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 |
Documentation:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=LEAP
to link to this page.