Display Settings:


Send to:

Choose Destination
See comment in PubMed Commons below
BMC Bioinformatics. 2010 Nov 16;11:564. doi: 10.1186/1471-2105-11-564.

Empirical bayes analysis of sequencing-based transcriptional profiling without replicates.

Author information

  • 1Center for Statistical Sciences and Department of Community Health, Box G-121S-7, Brown University, Providence RI 02912, USA. zwu@stat.brown.edu



Recent technological advancements have made high throughput sequencing an increasingly popular approach for transcriptome analysis. Advantages of sequencing-based transcriptional profiling over microarrays have been reported, including lower technical variability. However, advances in technology do not remove biological variation between replicates and this variation is often neglected in many analyses.


We propose an empirical Bayes method, titled Analysis of Sequence Counts (ASC), to detect differential expression based on sequencing technology. ASC borrows information across sequences to establish prior distribution of sample variation, so that biological variation can be accounted for even when replicates are not available. Compared to current approaches that simply tests for equality of proportions in two samples, ASC is less biased towards highly expressed sequences and can identify more genes with a greater log fold change at lower overall abundance.


ASC unifies the biological and statistical significance of differential expression by estimating the posterior mean of log fold change and estimating false discovery rates based on the posterior mean. The implementation in R is available at http://www.stat.brown.edu/Zwu/research.aspx.

[PubMed - indexed for MEDLINE]
Free PMC Article

Images from this publication.See all images (7)Free text

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
PubMed Commons home

PubMed Commons

How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for BioMed Central Icon for PubMed Central
    Loading ...
    Write to the Help Desk