LEAP: Constructing Gene Co-Expression Networks for Single-Cell RNA-Sequencing Data Using Pseudotime Ordering
Version 0.2

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.

Package details

AuthorAlicia T. Specht and Jun Li
Date of publication2016-09-13 21:19:05
MaintainerAlicia T. Specht <[email protected]>
LicenseGPL-2
Version0.2
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("LEAP")

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LEAP documentation built on May 29, 2017, 12:15 p.m.