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.
|Author||Alicia T. Specht and Jun Li|
|Date of publication||2016-09-13 21:19:05|
|Maintainer||Alicia T. Specht <firstname.lastname@example.org>|
|Package repository||View on CRAN|
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