Dataset Information

Systematic Evaluation of Statistical Methods for Identifying Looping Interactions in 5C Data.

ABSTRACT: Chromosome-Conformation-Capture-Carbon-Copy (5C) is a molecular technology based on proximity ligation that enables high-resolution and high-coverage inquiry of long-range looping interactions. Computational pipelines for analyzing 5C data involve a series of interdependent normalization procedures and statistical methods that markedly influence downstream biological results. A detailed analysis of the trade-offs inherent to all stages of 5C data analysis has not been reported. Here, we provide a comparative assessment of method performance at each step in the 5C analysis pipeline, including sequencing depth and library complexity correction, bias mitigation, spatial noise reduction, distance-dependent expected and variance estimation, statistical modeling, and loop detection. We discuss methodological advantages and disadvantages at each step and provide a full suite of algorithms, lib5C, to allow investigators to test the range of approaches on their own 5C data. Principles learned from our comparative analyses can be applied to protein-independent proximity ligation-based data, including Hi-C, 4C, and Capture-C.

SUBMITTER: Gilgenast TG

PROVIDER: S-EPMC6696950 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Systematic Evaluation of Statistical Methods for Identifying Looping Interactions in 5C Data.

Gilgenast Thomas G TG Phillips-Cremins Jennifer E JE

Cell systems 20190320 3

Chromosome-Conformation-Capture-Carbon-Copy (5C) is a molecular technology based on proximity ligation that enables high-resolution and high-coverage inquiry of long-range looping interactions. Computational pipelines for analyzing 5C data involve a series of interdependent normalization procedures and statistical methods that markedly influence downstream biological results. A detailed analysis of the trade-offs inherent to all stages of 5C data analysis has not been reported. Here, we provide ...[more]

PMID: 30904376

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Project description:BACKGROUND:DNA methylation offers an excellent example for elucidating how epigenetic information affects gene expression. ? values and M values are commonly used to quantify DNA methylation. Statistical methods applicable to DNA methylation data analysis span a number of approaches such as Wilcoxon rank sum test, t-test, Kolmogorov-Smirnov test, permutation test, empirical Bayes method, and bump hunting method. Nonetheless, selection of an optimal statistical method can be challenging when different methods generate inconsistent results from the same data set. RESULTS:We compared six statistical approaches relevant to DNA methylation microarray analysis in terms of false discovery rate control, statistical power, and stability through simulation studies and real data examples. Observable differences were noticed between ? values and M values only when methylation levels were correlated across CpG loci. For small sample size (n=3 or 6 in each group), both the empirical Bayes and bump hunting methods showed appropriate FDR control and the highest power when methylation levels across CpG loci were independent. Only the bump hunting method showed appropriate FDR control and the highest power when methylation levels across CpG sites were correlated. For medium (n=12 in each group) and large sample sizes (n=24 in each group), all methods compared had similar power, except for the permutation test whenever the proportion of differentially methylated loci was low. For all sample sizes, the bump hunting method had the lowest stability in terms of standard deviation of total discoveries whenever the proportion of differentially methylated loci was large. The apparent test power comparisons based on raw p-values from DNA methylation studies on ovarian cancer and rheumatoid arthritis provided results as consistent as those obtained in the simulation studies. Overall, these results provide guidance for optimal statistical methods selection under different scenarios. CONCLUSIONS:For DNA methylation studies with small sample size, the bump hunting method and the empirical Bayes method are recommended when DNA methylation levels across CpG loci are independent, while only the bump hunting method is recommended when DNA methylation levels are correlated across CpG loci. All methods are acceptable for medium or large sample sizes.

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Dataset Information

Systematic Evaluation of Statistical Methods for Identifying Looping Interactions in 5C Data.

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Systematic Evaluation of Statistical Methods for Identifying Looping Interactions in 5C Data.

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