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Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data

Franck Rapaport1, Raya Khanin1, Yupu Liang1, Mono Pirun1, Azra Krek1, Paul Zumbo23, Christopher E Mason23, Nicholas D Socci1 and Doron Betel34*

Author Affiliations

1 Bioinformatics Core, Memorial Sloan-Kettering Cancer Center, New York, NY, 10065, USA

2 Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY, 10021, USA

3 Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY, 10021, USA

4 Division of Hematology/Oncology, Department of Medicine, Weill Cornell Medical College, New York, NY, 10021, USA

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Genome Biology 2013, 14:R95  doi:10.1186/gb-2013-14-9-r95

Published: 10 September 2013

Additional files

Additional file 1:

Supplementary figures. All the supplementary figures referenced in the main text. 1 Hierarchical clustering of the SEQC libraries from sample A and B . . . . . . . . . . 3. 2 Hierarchical clustering of the ENCODE samples .................... 4. 3 Dunn clustering validity index............................... 5. 4 Normalized read counts .................................. 6. 5 ROC analysis of ERCC spike-in controls.........................7. 6 Null model p-values distribution without replicate samples . . . . . . . . . . . . . . . 8. 7 Evaluating monotonic correlation between signal-to-noise and p-values in genes expressed in only one condition ............................... 9. 8 Correlation of signal-to-noise ratio and DE p-values from SEQC data set . . . . . . . 10. 9 Methods performances with reduced sequencing depth and number of replicates for detecting DE between GM12892 and H1-hESC............... 11. 10 Methods performances with reduced sequencing depth and number of replicates for detecting DE between H1-hESC and MCF-7.................. 12. 11 Impact of sequencing depth and number of replicate samples on DE detection by DESeq using SEQC data.................................. 13. 12 Impact of sequencing depth and number of replicate samples on DE detection by edger using SEQC data.................................. 14. 13 Impact of sequencing depth and number of replicate samples on DE detection by limmaQN using SEQC data................................ 15. 14 Impact of sequencing depth and number of replicate samples on DE detection by limmaVoom using SEQC data............................. 16. 15 Impact of sequencing depth and number of replicate samples on DE detection by PoissonSeq using SEQC data ............................. 17. 16 Over-dispersion of the ENCODE dataset ........................ 18}.

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