Beautiful plots made via myTAI • myTAI

Gallery of different plots available with myTAI

Here are example outputs of plotting functions from myTAIv2.

We hope these plots can inspire your analysis!

Bulk RNA-seq data

example_phyex_set is an example BulkPhyloExpressionSet object.

To learn more about bringing your dataset into myTAI, follow this vignette here:
→ 📊

library(myTAI); library(S7); library(ggplot2); library(patchwork)
data("example_phyex_set")

myTAI plots can be modified as a ggplot2 object.

myTAI::plot_signature(example_phyex_set, 
                      show_p_val = TRUE, 
                      conservation_test = stat_flatline_test,
                      colour = "lavender") +
  # as the plots are ggplot2 objects, we can simply modify them using ggplot2
  ggplot2::labs(title = "Developmental stages of A. thaliana")

plot_signature function output with stat_flatline_test

module_info <- list(early = 1:3, mid = 4:6, late = 7:8)
myTAI::plot_signature(example_phyex_set,
                      show_p_val = TRUE,
                      conservation_test = stat_reductive_hourglass_test,
                      modules = module_info,
                      colour = "lavender")

plot_signature function output with stat_reductive_hourglass_test

Transformation and robustness checks

See more here:
→ 🛡️

myTAI::plot_signature_transformed(
  example_phyex_set)

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plot_signature_transformed function output

myTAI::plot_signature_gene_quantiles(
  example_phyex_set)

plot_signature_gene_quantiles function output

Statistical tests and plotting results

See more here:
→ 📈

myTAI::stat_flatline_test(
  example_phyex_set, plot_result = TRUE)

stat_flatline_test function output

## 
## Statistical Test Result
## =======================
## Method: Flat Line Test 
## Test statistic: 0.164405 
## P-value: 0.4755228 
## Alternative hypothesis: greater 
## Data: Embryogenesis 2019

res_flt <- myTAI::stat_flatline_test(example_phyex_set, plot_result = FALSE)

myTAI::plot_cullen_frey(res_flt)

plot_cullen_frey function output for stat_flatline_test

## summary statistics
## ------
## min:  0.002580694   max:  3.953036 
## median:  0.1555222 
## mean:  0.2509132 
## estimated sd:  0.299606 
## estimated skewness:  3.27823 
## estimated kurtosis:  20.12338

myTAI::plot_null_txi_sample(res_flt) +
  ggplot2::guides(x =  guide_axis(angle = 90))

plot_null_txi_sample function output for stat_flatline_test

module_info <- list(early = 1:3, mid = 4:6, late = 7:8)
myTAI::stat_reductive_hourglass_test(
  example_phyex_set, plot_result = TRUE,
  modules = module_info)

stat_reductive_hourglass_test function output

## 
## Statistical Test Result
## =======================
## Method: Reductive Hourglass Test 
## Test statistic: 0.3747398 
## P-value: 0.03720072 
## Alternative hypothesis: greater 
## Data: Embryogenesis 2019

Average gene expression level by phylostratum

myTAI::plot_strata_expression(example_phyex_set)

plot_strata_expression function output

plot_strata_expression with scaled y axis

myTAI::plot_strata_expression(example_phyex_set) +
  ggplot2::scale_y_log10() +
  ggplot2::labs(x = "Expression aggregated by mean (log-scaled)")

plot_strata_expression function output ggplot2

plot_strata_expression with explicit transformation

library(patchwork)
p1 <- myTAI::plot_strata_expression(example_phyex_set |> myTAI::tf(log1p))

# equivalent to 
p2 <- example_phyex_set |> myTAI::tf(log1p) |> myTAI::plot_strata_expression() 

p1+p2

plot_strata_expression function output

As you can see, both plots are identical. This example demonstrates that there are multiple ways to achieve the same result through piping (|>) operator in R. |> is basically the same as %>%.

Contribution to the overall TAI by phylostratum

myTAI::plot_contribution(example_phyex_set)

plot_contribution function output

Curious about methods to obtain gene age information? See more here:
→ 📚

For other analogous methods to assign evolutionary or expression information to each gene for TDI, TSI etc., see here:
→ 🧬

myTAI::plot_distribution_expression(example_phyex_set)

plot_distribution_expression function output

Contribution to the overall TAI by partial TAI (pTAI)

pTAI, or

$\mathrm{pTAI}_i = \frac{\mathrm{ps}_i \cdot e_{is}}{\sum_{i=1}^{n} e_{is}}$

where $e_{is}$ denotes the expression level of a given gene $) i$ in sample $s$ , ${ps}_i$ is its gene age assignment, and $n$ is the total number of genes, is the per-gene contribution to the overall TAI. (Summing pTAI across all genes gives in a given sample $s$ gives the overall ${TAI}_s$ )

pTAI QQ plot compares the partial TAI distributions of various developmental stages against a reference stage (default is stage 1).

myTAI::plot_distribution_pTAI_qqplot(example_phyex_set)

plot_distribution_pTAI_qqplot function output

Phylostratum distribution

myTAI::plot_distribution_strata(example_phyex_set@strata) /
myTAI::plot_distribution_strata(
  example_phyex_set@strata,
  selected_gene_ids = myTAI::genes_top_variance(example_phyex_set, p = 0.95),
  as_log_obs_exp = TRUE
) + plot_annotation(title = "Distribution of gene ages (top), Observed vs Expected plot of top 5% variance genes (bottom)")

plot_distribution_strata function output

Expression heatmap

myTAI::plot_gene_heatmap(example_phyex_set)

plot_gene_heatmap function output default

myTAI::plot_gene_heatmap(example_phyex_set, cluster_rows = TRUE, show_reps=TRUE, show_gene_ids=TRUE, top_p=0.005)

plot_gene_heatmap function output clustered

myTAI::plot_gene_heatmap(example_phyex_set, cluster_rows = TRUE, show_reps=TRUE, top_p=0.005, std=FALSE, show_gene_ids=TRUE)

plot_gene_heatmap function output nonstd

Dimension reduction

At the gene level

myTAI::plot_gene_space(example_phyex_set)

plot_gene_space function output

myTAI::plot_gene_space(example_phyex_set,colour_by = "strata")

plot_gene_space function output by strata

At the sample level

myTAI::plot_sample_space(example_phyex_set) | myTAI::plot_sample_space(example_phyex_set, colour_by = "TXI")

plot_sample_space function output by TXI

# we can even do a UMAP
myTAI::plot_sample_space(example_phyex_set, method = "UMAP")

plot_sample_space function output by TXI

Inspecting mean-variance relationship

# highlighting top variance genes
top_var_genes <- myTAI::genes_top_variance(example_phyex_set, p = 0.9995)
p1 <- myTAI::plot_mean_var(example_phyex_set)
p2 <- myTAI::plot_mean_var(example_phyex_set, 
                     highlight_genes = top_var_genes)

p1 + p2 + plot_annotation(title = "Mean-variance: simple vs. highlighted top variance genes")

plot_mean_var function output simple vs highlighted

# with log transform and colouring by phylostratum
myTAI::plot_mean_var(example_phyex_set |> myTAI::tf(log1p), 
                     colour_by = "strata") +
  ggplot2::guides(colour = guide_legend(ncol=2))

plot_gene_space function output log transform coloured by strata

Individual gene expression profiles

# side by side: manual coloring vs strata coloring
p1 <- myTAI::plot_gene_profiles(example_phyex_set, max_genes = 10, colour_by = "manual")
p2 <- myTAI::plot_gene_profiles(example_phyex_set, max_genes = 10, colour_by = "strata")

p1 + p2 + plot_annotation(title = "Gene profiles: manual vs. strata coloring")

plot_gene_profiles function output manual vs strata coloring

# stage colouring with standardized log transformation
myTAI::plot_gene_profiles(example_phyex_set, max_genes = 10, 
                          transformation = "std_log", colour_by = "stage")

plot_gene_profiles function output stage std_log transformation

# faceted by phylostratum
myTAI::plot_gene_profiles(example_phyex_set, max_genes = 1000, 
                          colour_by = "strata", facet_by_strata = TRUE, show_set_mean = TRUE,
                          show_labels = FALSE)

plot_gene_profiles function output faceted by strata

These plots are examples of plots that myTAIv2 can generate. To check out the functions, use ? before the function (i.e. ?myTAI::plot_mean_var().

You can also find a list of plotting functions in Reference.

Single cell RNA-seq data

Most of the plotting functions shown above also apply for single cell RNA-seq data, as long as it is a ScPhyloExpressionSet object.

Plot away!