MAC_counter: Function to perform lag-based correlation anaylsis of...

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

Description

See vignette for more details.

Usage

MAC_counter(data, max_lag_prop = 1/3, MAC_cutoff = 0.2, 
file_name = F, lag_matrix = T, symmetric = F)

Arguments

data	A data matrix for which the rows are genes and the columns are experiments, sorted by their pseudotime.
max_lag_prop	The largest proportion of your experiments that you want the size of the lag to be. Recommended not to go beyond 1/3. Default value is 1/3.
MAC_cutoff	The lowest MAC desired to be shown in results. Default value is 0.2.
file_name	The name to be used for resulting .csv files. I.e., using file_name="mine" would create the file "MAC_mine.csv" and "lag_mine.csv"
lag_matrix	Logical. TRUE indicates that the resulting matrix of lags should be saved to a csv. Default value is TRUE.
symmetric	Logical. TRUE indicates that a symmetric matrix is required for output MAC .csv file. Results in the absolute maximum value for each pair (i,j) and (j,i). Default value is FALSE.

Details

See vignette for more details.

Value

Returns a dataset with four columns: Row gene index and Column gene index correspond to the indeces for the gene pair (i,j), Correlation is the maximum absolute correlation (MAC) achieved for the pair, and Lag is the lag at which the MAC occurred.

Examples

x <- matrix(rnorm(6),2,3)
y <- MAC_counter(x)

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

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