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>|
example_data: Numeric data frame of example data
lag_example: Integer data frame
LEAP-package: Constructing Gene Co-Expression Networks for Single-Cell...
MAC_counter: Function to perform lag-based correlation anaylsis of...
MAC_example: Numerical matrix
MAC_lags: Internal function used by MAC_counter and MAC_perm
MAC_perm: Function to perform a permutation analysis to determine a...
MAC_symmetric: Numeric data frame
perm_example: The resulting data ouptut from applying MAC_perm() to...
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