Finds "Local Subnetworks" Within an Interaction Network which
Show Enrichment for Differentially Expressed Genes
Implements the method described in "Network-based analysis of omics data: The LEAN method" [Gwinner Boulday (2016) <DOI:10.1093/bioinformatics/btw676>]
Given a protein interaction network and a list of p-values describing a measure of interest (as e.g. differential gene expression) this method
computes an enrichment p-value for the protein neighborhood of each gene and compares it to a background distribution of randomly drawn p-values.
The resulting scores are corrected for multiple testing and significant hits are returned in tabular format.

Constructing Gene Co-Expression Networks for Single-Cell
RNA-Sequencing Data Using Pseudotime Ordering
Advances in sequencing technology now allow researchers to capture the expression profiles of individual cells. Several algorithms have been developed to attempt to account for these effects by determining a cell's so-called `pseudotime', or relative biological state of transition. By applying these algorithms to single-cell sequencing data, we can sort cells into their pseudotemporal ordering based on gene expression. LEAP (Lag-based Expression Association for Pseudotime-series) then applies a time-series inspired lag-based correlation analysis to reveal linearly dependent genetic associations.

Likelihood Estimation of Admixture in Parents From Offspring
Genotypes
Contains LEAPFrOG Gradient Optimisation and Expectation
Maximisation functions for inferring parental admixture
proportions from an offspring with SNP genotypes.

Contains LEAPFrOG Gradient Optimisation and Expectation Maximisation functions for inferring parental admixture proportions from an offspring with SNP genotypes.

Latent Environmental & Genetic InTeraction (LEGIT) Model
Constructs genotype x environment interaction (GxE) models where
G is a weighted sum of genetic variants (genetic score) and E is a weighted
sum of environments (environmental score). This approach has greatly enhanced
predictive power over traditional GxE models which include only a single genetic
variant and a single environmental exposure. Gene x gene interactions (GxG)
and environment x environment interactions (ExE) can be incorporated into the
genetic and environmental scores. Any standard model formula can be used, it
doesn't have to be a two-way interaction model. Although this approach was made
for GxE modelling, the genetic score doesn't need to contain genetic variables
and the environmental score doesn't need to contain environmental variables,
therefore this approach is highly flexible. A paper discussing the method used
for estimating the weights should be released as pre-print and sent for peer-
review before Summer 2017.