MOTIVATION: Recent technological advances produce a wealth of high-dimensional descriptions of biological processes, yet extracting meaningful insight and mechanistic understanding from these data remains challenging. For example, in developmental biology, the dynamics of differentiation can now be mapped quantitatively using single-cell RNA sequencing, yet it is difficult to infer molecular regulators of developmental transitions. Here, we show that discovering informative features in the data is crucial for statistical analysis as well as making experimental predictions. RESULTS: We identify features based on their ability to discriminate between clusters of the data points. We define a class of problems in which linear separability of clusters is hidden in a low-dimensional space. We propose an unsupervised method to identify the subset of features that define a low-dimensional subspace in which clustering can be conducted. This is achieved by averaging over discriminators trained on an ensemble of proposed cluster configurations. We then apply our method to single-cell RNA-seq data from mouse gastrulation, and identify 27 key transcription factors (out of 409 total), 18 of which are known to define cell states through their expression levels. In this inferred subspace, we find clear signatures of known cell types that eluded classification prior to discovery of the correct low-dimensional subspace. AVAILABILITY AND IMPLEMENTATION: https://github.com/smelton/SMD. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

We present two centralized algorithms that dynamically deform the topology of a multi-hop wireless network in response to changing traffic conditions, so as to reduce the end-to-end packet delay. These algorithms operate by reducing an easily calculable traffic-and-topology-dependent metric that has strong positive correlation to the mean end-to-end network delay. This makes it possible to change the network topology in real time. The first algorithm involves moving existing nodes to create new links, while the second involves adding a new node to the existing network. The new links are added judiciously between nodes with the most traffic fluctuations, in a way that often decreases the total number of network links. Multiple simulations on the original and the deformed networks using the \ns simulator demonstrate that the reductions in end-to-end delay are significantly higher than with the alternative approach of increasing the capacities of the most congested links.

This paper defines the metrics to characterize the performance of ad hoc networks based on timescales for information flow, power consumption and interference. The statistical distribution of timescales has not been previously considered. Yet, it is important for understanding the feasibility of communicating over such networks, for comparing different algorithms for building up network topology and for distinguishing regimes of routing. We quantify the longest timescale for information flow and estimate its distribution. We also introduce a decentralized adaptive power algorithm, that uses only information local to each device, for building ad hoc networks. This algorithm is shown to perform significantly better by all our metrics when compared with a standard, constant power, algorithm.

We present a routing paradigm called PBR that utilizes steepest gradient search methods to route data packets. More specifically, the PBR paradigm assigns scalar potentials to network elements and forwards packets in the direction of maximum positive force. We show that the family of PBR schemes are loop free and that the standard shortest path routing algorithms are a special case of the PBR paradigm. We then show how to design a potential function that accounts for traffic conditions at a node. The resulting routing algorithm routes around congested areas while preserving the key desirable properties of IP routing mechanisms including hop-by-hop routing, local route computations and statistical multiplexing. Our simulations using the ns simulator indicate that the traffic aware routing algorithm shows significant improvements in end-to-end delay and jitter when compared to standard shortest path routing algorithms. The simulations also indicate that our algorithm does not incur too much control overheads and is fairly stable even when traffic conditions are dynamic.