Rashid Sabrina, Kotton Darrell N, Bar-Joseph Ziv
Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Department of Medicine, Department of Pathology and Laboratory Medicine, Center for Regenerative Medicine (CReM) of Boston University and Boston Medical Center, Boston, MA 02118, USA.
Bioinformatics. 2017 Aug 15;33(16):2504-2512. doi: 10.1093/bioinformatics/btx173.
Single cell RNA-Seq analysis holds great promise for elucidating the networks and pathways controlling cellular differentiation and disease. However, the analysis of time series single cell RNA-Seq data raises several new computational challenges. Cells at each time point are often sampled from a mixture of cell types, each of which may be a progenitor of one, or several, specific fates making it hard to determine which cells should be used to reconstruct temporal trajectories. In addition, cells, even from the same time point, may be unsynchronized making it hard to rely on the measured time for determining these trajectories.
We present TASIC a new method for determining temporal trajectories, branching and cell assignments in single cell time series experiments. Unlike prior approaches TASIC uses on a probabilistic graphical model to integrate expression and time information making it more robust to noise and stochastic variations. Applying TASIC to in vitro myoblast differentiation and in-vivo lung development data we show that it accurately reconstructs developmental trajectories from single cell experiments. The reconstructed models enabled us to identify key genes involved in cell fate determination and to obtain new insights about a specific type of lung cells and its role in development.
The TASIC software package is posted in the supporting website. The datasets used in the paper are publicly available.
Supplementary data are available at Bioinformatics online.
单细胞RNA测序分析在阐明控制细胞分化和疾病的网络及通路方面具有巨大潜力。然而，对时间序列单细胞RNA测序数据的分析带来了一些新的计算挑战。每个时间点的细胞通常是从多种细胞类型的混合物中采样的，每种细胞类型可能是一种或几种特定命运的祖细胞，这使得很难确定应该使用哪些细胞来重建时间轨迹。此外，即使是来自同一时间点的细胞也可能不同步，这使得很难依靠测量的时间来确定这些轨迹。
我们提出了TASIC，这是一种用于在单细胞时间序列实验中确定时间轨迹、分支和细胞分配的新方法。与先前的方法不同，TASIC使用概率图形模型来整合表达和时间信息，使其对噪声和随机变化更具鲁棒性。将TASIC应用于体外成肌细胞分化和体内肺发育数据，我们表明它能从单细胞实验中准确重建发育轨迹。重建的模型使我们能够识别参与细胞命运决定的关键基因，并获得关于一种特定类型的肺细胞及其在发育中的作用的新见解。
TASIC软件包发布在支持网站上。本文中使用的数据集可公开获取。
补充数据可在《生物信息学》在线获取。
