Empirical bayes analysis of sequencing-based transcriptional profiling without replicates
1 Center for Statistical Sciences and Department of Community Health, Box G-121S-7, Brown University, Providence RI 02912, USA
2 Department of Cell and Molecular Biology The University of Rhode Island, 120 Flagg Road, Kingston, RI 02881, USA
3 The Graduate School of Oceanography, University of Rhode Island, South Ferry Road, Narragansett, RI 02882, USA
4 Biology Department, Woods Hole Oceanographic Institution, Woods Hole MA 02543, USA
5 Marine Chemistry and Geochemistry Department, Woods Hole Oceanographic Institution, 360 Woods Hole Rd, Woods Hole MA 02543, USA
BMC Bioinformatics 2010, 11:564 doi:10.1186/1471-2105-11-564Published: 16 November 2010
Additional file 1:
Figure S1. A. Scatter plot of the log10 rpm in the A and B samples. Differentially expressed genes identified by DGEseq with estimated q-value (Storey FDR) less than 0.01 highlighted in red and the genes with smallest q-value 1000 of which highlighted in blue. B. Distribution of average rpm for the highlighted red or blue genes.
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Additional file 2:
Figure S2. Histogram of the apparent fold change of the top 1000 genes found by DGEseq or ASC.
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Additional file 3:
Figure S3. Sensitivity of to hyper-parameter estimation. Scatter plot of and , based on q1 = 0.8, q2 = 0.9 and q1 = .9, q2 = 0.95, respectively. The maximum difference in estimated fold change is less than 0.04, indicating that is not sensitive to the choice of q.
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