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.


Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.


0.2 by Alicia T. Specht, 5 years ago

Browse source code at

Authors: Alicia T. Specht and Jun Li

Documentation:   PDF Manual  

GPL-2 license

Suggests ggplot2

See at CRAN