Package 'goatea' reference manual

Title:	Interactive Exploration of GSEA by the GOAT Method
Description:	Geneset Ordinal Association Test Enrichment Analysis (GOATEA) provides a 'Shiny' interface with interactive visualizations and utility functions for performing and exploring automated gene set enrichment analysis using the 'GOAT' package. 'GOATEA' is designed to support large-scale and user-friendly enrichment workflows across multiple gene lists and comparisons, with flexible plotting and output options. Visualizations pre-enrichment include interactive 'Volcano' and 'UpSet' (overlap) plots. Visualizations post-enrichment include interactive geneset dotplot, geneset treeplot, gene-effectsize heatmap, gene-geneset heatmap and 'STRING' database of protein-protein-interactions network graph. 'GOAT' reference: Frank Koopmans (2024) <doi:10.1038/s42003-024-06454-5>.
Authors:	Maurits Unkel [aut, cre, fnd, cph] (ORCID: <https://orcid.org/0000-0003-1920-6001>)
Maintainer:	Maurits Unkel <[email protected]>
License:	Apache License (>= 2)
Version:	2.1.0
Built:	2026-05-30 08:22:09 UTC
Source:	https://github.com/bioc/goatea

Calculate geneSetRatio per gene per enrichment contrast

Description

Get a percentage of genesets the specific gene is included

Usage

calculate_geneSetRatio(enrichment_results, gene_overview_df)
calculate_geneSetRatio(enrichment_results, gene_overview_df)

Arguments

enrichment_results

list of enrichment results

gene_overview_df

dataframe with gene-wise information

Value

numerical vector of gene set ratios

Examples

calculate_geneSetRatio(
list(
  A = get(load(system.file("extdata", "example_enrichment.rda", package = "goatea"))), 
  B = get(load(system.file("extdata", "example_enrichment.rda", package = "goatea")))
), 
get(load(system.file("extdata", "example_genes_overview.rda", package = "goatea"))))
calculate_geneSetRatio(
list(
  A = get(load(system.file("extdata", "example_enrichment.rda", package = "goatea"))), 
  B = get(load(system.file("extdata", "example_enrichment.rda", package = "goatea")))
), 
get(load(system.file("extdata", "example_genes_overview.rda", package = "goatea"))))

Create and/or modify color/gradient palettes

Description

Note for colorblind use: "Okabe-Ito"

Addition of values happens before multiplication with factors.

Palette names are stripped of whitespace and lowered for name matching. All RColorBrewer and Viridis palettes are included.

All grDevices plotting functions are provided as palettes, simply use colors = "rainbow", "heat", "terrain", "topo" or "cm".

Usage

colorify(
  n = NULL,
  colors = character(0),
  colors_lock = NULL,
  colors_names = character(0),
  colors_breakpoints = numeric(0),
  gradient_n = n,
  gradient_space = c("rgb", "Lab"),
  gradient_interpolate = c("linear", "spline"),
  hf = 1,
  sf = 1,
  lf = 1,
  rf = 1,
  gf = 1,
  bf = 1,
  hv = 0,
  sv = 0,
  lv = 0,
  rv = 0L,
  gv = 0L,
  bv = 0L,
  alpha = 1,
  rev = FALSE,
  plot = FALSE,
  export = FALSE,
  verbose = TRUE,
  ...
)
colorify(
  n = NULL,
  colors = character(0),
  colors_lock = NULL,
  colors_names = character(0),
  colors_breakpoints = numeric(0),
  gradient_n = n,
  gradient_space = c("rgb", "Lab"),
  gradient_interpolate = c("linear", "spline"),
  hf = 1,
  sf = 1,
  lf = 1,
  rf = 1,
  gf = 1,
  bf = 1,
  hv = 0,
  sv = 0,
  lv = 0,
  rv = 0L,
  gv = 0L,
  bv = 0L,
  alpha = 1,
  rev = FALSE,
  plot = FALSE,
  export = FALSE,
  verbose = TRUE,
  ...
)

Arguments

n

default: NULL, else integer, amount of colors to create, if palette selected and more colors requested they will be generated

colors

character (vector), combination of selecting palette(s) by name (options: see display_palettes()), and/or vector of R color names and/or color hexcodes

colors_lock

default: rep(FALSE, length(colors), numerical or logical index of colors (not) to be modified, if logical length != colors it will be cut or filled with TRUE/FALSE, prefix with '!' for logical vectors and '-' for numerical vectors to get inverse, see examples. If gradient_n %% length(colors) == 0, i.e. if gradient_n divisive by amount of colors without rest, set repeat given locking pattern

colors_names

default: character(0), else character vector of color names

colors_breakpoints

default: numeric(0), else numeric vector of breakpoints to colorRamp in between

gradient_n

default: n, else integer, amount of colors to output as gradient, after completing palette for n colors

gradient_space

default: "rgb", else "Lab", see ?grDevices::colorRamp()

gradient_interpolate

default: "linear", else "spline", see ? grDevices::colorRamp()

hf

hue factor, default: 1, multiply values by factor, proportional to base value of 1

sf

saturation factor, default: 1, multiply values by factor, proportional to base value of 1

lf

lightness/brightness factor, default: 1, multiply values by factor, proportional to base value of 1

rf

red factor, default: 1, multiply values by factor, proportional to base value of 1

gf

green factor, default: 1, multiply values by factor, proportional to base value of 1

bf

blue factor, default: 1, multiply values by factor, proportional to base value of 1

hv

hue value, default: 0, add value to values, linear from base value of 0

sv

saturation value, default: 0, add value to values, linear from base value of 0

lv

lightness/brightness value, default: 0, add value to values, linear from base value of 0

rv

red value, default: 0, add value to values, linear from base value of 0

gv

green value, default: 0, add value to values, linear from base value of 0

bv

blue value, default: 0, add value to values, linear from base value of 0

alpha

numeric, sets color alpha values

rev

default: FALSE, if TRUE, reverse order of colors

plot

default: FALSE, if TRUE plot pie chart of color palette

export

default: FALSE, if TRUE: export = getwd(), if export = "string/", save hexcodes, rgb, and hsl values to export/colorify.csv

verbose

default: TRUE, else FALSE - to log status messages

...

additional arguments to pass on

Details

Either generate theoretically maximally different colors, select an available R grDevices palette and/or modify the colors of the given gradient/palette

Value

vector of color hexcodes

Examples

colorify(10, plot = TRUE)
colorify(10, plot = TRUE)

Colorify colorRamp between colors mapping to breakpoint values

Description

Note that breakpoints and colors will be ordered ascendingly by breakpoints values

Usage

colorify_map(colors, breakpoints, ...)
colorify_map(colors, breakpoints, ...)

Arguments

colors

hexcolor character vector

breakpoints

numeric vector matching colors per value

...

to pass arguments to grDevices::colorRamp

Value

function with colors and breaks attributes, can be called as function(c(values)) to return hexcolorcodes

Display R grDevices palettes

Description

Use colorify() to select and modify the palettes, see its documentation. Note that discrete palettes with maximum n colors will be repeated in plotting.

Any numeric i_palettes over maximum amount of palettes are not displayed.

Contains all Viridis palettes, excluding Turbo.

Usage

display_palettes(n = 10, i_palettes = seq_len(1000), border = FALSE)
display_palettes(n = 10, i_palettes = seq_len(1000), border = FALSE)

Arguments

n

integer, amount of colors to display

i_palettes

default: numeric vector as index/range for choosing palettes, or a combination of 'rcolorbrewer', 'viridis', 'rainbow' (grDevices Palettes) to show specific palettes

border

default: FALSE, if TRUE show color rectangle borders

Value

named vector with source and name of palettes, 'hcl' for grDevices::hcl.pals() and 'pal' for grDevices::palette.pals()

Example Colameo MS data

Description

Mass spectrometry genelist from Colameo et al. 2021.

Usage

example_Colameo_MS
example_Colameo_MS

Format

A data frame with columns gene, symbol, effectsize, pvalue.

Source

Colameo et al. 2021 (PMID: 34396684)

Example Colameo RNA data

Description

RNA-seq genelist from Colameo et al. 2021.

Usage

example_Colameo_RNA
example_Colameo_RNA

from_symbol: gene_A, gene_B
to_symbol: gene_A, gene_B
combined_score: 0, 1000
from: gene_A_ID, gene_B_ID
to: gene_A_ID, gene_B_ID

...

Source

generated with data-raw/example_data.R

Get file extension

Description

Get file extension

Usage

file_extension(x)
file_extension(x)

Arguments

x

string filepath

Value

string file extension

Examples

file_extension('filename.ext')
file_extension('filename.ext')

Filter enrichment

Description

Search and filter and sort or summarize (compiled) enrichment output.

Usage

filter_enrichment(
  df,
  genes_input = "",
  genes_any_all = c("any", "all"),
  terms_query = "",
  terms_query_all_any = c("any", "all"),
  terms_antiquery = "",
  terms_antiquery_all_any = c("any", "all"),
  min_ngenes = 0,
  min_ngenes_input = 0,
  min_ngenes_signif = 0,
  min_abs_zscore = 0,
  min_pvalue_adjust = 0,
  max_ngenes = 1e+06,
  max_ngenes_input = 1e+06,
  max_ngenes_signif = 1e+06,
  max_abs_zscore = 1e+06,
  max_pvalue_adjust = 1
)
filter_enrichment(
  df,
  genes_input = "",
  genes_any_all = c("any", "all"),
  terms_query = "",
  terms_query_all_any = c("any", "all"),
  terms_antiquery = "",
  terms_antiquery_all_any = c("any", "all"),
  min_ngenes = 0,
  min_ngenes_input = 0,
  min_ngenes_signif = 0,
  min_abs_zscore = 0,
  min_pvalue_adjust = 0,
  max_ngenes = 1e+06,
  max_ngenes_input = 1e+06,
  max_ngenes_signif = 1e+06,
  max_abs_zscore = 1e+06,
  max_pvalue_adjust = 1
)

Arguments

df

enrichment output dataframe

genes_input

default: UI input/character vector of genes to select df terms for

genes_any_all

default: 'any', else 'all', use to define to take only specific terms containing any or all associated genes

terms_query

dfeault: UI input/character vector of keywords to match (grepl) term names

terms_query_all_any

default: 'any', else 'all', defines if terms should match any or all of the query keywords given

terms_antiquery

dfeault: UI input/character vector of keywords to NOT match (grepl) term names

terms_antiquery_all_any

default: 'any', else 'all', defines if terms should NOT match any or all of the query keywords given

min_ngenes

default: 0, set higher to filter terms with less n genes

min_ngenes_input

default: 0, else set higher to filter terms with less n input genes

min_ngenes_signif

default: 0, set higher to filter terms with less n significant genes

min_abs_zscore

default: 0, set higher to filter terms with less absolute zscore

min_pvalue_adjust

default: 0, set higher to filter terms with lower multiple testing corrected p-value

max_ngenes

default: 0, set lower to filter terms with more n genes

max_ngenes_input

default: 0, else set lower to filter terms with more n input genes

max_ngenes_signif

default: 0, set lower to filter terms with more n significant genes

max_abs_zscore

default: 0, set lower to filter terms with more absolute zscore

max_pvalue_adjust

default: 1, set lower to filter terms with higher adjusted p-value for multiple correction

Value

filtered dataframe

Examples

filter_enrichment(
get(load(system.file("extdata", "example_enrichment.rda", package = "goatea"))), 
min_ngenes = 15)
filter_enrichment(
get(load(system.file("extdata", "example_enrichment.rda", package = "goatea"))), 
min_ngenes = 15)

Set base folder

Description

sets/gets given folder path if provided else checks in order:

path.expand("~") (tilde (~) expands to HOME folder path)
Sys.getenv("R_USER") (set on R session start)
Sys.getenv("USERPROFILE") (Windows specific)

Usage

get_base_folder(folder_path = NULL)
get_base_folder(folder_path = NULL)

Arguments

folder_path

character, default NULL, else existing directory

Value

character folder path

Examples

get_base_folder()
get_base_folder()

Get PPI igraph

Description

Uses Leiden clustering on modularity for community detection. Leiden was chosen as default as expected PPI data is not inherently hierarchical, which is why modularity optimalization is used on the graph topology. Expected PPI data comes from genes/proteins (of interest) selected from gene set enrichment analysis or differential expression analysis. Using clustering from terms is not possible, as genes can be in multiple terms. Leiden also scales well to large graphs, has consistent clustering outcomes and provides some inherent guarantees by its method, e.g. locally optimal assignment.

Usage

get_ppigraph(ppi_data, vertex_clustering = NULL)
get_ppigraph(ppi_data, vertex_clustering = NULL)

Arguments

ppi_data

dataframe, PPI by aliases/ids in columns 'from' and 'to'

vertex_clustering

NULL, else numerical vector of cluster IDs

Value

igraph object of PPI data

References

Traag, V.A., Waltman, L. & van Eck, N.J. From Louvain to Leiden: guaranteeing well-connected communities. Sci Rep 9, 5233 (2019). https://doi.org/10.1038/s41598-019-41695-z

Examples

get_ppigraph(
  get(load(system.file("extdata", "example_ppi_data.rda", package = "goatea")))
)
get_ppigraph(
  get(load(system.file("extdata", "example_ppi_data.rda", package = "goatea")))
)

Get STRING database Protein-Protein Interactions

Description

STRING documentation: https://string-db.org/cgi/help?sessionId=baEZCS5u1RdM

Protocol used for downloading STRING files is https

Usage

get_string_ppi(
  aliases,
  score_threshold = 0L,
  organism = 9606L,
  network_type = "full",
  link_data = "combined_only",
  folder = tempdir(),
  version = "latest",
  versions = NULL
)
get_string_ppi(
  aliases,
  score_threshold = 0L,
  organism = 9606L,
  network_type = "full",
  link_data = "combined_only",
  folder = tempdir(),
  version = "latest",
  versions = NULL
)

Arguments

aliases

character, vector with protein/gene symbols/aliases

score_threshold

integer, default: 0, to get all PPI, ranges between [0-1000], 200 for low, 400 for medium and 700 for high/stringent scoring PPI

organism

integer, default: 9606 (Homo Sapiens), see ?goat::load_genesets_go_bioconductor taxid parameter for possible organism taxIDs

network_type

character, default: 'full', else 'physical' for only STRING documented physical interactions

link_data

character, default: 'combined_only', else 'full' or 'detailed', see STRING documentation

folder

character, default: tempdir(), else given folder path for where to download STRING files, converted to .parquet for compression and query efficiency, if tempdir() the temporary directory with the downloaded files are removed after the R session

version

character, default: 'latest', else a version to check availability, e.g. "12.0", if version not available the available versions are printed

versions

NULL, else character vector with versions to choose from with version

Value

dataframe (tibble) with protein-protein interactions (symbols and STRING IDs) and STRING combined score

Examples


get_string_ppi(c("TP53", "EGFR", "BRCA1", "MTOR", "MYC", "SOX2"))

get_string_ppi(c("TP53", "EGFR", "BRCA1", "MTOR", "MYC", "SOX2"))

Get term names by searching with (partial) keywords

Description

Get term names by searching with (partial) keywords

Usage

get_terms_by_keywords(patterns, terms, pos_neg = "pos", all_any = "all")
get_terms_by_keywords(patterns, terms, pos_neg = "pos", all_any = "all")

Arguments

patterns

keywords to match (grepl) term names

terms

character vector to be grepl searched

pos_neg

return positive matches or negate matches

all_any

need all or any patterns to match search terms

Value

character vector with matching terms by patterns

Examples

get_terms_by_keywords('circa', c('circadian rhythm', 'no match', 'circadian clock'))
get_terms_by_keywords('circa', c('circadian rhythm', 'no match', 'circadian clock'))

Get visNetwork graph

Description

Gets visNetwork graph with ppigraph, and optionally genes overview, metadata

Usage

get_visNetwork(ppigraph, genes_overview = NULL, sample_name = NULL)
get_visNetwork(ppigraph, genes_overview = NULL, sample_name = NULL)

Arguments

ppigraph

igraph object, get from get_ppigraph()

genes_overview

(optional) dataframe, default: NULL, else metadata dataframe for ppigraph proteins/genes aliases

sample_name

(optional) character, default: NULL, else sample name found in genes_overview columns

Value

list of visNetwork nodes and edges and given ppigraph

Examples

ppi_graph <- get_ppigraph(
  get(load(system.file("extdata", "example_ppi_data.rda", package = "goatea")))
)
get_visNetwork(ppi_graph)
ppi_graph <- get_ppigraph(
  get(load(system.file("extdata", "example_ppi_data.rda", package = "goatea")))
)
get_visNetwork(ppi_graph)

Server for goatea package

Description

Server for goatea package

Usage

goatea_server(input, output, session, css_colors)
goatea_server(input, output, session, css_colors)

Arguments

input

Shiny input elements handling

output

Shiny input elements handling

session

Shiny handling reactivity in app

css_colors

see app.R, user set manual colors for the GOATEA UI

Value

Shiny server function

UI for GOATEA package

Description

UI for GOATEA package

Usage

goatea_ui()
goatea_ui()

Value

Shiny UI function

Hex code colors to rgba format

Description

Hex code colors to rgba format

Usage

hexcolor2rgba(hexcolors, alpha = NULL)
hexcolor2rgba(hexcolors, alpha = NULL)

Arguments

hexcolors

character (vector), hexcode colors (e.g. #FFFFFF)

alpha

numeric in range [0-1], default: NULL to use full opacity or given opacity (AA) in hexcolors (#RRGGBBAA)

Value

colors in rgba format

Examples

colors <- colorify(5)
hexcolor2rgba(colors)
hexcolor2rgba(colors, alpha = .5)
colors <- gsub('FF$', 75, colors)
hexcolor2rgba(colors)
hexcolor2rgba(colors, alpha = .5)
colors <- colorify(5)
hexcolor2rgba(colors)
hexcolor2rgba(colors, alpha = .5)
colors <- gsub('FF$', 75, colors)
hexcolor2rgba(colors)
hexcolor2rgba(colors, alpha = .5)

Palette original name mapping

Description

All ColorBrewer palettes overlap with grDevices palettes Viridis palettes, except "Magma", overlap with grDevices palettes

Usage

palette_name_mapping(palette)
palette_name_mapping(palette)

Arguments

palette

string: name of palette, will be lower()ed and stripped of whitespace

Value

original palette name

Plot ComplexHeatmap

Description

Plot ComplexHeatmap from enrichment analysis results and corresponding genelist

Usage

plot_ComplexHeatmap(
  enrichment_result,
  genelist,
  genes = NULL,
  cluster_method = "single",
  n_cluster = 1,
  n_top_terms = NA,
  n_top_genes = NA,
  genelist_overlap = NULL,
  plot = FALSE
)
plot_ComplexHeatmap(
  enrichment_result,
  genelist,
  genes = NULL,
  cluster_method = "single",
  n_cluster = 1,
  n_top_terms = NA,
  n_top_genes = NA,
  genelist_overlap = NULL,
  plot = FALSE
)

Arguments

enrichment_result

dataframe containing enrichment analysis results. Must include name (gene set names) and symbol (listed genes associated with gene sets)

genelist

dataframe with gene-level statistics, including at least symbol, pvalue, effectsize, and signif columns

genes

character, default: NULL, if genes given, these are prioritized for visualization

cluster_method

default: 'single', else one of hclust methods

n_cluster

default: 1, integer, number of hierarchical clusters to define

n_top_terms

default: NULL, if integer, plot only top genesets (recommended for visual clarity: 70)

n_top_genes

default: NULL, if integer, plot only top genes (recommended for visual clarity: 150)

genelist_overlap

(Optional) dataframe with gene overlap information, including symbol and genelist_overlap, see run_genelists_overlap()

plot

default: FALSE, if TRUE, display drawn ComplexHeatmap

Value

A ComplexHeatmap object displaying genesets (rows) and genes (columns), potentially clustered based on their binary associations. The heatmap includes:

Row annotations: Gene set size, p-value, and average effect size.
Column annotations: Gene p-values, effect sizes, and optional overlap categories.
Customized row/column labels highlighting significant elements.
A color-mapped heatmap showing clustering results.

Examples

plot_ComplexHeatmap(
  get(load(system.file("extdata", "example_enrichment.rda", package = "goatea")))[seq.int(1, 3), ], 
  get(load(system.file("extdata", "example_genelist.rda", package = "goatea"))), 
  n_cluster = 3, 
  n_top_genes = 10
)
plot_ComplexHeatmap(
  get(load(system.file("extdata", "example_enrichment.rda", package = "goatea")))[seq.int(1, 3), ], 
  get(load(system.file("extdata", "example_genelist.rda", package = "goatea"))), 
  n_cluster = 3, 
  n_top_genes = 10
)

Plot EnhancedVolcano

Description

Plot EnhancedVolcano

Usage

plot_EnhancedVolcano(
  genelist,
  effectsize_threshold = 1,
  pvalue_threshold = 0.05,
  background_color = "black",
  foreground_color = "white",
  interactive = FALSE,
  legend_labels = c("NS", "FC", "P", "FC & P"),
  x_label = "effectsize (FC)",
  y_label = "-log10(pvalue) (P)",
  title = "Volcano plot",
  subtitle = "EnhancedVolcano",
  caption = paste0("N genes: ", nrow(genelist)),
  label_size = 3,
  legend_label_size = 14,
  axes_label_size = 18,
  point_size = 2
)
plot_EnhancedVolcano(
  genelist,
  effectsize_threshold = 1,
  pvalue_threshold = 0.05,
  background_color = "black",
  foreground_color = "white",
  interactive = FALSE,
  legend_labels = c("NS", "FC", "P", "FC & P"),
  x_label = "effectsize (FC)",
  y_label = "-log10(pvalue) (P)",
  title = "Volcano plot",
  subtitle = "EnhancedVolcano",
  caption = paste0("N genes: ", nrow(genelist)),
  label_size = 3,
  legend_label_size = 14,
  axes_label_size = 18,
  point_size = 2
)

Arguments

genelist

UI value/list of tibbles/dataframes

effectsize_threshold

numeric, default: 1, threshold for showing significance on effectsize axis

pvalue_threshold

numeric, default: 0.05, threshold for showing significance on pvalue axis

background_color

default: 'black', else character hexcolor or colorname

foreground_color

default: 'white', else character hexcolor or colorname

interactive

default: FALSE, else TRUE

legend_labels

character vector, default: c('NS', 'FC', 'P', 'FC & P'), plot legend labels

x_label

character, default: 'effectsize (FC)', plot x-axis label

y_label

character, default: "'-log10(pvalue) (P)', plot y-axis label

title

character, default: 'Volcano plot', plot title

subtitle

character, default: 'EnhancedVolcano', plot subtitle

caption

character, default: paste0("N genes: ", nrow(genelist)), plot caption

label_size

numeric, default: 3, plot variable label size

legend_label_size

numeric, default: 14, plot legend label size

axes_label_size

numeric, default: 18, plot x- and y-axis lable sizes

point_size

numeric, default: 2, plot point size

Value

plotly or ggplot2 object

Examples

plot_EnhancedVolcano(
  get(load(system.file("extdata", "example_genelist.rda", package = "goatea")))
)
plot_EnhancedVolcano(
  get(load(system.file("extdata", "example_genelist.rda", package = "goatea")))
)

Plot gene2effectsize ComplexHeatmap

Description

Plot gene2effectsize ComplexHeatmap

Usage

plot_gene_effectsize_ComplexHeatmap(
  genes,
  genes_overview,
  rows_dendrogram = TRUE,
  cols_dendrogram = TRUE,
  plot_n_genes = 50
)
plot_gene_effectsize_ComplexHeatmap(
  genes,
  genes_overview,
  rows_dendrogram = TRUE,
  cols_dendrogram = TRUE,
  plot_n_genes = 50
)

Arguments

genes

character, genes to visualize

genes_overview

dataframe, containing columns: 'symbol', 'SAMPLE_efsi' and 'SAMPLE_pval'

rows_dendrogram

default: FALSE, TRUE to cluster rows and show dendrogram

cols_dendrogram

default: FALSE, TRUE to cluster columns and show dendrogram

plot_n_genes

integer, default: 50, NULL to plot all genes

Value

ComplexHeatmap object

Examples

plot_gene_effectsize_ComplexHeatmap(
  c('gene_1', 'gene_2', 'gene_3', 'gene_4', 'gene_5'), 
  get(load(system.file("extdata", "example_genes_overview.rda", package = "goatea")))
)
plot_gene_effectsize_ComplexHeatmap(
  c('gene_1', 'gene_2', 'gene_3', 'gene_4', 'gene_5'), 
  get(load(system.file("extdata", "example_genes_overview.rda", package = "goatea")))
)

Visualize genelists gene overlap in an interactive UpSet plot

Description

UpSetJS examples: https://upset.js.org/integrations/r/articles/combinationModes.html#distinct-intersection-mode

Usage

plot_genelists_overlap_upsetjs(
  genelists,
  mode = "distinct",
  interactive = FALSE,
  main.color = "black",
  highlight.color = "green"
)
plot_genelists_overlap_upsetjs(
  genelists,
  mode = "distinct",
  interactive = FALSE,
  main.color = "black",
  highlight.color = "green"
)

Arguments

genelists

UI value/list of tibbles/dataframes

mode

string, default: 'intersect', else 'distinct' or 'union' - how to overlap the listed genes

interactive

default: FALSE, else TRUE

main.color

default: 'white' else character hexcolor or colorname

highlight.color

default: 'green' else character hexcolor or colorname

Value

upset plot

Examples

plot_genelists_overlap_upsetjs(list(
  A = get(load(system.file("extdata", "example_genelist.rda", package = "goatea"))), 
  B = get(load(system.file("extdata", "example_genelist.rda", package = "goatea")))
))
plot_genelists_overlap_upsetjs(list(
  A = get(load(system.file("extdata", "example_genelist.rda", package = "goatea"))), 
  B = get(load(system.file("extdata", "example_genelist.rda", package = "goatea")))
))

Plot splitdot plot

Description

Plot splitdot plot

Usage

plot_splitdot(enrichment, topN = NA)
plot_splitdot(enrichment, topN = NA)

Arguments

enrichment

GOAT enrichment result

topN

default: NA to plot all, else integer to plot topN terms by adjusted pvalue

Value

ggplot2 object

Examples

plot_splitdot(
  get(load(system.file("extdata", "example_enrichment.rda", package = "goatea")))
)
plot_splitdot(
  get(load(system.file("extdata", "example_enrichment.rda", package = "goatea")))
)

Plot semantic similarity termtree

Description

Plot semantic similarity termtree

Usage

plot_termtree(
  genelist,
  genesets,
  map_organism = 9606,
  effectsize_threshold = 1,
  Nterms = NA,
  Nwords = 5,
  Nclusters = 3
)
plot_termtree(
  genelist,
  genesets,
  map_organism = 9606,
  effectsize_threshold = 1,
  Nterms = NA,
  Nwords = 5,
  Nclusters = 3
)

Arguments

genelist

GOAT current genelist from selected enrichment sample

genesets

GOAT filtered genesets

map_organism

integer, default: 9606 (human) - input organism ID that will be mapped to org.Xx.eg.db

effectsize_threshold

numerical, default: 1 - genelist effectsize threshold

Nterms

integer, default: NA to plat all terms, integer sets amount of terms to plot

Nwords

integer, default: 5, sets N summarized words per cluster

Nclusters

integer, default: 1, sets N clusters of terms

Value

ggtree/gg/ggplot object

Examples

plot_termtree(
  genelist = get(load(system.file("extdata", "example_genelist.rda", package = "goatea"))),
  genesets = get(load(system.file("extdata", "example_genesets.rda", package = "goatea")))
)
plot_termtree(
  genelist = get(load(system.file("extdata", "example_genelist.rda", package = "goatea"))),
  genesets = get(load(system.file("extdata", "example_genesets.rda", package = "goatea")))
)

Process Shiny area input string

Description

Process Shiny area input string

Usage

process_string_input(string_input)
process_string_input(string_input)

Arguments

string_input

siny string

Value

processed string - no whitespace, enters, only letters and numbers

Examples

process_string_input("test string \n")
process_string_input("test string \n")

Read and validate a table with genes (that should be tested in overrepresentation-analysis) for compatibility with this R package#'

Description

if 'pvalue' is not in the genelist columns, it is set and defaulted to 1 for visualization purposes if 'effectsize' is not in the genelist columns, it is set and defaulted to 0 for visualization purposes

Usage

read_validate_genelist(
  file,
  remove_non_numerical_ids = TRUE,
  remove_duplicated = TRUE,
  remove_Rik_genes = TRUE,
  remove_Gm_genes = TRUE,
  map_organism = NULL
)
read_validate_genelist(
  file,
  remove_non_numerical_ids = TRUE,
  remove_duplicated = TRUE,
  remove_Rik_genes = TRUE,
  remove_Gm_genes = TRUE,
  map_organism = NULL
)

Arguments

file

full filepath to gene tibble in .csvs/.xlsx/.tsv

remove_non_numerical_ids

boolean, default TRUE, if non-numerical in gene column, remove

remove_duplicated

boolean, default TRUE, removes duplicated gene symbols/ids

remove_Rik_genes

boolean, default TRUE, grepl("Rik$") search and remove Riken non-canonical mouse genes

remove_Gm_genes

boolean, default TRUE, grepl("^Gm") search and remove Gm non-canonical mouse genes

map_organism

default: NULL, if numeric taxid, used for selecting org.Xx.eg.db to map gene symbols to gene column via AnnotationDbi::mapIds(keytype = 'ALIAS') - if mapped to NA the genes are removed - need to download org.Xx.eg.db manually! Symbols are set toupper() to match formatting. Protein symbols could be used too.

9606 = Human (Homo sapiens) (org.Hs.eg.db)
9544 = Rhesus monkey (Macaca mulatta) (org.Mmu.eg.db)
10090 = Mouse (Mus musculus) (org.Mm.eg.db)
10116 = Rat (Rattus norvegicus) (org.Rn.eg.db)
7227 = Fruit fly (Drosophila melanogaster) (org.Dm.eg.db)
6239 = Worm (Caenorhabditis elegans) (org.Ce.eg.db)

Value

tibble dataframe with columns: symbol (string), gene (string as integer ID), pvalue (numeric), effestsize (numeric)

Examples

file_path <- system.file("extdata", "example_genelist.csv", package = "goatea")
read_validate_genelist(file = file_path)
file_path <- system.file("extdata", "example_genelist.csv", package = "goatea")
read_validate_genelist(file = file_path)

Rename the gene overview

Description

Rename the gene overview

Usage

rename_gene_overview(names, genes_overview)
rename_gene_overview(names, genes_overview)

Arguments

names

names to rename gene overview

genes_overview

UI given genes overview dataframe (rv_genelists_overlap$gene_overview)

Value

genes overview renamed

Create gene overview through overlapping genelists information by overlapping significant genes

Description

Create gene overview through overlapping genelists information by overlapping significant genes

Usage

run_genelists_overlap(genelists)
run_genelists_overlap(genelists)

Arguments

genelists

UI value/list of tibbles/dataframes

Value

tibble/dataframe with (annotated) genes and p-value/effectsize info for each genelist, concluding with overlapping genelists by significant genes

Examples

run_genelists_overlap(list(
  A = get(load(system.file("extdata", "example_genelist.rda", package = "goatea"))), 
  B = get(load(system.file("extdata", "example_genelist.rda", package = "goatea")))
))
run_genelists_overlap(list(
  A = get(load(system.file("extdata", "example_genelist.rda", package = "goatea"))), 
  B = get(load(system.file("extdata", "example_genelist.rda", package = "goatea")))
))

Perform geneset enrichment testing using any supported method

Description

See original documentation at test_genesets

Usage

run_geneset_enrichment(
  genesets,
  genelist,
  method = "goat",
  score_type = "effectsize",
  padj_method = "BH",
  padj_sources = TRUE,
  padj_cutoff = 0.01,
  padj_min_signifgenes = 0L,
  ...
)
run_geneset_enrichment(
  genesets,
  genelist,
  method = "goat",
  score_type = "effectsize",
  padj_method = "BH",
  padj_sources = TRUE,
  padj_cutoff = 0.01,
  padj_min_signifgenes = 0L,
  ...
)

Arguments

genesets

tibble with genesets, must contain columns 'source', 'source_version', 'id', 'name', 'genes', 'ngenes', 'ngenes_signif'

genelist

tibble with genes, must contain column 'gene' and 'test'. gene = character column, which are matched against list column 'genes' in genesets tibble. test = boolean column (you can set all to FALSE if not performing Fisher-exact or hypergeometric test downstream)

method

method for overrepresentation analysis. Options: "goat", "hypergeometric", "fisherexact", "fisherexact_ease", "gsea", "idea"

score_type

string, default: "effectsize", alternatively set to "pvalue", "effectsize_up", "effectsize_down", "effectsize_abs"

padj_method

first step of multiple testing correction; method for p-value adjustment, passed to stats::p.adjust() via padjust_genesets(), e.g. set "BH" to compute FDR adjusted p-values (default) or "bonferroni" for a more stringent procedure

padj_sources

second step of multiple testing correction; apply Bonferroni adjustment to all p-values according to the number of geneset sources that were tested. Boolean parameter, set TRUE to enable (default) or FALSE to disable

padj_cutoff

cutoff for adjusted p-value, signif column is set to TRUE for all values lesser-equals

padj_min_signifgenes

if a value larger than zero is provided, this will perform additional post-hoc filtering; after p-value adjustment, set the pvalue_adjust to NA and signif to FALSE for all genesets with fewer than padj_min_signifgenes 'input genes that were significant' (ngenes_signif column in genesets table). So this does not affect the accuracy of estimated p-values, in contrast to prefiltering genesets prior to p-value computation or adjusting p-values

...

further parameters are passed to the respective stats method

Value

the input genesets, with results stored in columns 'pvalue', 'pvalue_adjust', 'signif' and 'zscore'

Examples

run_geneset_enrichment(
  get(load(system.file("extdata", "example_genesets.rda", package = "goatea"))),
  get(load(system.file("extdata", "example_genelist.rda", package = "goatea")))
)
run_geneset_enrichment(
  get(load(system.file("extdata", "example_genesets.rda", package = "goatea"))),
  get(load(system.file("extdata", "example_genelist.rda", package = "goatea")))
)

Scale values between given min/max

Description

Scale values between given min/max

Usage

scale_values_between(
  values,
  old_min = min(values),
  old_max = max(values),
  new_min = 0,
  new_max = 100
)
scale_values_between(
  values,
  old_min = min(values),
  old_max = max(values),
  new_min = 0,
  new_max = 100
)

Arguments

values

numeric (vector)

old_min

numeric, default: min(values), else set as current expected minimum of values

old_max

numeric, default: max(values), else set as current expected maximum of values

new_min

numeric, default: 0, else set to wanted new minimum value

new_max

numeric, default: 100, else set to wanted new maximum value

Value

scaled numeric values

Examples

scale_values_between(c(1,3,1,4,1,6,1,6,5,7))
scale_values_between(c(1,3,1,4,1,6,1,6,5,7))

Set significant and number of genes

Description

Set significant and number of genes

Usage

set_significant_N_genes(
  genelist,
  significance_by = "pvalue_effectsize",
  pvalue_threshold = 0.05,
  effectsize_threshold = 1,
  keep_max_n_genes = FALSE,
  keep_max_n_genes_by = "pvalue"
)
set_significant_N_genes(
  genelist,
  significance_by = "pvalue_effectsize",
  pvalue_threshold = 0.05,
  effectsize_threshold = 1,
  keep_max_n_genes = FALSE,
  keep_max_n_genes_by = "pvalue"
)

Arguments

genelist

list, loaded genelist with goatea::read_validate_genelist()

significance_by

string, default: 'pvalue_effectsize', else 'pvalue' or 'effectsize' to set gene significance to TRUE/FALSE in 'signif' column

pvalue_threshold

numeric, default: 0.05, to set gene significance based on pvalue

effectsize_threshold

numeric, default: 1, to set gene significance based on effectsize

keep_max_n_genes

boolean, default: TRUE, filter down by pvalue to max n genes allowed by goat (max(goat::goat_nulldistributions$N))

keep_max_n_genes_by

string, default: 'pvalue', else 'effectsize', order genes based on lowest pvalues or highest absolute effect sizes

Value

genelist with added 'signif' column with TRUE/FALSE values

Examples

set_significant_N_genes(
  get(load(system.file("extdata", "example_genelist.rda", package = "goatea")))
)
set_significant_N_genes(
  get(load(system.file("extdata", "example_genelist.rda", package = "goatea")))
)

Wrap Shiny UI element with a hoverable tooltip contained in html div tags

Description

Wrap Shiny UI element with a hoverable tooltip contained in html div tags

Usage

wrap_hovertip(ui_element, hovertip)
wrap_hovertip(ui_element, hovertip)

Arguments

ui_element

Shiny UI element to wrap with hovertext

hovertip

text that will show as hover popup

Value

tags$div element around given Shiny UI element

Examples

wrap_hovertip(shiny::actionButton('id_example', 'example'), 'example')
wrap_hovertip(shiny::actionButton('id_example', 'example'), 'example')

Wrap Shiny UI element with a loading spinner contained in html div tags

Description

Wrap Shiny UI element with a loading spinner contained in html div tags

Usage

wrap_loader(id, ui_element)
wrap_loader(id, ui_element)

Arguments

id

string: id of loader, used with show/hide in server side

ui_element

wrapped Shiny UI element

Value

html div element wrapped around given Shiny UI element

Examples

wrap_loader('id_example', shiny::actionButton('id_example', 'example'))
wrap_loader('id_example', shiny::actionButton('id_example', 'example'))

Package 'goatea'

Help Index

Calculate geneSetRatio per gene per enrichment contrast

Description

Usage

Arguments

Value

Examples

Create and/or modify color/gradient palettes

Description

Usage

Arguments

Details

Value

Examples

Colorify colorRamp between colors mapping to breakpoint values

Description

Usage

Arguments

Value

Display R grDevices palettes

Description

Usage

Arguments

Value

Example Colameo MS data

Description

Usage

Format

Source

Example Colameo RNA data

Description

Usage

Format

Source

An example enrichment

Description

Format

enrichment

Source

An example genelist

Description

Format

genelist

Source

An example genes overview

Description

Format

⁠genes overview⁠

Source

Example genesets

Description

Format

genesets

Source

An example ppi data

Description

Format

⁠ppi data⁠

Source

Get file extension

Description

Usage

Arguments

Value

Examples

Filter enrichment

Description

Usage

Arguments

Value

Examples

Set base folder

Description

Usage

Arguments

Value

Examples

Get PPI igraph

Description

`enrichment`

`genelist`

`⁠genes overview⁠`

`genesets`

`⁠ppi data⁠`