LEAP-package: Constructing Gene Co-Expression Networks for Single-Cell...

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

Description

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.

Details

The DESCRIPTION file: This package was not yet installed at build time.

Index: This package was not yet installed at build time.
~~ An overview of how to use the package, including the most important functions ~~

Author(s)

Alicia T. Specht and Jun Li

Maintainer: Alicia T. Specht <[e-mail]>

References

Shalek AK, Satija R., Shuga J., Trombetta J.J. et al. (2014) Single-cell RNA-seq reveals dynamic paracrine control of cellular variation. Nature, 510(7505), 363-369. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4193940/

Examples

## Not run: MAC_results = MAC_counter(data=example_data)

## Not run: MAC_perm(data=example_data, MACs_observ=MAC_example)

Example output

LEAP documentation built on May 1, 2019, 9:23 p.m.