Table 1

Parameter settings for biclustering algorithms and post-filtering in the experiments on artificial datasets

Experiment

Algorithm/post-filtering

Parameter settings*

Artificial datasets for additive models

PA

ε = 0.5 – 2.0, N_r = 21, N_c = 5, P_o = 50

C&C

δ = 0.04 – 0.5, α = 1.2, M = 40

pCluster

δ = 0.5 – 1.0, N_r = 21, N_c = 5

Post-filtering

N_r = 21, N_c = 5, P_o = 50 and M = 10

Artificial datasets for multiplicative models

PM

ε = 0.2 – 0.6, N_r = 18, N_c = 4, P_o = 25

PAL

ε = 0.4 – 1.0, N_r = 18, N_c = 4, P_o = 25

C&C

δ = 0.04 – 0.5, α = 1.2, M = 20

pCluster

δ = 0.5 – 1.0, N_r = 18, N_c = 4

Post-filtering

N_r = 18, N_c = 4, P_o = 25 and M = 5

* The definitions of parameters ε, N_r, N_c and P_o follow those defined for the proposed algorithm, i.e. noise threshold, minimum number of rows, minimum number of columns and maximum percentage in overlap allowed in biclusters respectively. Furthermore, M denotes the maximum number of biclusters required and δ of C&C and the pCluster algorithm is defined as in the original publications [12, 25].

Cheng et al. BMC Bioinformatics 2008 9:210   doi:10.1186/1471-2105-9-210