TY  - GEN
N2  - Complex networks that connect hundreds or thousands of nodes together can function properly through the use of nodes interacting, affecting and regulating one another. Genetic networks underlying high-level hormonal changes are also believed to be complex and most of the time are not yet very well understood. There are also different networks with common genes that start to activate or deactivate at the same time, which also adds to the difficulty of the problem of understanding genetic networks. Therefore, finding genes transcriptionally active in a gene set that are responsible for a change in the human body is a key point in reaching the underlying network structure. Here, we present a new technique that searches for these nodes in a set of variables that connect to form a network. It is a stepwise greedy search that investigates the change in a chosen network when one node is taken out at a time. Our simulated genetic data results show that our method is successful for differentiating between transcriptionally active nodes and background nodes with a p-value of less than 0.05. As real biological data, we used rat RNA-seq data taken for initiation-of-puberty research. Our method found new genes as well as confirming previously known genes with significant enrichment results taking charge in functions such as transcriptional binding, histone modifications, and reproductive development.
DO  - 10.6083/bc386j87q
DO  - DOI
AB  - Complex networks that connect hundreds or thousands of nodes together can function properly through the use of nodes interacting, affecting and regulating one another. Genetic networks underlying high-level hormonal changes are also believed to be complex and most of the time are not yet very well understood. There are also different networks with common genes that start to activate or deactivate at the same time, which also adds to the difficulty of the problem of understanding genetic networks. Therefore, finding genes transcriptionally active in a gene set that are responsible for a change in the human body is a key point in reaching the underlying network structure. Here, we present a new technique that searches for these nodes in a set of variables that connect to form a network. It is a stepwise greedy search that investigates the change in a chosen network when one node is taken out at a time. Our simulated genetic data results show that our method is successful for differentiating between transcriptionally active nodes and background nodes with a p-value of less than 0.05. As real biological data, we used rat RNA-seq data taken for initiation-of-puberty research. Our method found new genes as well as confirming previously known genes with significant enrichment results taking charge in functions such as transcriptional binding, histone modifications, and reproductive development.
T1  - Iterative graph perturbation to identify high impact nodes with application to genetic regulation of onset of puberty
DA  - 2020-04-27
AU  - Selcuk, Basak
L1  - https://digitalcollections.ohsu.edu/record/7906/files/Selcuk.Basak.2020.pdf
PB  - Oregon Health and Science University
PY  - 2020-04-27
ID  - 7906
L4  - https://digitalcollections.ohsu.edu/record/7906/files/Selcuk.Basak.2020.pdf
KW  - Gene Regulatory Networks
KW  - Puberty
KW  - partial correlation networks
TI  - Iterative graph perturbation to identify high impact nodes with application to genetic regulation of onset of puberty
Y1  - 2020-04-27
L2  - https://digitalcollections.ohsu.edu/record/7906/files/Selcuk.Basak.2020.pdf
LK  - https://digitalcollections.ohsu.edu/record/7906/files/Selcuk.Basak.2020.pdf
UR  - https://digitalcollections.ohsu.edu/record/7906/files/Selcuk.Basak.2020.pdf
ER  -