26,326 research outputs found
Multiple testing procedures under confounding
While multiple testing procedures have been the focus of much statistical
research, an important facet of the problem is how to deal with possible
confounding. Procedures have been developed by authors in genetics and
statistics. In this chapter, we relate these proposals. We propose two new
multiple testing approaches within this framework. The first combines
sensitivity analysis methods with false discovery rate estimation procedures.
The second involves construction of shrinkage estimators that utilize the
mixture model for multiple testing. The procedures are illustrated with
applications to a gene expression profiling experiment in prostate cancer.Comment: Published in at http://dx.doi.org/10.1214/193940307000000176 the IMS
Collections (http://www.imstat.org/publications/imscollections.htm) by the
Institute of Mathematical Statistics (http://www.imstat.org
Size, power and false discovery rates
Modern scientific technology has provided a new class of large-scale
simultaneous inference problems, with thousands of hypothesis tests to consider
at the same time. Microarrays epitomize this type of technology, but similar
situations arise in proteomics, spectroscopy, imaging, and social science
surveys. This paper uses false discovery rate methods to carry out both size
and power calculations on large-scale problems. A simple empirical Bayes
approach allows the false discovery rate (fdr) analysis to proceed with a
minimum of frequentist or Bayesian modeling assumptions. Closed-form accuracy
formulas are derived for estimated false discovery rates, and used to compare
different methodologies: local or tail-area fdr's, theoretical, permutation, or
empirical null hypothesis estimates. Two microarray data sets as well as
simulations are used to evaluate the methodology, the power diagnostics showing
why nonnull cases might easily fail to appear on a list of ``significant''
discoveries.Comment: Published in at http://dx.doi.org/10.1214/009053606000001460 the
Annals of Statistics (http://www.imstat.org/aos/) by the Institute of
Mathematical Statistics (http://www.imstat.org
aFold – using polynomial uncertainty modelling for differential gene expression estimation from RNA sequencing data
Data normalization and identification of significant differential expression represent crucial steps in RNA-Seq analysis. Many available tools rely on assumptions that are often not met by real data, including the common assumption of symmetrical distribution of up- and down-regulated genes, the presence of only few differentially expressed genes and/or few outliers. Moreover, the cut-off for selecting significantly differentially expressed genes for further downstream analysis often depend on arbitrary choices
Differential meta-analysis of RNA-seq data from multiple studies
High-throughput sequencing is now regularly used for studies of the
transcriptome (RNA-seq), particularly for comparisons among experimental
conditions. For the time being, a limited number of biological replicates are
typically considered in such experiments, leading to low detection power for
differential expression. As their cost continues to decrease, it is likely that
additional follow-up studies will be conducted to re-address the same
biological question. We demonstrate how p-value combination techniques
previously used for microarray meta-analyses can be used for the differential
analysis of RNA-seq data from multiple related studies. These techniques are
compared to a negative binomial generalized linear model (GLM) including a
fixed study effect on simulated data and real data on human melanoma cell
lines. The GLM with fixed study effect performed well for low inter-study
variation and small numbers of studies, but was outperformed by the
meta-analysis methods for moderate to large inter-study variability and larger
numbers of studies. To conclude, the p-value combination techniques illustrated
here are a valuable tool to perform differential meta-analyses of RNA-seq data
by appropriately accounting for biological and technical variability within
studies as well as additional study-specific effects. An R package metaRNASeq
is available on the R Forge
On the utility of RNA sample pooling to optimize cost and statistical power in RNA sequencing experiments
Background: In gene expression studies, RNA sample pooling is sometimes considered because of budget constraints or lack of sufficient input material. Using microarray technology, RNA sample pooling strategies have been reported to optimize both the cost of data generation as well as the statistical power for differential gene expression (DGE) analysis. For RNA sequencing, with its different quantitative output in terms of counts and tunable dynamic range, the adequacy and empirical validation of RNA sample pooling strategies have not yet been evaluated. In this study, we comprehensively assessed the utility of pooling strategies in RNA-seq experiments using empirical and simulated RNA-seq datasets.
Result: The data generating model in pooled experiments is defined mathematically to evaluate the mean and variability of gene expression estimates. The model is further used to examine the trade-off between the statistical power of testing for DGE and the data generating costs. Empirical assessment of pooling strategies is done through analysis of RNA-seq datasets under various pooling and non-pooling experimental settings. Simulation study is also used to rank experimental scenarios with respect to the rate of false and true discoveries in DGE analysis. The results demonstrate that pooling strategies in RNA-seq studies can be both cost-effective and powerful when the number of pools, pool size and sequencing depth are optimally defined.
Conclusion: For high within-group gene expression variability, small RNA sample pools are effective to reduce the variability and compensate for the loss of the number of replicates. Unlike the typical cost-saving strategies, such as reducing sequencing depth or number of RNA samples (replicates), an adequate pooling strategy is effective in maintaining the power of testing DGE for genes with low to medium abundance levels, along with a substantial reduction of the total cost of the experiment. In general, pooling RNA samples or pooling RNA samples in conjunction with moderate reduction of the sequencing depth can be good options to optimize the cost and maintain the power
The Reproducibility of Lists of Differentially Expressed Genes in Microarray Studies
Reproducibility is a fundamental requirement in scientific experiments and clinical contexts. Recent publications raise concerns about the reliability of microarray technology because of the apparent lack of agreement between lists of differentially expressed genes (DEGs). In this study we demonstrate that (1) such discordance may stem from ranking and selecting DEGs solely by statistical significance (P) derived from widely used simple t-tests; (2) when fold change (FC) is used as the ranking criterion, the lists become much more reproducible, especially when fewer genes are selected; and (3) the instability of short DEG lists based on P cutoffs is an expected mathematical consequence of the high variability of the t-values. We recommend the use of FC ranking plus a non-stringent P cutoff as a baseline practice in order to generate more reproducible DEG lists. The FC criterion enhances reproducibility while the P criterion balances sensitivity and specificity
Computational Models for Transplant Biomarker Discovery.
Translational medicine offers a rich promise for improved diagnostics and drug discovery for biomedical research in the field of transplantation, where continued unmet diagnostic and therapeutic needs persist. Current advent of genomics and proteomics profiling called "omics" provides new resources to develop novel biomarkers for clinical routine. Establishing such a marker system heavily depends on appropriate applications of computational algorithms and software, which are basically based on mathematical theories and models. Understanding these theories would help to apply appropriate algorithms to ensure biomarker systems successful. Here, we review the key advances in theories and mathematical models relevant to transplant biomarker developments. Advantages and limitations inherent inside these models are discussed. The principles of key -computational approaches for selecting efficiently the best subset of biomarkers from high--dimensional omics data are highlighted. Prediction models are also introduced, and the integration of multi-microarray data is also discussed. Appreciating these key advances would help to accelerate the development of clinically reliable biomarker systems
Latent rank change detection for analysis of splice-junction microarrays with nonlinear effects
Alternative splicing of gene transcripts greatly expands the functional
capacity of the genome, and certain splice isoforms may indicate specific
disease states such as cancer. Splice junction microarrays interrogate
thousands of splice junctions, but data analysis is difficult and error prone
because of the increased complexity compared to differential gene expression
analysis. We present Rank Change Detection (RCD) as a method to identify
differential splicing events based upon a straightforward probabilistic model
comparing the over- or underrepresentation of two or more competing isoforms.
RCD has advantages over commonly used methods because it is robust to false
positive errors due to nonlinear trends in microarray measurements. Further,
RCD does not depend on prior knowledge of splice isoforms, yet it takes
advantage of the inherent structure of mutually exclusive junctions, and it is
conceptually generalizable to other types of splicing arrays or RNA-Seq. RCD
specifically identifies the biologically important cases when a splice junction
becomes more or less prevalent compared to other mutually exclusive junctions.
The example data is from different cell lines of glioblastoma tumors assayed
with Agilent microarrays.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS389 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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