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Table 4 – Classification of expression patterns for DEGs

From: Evaluation of methods for differential expression analysis on multi-group RNA-seq count data

 

G1 = G2 = G3

G1 > G2 = G3

G1 > G2 > G3

G1 > G3 > G2

G2 > G1 = G3

G2 > G1 > G3

G2 > G3 > G1

G3 > G1 = G2

G3 > G1 > G2

G3 > G2 > G1

Total

all_genes

13.5

2.2

15.1

8.7

2.3

15.9

9.4

2.9

15.1

14.8

20689

common

0.0

0.1

23.2

5.8

0.2

26.4

5.7

0.7

18.6

19.2

2376

EEE-E

0.0

0.6

20.7

7.4

0.7

21.9

8.1

1.6

19.9

19.2

7247

DDD-D

0.0

0.4

25.0

7.3

0.6

25.0

6.0

1.4

17.3

17.1

3850

SSS-S

0.0

0.2

19.3

7.1

0.3

21.7

9.4

0.9

19.9

21.2

7295

E-E (edgeR)

0.0

0.6

20.4

7.3

0.7

22.1

8.3

1.6

19.7

19.3

7247

edgeR_robust

0.0

0.3

20.6

8.4

0.5

22.0

8.8

1.2

19.1

18.9

8076

D-D (DESeq)

0.0

0.4

24.3

7.2

0.6

24.2

6.0

1.4

17.8

18.1

3832

S-S (DESeq2)

0.0

0.2

20.4

8.0

0.3

21.8

8.9

0.8

19.7

19.9

7585

voom

0.0

0.7

21.3

7.7

0.7

22.5

8.2

1.3

18.7

19.0

7016

SAMseq

0.0

0.2

20.9

9.7

0.3

21.8

9.2

0.8

18.9

18.3

9453

PoissonSeq

0.0

0.0

19.5

8.9

0.1

22.2

9.4

0.3

20.3

19.3

6613

baySeq

0.0

0.8

21.0

5.5

1.3

23.7

6.3

2.8

19.0

19.6

3975

EBSeq

0.0

0.0

21.0

7.0

0.1

23.7

7.1

0.3

20.8

19.9

5699

  1. Percentages of genes assigned to each of the ten possible patterns defined as baySeq. Numbers in the “Total” column indicate the numbers of genes. For example, baySeq assigned 13.5 % of 20,689 genes as “G1 = G2 = G3.”