Table 2

The seven most significant linear ICA clusters from the yeast cell cycle data (Dataset 2)

Cluster

Number of ORFs

GO/KEGG functional categories

Number of ORFs within functional category

p value (log10)


1

215

Protein biosynthesis (175)

93

-60.1

217

Structural constituent of ribosome (118)

83

-67.6

157

Cytosolic ribosome (94)

83

-73.1

229

Ribosome (96)

83

-82.5

5

208

Cell cycle (220)

61

-19.5

202

DNA-directed DNA polymerase (13)

7

-4.7

115

Replication fork (30)

16

-9.6

229

Cell cycle (58)

18

-6.6

11

2,072

Sulfur amino acid metabolism (12)

11

-11.1

211

Structural constituent of cytoskeleton (25)

11

-5.9

125

Spindle (32)

18

-10.62

9

209

Ribosome biogenesis (38)

15

-7.1

207

RNA binding (75)

7

-3.4

111

Nucleus (334)

54

-7.3

7

198

Glutamine family amino acid biosynthesis (11)

8

-6.8

99

Mitochondrion (353)

22

-3.8

3

209

Protein folding (26)

11

-5.7

212

Heat shock protein (14)

9

-6.7

11

199

DNA unwinding (10)

6

-4.5

192

ATP-dependent DNA helicase (7)

6

-6.0

85

Pre-replicative complex (8)

6

-5.7

216

Cell cycle (58)

13

-3.6


The cluster IDs are shown, where cluster Ci,1in Equation 3 is denoted by 2i-1 and a cluster Ci,2 is denoted by 2i. The number of genes in the cluster that have at least one annotation in GO or KEGG are listed along with the functional category with the smallest p-value among those in each annotation system. Four annotation systems are used: biological process (GO), molecular function (GO), cellular component (GO) and KEGG. Numbers in parentheses show the number of genes within the functional category that are present in the microarray data. Functional categories with p-values higher than 10-3 are discarded, and those with values higher than 10-7 are not considered to be significant. The number of genes shared by the cluster and the functional category is shown with the log10 of the p-values corresponding to each functional category for the cluster.

Lee and Batzoglou Genome Biology 2003 4:R76   doi:10.1186/gb-2003-4-11-r76

Open Data