Power analysis and sample size estimation for RNA-Seq differential expression

It is crucial for researchers to optimize RNA-seq experimental designs for differential expression detection. Currently, the field lacks general methods to estimate power and sample size for RNA-Seq in complex experimental designs, under the assumption of the negative binomial distribution. We simulate RNA-Seq count data based on parameters estimated from six widely different public data sets (including cell line comparison, tissue comparison, and cancer data sets) and calculate the statistical power in paired and unpaired sample experiments. We comprehensively compare five differential expression analysis packages (DESeq, edgeR, DESeq2, sSeq, and EBSeq) and evaluate their performance by power, receiver operator characteristic (ROC) curves, and other metrics including areas under the curve (AUC), Matthews correlation coefficient (MCC), and F-measures. DESeq2 and edgeR tend to give the best performance in general. Increasing sample size or sequencing depth increases power; however, increasing sample size is more potent than sequencing depth to increase power, especially when the sequencing depth reaches 20 million reads. Long intergenic noncoding RNAs (lincRNA) yields lower power relative to the protein coding mRNAs, given their lower expression level in the same RNA-Seq experiment. On the other hand, paired-sample RNA-Seq significantly enhances the statistical power, confirming the importance of considering the multifactor experimental design. Finally, a local optimal power is achievable for a given budget constraint, and the dominant contributing factor is sample size rather than the sequencing depth. In conclusion, we provide a power analysis tool (http://www2.hawaii.edu/~lgarmire/RNASeqPowerCalculator.htm) that captures the dispersion in the data and can serve as a practical reference under the budget constraint of RNA-Seq experiments.     

Comparison of false discovery rate methods in identifying genes with differential expression

Current high-throughput techniques such as microarray in genomics or mass spectrometry in proteomics usually generate thousands of hypotheses to be tested simultaneously. The usual purpose of these techniques is to identify a subset of interesting cases that deserve further investigation. As a consequence, the control of false positives among the tests called "significant" becomes a critical issue for researchers. Over the past few years, several false discovery rate (FDR)-controlling methods have been proposed; each method favors certain scenarios and is introduced with the purpose of improving the control of FDR at the targeted level. In this paper, we compare the performance of the five FDR-controlling methods proposed by Benjamini et al., the qvalue method proposed by Storey, and the traditional Bonferroni method. The purpose is to investigate the "observed" sensitivity of each method on typical microarray experiments in which the majority (or all) of the truth is unknown. Based on two well-studied microarray datasets, it is found that in terms of the "apparent" test power, the ranking of the FDR methods is given as Step-down相似文献   

Effectively identifying regulatory hotspots while capturing expression heterogeneity in gene expression studies

Expression quantitative trait loci (eQTL) mapping is a tool that can systematically identify genetic variation affecting gene expression. eQTL mapping studies have shown that certain genomic locations, referred to as regulatory hotspots, may affect the expression levels of many genes. Recently, studies have shown that various confounding factors may induce spurious regulatory hotspots. Here, we introduce a novel statistical method that effectively eliminates spurious hotspots while retaining genuine hotspots. Applied to simulated and real datasets, we validate that our method achieves greater sensitivity while retaining low false discovery rates compared to previous methods.     

A novel strategy for identifying differential gene expression: An improved method of differential display analysis.

We propose a novel alternative approach, an advanced method for recently developed strategies, for identifying differentially expressed genes. Firstly, double-stranded cDNAs were digested using Sau3AI and the 3'-end restriction fragments of the cDNA were ligated to a double-stranded adapter. Next, the restriction fragments were directly amplified using several combinations of adapter-specific primers and FITC-labeled oligo dT primers. The selected cDNA fragments were displayed on a polyacrylamide gel. Neither nested PCR nor purification of 3'-end fragments are necessary. We examined the validity of this approach by evaluating gene expression changes during granulocytic differentiation of HL-60 cells. This method can theoretically detect almost all gene expression changes more rapidly and through simpler manipulations than by any other approach.     

Leveraging two-way probe-level block design for identifying differential gene expression with high-density oligonucleotide arrays

Background  

To identify differentially expressed genes across experimental conditions in oligonucleotide microarray experiments, existing statistical methods commonly use a summary of probe-level expression data for each probe set and compare replicates of these values across conditions using a form of the t-test or rank sum test. Here we propose the use of a statistical method that takes advantage of the built-in redundancy architecture of high-density oligonucleotide arrays.     

A practical false discovery rate approach to identifying patterns of differential expression in microarray data

SUMMARY: Searching for differentially expressed genes is one of the most common applications for microarrays, yet statistically there are difficult hurdles to achieving adequate rigor and practicality. False discovery rate (FDR) approaches have become relatively standard; however, how to define and control the FDR has been hotly debated. Permutation estimation approaches such as SAM and PaGE can be effective; however, they leave much room for improvement. We pursue the permutation estimation method and describe a convenient definition for the FDR that can be estimated in a straightforward manner. We then discuss issues regarding the choice of statistic and data transformation. It is impossible to optimize the power of any statistic for thousands of genes simultaneously, and we look at the practical consequences of this. For example, the log transform can both help and hurt at the same time, depending on the gene. We examine issues surrounding the SAM 'fudge factor' parameter, and how to handle these issues by optimizing with respect to power.     

Robust method for detecting differential gene expression in twin studies

MOTIVATION: A steadily increasing number of experiments with microarrays stimulate the further development of the statistical methods of the analysis of gene expression data. One of the central problems in this area is detecting differential gene expression under two or more conditions. Unfortunately, up to now it has not been studied how the correlations between related individuals, such as twins influence the estimates of differential gene expression. RESULTS: In this paper, we discuss this problem and propose a new method that is robust with respect to correlations of gene expression data for twins.     

Probabilistic analysis of probe reliability in differential gene expression studies with short oligonucleotide arrays

Probe defects are a major source of noise in gene expression studies. While existing approaches detect noisy probes based on external information such as genomic alignments, we introduce and validate a targeted probabilistic method for analyzing probe reliability directly from expression data and independently of the noise source. This provides insights into the various sources of probe-level noise and gives tools to guide probe design.     

Local deformation studies of chain molecules: differential conditions for changes of dihedral angles

New first and second-order differential equations for changes of dihedral angles characterizing local deformations of chain molecules with fixed bond lengths and bond angles are derived. Two methods for integrating the differential relations are given. The proposed method is used to generate a path of locally deformed conformations around a β-turn region of a small protein, bovine pancreatic trypsin inhibitor. The variable regions change their conformations by more than 3 Å root-mean-square distance value whereas the fixed regions stay within 0.02 Å. Possible applications of this method are in the field of computer graphics, Monte Carlo simulations, and energy minimization calculations of chain molecules.