Exabytes of images, sequences, tabular data, and unstructured data are now available for analysis to advance science.These data support efforts to visualize the image of the black hole [1], characterize the spectrum of mutations across cancer types [2], create a sophisticated language model [3], and many other tasks.Defining a data analysis as large scale or not is likely to be a moving target as computing and data transfer technologies advance.We consider an analysis large scale when the data size exceeds the capacity of local resources or when the amount of time spent waiting for high-performance computing (HPC) compute capacity would disrupt the pace of the research project.For example, the recount2 [4] analysis processed petabytes of data, so we consider it to be large-scale data processing for the current day standard.We provide 10 simple rules that apply whether the infrastructure used is traditional HPC or a more modern cloud service.Our work and experience are in the space of genomics, but the 10 rules we provide here are more general and broadly applicable given our definition of largescale data analysis.The particular characteristics of genomics data that influence our recommendations but that may not be universally true are that the full set of such data does not have to be present to begin processing, that many processing steps can be completed in a streaming manner, and that the size of processed data required for downstream analysis and interpretation is typically orders of magnitude smaller than the unprocessed form.

Machine learning is a modern approach to problem-solving and task automation.In particular, machine learning is concerned with the development and applications of algorithms that

Dynamic networks can be challenging to analyze visually, especially if they span a large time range during which new nodes and edges can appear and disappear. Although it is straightforward to provide interfaces for visualization that represent multiple states of the network (i.e., multiple timeslices) either simultaneously (e.g., through small multiples) or interactively (e.g., through interactive animation), these interfaces might not support tasks in which disjoint timeslices need to be compared. Since these tasks are key for understanding the dynamic aspects of the network, understanding which interactive visualizations best support these tasks is important. We present the results of a series of laboratory experiments comparing two traditional approaches (small multiples and interactive animation), with a more recent approach based on interactive timeslicing. The tasks were performed on a large display through a touch interface. Participants completed 24 trials of three tasks with all techniques. The results show that interactive timeslicing brings benefit when comparing distant points in time, but less benefits when analyzing contiguous intervals of time.

Network data that changes over time can be very useful for studying a wide range of important phenomena, from how social network connections change to epidemiology. However, it is challenging to analyze, especially if it has many actors, connections or if the covered timespan is large with rapidly changing links (e.g., months of changes with changes at second resolution). In these analyses one would often like to compare many periods of time to others, without having to look at the full timeline. To support this kind of analysis we designed and implemented a technique and system to visualize this dynamic data. The Dynamic Network Plaid (DNP) is designed for large displays and based on user-generated interactive timeslicing on the dynamic graph attributes and on linked provenance-preserving representations. We present the technique, interface and the design/evaluation with a group of public health researchers investigating non-suicidal self-harm picture sharing in Instagram.

BACKGROUND: A fundamental characteristic of multicellular organisms is the specialization of functional cell types through the process of differentiation. These specialized cell types not only characterize the normal functioning of different organs and tissues, they can also be used as cellular biomarkers of a variety of different disease states and therapeutic/vaccine responses. In order to serve as a reference for cell type representation, the Cell Ontology has been developed to provide a standard nomenclature of defined cell types for comparative analysis and biomarker discovery. Historically, these cell types have been defined based on unique cellular shapes and structures, anatomic locations, and marker protein expression. However, we are now experiencing a revolution in cellular characterization resulting from the application of new high-throughput, high-content cytometry and sequencing technologies. The resulting explosion in the number of distinct cell types being identified is challenging the current paradigm for cell type definition in the Cell Ontology. RESULTS: In this paper, we provide examples of state-of-the-art cellular biomarker characterization using high-content cytometry and single cell RNA sequencing, and present strategies for standardized cell type representations based on the data outputs from these cutting-edge technologies, including "context annotations" in the form of standardized experiment metadata about the specimen source analyzed and marker genes that serve as the most useful features in machine learning-based cell type classification models. We also propose a statistical strategy for comparing new experiment data to these standardized cell type representations. CONCLUSION: The advent of high-throughput/high-content single cell technologies is leading to an explosion in the number of distinct cell types being identified. It will be critical for the bioinformatics community to develop and adopt data standard conventions that will be compatible with these new technologies and support the data representation needs of the research community. The proposals enumerated here will serve as a useful starting point to address these challenges.

Many algorithms have been created to automatically detect community structures in social networks. These algorithms have been studied from the perspective of optimisation extensively. However, which community finding algorithm most closely matches the human notion of communities? In this paper, we conduct a user study to address this question. In our experiment, users collected their own Facebook network and manually annotated it, indicating their social communities. Given this annotation, we run state-of-the-art community finding algorithms on the network and use Normalised Mutual Information (NMI) to compare annotated communities with automatically detected ones. Our results show that the Infomap algorithm has the greatest similarity to user defined communities, with Girvan-Newman and Louvain algorithms also performing well.