LEAP: 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.

Version: 0.2
Suggests: ggplot2
Published: 2016-09-13
Author: Alicia T. Specht and Jun Li
Maintainer: Alicia T. Specht <aspecht2 at nd.edu>
License: GPL-2
NeedsCompilation: no
CRAN checks: LEAP results


Reference manual: LEAP.pdf
Vignettes: LEAP_Vignette


Package source: LEAP_0.2.tar.gz
Windows binaries: r-devel: LEAP_0.2.zip, r-release: LEAP_0.2.zip, r-oldrel: LEAP_0.2.zip
macOS binaries: r-release (arm64): LEAP_0.2.tgz, r-oldrel (arm64): LEAP_0.2.tgz, r-release (x86_64): LEAP_0.2.tgz, r-oldrel (x86_64): LEAP_0.2.tgz
Old sources: LEAP archive


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