flatten_array

This function takes in a 3D array of values, effects or t_stats, etc.
and returns a 2D matrix in which each entry contains a single value
representing all corresponding entries in the 3rd dimension. This
number is determined by the function specified by the user. It could be min,
max, mean, etc.

internal

Combined Analysis of Pleiotropy and Epistasis infers predictive
networks between genetic variants and phenotypes. It can be used with
standard two-parent populations as well as multi-parent populations, such
as the Diversity Outbred (DO) mice, Collaborative Cross (CC) mice, or the
multi-parent advanced generation intercross (MAGIC) population of Arabidopsis
thaliana. It uses complementary information of pleiotropic gene variants across
different phenotypes to resolve models of epistatic interactions between alleles.
To do this, cape reparametrizes main effect and interaction coefficients from
pairwise variant regressions into directed influence parameters. These
parameters describe how alleles influence each other, in terms of suppression
and enhancement, as well as how gene variants influence phenotypes. All of the
final interactions are reported as directed interactions between pairs of
parental alleles. For detailed descriptions of the methods used in this package
please see the following references.
Carter, G. W., Hays, M., Sherman, A. & Galitski, T. (2012) <doi:10.1371/journal.pgen.1003010>.
Tyler, A. L., Lu, W., Hendrick, J. J., Philip, V. M. & Carter, G. W. (2013) <doi:10.1371/journal.pcbi.1003270>.

Anna L Tyler

cape

Combined Analysis of Pleiotropy and Epistasis for Diversity
Outbred Mice

Anna Tyler

Jake Emerson

Baha El Kassaby

Ann Wells

Georgi Kolishovski

Vivek Philip

Gregory Carter

flatten_array function

<dl><dt>arrayX</dt>
<dd>3D array of values, effects or t_stats, etc.</dd>
<dt>margin1</dt>
<dd>dimension that will end up as the columns of the final matrix</dd>
<dt>margin2</dt>
<dd>dimension that will end up as the rows of the final matrix</dd>
<dt>slice_fun</dt>
<dd>summary function. The function by which to summarize 
the remaining dimension e.g., <code>function(x) mean(x, na.rm = TRUE)</code></dd></dl>

Arguments

Drop the 3rd dimension of an array using a summary 
function, e.g., min max, mean — flatten_array

<dl>

<dt>arrayX</dt>
<dd>3D array of values, effects or t_stats, etc.</dd>


<dt>margin1</dt>
<dd>dimension that will end up as the columns of the final matrix</dd>


<dt>margin2</dt>
<dd>dimension that will end up as the rows of the final matrix</dd>


<dt>slice_fun</dt>
<dd>summary function. The function by which to summarize 
the remaining dimension e.g., <code>function(x) mean(x, na.rm = TRUE)</code></dd>

</dl>

Drop the 3rd dimension of an array using a summary 
function, e.g., min max, mean

flatten_array: Drop the 3rd dimension of an array using a summary function, e.g., min max, mean

Description

Usage

Value

Arguments