Description Details Author(s) References Examples
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.
The DESCRIPTION file:
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~~ An overview of how to use the package, including the most important functions ~~
Alicia T. Specht and Jun Li
Maintainer: Alicia T. Specht <aspecht2@nd.edu>
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/
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