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Statistics for Immunologists

ABSTRACT: Statistical tests and graphs are an important part of any immunological research publication or presentation, but not all immunologists have the statistical expertise to choose the best methods to evaluate and represent their experiments. These protocols provide a brief overview of the statistical methods most relevant to immunology researchers, so they can either analyze their own data or better understand their statistician collaborators. Protocols cover the basics of t?tests, ANOVA, nonparametric tests, linear and nonlinear regression models, categorical response models, statistical design of experiments, and advanced methods like re?sampling tests, Bayesian statistics, and methods for high?throughput ?omics data. Each topic receives a brief overview of the theory and usage, followed by brief instructions and code examples for R software. These protocols should be useful to biologists who are learning statistics for the first time and biologists who would like a refresher on basic statistical methods.

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PROVIDER: S-EPMC6223127 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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Statistics for Immunologists

Current protocols in immunology 20180802 1

Statistical tests and graphs are an important part of any immunological research publication or presentation, but not all immunologists have the statistical expertise to choose the best methods to evaluate and represent their experiments. These protocols provide a brief overview of the statistical methods most relevant to immunology researchers, so they can either analyze their own data or better understand their statistician collaborators. Protocols cover the basics of t‐tests, ANOVA, nonparame ...[more]

PMID: 30400720

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Project description:BackgroundThe optimal score for ungapped local alignments of infinitely long random sequences is known to follow a Gumbel extreme value distribution. Less is known about the important case, where gaps are allowed. For this case, the distribution is only known empirically in the high-probability region, which is biologically less relevant.ResultsWe provide a method to obtain numerically the biologically relevant rare-event tail of the distribution. The method, which has been outlined in an earlier work, is based on generating the sequences with a parametrized probability distribution, which is biased with respect to the original biological one, in the framework of Metropolis Coupled Markov Chain Monte Carlo. Here, we first present the approach in detail and evaluate the convergence of the algorithm by considering a simple test case. In the earlier work, the method was just applied to one single example case. Therefore, we consider here a large set of parameters: We study the distributions for protein alignment with different substitution matrices (BLOSUM62 and PAM250) and affine gap costs with different parameter values. In the logarithmic phase (large gap costs) it was previously assumed that the Gumbel form still holds, hence the Gumbel distribution is usually used when evaluating p-values in databases. Here we show that for all cases, provided that the sequences are not too long (L > 400), a "modified" Gumbel distribution, i.e. a Gumbel distribution with an additional Gaussian factor is suitable to describe the data. We also provide a "scaling analysis" of the parameters used in the modified Gumbel distribution. Furthermore, via a comparison with BLAST parameters, we show that significance estimations change considerably when using the true distributions as presented here. Finally, we study also the distribution of the sum statistics of the k best alignments.ConclusionOur results show that the statistics of gapped and ungapped local alignments deviates significantly from Gumbel in the rare-event tail. We provide a Gaussian correction to the distribution and an analysis of its scaling behavior for several different scoring parameter sets, which are commonly used to search protein data bases. The case of sum statistics of k best alignments is included.

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Statistics for Immunologists

Publications

Statistics for Immunologists

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