![]() Vi SNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. 2008 9:2579–605.Īmir ED, Davis KL, Tadmor MD, Simonds EF, Levine JH, Bendall SC, et al. Mathematical methods and algorithms for signal processing. Local multidimensional scaling for nonlinear dimension reduction, graph drawing, and proximity analysis. A global geometric framework for nonlinear dimensionality reduction. Nonlinear dimensionality reduction by locally linear embedding. Visualizing structure and transitions in high-dimensional biological data. Moon KR, van Dijk D, Wang Z, Gigante S, Burkhardt DB, Chen WS, et al. Confidence interval or p-value?: part 4 of a series on evaluation of scientific publications. Confidence intervals and p-values in clinical decision making. The relevance of confidence interval and P-value in inferential statistics. Common pitfalls in statistical analysis: “P” values, statistical significance and confidence intervals. Common pitfalls in statistical analysis: the perils of multiple testing. Characterization of haplotypes and single nucleotide polymorphisms associated with Gn1a for high grain number formation in rice plant. Gouda G, Gupta MK, Donde R, Kumar J, Parida M, Mohapatra T, et al. Divergent evolution and purifying selection of the type 2 diabetes gene sequences in Drosophila: a phylogenomic study. Genetic Basis of Adaptation and Maladaptation via Balancing Selection. Computational approach to understand molecular mechanism involved in BPH resistance in Bt- rice plant. Gupta MK, Vadde R, Gouda G, Donde R, Kumar J, Behera L. Computational biology: toward early detection of pancreatic Cancer. Gupta MK, Sarojamma V, Reddy MR, Shaik JB, Vadde R. Statistical contributions to bioinformatics: design, Modeling, structure learning, and integration. ![]() Few researchers have proposed that statistician participation in the initial data collection and preprocessing phase will minimize errors and contribute to more critical scientific conclusions. Additionally, while statisticians play an essential role in numerous bioinformatics studies, most of them are only interested in obtained and preprocessed data. However, a larger dataset analysis often faces various challenges, like multiple comparisons, high dimensionality, small n and large p problem, noise, and heterogeneous information. Results obtained revealed that, to date, several statistical methods have been developed for analyzing large-scale biological data, like multiple testing, unsupervised learning and data visualization, clustering, and bootstrapping. In this chapter, the authors attempted to understand how statisticians develop and employ various strategies to investigate and analyze these big datasets. Thus, more rigorous statistical techniques are required to accurately predict the resulting big datasets. High-throughput methods are rapidly becoming prevalent in biological sciences and clinical studies.
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