School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Bioinformatics. 2011 Jul 1;27(13):i196-204. doi: 10.1093/bioinformatics/btr239.
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.
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.
Software will be available at http://www.sailing.cs.cmu.edu/.
估计生物谱系上的基因调控网络对于深入了解细胞在发育和分化过程中如何进化至关重要。然而，估计这种进化网络的一个挑战是，它们的宿主细胞不仅连续进化，而且随着时间的推移也会分支。例如，干细胞在每次分裂时都会进化成两个更专门化的子细胞，形成一个网络树。另一个例子是在实验室环境中：生物学家可能会单独应用几种不同的药物来处理恶性癌细胞，以分析每种药物对细胞的影响；用一种药物处理的细胞与用另一种药物处理的细胞不一定内在相似，而是与它们起源的恶性癌细胞相似。
我们提出了一种新的算法 Treegl，这是一种基于 ℓ(1)范数和总变差惩罚的线性回归方法，可以有效地估计与树谱系相关的多个基因网络，而每个细胞类型只需几个样本。Treegl 利用了生物谱系中相关网络之间的相似性，同时也揭示了网络之间的明显差异。我们通过模拟证明了我们的算法明显优于现有方法。此外，我们还探索了在乳腺癌数据集上的应用，并表明我们的算法能够产生有生物学意义的结果，深入了解乳腺癌细胞的进展和逆转。
软件将可在 http://www.sailing.cs.cmu.edu/ 获得。
