Comparison of RNA-Seq Differential Expression Methods

1. Anders, S., W. Huber. Differential Expression Analysis for Sequence Count Data. - Genome Biology, Vol. 11, 2010, R106. https://doi.org/10.1186/gb-2010-11-10-r10610.1186/gb-2010-11-10-r106Open DOISearch in Google Scholar

2. Love, M. I., W. Huber, S. Anders. Moderated Estimation of Fold Change and Dispersion for RNA-seq Data with DESeq2. - Genome Biology, Vol. 12, 2014, No 12, 550. https://doi.org/10.1186/s13059-014-0550-810.1186/s13059-014-0550-8Open DOISearch in Google Scholar

3. Robinson, M. D., D. J. Mccarthy, G. K. Smyth. EdgeR: A Bioconductor Package for Differential Expression Analysis of Digital Gene Expression Data. - Bioinformatics, Vol. 26, 2010, No 1, 149-140. https://doi.org/10.1093/bioinformatics/btp61610.1093/bioinformatics/btp616Open DOISearch in Google Scholar

4. Law, C. W., Y. Chen, W. Shi, G. Smyth. Voom: Precision Weights Unlock Linear Model Analysis Tools for RNA-seq Read Counts. - Genome Biology, Vol. 15, 2014, R29. https://doi.org/10.1186/gb-2014-15-2-r2910.1186/gb-2014-15-2-r29Open DOISearch in Google Scholar

5. Bottomly, D., N. A. R. Walter, J. E. Hunter et al. Evaluating Gene Expression in C57BL/6J and DBA/2J Mouse Striatum Using RNA-Seq and Microarrays. - PLoS ONE, Vol. 6, 2011, No 3, e17820. https://doi.org/10.1371/journal.pone.001782010.1371/journal.pone.0017820Open DOISearch in Google Scholar

6. Frazee, A. C., B. Langmead, J. T. Leak. ReCount: A Multi-Experiment Resource of Analysis-Ready RNA-Seq Gene Count Datasets. - BMC Bioinformatics, Vol. 12, 2011, 449. https://doi.org/10.1186/1471-2105-12-44910.1186/1471-2105-12-449Open DOISearch in Google Scholar

7. Soneson, C., M. Delorenz i. A Comparison of Methods for Differential Expression Analysis of RNA-Seq Data. - BMC Bioinformatics, Vol. 14, 2013, 91. https://doi.org/10.1186/1471-2105-14-9110.1186/1471-2105-14-91Open DOISearch in Google Scholar

8. Conesa, A., P. Madrigal, S. Tarazona et al. A Survey of Best Practices for RNA-Seq Data Analysis. - Genome Biology, Vol. 17, 2016, 13. https://doi.org/10.1186/s13059-016-0881-810.1186/s13059-016-0881-8Search in Google Scholar

9. Rapaport, F., R. Khanin, Y. Lianget al. Comprehensive Evaluation of Differential Gene Expression Analysis Methods for RNA-Seq Data - Genome Biology, Vol. 19, 2013, No 9, R95. https://doi.org/10.1186/gb-2013-14-9-r9510.1186/gb-2013-14-9-r95Open DOISearch in Google Scholar

10. Atanassov, A., T. Gurov, A. Karaivan o v a et al. On the Parallelization Approaches for Intel MIC Architecture. - AIP Conference Proceedings, Vol. 1773, 2016, No 070001. https://doi.org/10.1063/1.496498310.1063/1.4964983Search in Google Scholar

11. Wu, H., C. Wang, Z. Wu. A New Shrinkage Estimator for Dispersion Improves Differential Expression Detection in RNA-Seq Data. - Biostatistics, Vol. 14, 2013, No 2, 232-43. https://doi.org/10.1093/biostatistics/kxs03310.1093/biostatistics/kxs033Open DOISearch in Google Scholar

12. Di, Y., D. Schafer, J. S. Cumbie, J. H. Chang. The NBP Negative Binomial Model for Assessing Differential Gene Expression from RNA-Seq. - Statistical Applications in Genetics and Molecular Biology, Vol. 10, 2011, No 1. https://doi.org/10.2202/1544-6115.163710.2202/1544-6115.1637Search in Google Scholar

13. Sun, J., T. Nishiyama, K. Shimizu, K. Kadota. TCC: An R Package for Comparing Tag Count Data with Robust Normalization Strategies. - BMC Bioinformatics, Vol. 14, 2013, 219. https://doi.org/10.1186/1471-2105-14-21910.1186/1471-2105-14-219Open DOISearch in Google Scholar

14. Benjamini, Y., Y. Hohber g. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. - Journal of the Royal Statistical Society. Series B (Methodological), Vol. 59, 1995, No 1, pp. 289-300.10.1111/j.2517-6161.1995.tb02031.xSearch in Google Scholar

15. Stoimenova, E. Comparison of Partially Ranked Lists. - Austrian Journal of Statistics, Vol. 46, 2014, No 3-4, pp. 107-115. https://doi.org/10.17713/ajs.v46i3-4.676 10.17713/ajs.v46i3-4.676Open DOISearch in Google Scholar

16. Ferguson, J. P., D. Paleje v. p-Value Calibration for Multiple Testing Problems in Genomics - Statistical Applications in Genetics and Molecular Biology, Vol. 13, 2014, No 6, pp. 659-673. https://doi.org/10.1515/sagmb-2013-007410.1515/sagmb-2013-0074Open DOISearch in Google Scholar