Type: Package
Title: Enrichment Analysis and Intersecting Sankey Diagram
Version: 0.7
Date: 2024-03-14
URL: https://github.com/ocbe-uio/EnrichIntersect
BugReports: https://github.com/ocbe-uio/EnrichIntersect/issues
Description: A flexible tool for enrichment analysis based on user-defined sets. It allows users to perform over-representation analysis of the custom sets among any specified ranked feature list, hence making enrichment analysis applicable to various types of data from different scientific fields. 'EnrichIntersect' also enables an interactive means to visualize identified associations based on, for example, the mix-lasso model (Zhao et al., 2022 <doi:10.1016/j.isci.2022.104767>) or similar methods.
License: MIT + file LICENSE
Depends: R (≥ 4.0)
Encoding: UTF-8
RoxygenNote: 7.2.3
Imports: ggplot2, dplyr, networkD3, jsonlite, htmlwidgets, webshot2, stats, methods, grDevices
Suggests: knitr
LazyData: true
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2024-03-14 21:53:20 UTC; zhiz
Author: Zhi Zhao [aut, cre], Manuela Zucknick [aut], Tero Aittokallio [ctb]
Maintainer: Zhi Zhao <zhi.zhao@medisin.uio.no>
Repository: CRAN
Date/Publication: 2024-03-14 22:10:02 UTC

Data set 'cancers_drug_groups'

Description

Data set 'cancers_drug_groups' is a list including a score dataframe with 147 drugs as rows and 19 cancer types as columns, and a dataframe with 9 self-defined drug groups (1st column) of the 147 drugs (2nd column). More details of the data can be found in the results of paper 'Tissue-specific identification of multi-omics features for pan-cancer drug response prediction' by Zhao Z., Wang S. Zucknick M. and Aittokallio T. (2022).

Usage

cancers_drug_groups

Format

An object of class list of length 2.

Value

No return value.

References

Zhao Z, Wang S, Zucknick M and Aittokallio T (2022). Tissue-specific identification of multi-omics features for pan-cancer drug response prediction. iScience, 25(8): 104767.

Examples

# Load data
data(cancers_drug_groups, package = "EnrichIntersect")

Data set 'cancers_genes_drugs'

Description

Data set 'cancers_genes_drugs' is an array with association scores between 56 genes (1st dimension), three cancer types (2nd dimension) and two drugs (3rd dimension). More details of the data can be found in the results of paper 'Tissue-specific identification of multi-omics features for pan-cancer drug response prediction' by Zhao Z., Wang S. Zucknick M. and Aittokallio T. (2022).

Usage

cancers_genes_drugs

Format

An object of class array of dimension 56 x 3 x 2.

Value

No return value.

References

Zhao Z, Wang S, Zucknick M and Aittokallio T (2022). Tissue-specific identification of multi-omics features for pan-cancer drug response prediction. iScience, 25(8): 104767.

Examples

# Load data
data(cancers_genes_drugs, package = "EnrichIntersect")

Plot enrichment map

Description

Plot enrichment map through a vector (matrix) of scores and a self-defined set that summarizes a few groups of the names (rownames) of the vector (matrix)

Usage

enrichment(
  x,
  custom.set,
  alpha = 0,
  normalize = TRUE,
  permute.n = 100,
  padj.method = "none",
  pvalue.cutoff = 0.05,
  angle = 45,
  ...
)

Arguments

x

a vector (matrix) of scores to be enriched

custom.set

a self-defined set that summarizes a few groups of the names (rownames) of x

alpha

exponent weight of the score of ordered features. Default is 0 for calculating enrichment score via classic Kolmogorov-Smirnov statistic

normalize

logic value to determine if normalizing enrichment scores, accounting for custom set size. Default is TRUE

permute.n

number of custom-set permutations for significance testing. Default is 100

padj.method

correction method, one of "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none". Default is "none"

pvalue.cutoff

a cutoff for both unadjusted and adjusted p-value to mark significantly enriched classes. Default is 0.05

angle

angle of rotating x-axis labels. Default is 45

...

other arguments

Value

Return a list including a matrix of (normalized) enrichment score, a matrix of corresponding p-value and ggplot object:

References

Reimand J, Isserlin R, Voisin V, et al (2019). Pathway enrichment analysis and visualization of omics data using g:profiler, gsea, cytoscape and enrichmentmap. Nature protocols, 14:482–517.

Examples

# Data set 'cancers_drug_groups' is a list including a score dataframe with 147 drugs as rows
# and 19 cancer types as columns, and a dataframe with 9 self-defined drug groups (1st column)
# of the 147 drugs (2nd column).
data(cancers_drug_groups, package = "EnrichIntersect")

x <- cancers_drug_groups$score
custom.set <- cancers_drug_groups$custom.set
set.seed(123)
enrich <- enrichment(x, custom.set, permute.n = 5)

Plot Sankey diagram for intersecting sets

Description

Plot Sankey diagram for intersecting set through an array

Usage

intersectSankey(
  x,
  out.fig = NULL,
  color = NULL,
  step.names = c("Levels", "Variables", "Tasks"),
  fontSize = c(20, 13, 20),
  nodePadding = 11,
  nodeWidth = 5,
  margin = list(right = 180),
  ...
)

Arguments

x

an array for constructing intersecting set

out.fig

print the figure into "html", "pdf" or "png" file. Default is NULL with R graphics device

color

a vector of colors corresponding to individual tasks

step.names

names of the three dimensions of the array x, i.e. names of multiple levels, intermediate variables and tasks. Default is c("Levels","Variables","Tasks"). If step.names=NULL, it will not show the names

fontSize

a value or vector of three values. If it is one value, it is the font size for all labels. But a vector of three values specifies the font size of the labels in the left, middle and right, respectively. Default is c(20,10,20)

nodePadding

numeric essentially influences the width height

nodeWidth

numeric width of each node

margin

an integer or a named list/vector of integers for the plot margins

...

graphics parameters to be passed to sankeyNetwork() from R package networkD3

Value

An object of a D3 JavaScript intersecting Sankey diagram for visualising associations based on the input array.

Examples

# Data set 'cancers_genes_drugs' is an array with association scores between 56 genes (1st
# dimension), three cancer types (2nd dimension) and two drugs (3rd dimension)
data(cancers_genes_drugs, package = "EnrichIntersect")

intersectSankey(cancers_genes_drugs, step.names = c("Cancers", "Genes", "Drugs"))