Supplementary Information


Problem statements

BiVisu can detect biclusters of constant, constant rows, constant columns, additive and multiplicative types as defined in [5]. Fig. 1 shows examples of these biclusters. The additive type biclusters can be described by the following equation

aij = m + ai + bj + eij

where aij is an expression value at row i and column j. m is a constant, ai is a parameter varying with row, bj is a parameter varying with column and eij is the error term of the model. It can be easily verified that the additive models are able to describe constant, constant rows and constant columns but not multiplicative models. For multiplicative models, the expression values are given by

aij = m ´ ai ´ bj + eij

where the symbols are defined in the same way as in the additive model. Apart from the additive-related biclusters, the biclusters of constant, constant rows and constant columns types can be formulated as the multiplicative models. In both additive and multiplicative models, the error term eij is used to compensate for the noise in the data. Besides noise, the real gene expression data usually has very high dimension (especially in rows). A good biclustering algorithm should be able to detect biclusters with minimum errors under noisy environment within an acceptable time period.

6 6 6
6 6 6
6 6 6
8 8 8
5 5 5
11 11 11
8 5 11
8 5 11
8 5 11
(a) (b) (c)
2 7 4
1 6 3
3 8 5
8 4 20
4 2 10
12 6 30
(d) (e)
Fig.1. Different types of biclusters: (a) constant (b) constant rows (c) constant Columns (d) additive and (e) multiplicative.



(a) (b) (c)
(d) (e)
Fig.2. Parallel Coordinate (PC) plots of biclusters: (a) constant (b) constant rows (c) constant columns (d) additive and (e) multiplicative.

Parallel Coordinate Plots

Parallel coordinate (PC) plots allow visualization of high dimensional data in a 2D plane. Besides visualization, it has been exploited to re-formulate the biclustering problem [1]. The PC plots of constant, constant rows, constant columns, additive and multiplicative types are illustrated in Fig. 2. The coherence of the first four bicluster types can be easily seen in their PC plots. The formulation can be further simplified if a difference matrix is considered. Fig. 3 shows the PC plot of the element-wise difference between each column and the first column (reference column) for the additive-related bicluster in Fig. 1(d). As seen in Fig. 2(e), the multiplicative-related bicluster is usually harder to observe. With similar formulation for additive biclusters, the observation of coherence of multiplicative-related biclusters can be facilitated by considering the element-wise ratio between each column and the first column (reference column) as demonstrated in Fig. 4. These observations form the basic idea of clustering operations in the biclustering algorithm as described in [1].

Fig. 3. The PC plot of the element-wise difference between each column and the first column (reference column) for the additive-related bicluster in Fig. 2(d).
Fig. 4. The PC plot of the element-wise ratio between each column and the first column (reference column) for the multiplicative-related bicluster in Fig. 2(e).

Biclustering Algorithms

As seen in Fig.3 and Fig.4, coherent genes in a bicluster are clustered along the modified axes in the PC plot. In the problem of biclustering, the conditions at which a subset of genes co-expresses are not known in advance. In order to cluster the genes (rows) and conditions (columns) simultaneously, clustering of rows is first performed for each pair of columns in our proposed algorithm [1]. Further columns are then merged to form a big bicluster. Here we demonstrate the idea using an example with one constant bicluster, one constant column bicluster and one additive-related bicluster as given in Fig.5. These biclusers will be referred as biclusters 1, 2 and 3 respectively in the subsequent discussion.

  C1 C2 C3 C4 C5 C6
R1 2 4 6 1 8 8
R2 2 5 3 6 1 2
R3 2 4 6 3 8 8
R4 1 0 2 5 0 1
R5 0 4 4 6 2 3
R6 2 4 6 2 2 8
R7 6 3 5 1 3 4
R8 2 4 6 0 1 8
R9 4 1 9 1 1 0
R10 2 1 7 1 1 8
R11 3 7 0 8 4 6
R12 5 1 8 1 1 9
Fig.5. An expresson matrix which consists of three biclusters: a constant bicluster (bicluster 1 highlighted in light blue color), a constant column bicluster (bicluster 2 highlighted in green color) and an additive-related bicluster (bicluster 3 highlighted in yellow color).

As the constant bicluster and constant column bicluster can be described using the additive model, difference matrix given in Fig.6 is thus computed. In the seventh column of the difference matrix, which contains the element-wise differences between C2 and C4, there are eight distinct values. According to these eight distinct values, the elements can be clustered into eight groups. For example, elements in R9, R10 and R12 are grouped as their entries are all zero. The overall clustering results are summarized in Table.I. As seen, besides group 4, the counts for all other groups are at most two. These groups are too small to be considered as valid biclusters. Thus only group 4 which consists of R9, R10 and R12 is retained. In this subset of rows, C2 and C4 can be merged together as in Fig. 7(a). Also, C2 can be selected to compare with other columns. From the difference matrix given in Fig.7(b), only the column differences between C2 and C5 can form a sufficient large cluster. Therefore, C5 is joined with C2 and C4. The subset of rows then becomes {R9, R10, R12} while the subset of columns is {C2, C4, C5}. Thus, bicluster 1 is detected.

  C1-C2 C1-C3 C1-C4 C1-C5 C1-C6 C2-C3 C2-C4 C2-C5 C2-C6 C3-C4 C3-C5 C3-C6 C4-C5 C4-C6 C5-C6
R1 -2 -4 1 -6 -6 -2 3 -4 -4 5 -2 -2 -7 -7 0
R2 -3 -1 -4 1 0 2 -1 4 3 -3 2 1 5 4 -1
R3 -2 -4 -1 -6 -6 -2 1 -4 -4 3 -2 -2 -5 -5 0
R4 1 -1 -4 1 0 -2 -5 0 -1 -3 2 1 5 4 -1
R5 -4 -4 -6 -2 -3 0 -2 2 1 -2 2 1 4 3 -1
R6 -2 -4 0 0 -6 -2 2 2 -4 4 4 -2 0 -6 -6
R7 3 1 5 3 2 -2 2 0 -1 4 2 1 -2 -3 -1
R8 -2 -4 2 1 -6 -2 4 3 -4 6 5 -2 -1 -8 -7
R9 3 -5 3 3 4 -8 0 0 1 8 8 9 0 1 1
R10 1 -5 1 1 -6 -6 0 0 -7 6 6 -1 0 -7 -7
R11 -4 3 -5 -1 -3 7 -1 3 1 -8 -4 -6 4 2 -2
R12 4 -3 4 4 -4 -7 0 0 -8 7 7 -1 0 -8 -8
Fig.6. The difference matrix of the whole expression matrix in Fig. 5.


Table I. Results of clustering based on distinct difference values between C2 and C4.
group 1 2 3 4 5 6 7 8
values -5 -2 -1 0 1 2 3 4
counts 1 1 2 3 1 2 1 1


  {C2,C4} C1 C3 C5 C6
R9 {1,1} 4 9 1 0
R10 {1,1} 2 7 1 8
R12 {1,1} 5 8 1 9
  C1-C2 C3-C2 C5-C2 C6-C2
R9 3 8 0 -1
R10 1 6 0 7
R12 4 7 0 8
(a) (b)
Fig.7. Comparison of columns C1, C3, C5 and C6 with {C2, C4}: (a) in expression values and (b) in a difference matrix.

The other two biclusters can be found similarly by considering the twelfth column of the difference matrix (Fig. 6), which consists of the element-wise differences between C3 and C6 . The twelfth column of the difference matrix has five distinct values so five groups can be formed as shown in Table.II. Among these five groups, only groups 2 and 4 are large enough to be considered as bicluster candidates. In this way, the rows are split into two groups; group 2 consisting of R1, R3, R6 and R8 and group 4 consisting of R2, R4, R5 and R7. After that, C3 is compared with columns other than C3 and C6 over the rows in group 2. A difference matrix shown in Fig.8(b) is calculated. As seen from Fig. 8(b), the difference values in C1-C3 are all equal to -4 Therefore, {C3, C6} and {C1} are merged to a single group for rows {R1, R3, R6 and R8}. This “merge and split process”, i.e. merging paired columns and splitting in rows is performed for other columns. It can be found that C2 can be further merged into the column cluster while the row cluster remains unchanged. The final clustering in rows and columns gives bicluster 2. In the other row group split at the beginning, i.e. group 4, the “merge and split” process is applied. As demonstrated in Fig.9, this results in bicluster 3. Actually, the "merge and split" process is repeated for each column-pair. Since there is either no significantly large bicluster found or the same biclusters are detected, only the three biclusters are obtained from BiVisu. That is, the same set of bicluster embedded in the dataset are detected.

Table II. The distinct difference values between C3 and C6 and the corresponding number of counts.
group 1 2 3 4 5
values -6 -2 -1 1 9
counts 1 4 2 4 1


  {C3,C6} C1 C2 C4 C5
R1 {6,8} 2 4 1 8
R3 {6,8} 2 4 3 8
R6 {6,8} 2 4 2 2
R8 {6,8} 2 4 0 1
  C1-C3 C2-C3 C4-C3 C5-C3
R1 -4 -2 -5 2
R3 -4 -2 -3 2
R6 -4 -2 -4 -4
R8 -4 -2 -6 -5
(a) (b)
Fig.8. Comparison of columns C1, C2, C4 and C5 with {C3, C6}: (a) in expression values and (b) in a difference matrix.


  {C3,C6} C1 C2 C4 C5
R2 {3,2} 2 5 6 1
R4 {2,1} 1 0 5 0
R5 {4,3} 0 4 6 2
R7 {5,4} 6 3 1 3
  C1-C3 C2-C3 C4-C3 C5-C3
R2 -1 2 3 -2
R4 -1 -2 3 -2
R5 -4 0 2 -2
R7 1 -2 -4 -2
(a) (b)
Fig.9. Comparison of columns C1, C2, C4 and C5 with {C3, C6}: (a) in expression values and (b) in a difference matrix.

References

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