Delta-statistics

Kalle Leppälä

This repository contains the code for the article (add a link here).

The article is joint work between Kalle Leppälä, Flavio Augusto da Silva Coelho, Michaela Richter, Victor A. Albert and Charlotte Lindqvist.

Simulation

• tools.md has the R functions and documentation used in all the coalescence simulations.
• S_big.md is the simulation and documentation in tree $S$ using 1,000,000 allelic patterns.
• S_medium.md is the simulation in tree $S$ using 100,000 allelic patterns.
• S_small.md is the simulation in tree $S$ using 10,000 allelic patterns.
• A_big.md is the simulation and documentation in tree $A$ using 1,000,000 allelic patterns.
• A_medium.md is the simulation in tree $A$ using 100,000 allelic patterns.
• A_small.md is the simulation in tree $A$ using 10,000 allelic patterns.
• Q_big.md is the simulation and documentation in tree $Q$ using 1,000,000 allelic patterns.
• Q_medium.md is the simulation in tree $Q$ using 100,000 allelic patterns.
• Q_small.md is the simulation in tree $Q$ using 10,000 allelic patterns.
• DFOIL.md is the simulation in tree $S$ using $D_\text{FOIL}$.
• dependence.md explores the dependence of the $D_\text{FOIL}$- and $\Delta$-statistics on the synchronization assumption (2).

Empirical example

• Delta.py contains Python code for computing $\Delta$-statistics form PLINK .traw -files, either in windows or using the block jackknife.
• Summary_script_without_singletons.R contains R code used for classifying the $\Delta$-statistic signatures, without singleton patterns.
• Summary_script_singletons.R contains R code used for classifying the $\Delta$-statistic signatures, with singleton patterns.