EnrichIntersect is a flexible tool for enrichment analysis based on
user-defined sets. It allows users to perform over-representation
analysis of custom sets among any specified ranked feature list, making
enrichment analysis applicable to various types of data from different
scientific fields. EnrichIntersect also provides interactive
visualization of intersecting sets, for example based on the mix-lasso
model (Zhao et al., 2022)
or similar methods.
Install the latest released version from CRAN:
library("EnrichIntersect")Install the latest development version from GitHub:
# library("pak")
pak::pak("ocbe-uio/EnrichIntersect")The example data object cancers_drug_groups is an R list provided in
the package. It includes a data.frame with 147 cancer drugs as rows
and nine cancer types as columns, and another data.frame that assigns
the 147 drugs, listed in the first column, to nine user-defined drug
classes, listed in the second column.
The default setup of enrichment() uses a classic
Kolmogorov-Smirnov-like test statistic to calculate the normalized
enrichment score. This score quantifies the degree to which features in
a user-defined set are over-represented at the top of a ranked feature
list. By default, enrichment() uses 100 permutations for the empirical
null test statistic.
In the visualization, statistically significantly enriched feature sets
are marked with red circles at a pre-specified significance level. The
p-values can be adjusted by specifying padj.method, using one of
c("holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none").
Users can specify alpha for calculating a weighted enrichment score,
normalize = FALSE for using the standard enrichment score rather than
the normalized score, permute.n for the number of permutations, and
pvalue.cutoff for marking enriched categories at a specific
significance level.
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 = 1000)
The EnrichIntersect function intersectSankey() creates a Sankey
diagram to visualize intersecting sets from an array object. The first
dimension represents intermediate variables, while the second and third
dimensions represent multiple levels and multiple tasks, respectively.
One intersecting set is a list of intermediate variables associated with
a combination of a subset of levels and a subset of tasks. Such
relationships can be difficult to visualize when there are many possible
combinations. The function intersectSankey() adapts sankeyNetwork()
from the R package networkD3 to create a D3 JavaScript interactive
Sankey diagram suitable for multiple levels, multiple tasks, and many
intermediate variables.
Besides displaying the Sankey diagram in the R graphics device, users
can save it as an interactive HTML file, or as a PDF or PNG file via the
R package webshot2. The argument
out.fig = c(NA, "html", "pdf", "png") controls whether the figure is
displayed or saved as an HTML, PDF, or PNG file.
The example data object cancers_genes_drugs is an array with
associations between 56 genes, two cancer types, and two drugs. Users
can adjust out.fig for different output formats and use step.names
to label the three dimensions in the Sankey diagram.
data(cancers_genes_drugs, package = "EnrichIntersect")
intersectSankey(
cancers_genes_drugs,
step.names = c("Cancers", "Genes", "Drugs")
)
Zhi Zhao, Manuela Zucknick, Tero Aittokallio (2022).
EnrichIntersect: an R package for custom set enrichment analysis and
interactive visualization of intersecting sets.
Bioinformatics Advances, 2(1), vbac073. DOI:
10.1093/bioadv/vbac073.
Zhi Zhao, Shixiong Wang, Manuela Zucknick, Tero Aittokallio (2022).
Tissue-specific identification of multi-omics features for pan-cancer
drug response prediction.
iScience, 25(8), 104767. DOI:
10.1016/j.isci.2022.104767.