Computation Of Large Datasets Research Articles

The need to reassess single-cell RNA sequencing datasets: the importance of biological sample processing

Background: The advent of single-cell RNA sequencing (scRNAseq) and additional single-cell omics technologies have provided scientists with unprecedented tools to explore biology at cellular resolution. However, reaching an appropriate number of good quality reads per cell and reasonable numbers of cells within each of the populations of interest are key to infer relevant conclusions about the underlying biology of the dataset. For these reasons, scRNAseq studies are constantly increasing the number of cells analysed and the granularity of the resultant transcriptomics analyses. Methods: We aimed to identify previously described fibroblast subpopulations in healthy adult human skin by using the largest dataset published to date (528,253 sequenced cells) and an unsupervised population-matching algorithm. Results: Our reanalysis of this landmark resource demonstrates that a substantial proportion of cell transcriptomic signatures may be biased by cellular stress and response to hypoxic conditions. Conclusions: We postulate that careful design of experimental conditions is needed to avoid long processing times of biological samples. Additionally, computation of large datasets might undermine the extent of the analysis, possibly due to long processing times.

Improving Efficiency of K-Means Algorithm for Large Datasets

Clustering is a process of grouping objects into different classes based on their similarities. K-means is a widely studied partitional based algorithm. It is reported to work efficiently for small datasets; however the performance is not very appreciable in terms of time of computation for large datasets. Several modifications have been made by researchers to address this issue. This paper proposes a novel way of handling the large datasets using K-means in a distributed manner to obtain efficiency. The concept of parallel processing is exploited by dividing the datasets to a number of baskets and then applying K-means in parallel manner to each such basket. The proposed BasketK-means provides a very competitive performance with considerably less computation time. The simulation results on various real datasets and synthetic datasets presented in the work clearly emphasize the effectiveness of the proposed approach.

Big Data, social physics, and spatial analysis: The early years

This paper examines one of the historical antecedents of Big Data, the social physics movement. Its origins are in the scientific revolution of the 17th century in Western Europe. But it is not named as such until the middle of the 19th century, and not formally institutionalized until another hundred years later when it is associated with work by George Zipf and John Stewart. Social physics is marked by the belief that large-scale statistical measurement of social variables reveals underlying relational patterns that can be explained by theories and laws found in natural science, and physics in particular. This larger epistemological position is known as monism, the idea that there is only one set of principles that applies to the explanation of both natural and social worlds. Social physics entered geography through the work of the mid-20th-century geographer William Warntz, who developed his own spatial version called “macrogeography.” It involved the computation of large data sets, made ever easier with the contemporaneous development of the computer, joined with the gravitational potential model. Our argument is that Warntz's concerns with numeracy, large data sets, machine-based computing power, relatively simple mathematical formulas drawn from natural science, and an isomorphism between natural and social worlds became grounds on which Big Data later staked its claim to knowledge; it is a past that has not yet passed.

Wavelet analysis for single molecule localization microscopy

Localization of single molecules in microscopy images is a key step in quantitative single particle data analysis. Among them, single molecule based super-resolution optical microscopy techniques require high localization accuracy as well as computation of large data sets in the order of 10(5) single molecule detections to reconstruct a single image. We hereby present an algorithm based on image wavelet segmentation and single particle centroid determination, and compare its performance with the commonly used gaussian fitting of the point spread function. We performed realistic simulations at different signal-to-noise ratios and particle densities and show that the calculation time using the wavelet approach can be more than one order of magnitude faster than that of gaussian fitting without a significant degradation of the localization accuracy, from 1 nm to 4 nm in our range of study. We propose a simulation-based estimate of the resolution of an experimental single molecule acquisition.

NPUA: A new approach for the analysis of computer experiments

An important problem in the analysis of computer experiments is the specification of the uncertainty of the prediction according to a meta-model. The Bayesian approach, developed for the uncertainty analysis of deterministic computer models, expresses uncertainty by the use of a Gaussian process. There are several versions of the Bayesian approach, which are different in many regards but all of them lead to time consuming computations for large data sets. In the present paper we introduce a new approach in which the distribution of uncertainty is obtained in a general nonparametric form. The proposed approach is called non-parametric uncertainty analysis (NPUA), which is computationally simple since it combines generic sampling and regression techniques. We compare NPUA with the Bayesian and Kriging approaches and show the advantages of NPUA for finding points for the next runs by reanalyzing the ASET model.

Genetic Evaluation of Dairy Goat Does for Milk and Fat as an Extension of Buck Evaluation

The current procedure for calculating genetic evaluations of dairy goat bucks provides easily accessible information for constructing genetic evaluations for dairy goat does (doe indexes). Relationships among sires are accounted for by using the additive genetic relationship matrix among sires if the doe's sire was evaluated. The sire evaluation plus a portion of residual doe effect is the basis of doe evaluation. Although direct incorporation of maternal relationships among does may be accomplished, it requires extensive computations for large data sets. An approximate procedure based on selection index principles includes information from a doe's dam with only a modest increase in computing expense over that for genetic evaluations of bucks. Doe indexes were computed and adjusted to separate breed bases. Trend in doe indexes was positive for all breeds and ranged from .54 to 1.37 kg/yr for milk yield and from .021 to .052 kg/yr for fat yield.

Improved Method for Comparing Rank-Order Preferences of Two Groups of Consumers

Ryans recently proposed several measures that could be used to determine the degree of agreement between sets of preference rankings from two separate groups of consumers. The most satisfactory of these measures required burdensome computations for large data sets. In this article a much simpler method of calculating the measure is described that makes feasible its computation for large data sets.