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Jun 24th, 2019
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  1. > dim(exprs_mat)
  2. [1] 112830 735
  3. > exprs_mat[1:4, 1:3]
  4. Tarca_001_P1A01 Tarca_003_P1A03 Tarca_004_P1A04
  5. 1_at 6.062215 6.125023 5.875502
  6. 10_at 3.796484 3.805305 3.450245
  7. 100_at 5.849338 6.191562 6.550525
  8. 1000_at 3.567779 3.452524 3.316134
  9.  
  10. > dim(ano)
  11. [1] 735 6
  12. > head(ano)
  13. SampleID GA Batch Set Train Platform
  14. Tarca_001_P1A01 Tarca_001_P1A01 11.0 1 PRB_HTA 1 HTA20
  15. Tarca_013_P1B01 Tarca_013_P1B01 15.3 1 PRB_HTA 1 HTA20
  16. Tarca_025_P1C01 Tarca_025_P1C01 21.7 1 PRB_HTA 1 HTA20
  17. Tarca_037_P1D01 Tarca_037_P1D01 26.7 1 PRB_HTA 1 HTA20
  18. Tarca_049_P1E01 Tarca_049_P1E01 31.3 1 PRB_HTA 1 HTA20
  19. Tarca_061_P1F01 Tarca_061_P1F01 32.1 1 PRB_HTA 1 HTA20
  20.  
  21. library(limma)
  22. fit <- limma::lmFit(exprs_mat, design = model.matrix( ~ 0 + t(ano$GA))
  23. fit <- eBayes(fit)
  24. topTable(fit, coef=2)
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