visGSEA

eTerm

which sample will be used. It can be index or
sample names

which_sample

which term will be used. It can be index or term ID
or term names

which_term

logical to indicate whether to plot

plot

the orientation of the plots. It can be either
"vertical" (default) or "horizontal"

orientation

the line width for the hits (ie genes in the gene
set)

hit.linewidth

logical to indicate whether to open a new page. By
default, it sets to true for opening a new page

newpage

<code>visGSEA</code> is supposed to visualise running enrichment score for a
given sample and a gene set. To help understand the underlying running
enrichment score, the input gene scores are also displayed. Positions
for members in the given gene set are color-coded in both displays (red
line for the positive gene scores, and green line for the negative).

The focus of the dnet by Fang and Gough (2014) <doi:10.1186/s13073-014-0064-8> is to make sense of omics data (such as gene expression and mutations) from different angles including: integration with molecular networks, enrichments using ontologies, and relevance to gene evolutionary ages. Integration is achieved to identify a gene subnetwork from the whole gene network whose nodes/genes are labelled with informative data (such as the significant levels of differential expression or survival risks). To help make sense of identified gene networks, enrichment analysis is also supported using a wide variety of pre-compiled ontologies and phylostratific gene age information in major organisms including: human, mouse, rat, chicken, C.elegans, fruit fly, zebrafish and arabidopsis. Add-on functionalities are supports for calculating semantic similarity between ontology terms (and between genes) and for calculating network affinity based on random walk; both can be done via high-performance parallel computing.

visGSEA: Function to visualise running enrichment score for a given sample and a gene set

Description

Usage

Arguments

Value

See Also

Examples