MAC_symmetric


Contains the results of applying MAC_counter(symmetric=T) to example_data


datasets

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.

Alicia T Specht

LEAP

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

MAC_symmetric function

A data frame with 20 observations on 20 variables.

MAC_symmetric: Numeric data frame

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Examples