Richard J. Acton 
paper doi: 10.1093/g3journal/jkae017
We will be Focusing out attention on figure 7 E & F from this paper (Xu et al. 2024). This is as something of a pre-text to discuss the challenging problem that is the useful visual summary of the results of functional enrichment analyses.
Figures 7 E & F do not do a great job of highlighting the insights that the authors drew from the underlying data analysis. In their original context in the easyGSEA dashboard on eVITTA referenced by the authors these graphic make a little more sense where they are an interactive graphics with tooltips, and where clicking on the bars for a terms exposes further information. They are one of a number of visuals with which you can explore this data, so do not have to do as much heavy lifting on their own. Designing a graph to accommodate arbitrary dynamically generated contents is a harder problem than designing a one-off visual especially where data ranges can vary quite dramatically. Generally you have to make certain compromises to achieve generality for a graph. That said there are some critiques which still apply even with the benefit of this context.
There is a problem of emphasis in these plots. The terms are arguably the most important thing for people to be able to read and interpret. Yet the terms are buried in strings with the ‘aspect’ or source of the term and the terms identifier. They are separated with underscores, not aligned to the terms’ starts in a predictable place and sometimes truncated. The direction is for the most part as important as the terms, as knowing the know the direction of change of a term can also be important for its interpretation. The specific significance, score and rank order of terms is less cruital, interpretation of functional enrichment results is a pretty qualitative process.
The ID of the term is very helpful for anyone doing follow up to weigh other factors in how to interpret these functional enrichment results but is only needed for reference and it need not feature prominently. (Having it sometimes truncated is not optimal for the ability to look up the term.)
Other factors that are potentially relevant for interpretation are:
A smaller term with all or a very high fraction of genes showing a difference, that might not be as significant as a larger term where a mere substantial fraction of genes show a change, due to the effective ‘limit of resolution’ resulting from its small size, might be more biologically interesting to follow up.
The latter point is perhaps the hardest to capture in a summary
When assessing a single functional enrichment analysis, and not attempting to compare the enrichment of multiple different gene sets, the case can be made that a table is a preferable form of visual representation of this information than a graph. We are asking: ‘Which subsets of a set of genes that differ from some single point of comparison, by varying degrees, stand out in some way?’. Which subsets we look at, how we quantify the degree of difference from some baseline and what we mean by ‘stand out’ can all vary but we are starting from a single vector of functional elements, be they genes, transcripts, or proteins; with a single metric, be it their rank order or some score such as p-value or \(log_2\) fold change. Thus a single table of terms with columns to represent various parameters about these sets can potentially provide us with a more efficient and effective representation.
For anyone thinking about the design of tables for a scientific publication an good resource is chapter 2 of Maarten Boers’ excellent book: “Data Visualization for Biomedical Scientists” (Boers 2022).
suppressPackageStartupMessages({
library(purrr)
library(dplyr)
library(tidyr)
library(fs)
library(readr)
library(gt)
library(gtExtras)
library(DT)
library(reactable)
library(reactablefmtr)
library(webshot2)
library(htmltools)
library(tippy)
library(ggplot2)
library(uuid)
})
set.seed(42)A good case can be made that gene set enrichment analysis results lend themselves more to table and table-like representations than graphs.
There are number of reasons that a table or a hybrid table/graph may be better suited to this data than a purely graphical representation.
Key information is the term name which is text, often quite long and quite varied in length.
Rank order, precision, multiple variables for each term - that can provide important context for interpretation
How big the terms are. Which genes in the leading edge set (in GSEA, which differentially expressed genes are in over-represented terms in other types of functional enrichment analysis) Overlap between the genes in one term and another. broad terms can be parents of narrower terms and be enriched as a result of changes in the same or different sets of genes
Data underpinning these figure panels is available directly from figshare, great!
# Downloading data
data_file_urls <- c(
"table_s4.csv" = "https://ndownloader.figshare.com/files/44137139",
"table_s5.csv" = "https://ndownloader.figshare.com/files/44137145"
)
if(!all(file_exists(names(data_file_urls)))) {
purrr::iwalk(data_file_urls, ~download.file(.x, .y))
}R has lots of options for packages that format tables some examples include:
knitr::kable & {kableExtra}These have varying degrees of support for use in printed vs web based outputs. gt and kable tables have good print support and also work web outputs, gt is a little better at being consistent between different outputs formats. rhandsontable is somewhat spreadsheet like and can be good in shiny apps with with complex interactive tabular inputs. DT and reactable have good interactive sorting, searching and filtering capabilities which can run client-side in javascript providing quite a lot of interactivity from a static web page as long as the data is not too big. However reactable is perhaps a little better documented for R users and to perform some more advanced customisations with DT knowledge of JS/CSS is more necessary. flextable has quite nice syntax somewhat reminiscent of ggplot2. It is better at static rather than dynamics tables as opposed to DT, reactable, & rhandsontable, R native visual customization are also easier than DT or reactable where JS/CSS knowledge is helpful. It makes it very easy to embed arbitrary R plots into cells of your table so making custom ‘sparkline’ style plots with ggplot2 and adding them to your tables is easy. It also plays very well with embedding tables into other larger R graphics using grid.
#' process_gsea_table
#'
#' @param path the path to a csv file with GSEA data
#'
1process_gsea_table <- function(path) {
2 path %>%
3 readr::read_csv(show_col_types = FALSE) %>%
4 tidyr::extract(
pathway, into = c("aspect", "term", "id"),
regex = "(\\w+?)_(.*?)%(\\w+)",
remove = FALSE
) %>%
5 dplyr::mutate(
6 size = as.integer(size),
7 term = gsub("_", " ", term),
8 Genes = strsplit(Genes, ";"),
9 n_genes_in_leading_edge_subset = purrr::map_int(Genes, length),
10 direction = dplyr::if_else(sign(NES) == 1, "Up", "Down")
)
}table_s4 <- names(data_file_urls)[1] %>% process_gsea_table()
# table_s4table_s4 %>%
dplyr::group_by(direction) %>%
dplyr::slice(1:30) %>%
dplyr::ungroup() %>%
dplyr::arrange(desc(direction),desc(ES)) %>%
dplyr::mutate(
`-log10(pval)*sign(ES)` = -log10(pval) * sign(ES),
pathway = factor(pathway, levels = pathway, ordered = TRUE)
) %>%
ggplot2::ggplot(ggplot2::aes(ES, pathway)) +
ggplot2::geom_point(
ggplot2::aes(
size = n_genes_in_leading_edge_subset,
color = `-log10(pval)*sign(ES)`
), alpha = 0.8
) +
ggplot2::guides(size = "none") +
ggplot2::scale_color_distiller(palette = "RdBu", type = "div") +
ggplot2::geom_vline(xintercept = 0, color = "grey") +
ggplot2::theme_minimal() +
ggplot2::labs(x = "Enrichment Score (ES)", y = "")
table_s5 <- names(data_file_urls)[2] %>% process_gsea_table()
# table_s5only Biological process terms are present in table S4
table_s4 %>% distinct(aspect)# A tibble: 1 × 1
aspect
<chr>
1 BP tables are pre-filtered
table_s4 %>% nrow()[1] 83table_s4 %>% filter(pval < 0.05, padj < 0.25) %>% nrow()[1] 83gt exampletable_s4_gt_prep <- table_s4 %>%
dplyr::group_by(direction) %>%
dplyr::slice(1:30) %>%
dplyr::ungroup() %>%
arrange(dplyr::desc(direction), ES) %>%
dplyr::select(
dir = direction,
term, NES, padj,
n = n_genes_in_leading_edge_subset,
size, go_id = id
) %>%
dplyr::mutate(
leading_pc = round((n / size) * 100),
leading = paste0(n, "/", size, " (%", leading_pc, ")"),
go = paste0("GO:", go_id),
go_url = paste0("https://amigo.geneontology.org/amigo/term/GO:", go_id),
# `GO ID` = purrr::map2(go, go_url, gtExtras::gt_hyperlink)
`GO ID` = purrr::map2(go_url, go, ~{
lnk <- paste0('<a href="', .x, '">', .y, '</a>')
attributes(lnk)$html <- TRUE
attributes(lnk)$class <- c("html", "character")
lnk
})
) %>%
dplyr::group_by(dir) %>%
dplyr::arrange(dplyr::desc(dir), padj) %>%
dplyr::relocate(term, NES, padj, leading, `GO ID`)
table_s4_gt_table <- table_s4_gt_prep %>%
gt::gt() %>%
gt::tab_options(
table.width = 900,
data_row.padding = gt::px(3),
row_group.font.weight = "bold"# ,
# latex.use_longtable = TRUE # not supported until v0.11.1
) %>%
gtExtras::gt_color_rows(
columns = padj,
palette = "khroma::batlow",
domain = table_s4_gt_prep$padj #!!!
) %>%
gtExtras::gt_color_rows(
columns = NES,
palette = "RColorBrewer::RdBu",
direction = 1,
domain = table_s4_gt_prep$NES #!!!
) %>%
gt::fmt_number(columns = c(padj), decimals = 4) %>% # pval,
gt::fmt_number(columns = c(NES), decimals = 3) %>% # ES,
gt::cols_hide(c(n, size, leading_pc, go, go_id, go_url)) %>%
gt::tab_style(
style = gt::cell_borders(sides = "all", weight = gt::px(0)),
locations = gt::cells_body()
)Warning: Since gt v0.9.0, the `colors` argument has been deprecated.
• Please use the `fn` argument instead.
This warning is displayed once every 8 hours.table_s4_gt_table| term | NES | padj | leading | GO ID |
|---|---|---|---|---|
| Up | ||||
| peptidyl-tyrosine phosphorylation | 1.695 | 0.0802 | 31/77 (%40) | GO:0018108 |
| protein dephosphorylation | 1.889 | 0.0802 | 41/108 (%38) | GO:0006470 |
| peptidyl-serine phosphorylation | 1.975 | 0.0802 | 55/119 (%46) | GO:0018105 |
| olfactory behavior | 1.841 | 0.0802 | 37/67 (%55) | GO:0042048 |
| detection of chemical stimulus involved in sensory perception of smell | 2.116 | 0.0802 | 50/60 (%83) | GO:0050911 |
| nuclear-transcribed mRNA catabolic process | 1.811 | 0.0802 | 7/19 (%37) | GO:0000956 |
| response to stress | 1.781 | 0.0802 | 7/16 (%44) | GO:0006950 |
| DNA repair | 1.492 | 0.0813 | 45/128 (%35) | GO:0006281 |
| spermatogenesis | 1.542 | 0.0813 | 21/58 (%36) | GO:0007283 |
| RNA phosphodiester bond hydrolysis, endonucleolytic | 1.547 | 0.0813 | 13/57 (%23) | GO:0090502 |
| sensory perception of chemical stimulus | 1.642 | 0.0813 | 29/68 (%43) | GO:0007606 |
| nucleosome assembly | 1.653 | 0.0813 | 5/16 (%31) | GO:0006334 |
| RNA metabolic process | 1.787 | 0.0813 | 9/21 (%43) | GO:0016070 |
| proteolysis involved in protein catabolic process | 1.551 | 0.1063 | 16/48 (%33) | GO:0051603 |
| protein import into nucleus | 1.580 | 0.1063 | 16/30 (%53) | GO:0006606 |
| DNA replication | 1.512 | 0.1073 | 36/72 (%50) | GO:0006260 |
| mitotic sister chromatid segregation | 1.554 | 0.1244 | 23/33 (%70) | GO:0000070 |
| DNA recombination | 1.522 | 0.1436 | 18/32 (%56) | GO:0006310 |
| histone acetylation | 1.528 | 0.1464 | 6/28 (%21) | GO:0016573 |
| germ cell proliferation | 1.532 | 0.1581 | 12/16 (%75) | GO:0036093 |
| gastrulation | 1.504 | 0.1694 | 5/22 (%23) | GO:0007369 |
| polar body extrusion after meiotic divisions | 1.492 | 0.1865 | 17/21 (%81) | GO:0040038 |
| DNA replication initiation | 1.494 | 0.2034 | 11/16 (%69) | GO:0006270 |
| Down | ||||
| neuron differentiation | −1.765 | 0.0813 | 25/36 (%69) | GO:0030182 |
| chemosensory behavior | −1.824 | 0.0813 | 14/41 (%34) | GO:0007635 |
| mechanosensory behavior | −1.615 | 0.0813 | 7/29 (%24) | GO:0007638 |
| protein glycosylation | −1.804 | 0.0813 | 31/88 (%35) | GO:0006486 |
| endocytosis | −1.602 | 0.0813 | 28/92 (%30) | GO:0006897 |
| regulation of postsynaptic membrane potential | −1.530 | 0.0813 | 25/91 (%27) | GO:0060078 |
| regulation of membrane potential | −1.538 | 0.0813 | 25/93 (%27) | GO:0042391 |
| excitatory postsynaptic potential | −1.491 | 0.0813 | 23/85 (%27) | GO:0060079 |
| nervous system process | −1.423 | 0.0813 | 24/89 (%27) | GO:0050877 |
| fatty acid metabolic process | −1.403 | 0.0813 | 31/90 (%34) | GO:0006631 |
| locomotion | −1.440 | 0.0829 | 25/96 (%26) | GO:0040011 |
| nervous system development | −1.941 | 0.0996 | 42/114 (%37) | GO:0007399 |
| monoatomic ion transmembrane transport | −1.799 | 0.1063 | 36/125 (%29) | GO:0034220 |
| chemical synaptic transmission | −1.381 | 0.1063 | 30/128 (%23) | GO:0007268 |
| regulation of monoatomic ion transmembrane transport | −1.627 | 0.1196 | 17/28 (%61) | GO:0034765 |
| regulation of catalytic activity | −1.722 | 0.1223 | 44/140 (%31) | GO:0050790 |
| potassium ion transport | −1.576 | 0.1244 | 28/55 (%51) | GO:0006813 |
| axon guidance | −1.553 | 0.1313 | 22/64 (%34) | GO:0007411 |
| mitochondrion organization | −1.550 | 0.1419 | 18/34 (%53) | GO:0007005 |
| regulation of oviposition | −1.421 | 0.1436 | 22/48 (%46) | GO:0046662 |
| potassium ion transmembrane transport | −1.326 | 0.1439 | 43/85 (%51) | GO:0071805 |
| cytoskeleton organization | −1.519 | 0.1464 | 14/29 (%48) | GO:0007010 |
| molting cycle, collagen and cuticulin-based cuticle | −1.498 | 0.1464 | 16/58 (%28) | GO:0018996 |
| mitochondrial translation | −1.512 | 0.1479 | 23/32 (%72) | GO:0032543 |
| gonad morphogenesis | −1.488 | 0.1479 | 12/38 (%32) | GO:0035262 |
| glutathione metabolic process | −1.462 | 0.1641 | 28/55 (%51) | GO:0006749 |
| muscle contraction | −1.571 | 0.1657 | 19/26 (%73) | GO:0006936 |
| muscle organ development | −1.574 | 0.1845 | 12/18 (%67) | GO:0007517 |
| tRNA methylation | −1.513 | 0.1968 | 13/23 (%57) | GO:0030488 |
| proton motive force-driven ATP synthesis | −1.525 | 0.2171 | 13/17 (%76) | GO:0015986 |
As a rule of thumb it is good to use proportional fonts in your tables, they are generally easier to read than monospace fonts where all the letters are the same width. However some proportional fonts also have digit characters which are proportional in width. This can result in issues with the alignment of numbers in tables which should center around the decimal point.
Numerical formatting rules in some table packages can be somewhat limited so to produce correct alignments it may be necessary to convert numerical values to strings which you can space properly with more powerful numerical formatting functions like R’s sprintf. This, however, has the disadvantage that the table library no longer sees these columns as numerical values so cannot do things like sorting them interactively on a web page. Even table libraries with extensive numerical formatting options frequently fail to align their formatted result correctly around the decimal point. It may annoyingly be necessary in these cases to compromise on correct alignment to get these interactive features until such time as these alignment deficiencies can be fixed in the upstream table packages. gt does have decimal alignment in versions >=v0.8.0 but this is not the default behavior which I would content it should be. I’ve not got more optimal solution for DT
Unfortunately it is also a habit of some web based tables to ignore leading white-space characters, like spaces, which you may be relying upon to align your numbers correctly. So setting the CSS properties font-family to monospace and white-space to pre for the relevant columns of your table gets around these issues as an alignment based on the the character widths in a string should then be reliably correctly spaced when rendered on the web page.
gsea_reactable <- function(
data, element_id = paste0("gsea-reactable-", uuid::UUIDgenerate())
){
padj_color <- colourScaleR::universal_colour_scaler(
data$padj, type = "viridis", palette = "viridis",
n_breaks = 30, mode = "closure"
)
NES_color <- colourScaleR::universal_colour_scaler(
data$NES, type = "scico", palette = "roma",
n_breaks = 30, mode = "closure"
)
prop_color <- colourScaleR::universal_colour_scaler(
0:1, type = "viridis", palette = "plasma",
n_breaks = 30, mode = "closure"
)
reactable::reactable(
data = data,
elementId = element_id,
columns = list(
padj = reactable::colDef(
name = "Adjusted p-value",
align = "left",
width = 140,
cell = reactablefmtr::data_bars(
data, text_position = "inside-base",
fill_color = colourScaleR::universal_colour_scaler(
sort(data$padj), direction = -1, scale = "log",
palette = "Reds", type = "brewer"
)
),
style = list(fontFamily = "sans")
),
NES = reactable::colDef(
align = "center",
width = 380,
cell = reactablefmtr::data_bars(
data,
fill_color = c("#FE9915", "#73C08C")
),
style = list(fontFamily = "sans")
),
ES = reactable::colDef(show = FALSE),
n = reactable::colDef(show = FALSE),
leading_pc = reactable::colDef(show = FALSE),
Genes = reactable::colDef(
cell = function(){""},
width = 35,
details = function(index) {
htmltools::div(
"Genes: ",
paste(data$Genes[[index]], collapse = ", "),
style = list(fontSize = "0.75rem")
)
},
filterInput = function(values, name) {
htmltools::tags$input(
type = "text",
onchange = sprintf(
"Reactable.setFilter('%s', '%s', this.value)",
element_id, name
),
"aria-label" = sprintf("Filter %s", name),
style = "width: 100%;"
)
}
),
term = reactable::colDef(width = 300),
direction = reactable::colDef(
show = FALSE, defaultSortOrder = "desc"
),
size = reactable::colDef(width = 75, show = FALSE),
leading = reactable::colDef(
cell = reactablefmtr::data_bars(
data, fill_by = "leading_pc",
fill_color = viridisLite::viridis(5)
),
name = "(leading/size) %",
align = "left",
width = 400,
style = list(fontFamily = "sans")
)
),
highlight = TRUE, bordered = FALSE, borderless = TRUE, striped = TRUE,
pagination = FALSE, compact = TRUE,
height = 1000,
defaultSorted = c("direction", "padj")
# defaultSorted = c("direction", "ES")
)
}
table_s4 %>%
dplyr::group_by(direction) %>%
dplyr::slice(1:30) %>%
dplyr::ungroup() %>%
dplyr::select(
Genes, term,
ES, padj, NES,
direction,
n = n_genes_in_leading_edge_subset,
size
) %>%
dplyr::mutate(
leading_pc = round((n / size) * 100),
leading = paste0(n, "/", size, " (%",leading_pc,")"),
) %>%
gsea_reactable()table_s4_flxtbl_prep <- table_s4 %>%
dplyr::group_by(direction) %>%
dplyr::slice(1:30) %>%
dplyr::ungroup() %>%
# arrange(dplyr::desc(direction), ES) %>%
arrange(dplyr::desc(direction), padj) %>%
dplyr::select(
`⇅` = direction,
term, NES, padj, Genes,
n = n_genes_in_leading_edge_subset,
size, id
) %>%
dplyr::mutate(
leading_pc = round((n / size) * 100),
leading = paste0(n, "/", size, " (%",leading_pc,")"),
go = paste0("GO:", id),
go_url = paste0("https://amigo.geneontology.org/amigo/term/GO:", id)
) %>%
dplyr::mutate(
Genes = purrr::map_chr(Genes, ~paste0(.x, collapse = ", "))
)table_s4_flxtbl <- table_s4_flxtbl_prep %>%
flextable::flextable(
col_keys = c("⇅", "term", "NES", "padj", "leading", "go")
) %>%
flextable::bg(
i = ~`⇅` == "Up", j = "⇅", bg = "#73C08C"
) %>%
flextable::bg(
i = ~`⇅` == "Down", j = "⇅", bg = "#FE9915"
) %>%
flextable::merge_v(j = "⇅") %>%
flextable::colformat_double(
j = c("padj", "NES"),
big.mark=",", digits = 3, na_str = "N/A"
) %>%
flextable::compose(
j = "go", value = flextable::as_paragraph(
flextable::hyperlink_text(x = go, url = go_url)
)
) %>%
flextable::compose(
j = "term", value = flextable::as_paragraph(
flextable::as_chunk(
term,
props = flextable::fp_text_default(font.size = 10)
)
)
) %>%
flextable::bg(
j = c("padj"),#"pval",
bg = colourScaleR::universal_colour_scaler(
sort(c(table_s4_flxtbl_prep$padj)), ## !!
scale = "log", mode = "closure", direction = 1, end = 1, begin = 0.2,
palette = "plasma", type = "viridis"
)
) %>%
flextable::bold(j = c("padj")) %>%
flextable::colformat_double(
j = c("padj"),
big.mark=",", digits = 5, na_str = "N/A"
) %>%
flextable::bg(
j = c("leading"),#"pval",
bg = colourScaleR::universal_colour_scaler(
sort(c(table_s4_flxtbl_prep$leading_pc)),
mode = "closure", direction = -1, end = 1, begin = 0.2,
palette = "imola", type = "scico"
)(table_s4_flxtbl_prep$leading_pc)## !!
) %>%
flextable::compose(
j = "NES",
value = flextable::as_paragraph(
flextable::as_chunk(
sprintf("% .3f\n", NES),
props = flextable::fp_text_default(font.size = 8)
),
flextable::minibar(
value = abs(NES), height = 0.1,
barcol = colourScaleR::universal_colour_scaler(
table_s4_flxtbl_prep$NES, ## !!
mode = "closure", direction = 1,
palette = "roma", type = "scico"
)(NES)
)
)
) %>%
flextable::align(
j = c("padj", "NES"), align = "left", part = "all"
) %>%
flextable::hline(
i = ~{
x <- `⇅` == "Up"
x[c(which(x)[-length(which(x))], which(!x))] <- FALSE
x
},
border = flextable::fp_border_default(color = "#666666", width = 2)
) %>%
flextable::width(
width = c(0.5, 3, 0.8, 0.8, 0.5, 0.8)
) %>%
flextable::height(height = 8, part = "body", unit = "mm")
#flextable::set_table_properties(layout = "autofit", width = 1)
res <- table_s4_flxtbl %>% flextable::save_as_image("table_s4_flxtbl.png")Registered S3 method overwritten by 'webshot':
method from
print.webshot webshot2PhantomJS not found. You can install it with webshot::install_phantomjs(). If it is installed, please make sure the phantomjs executable can be found via the PATH variable.table_s4_flxtbl⇅ |
term |
NES |
padj |
leading |
go |
|---|---|---|---|---|---|
Up |
response to stress |
1.781 |
0.08016 |
7/16 (%44) |
|
nuclear-transcribed mRNA catabolic process |
1.811 |
0.08016 |
7/19 (%37) |
||
detection of chemical stimulus involved in sensory perception of smell |
2.116 |
0.08016 |
50/60 (%83) |
||
olfactory behavior |
1.841 |
0.08016 |
37/67 (%55) |
||
peptidyl-serine phosphorylation |
1.975 |
0.08016 |
55/119 (%46) |
||
protein dephosphorylation |
1.889 |
0.08016 |
41/108 (%38) |
||
peptidyl-tyrosine phosphorylation |
1.695 |
0.08016 |
31/77 (%40) |
||
RNA metabolic process |
1.787 |
0.08130 |
9/21 (%43) |
||
nucleosome assembly |
1.653 |
0.08130 |
5/16 (%31) |
||
sensory perception of chemical stimulus |
1.642 |
0.08130 |
29/68 (%43) |
||
RNA phosphodiester bond hydrolysis, endonucleolytic |
1.547 |
0.08130 |
13/57 (%23) |
||
spermatogenesis |
1.542 |
0.08130 |
21/58 (%36) |
||
DNA repair |
1.492 |
0.08130 |
45/128 (%35) |
||
protein import into nucleus |
1.580 |
0.10625 |
16/30 (%53) |
||
proteolysis involved in protein catabolic process |
1.551 |
0.10625 |
16/48 (%33) |
||
DNA replication |
1.512 |
0.10733 |
36/72 (%50) |
||
mitotic sister chromatid segregation |
1.554 |
0.12440 |
23/33 (%70) |
||
DNA recombination |
1.522 |
0.14359 |
18/32 (%56) |
||
histone acetylation |
1.528 |
0.14644 |
6/28 (%21) |
||
germ cell proliferation |
1.532 |
0.15807 |
12/16 (%75) |
||
gastrulation |
1.504 |
0.16936 |
5/22 (%23) |
||
polar body extrusion after meiotic divisions |
1.492 |
0.18646 |
17/21 (%81) |
||
DNA replication initiation |
1.494 |
0.20342 |
11/16 (%69) |
||
Down |
fatty acid metabolic process |
-1.403 |
0.08130 |
31/90 (%34) |
|
nervous system process |
-1.423 |
0.08130 |
24/89 (%27) |
||
excitatory postsynaptic potential |
-1.491 |
0.08130 |
23/85 (%27) |
||
regulation of membrane potential |
-1.538 |
0.08130 |
25/93 (%27) |
||
regulation of postsynaptic membrane potential |
-1.530 |
0.08130 |
25/91 (%27) |
||
endocytosis |
-1.602 |
0.08130 |
28/92 (%30) |
||
protein glycosylation |
-1.804 |
0.08130 |
31/88 (%35) |
||
mechanosensory behavior |
-1.615 |
0.08130 |
7/29 (%24) |
||
chemosensory behavior |
-1.824 |
0.08130 |
14/41 (%34) |
||
neuron differentiation |
-1.765 |
0.08130 |
25/36 (%69) |
||
locomotion |
-1.440 |
0.08291 |
25/96 (%26) |
||
nervous system development |
-1.941 |
0.09959 |
42/114 (%37) |
||
chemical synaptic transmission |
-1.381 |
0.10625 |
30/128 (%23) |
||
monoatomic ion transmembrane transport |
-1.799 |
0.10625 |
36/125 (%29) |
||
regulation of monoatomic ion transmembrane transport |
-1.627 |
0.11957 |
17/28 (%61) |
||
regulation of catalytic activity |
-1.722 |
0.12226 |
44/140 (%31) |
||
potassium ion transport |
-1.576 |
0.12440 |
28/55 (%51) |
||
axon guidance |
-1.553 |
0.13133 |
22/64 (%34) |
||
mitochondrion organization |
-1.550 |
0.14192 |
18/34 (%53) |
||
regulation of oviposition |
-1.421 |
0.14359 |
22/48 (%46) |
||
potassium ion transmembrane transport |
-1.326 |
0.14395 |
43/85 (%51) |
||
molting cycle, collagen and cuticulin-based cuticle |
-1.498 |
0.14644 |
16/58 (%28) |
||
cytoskeleton organization |
-1.519 |
0.14644 |
14/29 (%48) |
||
gonad morphogenesis |
-1.488 |
0.14791 |
12/38 (%32) |
||
mitochondrial translation |
-1.512 |
0.14791 |
23/32 (%72) |
||
glutathione metabolic process |
-1.462 |
0.16414 |
28/55 (%51) |
||
muscle contraction |
-1.571 |
0.16566 |
19/26 (%73) |
||
muscle organ development |
-1.574 |
0.18448 |
12/18 (%67) |
||
tRNA methylation |
-1.513 |
0.19675 |
13/23 (%57) |
||
proton motive force-driven ATP synthesis |
-1.525 |
0.21711 |
13/17 (%76) |
Representing the overlap between genes in different terms is challenging to do in a way that can be included in a combined visualisation.
table_s4_set_map <- table_s4 %>%
dplyr::select(Genes, term) %>%
tidyr::unnest(cols = c(Genes)) %>%
dplyr::filter(grepl("DNA", term)) %>%
#dplyr::filter(term %in% c(head(unique(table_s4$term)))) %>%
dplyr::group_by(Genes) %>%
dplyr::mutate(n = n()) %>%
dplyr::ungroup() %>%
dplyr::arrange(dplyr::desc(n)) %>%
dplyr::mutate(
Genes = factor(Genes, levels = unique(Genes), ordered = TRUE),
term = factor(term, levels = unique(term), ordered = TRUE)
) %>%
ggplot2::ggplot(aes(Genes, term)) +
ggplot2::geom_tile(fill = "red", colour = "grey") +
ggplot2::theme_void() +
ggplot2::theme(
#plot.background = ggplot2::element_rect(fill = "black"),
axis.text.y = ggplot2::element_text(hjust = 1, size = 8),
axis.text.x = ggplot2::element_text(
angle = 70, size = 8, vjust = 0.5, hjust = 0.5
)
)# +
#ggplot2::coord_equal()
ggplot2::ggsave("table_s4_set_map.png", table_s4_set_map, height = 1.5, width = 12)
table_s4_set_map
version is clear, where it is possible to use old versions, where any underlying database/dataset is also versioned and accessible
slides from the discussion session.
sessionInfo()R version 4.3.1 (2023-06-16)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.4 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-r0.3.20.so; LAPACK version 3.10.0
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] stats graphics grDevices datasets utils methods base
other attached packages:
[1] uuid_1.1-0 ggplot2_3.3.6 tippy_0.1.0
[4] htmltools_0.5.7 webshot2_0.1.0 reactablefmtr_2.0.0
[7] reactable_0.4.4 DT_0.23 gtExtras_0.4.0
[10] gt_0.10.1 readr_2.1.2 fs_1.6.3
[13] tidyr_1.2.0 dplyr_1.1.4 purrr_0.3.4
loaded via a namespace (and not attached):
[1] tidyselect_1.2.0 viridisLite_0.4.0 farver_2.1.0
[4] viridis_0.6.2 fastmap_1.1.0 promises_1.2.0.1
[7] digest_0.6.29 lifecycle_1.0.3 ellipsis_0.3.2
[10] processx_3.6.1 magrittr_2.0.3 compiler_4.3.1
[13] rlang_1.1.0 sass_0.4.8 tools_4.3.1
[16] utf8_1.2.2 yaml_2.3.5 data.table_1.14.2
[19] knitr_1.39 labeling_0.4.2 htmlwidgets_1.6.4
[22] bit_4.0.4 xml2_1.3.6 RColorBrewer_1.1-3
[25] scico_1.3.0 websocket_1.4.1 withr_2.5.0
[28] grid_4.3.1 fansi_1.0.3 gdtools_0.2.4
[31] colorspace_2.0-3 paletteer_1.4.0 scales_1.3.0
[34] cli_3.6.2 rmarkdown_2.14 crayon_1.5.1
[37] generics_0.1.2 tzdb_0.3.0 chromote_0.1.0
[40] stringr_1.4.0 parallel_4.3.1 base64enc_0.1-3
[43] vctrs_0.6.5 webshot_0.5.3 jsonlite_1.8.0
[46] hms_1.1.1 bit64_4.0.5 systemfonts_1.0.4
[49] crosstalk_1.2.0 fontawesome_0.2.2 glue_1.6.2
[52] reactR_0.5.0 rematch2_2.1.2 ps_1.7.1
[55] stringi_1.7.6 flextable_0.7.2 gtable_0.3.0
[58] shape_1.4.6 colourScaleR_1.1.1 later_1.3.0
[61] prismatic_1.1.0 munsell_0.5.0 tibble_3.2.1
[64] pillar_1.9.0 circlize_0.4.15 R6_2.5.1
[67] vroom_1.5.7 evaluate_0.15 renv_0.15.5
[70] Rcpp_1.0.8.3 zip_2.2.0 gridExtra_2.3
[73] officer_0.4.3 xfun_0.38 pkgconfig_2.0.3
[76] GlobalOptions_0.1.2