This is 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)) or similar methods.
To get started, load the package with
library("EnrichIntersect")
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 nice pre-defined drug groups (1st column) of the 147 drugs (2nd column).
<- cancers_drug_groups$score
x <- cancers_drug_groups$custom.set
custom.set set.seed(123)
<- enrichment(x, custom.set, permute.n = 100) enrich
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"))
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: https://doi.org/10.1016/j.isci.2022.104767.