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.


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install.packages("LEAP")

0.2 by Alicia T. Specht, a year ago


Browse source code at https://github.com/cran/LEAP


Authors: Alicia T. Specht and Jun Li


Documentation:   PDF Manual  


GPL-2 license


Suggests ggplot2


See at CRAN