Using the 10
S. pombe time course experimental data (
4,
11,
12), we first obtained the unconstrained phase angle estimates of genes in the core sets FB and FH (Supplementary Tables S2 and S3) which are then used for testing various hypotheses described in this article. Using the estimates in Supplementary Table S2, we tested the hypotheses appearing in Equation (
1) that all 35
S. pombe genes in FB satisfy the relative order specified by the
S. cerevisiae orthologs against the alternative hypothesis that they are not. The null hypothesis is rejected at
P
≤

0.15 in 5 out of 10 experiments (). Of these five experiments, two have a
P
<

0.0001. If the null hypothesis was true in each of the 10 experiments, then the binomial probability of observing two or more experiments (out of 10) with a
P
=

0.0001 is 4.49

×

10
−7, which is extremely small. This suggests that the relative order hypothesized in Equation (
1) may not be true and thus the 35
S. pombe genes do not follow the same relative order as their
S. cerevisiae orthologs. Of course, in the above argument we implicitly assume that the outcomes of the 10 experiments are identically and independently distributed. Although this is a commonly made assumption, we acknowledge that it may be restrictive.
| Table 3.Test for relative order of S. pombe genes in the core set FB (Order specified by S. cerevisiae orthologs) |
A question of interest is whether we can identify a subset of the 35 genes that conserve the relative order between the two yeasts. Since the number of all possible subsets (of various sizes) is extremely large, it would be practically impossible to enumerate all possible subsets of all sizes and then test the null hypotheses such as the one appearing in Equation (
1) for each subset. This problem resembles the classical problem of selection of variables (or model selection) in linear regression analysis. Accordingly, we developed a Forward Selection Algorithm (FSA), which is described in the Supplementary Data. Similar to forward selection procedure in classical linear regression analysis, the FSA proceeds systematically by entering one gene at a time into the test for relative order according to its periodicity rank assigned by the cyclebase. Smaller the rank, the more periodic the gene is and hence its phase angle estimate is more likely to be reliable. The proposed FSA begins with all ortholog pairs that have a cyclebase rank <100. Thus, a gene is included in Step 1 of FSA if both fission yeast as well as the budding yeast orthologs of the gene has a rank <100. Details of the subsequent steps and the implementation of FSA are provided in the Supplementary Data.
Using FSA (Supplementary Table S4), we discover that 28 out of 35 S. pombe genes, namely, cdc18, ssb1, cdc22, msh6, mrc1, pol1, psm3, rad21, cig2, pol2, mik1, h3.3, hhf1, hht3, hta2, htb1, pht1, klp5, fkh2, ace2, plo1, chs2, cdc15, imp2, sid2, slp1, SPAC1705.03C, eng1, potentially satisfy the same order as their S. cerevisiae orthologs. Thus, the relative order of these 28 genes seems to be conserved between the two species of yeasts. For these genes, the null hypothesis is rejected in none of the experiments even at a level of significance as high as 0.30 (). It is also interesting to note that the lack of fit criterion L based on all 35 genes was 46.75 and it dropped to 3.57 for the above 28 genes selected by FSA.
Similar to genes in FB, we also tested the Equation (
2) for genes in the core set FH and found that the relative order was rejected in 6 out of 10 experiments at a
P
<

0.001 (). Using FSA we found
ace2,
cdc18,
mik1,
histones (
hhf1,
hta2),
rnc1,
top2,
cdc25,
plo1 and
slp1, to satisfy the same relative order as their human orthologs (). Among these 10 genes,
ace2,
cdc18,
mik1,
histones (
hhf1,
hta2) and
plo1 also satisfied the relative order specified by their
S. cerevisiae orthologs. Recall that, evolutionarily, humans and fungi are ~1.5 billion years apart and budding yeast and fission yeasts are nearly billion years apart (
21). Thus, it appears that the above six genes are evolutionarily conserved in their relative order of peak expression during the cell division cycle ( and ). These six genes are well known in the literature to play a critical role during cell division cycle. For example, the transcription factor
ace2 and the polo-kinase
plo1 are well-known hubs of early M phase clusters (
22), the cell cycle gene
cdc18 is a key component of pre-replication complexes for the onset of S phase (
23), histones
hhf1,
hta2 play an important role during the S phase and
mik1 is critical in the establishment and maintenance of DNA damage check point (
24).
| Table 4.Test for relative order of S. pombe genes in the core set FH in the 10 experiments (Order specified by H. sapiens orthologs) |
| Table 5.A core set of signature cell cycle genes |
To ensure that our statistical test has sufficient power to detect the alternative hypothesis, i.e. reject the null hypothesis that the genes in both species satisfy the same relative order, we conducted a simulation study for the fission and budding yeast data by randomly permuting the order of the genes in Step 1 of FSA and applied the algorithm. We considered 100 permutations and performed the first step of FSA on each permuted data. The null was rejected for all 100 permutations. We also found that in at least 5 out of the 10 experiments the P <0.05 and this occurred in every one of the 100 random permutations we considered. Note that the binomial probability of observing a
P-value of 0.05 in at least 5 experiments out of 10 experiments by random chance is 6.36

×

10
−5, which is a very unlikely event. Yet, in all 100 random permutations we found 5 out of 10 experiments to have a
P
<0.05, thus suggesting that our test is reasonably powerful to reject the null hypothesis of relative order if the hypothesis is not true. In our simulation study, we did not investigate the power of our test for alternatives where the order among the genes is not well conserved but not entirely random order. As with any statistical test, there will be a reduction in power as we get closer to the null hypothesis. In other words, if the true order is a very minor perturbation of the null hypothesis then probability of rejecting the null hypothesis would be smaller than when true order is substantially different from the null hypothesis. In a future project, we plan to investigate this problem in greater detail.