A probabilistic framework for microarray data analysis: Fundamental probability models and statistical inference

Super-delta2: An Enhanced Differential Expression Analysis Procedure for Multi-Group Comparisons of RNA-seq Data

MOTIVATION

We developed super-delta2, a differential gene expression analysis pipeline designed for multi-group comparisons for RNA-seq data. It includes a customized one-way ANOVA F-test and a post-hoc test for pairwise group comparisons; both are designed to work with a multivariate normalization procedure to reduce technical noise. It also includes a trimming procedure with bias-correction to obtain robust and approximately unbiased summary statistics used in these tests. We demonstrated the asymptotic applicability of super-delta2 to log-transformed read counts in RNA-seq data by large sample theory based on Negative Binomial Poisson (NBP) distribution.

RESULTS

We compared super-delta2 with three commonly used RNA-seq data analysis methods: limma/voom, edgeR, and DESeq2 using both simulated and real datasets. In all three simulation settings, super-delta2 not only achieved the best overall statistical power, but also was the only method that controlled type I error at the nominal level. When applied to a breast cancer dataset to identify differential expression pattern associated with multiple pathologic stages, super-delta2 selected more enriched pathways than other methods, which are directly linked to the underlying biological condition (breast cancer).

CONCLUSIONS

By incorporating trimming and bias-correction in the normalization step, super-delta2 was able to achieve tight control of type I error. Because the hypothesis tests are based on asymptotic normal approximation of the NBP distribution, super-delta2 does not require computationally expensive iterative optimization procedures used by methods such as edgeR and DESeq2, which occasionally have convergence issues.

AVAILABILITY

Our method is implemented in a R-package, "superdelta2", freely available at: https://github.com/fhlsjs/superdelta2.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Super-delta: a new differential gene expression analysis procedure with robust data normalization

BACKGROUND

Normalization is an important data preparation step in gene expression analyses, designed to remove various systematic noise. Sample variance is greatly reduced after normalization, hence the power of subsequent statistical analyses is likely to increase. On the other hand, variance reduction is made possible by borrowing information across all genes, including differentially expressed genes (DEGs) and outliers, which will inevitably introduce some bias. This bias typically inflates type I error; and can reduce statistical power in certain situations. In this study we propose a new differential expression analysis pipeline, dubbed as super-delta, that consists of a multivariate extension of the global normalization and a modified t-test. A robust procedure is designed to minimize the bias introduced by DEGs in the normalization step. The modified t-test is derived based on asymptotic theory for hypothesis testing that suitably pairs with the proposed robust normalization.

RESULTS

We first compared super-delta with four commonly used normalization methods: global, median-IQR, quantile, and cyclic loess normalization in simulation studies. Super-delta was shown to have better statistical power with tighter control of type I error rate than its competitors. In many cases, the performance of super-delta is close to that of an oracle test in which datasets without technical noise were used. We then applied all methods to a collection of gene expression datasets on breast cancer patients who received neoadjuvant chemotherapy. While there is a substantial overlap of the DEGs identified by all of them, super-delta were able to identify comparatively more DEGs than its competitors. Downstream gene set enrichment analysis confirmed that all these methods selected largely consistent pathways. Detailed investigations on the relatively small differences showed that pathways identified by super-delta have better connections to breast cancer than other methods.

CONCLUSIONS

As a new pipeline, super-delta provides new insights to the area of differential gene expression analysis. Solid theoretical foundation supports its asymptotic unbiasedness and technical noise-free properties. Implementation on real and simulated datasets demonstrates its decent performance compared with state-of-art procedures. It also has the potential of expansion to be incorporated with other data type and/or more general between-group comparison problems.

Evaluation of bias-variance trade-off for commonly used post-summarizing normalization procedures in large-scale gene expression studies

Normalization procedures are widely used in high-throughput genomic data analyses to remove various technological noise and variations. They are known to have profound impact to the subsequent gene differential expression analysis. Although there has been some research in evaluating different normalization procedures, few attempts have been made to systematically evaluate the gene detection performances of normalization procedures from the bias-variance trade-off point of view, especially with strong gene differentiation effects and large sample size. In this paper, we conduct a thorough study to evaluate the effects of normalization procedures combined with several commonly used statistical tests and MTPs under different configurations of effect size and sample size. We conduct theoretical evaluation based on a random effect model, as well as simulation and biological data analyses to verify the results. Based on our findings, we provide some practical guidance for selecting a suitable normalization procedure under different scenarios.

Decreased secretion and unfolded protein response upregulation

Recombinant antibody fragments, for example, the classic monovalent single-chain antibody (scFv), are emerging as credible alternatives to monoclonal antibody (mAb) products. scFv fragments maintain a diverse range of potential applications in biotechnology and can be implemented as powerful therapeutic and diagnostic agents. As such, a variety of hosts have been used to produce antibody fragments resulting in varying degrees of success. Yeast, Saccharomyces cerevisiae, is an attractive host due to quality control mechanisms of the secretory pathway that ensure secreted proteins are properly folded. However, the expression of a recombinant protein in yeast is not trivial; neither are the quality control mechanisms the cell initiates to respond to overwhelming stress, such as an increased protein load, simplistic. The endoplasmic reticulum (ER) is a dynamic organelle, capable of sensing and adjusting its folding capacity in response to increased demand. When protein abundance or terminally misfolded proteins overwhelm the ER's capacity, the unfolded protein response (UPR) is activated. In the guidelines presented here, we discuss varying aspects of quality control, its modulation, and ways to design appropriate constructs for yeast recombinant protein expression. Furthermore, we have provided protocols and methods to monitor intracellular protein expression and trafficking as well as evaluation of the UPR, with essential controls. The latter part of this chapter will review considerations for the experimental design of microarray and quantitative polymerase chain reaction (q-PCR) techniques while suggesting appropriate means of data analysis.