
into = c("tissue", "cell.type"), sep = "-"
)
metadata_brain$conditions <- colnames(brain_codon_usage)
mean_brain_codon_usage <- computeMeanUsage(
data = brain_codon_usage, metadata = metadata_brain,
batch = "tissue", verbose = FALSE
)
#> - Detected metadata column: 'conditions'
#> - Final dataset contains 18 unique samples.
#> estimating size factors
#> estimating dispersions
#> gene-wise dispersion estimates
#> mean-dispersion relationship
#> final dispersion estimates
#> fitting model and testingBased on the calculations above, the plot illustrates: (i) the mean codon usage of the cerebellum cells as purple dots, (ii) the overall mean codon usage across conditions as grey bars, and (iii) the difference between the two as blue lines.
additional_metrics <- getMetadata(tTEobject, "CodonUsage_AdditionalMetrics")
plotTargetComparison(
target_data = data.frame(mean_brain_codon_usage),
overall_data = data.frame(additional_metrics$MeanCodonUsage),
x_axis_col = "feature", y_axis_col = "mean_usage_across_conditions",
show_difference = TRUE, add_titles = FALSE
)
The plotProportion() function provides
a summary visualization of a specific metric’s distribution across
features or conditions. It supports multiple display modes, including
bar, radar or donut plots, allowing users to
choose the most effective format for their data.
The choice of plot type in this and other functions within tTEscanR is left to the user’s discretion, depending on the nature of the data (e.g. number of features, distribution of counts) and the specific research question being addressed.
mean_codon_usage <- additional_metrics$MeanCodonUsage
mean_codon_usage$codon <- mean_codon_usage$feature
mean_codon_usage <- featuresToAA(
data = mean_codon_usage, position = "feature",
notation_from = "codon", notation_to = "aa", verbose = FALSE
)plotProportion(
data = mean_codon_usage, plot = "bar",
var_numerical = "mean_usage_across_conditions",
var_categorical = "codon", var_color = "feature", show_legend = "none"
) # Here feature corresponds to AA
#> $plot
# Data subset: mean codon usage across cerebellum cell types
aa_demand <- getAssay(tTEobject, "AADemand")
cerebellum_indexes_AA <- grep("Cerebellum", colnames(aa_demand))
cerebrum_indexes_AA <- grep("Cerebrum", colnames(aa_demand))
brain_indexes_AA <- c(cerebellum_indexes_AA, cerebrum_indexes_AA)
brain_AAdemand <- aa_demand[, brain_indexes_AA]
mean_brain_AAdemand <- computeMeanUsage(
data = brain_AAdemand, metadata = metadata_brain,
batch = "tissue", verbose = FALSE
)
#> - Detected metadata column: 'conditions'
#> - Final dataset contains 18 unique samples.
#> estimating size factors
#> estimating dispersions
#> gene-wise dispersion estimates
#> mean-dispersion relationship
#> -- note: fitType='parametric', but the dispersion trend was not well captured by the
#> function: y = a/x + b, and a local regression fit was automatically substituted.
#> specify fitType='local' or 'mean' to avoid this message next time.
#> final dispersion estimates
#> fitting model and testingplotProportion(
data = mean_brain_AAdemand, plot = "donut",
var_numerical = "mean_usage_across_conditions",
var_categorical = "feature", show_legend = "none"
) # Here feature corresponds to AA
#> $plot
The plotTEscore() function generates a
violin plot to compare the tTE scores
between a specified target condition and all other conditions in the
dataset. This visualization helps assess how distinct the translation
efficiency profile of the target group is relative to the rest.
As plotTEscore() uses violin plots, it is important to ensure that each group, especially the targeted condition, has a sufficient number of data points. Violin plots may not provide a reliable representation of the distribution if the sample size is too small. Alternatively, plotDistribution and plotProportion() could be used.
conditions_metadata <- getMetadata(tTEobject, "ConditionsLabels")
tTEresults_codon <- getMetadata(tTEobject, "tTEresults_codon")
tTEresults_AA <- getMetadata(tTEobject, "tTEresults_AA")plotTEscore(
data = tTEresults_codon, metadata = conditions_metadata,
index_col = "conditions", class_col = "tissue", add_stats = FALSE
)
#> Warning in defineMergedData(data = data, meta = metadata, index = index_col, :
#> One or more groups have fewer than 2 samples. Statistics (p-values) will return
#> NA.
#> $plot
#> Warning: Groups with fewer than two datapoints have been dropped.
#> ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
#>
#> $stats
#> NULL# Targeted condition: lymphoid and myeloid cells
conditions_metadata$group <- "other"
conditions_metadata$group[
grep("Myeloid", conditions_metadata$conditions)
] <- "myeloid"
conditions_metadata$group[
grep("Lymphoid", conditions_metadata$conditions)
] <- "lymphoid"# input data has 4 columns: condition, tTE, p_value, neg_log10_tTE_p_value
# Codon-anticodon tTE score
plotTEscore(
data = tTEresults_codon, metadata = conditions_metadata,
index_col = "conditions", class_col = "group",
color_palette = c(
myeloid = "#abd9e9", lymphoid = "#f1b6da", other = "#fee0b6"
),
add_stats = TRUE
)
#> $plot
#>
#> $stats
#> group1 group2 p_value comparison p_signif class
#> 1 lymphoid myeloid 0.7922078 lymphoid_vs_myeloid ns myeloid
#> 2 lymphoid other 0.5128688 lymphoid_vs_other ns other
#> 3 myeloid other 0.2770987 myeloid_vs_other ns other
# AA demand-supply tTE score
plotTEscore(
data = tTEresults_AA, metadata = conditions_metadata,
index_col = "conditions", class_col = "group",
color_palette = c(
myeloid = "#abd9e9", lymphoid = "#f1b6da", other = "#fee0b6"
),
add_stats = FALSE
)
#> $plot
#>
#> $stats
#> NULLIn cases where we want to refine the selection of target cell types,
such as including or excluding specific subtypes, it is essential to
encode this distinction explicitly in the input metadata
(targets) data frame.
For instance, if we aim to focus on neurons but exclude cells labeled as ENS neurons, we need to assign those excluded cells to a different category, referred to as “other” in the metadata. This ensures the function correctly recognizes which cells belong to the target group and which do not.
# Targeted condition: neurons
conditions_metadata$group <- "other"
conditions_metadata$group[grep(
"neuron", conditions_metadata$conditions
)] <- "neurons"
conditions_metadata$group[grep(
"ENS neuron", conditions_metadata$conditions
)] <- "other"# AA demand-supply tTE score
plotTEscore(
data = tTEresults_AA, metadata = conditions_metadata,
index_col = "conditions", class_col = "group",
color_palette = c(neurons = "#8073ac", other = "#fee0b6"),
add_stats = TRUE
)
#> $plot
#>
#> $stats
#> group1 group2 p_value comparison p_signif class
#> 1 neurons other 0.02818374 neurons_vs_other * other4. References
#> R version 4.6.1 (2026-06-24)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 26.04 LTS
#>
#> Matrix products: default
#> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.32.so; LAPACK version 3.12.0
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: Etc/UTC
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats4 stats graphics grDevices utils datasets methods
#> [8] base
#>
#> other attached packages:
#> [1] Seurat_5.5.1 SeuratObject_5.4.0 sp_2.2-1
#> [4] Signac_1.17.1 Rsamtools_2.29.0 Biostrings_2.81.3
#> [7] XVector_0.53.0 GenomicRanges_1.65.0 IRanges_2.47.2
#> [10] S4Vectors_0.51.5 Seqinfo_1.3.0 BiocGenerics_0.59.8
#> [13] generics_0.1.4 Matrix_1.7-5 dplyr_1.2.1
#> [16] biomaRt_2.69.0 tTEscanR_0.99.0 BiocStyle_2.41.0
#>
#> loaded via a namespace (and not attached):
#> [1] RcppAnnoy_0.0.23 splines_4.6.1
#> [3] later_1.4.8 bitops_1.0-9
#> [5] filelock_1.0.3 tibble_3.3.1
#> [7] polyclip_1.10-7 fastDummies_1.7.6
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#> [13] lattice_0.22-9 MASS_7.3-65
#> [15] backports_1.5.1 magrittr_2.0.5
#> [17] plotly_4.12.0 sass_0.4.10
#> [19] rmarkdown_2.31 jquerylib_0.1.4
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#> [25] spam_2.11-4 spatstat.sparse_3.2-0
#> [27] reticulate_1.46.0 cowplot_1.2.0
#> [29] pbapply_1.7-4 DBI_1.3.0
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#> [103] knitr_1.51 reshape2_1.4.5
#> [105] nlme_3.1-169 curl_7.1.0
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