Limits...
TREEGL: reverse engineering tree-evolving gene networks underlying developing biological lineages.

Parikh AP, Wu W, Curtis RE, Xing EP - Bioinformatics (2011)

Bottom Line: However, one challenge in estimating such evolving networks is that their host cells not only contiguously evolve, but also branch over time.For example, a stem cell evolves into two more specialized daughter cells at each division, forming a tree of networks.Treegl takes advantage of the similarity between related networks along the biological lineage, while at the same time exposing sharp differences between the networks.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.

ABSTRACT

Motivation: Estimating gene regulatory networks over biological lineages is central to a deeper understanding of how cells evolve during development and differentiation. However, one challenge in estimating such evolving networks is that their host cells not only contiguously evolve, but also branch over time. For example, a stem cell evolves into two more specialized daughter cells at each division, forming a tree of networks. Another example is in a laboratory setting: a biologist may apply several different drugs individually to malignant cancer cells to analyze the effects of each drug on the cells; the cells treated by one drug may not be intrinsically similar to those treated by another, but rather to the malignant cancer cells they were derived from.

Results: We propose a novel algorithm, Treegl, an ℓ(1) plus total variation penalized linear regression method, to effectively estimate multiple gene networks corresponding to cell types related by a tree-genealogy, based on only a few samples from each cell type. Treegl takes advantage of the similarity between related networks along the biological lineage, while at the same time exposing sharp differences between the networks. We demonstrate that our algorithm performs significantly better than existing methods via simulation. Furthermore we explore an application to a breast cancer dataset, and show that our algorithm is able to produce biologically valid results that provide insight into the progression and reversion of breast cancer cells.

Availability: Software will be available at http://www.sailing.cs.cmu.edu/.

Contact: epxing@cs.cmu.edu.

Show MeSH

Related in: MedlinePlus

Results on simulations. Our method (in blue) performs favorably to existing methods. See text for details.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3117339&req=5

Figure 2: Results on simulations. Our method (in blue) performs favorably to existing methods. See text for details.

Mentions: The results are shown in Figure 2 for two different sample sizes. Our method (in blue) performs favorably to estimating a single static network (green) or estimating each graph independently (red). It should be noted that our method can produce different graphs compared to the static method which only produces one. The independent method also produces different graphs but it performs very poorly.Fig. 2.


TREEGL: reverse engineering tree-evolving gene networks underlying developing biological lineages.

Parikh AP, Wu W, Curtis RE, Xing EP - Bioinformatics (2011)

Results on simulations. Our method (in blue) performs favorably to existing methods. See text for details.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3117339&req=5

Figure 2: Results on simulations. Our method (in blue) performs favorably to existing methods. See text for details.
Mentions: The results are shown in Figure 2 for two different sample sizes. Our method (in blue) performs favorably to estimating a single static network (green) or estimating each graph independently (red). It should be noted that our method can produce different graphs compared to the static method which only produces one. The independent method also produces different graphs but it performs very poorly.Fig. 2.

Bottom Line: However, one challenge in estimating such evolving networks is that their host cells not only contiguously evolve, but also branch over time.For example, a stem cell evolves into two more specialized daughter cells at each division, forming a tree of networks.Treegl takes advantage of the similarity between related networks along the biological lineage, while at the same time exposing sharp differences between the networks.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.

ABSTRACT

Motivation: Estimating gene regulatory networks over biological lineages is central to a deeper understanding of how cells evolve during development and differentiation. However, one challenge in estimating such evolving networks is that their host cells not only contiguously evolve, but also branch over time. For example, a stem cell evolves into two more specialized daughter cells at each division, forming a tree of networks. Another example is in a laboratory setting: a biologist may apply several different drugs individually to malignant cancer cells to analyze the effects of each drug on the cells; the cells treated by one drug may not be intrinsically similar to those treated by another, but rather to the malignant cancer cells they were derived from.

Results: We propose a novel algorithm, Treegl, an ℓ(1) plus total variation penalized linear regression method, to effectively estimate multiple gene networks corresponding to cell types related by a tree-genealogy, based on only a few samples from each cell type. Treegl takes advantage of the similarity between related networks along the biological lineage, while at the same time exposing sharp differences between the networks. We demonstrate that our algorithm performs significantly better than existing methods via simulation. Furthermore we explore an application to a breast cancer dataset, and show that our algorithm is able to produce biologically valid results that provide insight into the progression and reversion of breast cancer cells.

Availability: Software will be available at http://www.sailing.cs.cmu.edu/.

Contact: epxing@cs.cmu.edu.

Show MeSH
Related in: MedlinePlus