BACKGROUND: Differential expression (DE) analysis of RNA-seq data still poses 
inferential challenges, such as handling of transcripts characterized by low 
expression levels. In this study, we use a plasmode-based approach to assess the 
relative performance of alternative inferential strategies on RNA-seq 
transcripts, with special emphasis on transcripts characterized by a small 
number of read counts, so-called low-count transcripts, as motivated by an 
ecological application in prairie grasses. Big bluestem (Andropogon gerardii) is 
a wide-ranging dominant prairie grass of ecological and agricultural importance 
to the US Midwest while edaphic subspecies sand bluestem (A. gerardii ssp. 
Hallii) grows exclusively on sand dunes. Relative to big bluestem, sand bluestem 
exhibits qualitative phenotypic divergence consistent with enhanced drought 
tolerance, plausibly associated with transcripts of low expression levels. Our 
dataset consists of RNA-seq read counts for 25,582 transcripts (60% of which are 
classified as low-count) collected from leaf tissue of individual plants of big 
bluestem (n = 4) and sand bluestem (n = 4). Focused on low-count transcripts, we 
compare alternative ad-hoc data filtering techniques commonly used in RNA-seq 
pipelines and assess the inferential performance of recently developed 
statistical methods for DE analysis, namely DESeq2 and edgeR robust. These 
methods attempt to overcome the inherently noisy behavior of low-count 
transcripts by either shrinkage or differential weighting of observations, 
respectively.
RESULTS: Both DE methods seemed to properly control family-wise type 1 error on 
low-count transcripts, whereas edgeR robust showed greater power and DESeq2 
showed greater precision and accuracy. However, specification of the degree of 
freedom parameter under edgeR robust had a non-trivial impact on inference and 
should be handled carefully. When properly specified, both DE methods showed 
overall promising inferential performance on low-count transcripts, suggesting 
that ad-hoc data filtering steps at arbitrary expression thresholds may be 
unnecessary. A note of caution is in order regarding the approximate nature of 
DE tests under both methods.
CONCLUSIONS: Practical recommendations for DE inference are provided when 
low-count RNA-seq transcripts are of interest, as is the case in the comparison 
of subspecies of bluestem grasses. Insights from this study may also be relevant 
to other applications focused on transcripts of low expression levels.