Trang Le
#math graduate. Postdoc fellow with Jason Moore.
Detect network interactions with confounder control
Trang Lê
@trang1618
Rocky Mountain Bioinformatics Conference, 2019
y_i = \beta_0 + \beta_a a_i + \epsilon_i
d_{ij}(y) = \beta_0 + \beta_a d_{ij}(a) + \epsilon_{ij}
projected distances
nearest neighbors
attributes
d_{ij}(y) = \beta_0 + \beta_a d_{ij}(a) + \epsilon_{ij}
M_a \sim d_{ij}(a|y_i \neq y_j)
H_a \sim d_{ij}(a|y_i = y_j)
discrete outcome
e.g., y \in \{0, 1\}
| Supports regularization | No | No | Yes |
m = 200
p = 1000
10% functionally interacting
∗ NPDR 0.05 adjusted cutoffs
Genes most highly associated with sex are eliminated by adjustment
but remain in non-adjusted set.
Acknowledgement
Brett McKinney
Bryan Dawkins
Jason Moore
Ryan Urbanowicz
devtools::install_github('insilico/npdr')
Detect network interactions while controling for confounders
By Trang Le
Detect network interactions while controling for confounders
Presentation on 2019-12-06 at Rocky Mountain Bioinformatics Conference
