Background. A common research goal in transcriptome projects is to find genes 
that are differentially expressed in different phenotype classes. Biologists 
might wish to validate such gene candidates experimentally, or use them for 
downstream systems biology analysis. Producing a coherent differential gene 
expression analysis from RNA-seq count data requires an understanding of how 
numerous sources of variation such as the replicate size, the hypothesized 
biological effect size, and the specific method for making differential 
expression calls interact. We believe an explicit demonstration of such 
interactions in real RNA-seq data sets is of practical interest to biologists. 
Results. Using two large public RNA-seq data sets-one representing strong, and 
another mild, biological effect size-we simulated different replicate size 
scenarios, and tested the performance of several commonly-used methods for 
calling differentially expressed genes in each of them. We found that, when 
biological effect size was mild, RNA-seq experiments should focus on 
experimental validation of differentially expressed gene candidates. 
Importantly, at least triplicates must be used, and the differentially expressed 
genes should be called using methods with high positive predictive value (PPV), 
such as NOISeq or GFOLD. In contrast, when biological effect size was strong, 
differentially expressed genes mined from unreplicated experiments using NOISeq, 
ASC and GFOLD had between 30 to 50% mean PPV, an increase of more than 30-fold 
compared to the cases of mild biological effect size. Among methods with good 
PPV performance, having triplicates or more substantially improved mean PPV to 
over 90% for GFOLD, 60% for DESeq2, 50% for NOISeq, and 30% for edgeR. At a 
replicate size of six, we found DESeq2 and edgeR to be reasonable methods for 
calling differentially expressed genes at systems level analysis, as their PPV 
and sensitivity trade-off were superior to the other methods'. Conclusion. When 
biological effect size is weak, systems level investigation is not possible 
using RNAseq data, and no meaningful result can be obtained in unreplicated 
experiments. Nonetheless, NOISeq or GFOLD may yield limited numbers of gene 
candidates with good validation potential, when triplicates or more are 
available. When biological effect size is strong, NOISeq and GFOLD are effective 
tools for detecting differentially expressed genes in unreplicated RNA-seq 
experiments for qPCR validation. When triplicates or more are available, GFOLD 
is a sharp tool for identifying high confidence differentially expressed genes 
for targeted qPCR validation; for downstream systems level analysis, combined 
results from DESeq2 and edgeR are useful.