RNA-Sequencing
(RNA-Seq) provides valuable information for characterizing the molecular nature
of the cells, in particular, identification of differentially expressed
transcripts on a genome-wide scale. Unfortunately, cost and limited specimen
availability often lead to studies with small sample sizes, and hypothesis
testing on differential expression between classes with a small number of
samples is generally limited. The problem is especially challenging when only
one sample per each class exists. In this case, only a few methods among many
that have been developed are applicable for identifying differentially
expressed transcripts. Thus, the aim of this study was to develop a method able
to accurately test differential expression with a limited number of samples, in particular
non-replicated samples. We propose a local-pooled-error
method for RNA-Seq data (LPEseq) to account for non-replicated samples in the
analysis of differential expression. Our LPEseq method extends the existing LPE
method, which was proposed for microarray data, to allow examination of
non-replicated RNA-Seq experiments. We demonstrated the validity of the LPEseq method using both real and
simulated datasets. By comparing the results obtained using the LPEseq method
with those obtained from other methods, we found that the LPEseq method
outperformed the others for non-replicated datasets, and showed a similar
performance with replicated samples; LPEseq consistently showed high true discovery
rate while not increasing the rate of false positives regardless of the number
of samples. Our proposed LPEseq method can be effectively used to conduct
differential expression analysis as a preliminary design step or for
investigation of a rare specimen, for which a limited number of samples is
available<br
