thetahats

A function to compute the total connectivity of each gene from the association matrix.

A novel pseudo-value regression approach for the differential co-expression network analysis in expression data, which can incorporate additional clinical variables in the model. This is a direct regression modeling for the differential network analysis, and it is therefore computationally amenable for the most users. The full methodological details can be found in Ahn S et al (2023) <doi:10.1186/s12859-022-05123-w>.

Seungjun Ahn

PRANA

Pseudo-Value Regression Approach for Network Analysis (PRANA)

Somnath Datta

thetahats function

<dl><dt>asso.matinput</dt>
<dd>An association matrix that is estimated from the user-provided expression data is used
as an input to compute the total connectivity of each gene.</dd></dl>

Arguments

thetahats — thetahats

<dl>

<dt>asso.matinput</dt>
<dd>An association matrix that is estimated from the user-provided expression data is used
as an input to compute the total connectivity of each gene.</dd>

</dl>

thetahats: thetahats

Description

Usage

Value

Arguments