Web supplement to
"Analysis of a genome-wide set of gene deletions in the fission yeast Schizosaccharomyces pombe"

Analysis of microarray results

For growth profiling of pooled strains in rich media YES, the custom-made GeneChip, KRIBB_SP2 (48K) was used. The experimental strategy used in the S. cerevisiae growth fitness assay was slightly modified and used for the growth fitness profiling of S. pombe in rich media1,6. For the analysis of microarray results, the Analysis of Covariance (ANCOVA) model was used as a statistical tool.

1) Preprocessing and normalization:

For growth profiling, data was collected from six independent experiments using two different pool sets. Four independent experiments were performed with poolA and two with poolB (see the below Supplemental Methods 2 Table-1 for heterozygous pool Het_A and Het_B). First, "A qualified-tag set" which is defined as tags representing four-fold greater intensity than mean of hybridization signals (mean of array background) was selected based on six hybridizations at time-zero control (i.e., frozen stock). Total 1,193 tags were failed to meet the criteria of "qualified-tags". Out of 4,441 mutants in the deletion pool, 3523 mutants were represented by both UPTAG and DNTAG, and 811 mutants were represented by at least one of two tags (Supplemental Methods 2 Table-2). Therefore, at least one of the tags from 4,334 strains was detectable by chip analysis. The remaining, 107 non-qualified tags had an intensity less than four-fold of background intensity and were removed from the analysis. Each array signal was normalized by a mean-intensity (i.e., 2,500 arbitrary units).

[Supplemental Methods2] Table 1: Microarray dataset for growth profiling

CEL_file Media Pool Replicate number Cumulative generation
Exp032-01.CEL rich.media Het_A 1 3.618*
Exp032-02.CEL rich.media Het_A 1 6.676
Exp032-03.CEL rich.media Het_A 1 9.483
Exp032-04.CEL rich.media Het_A 1 12.7
Exp032-05.CEL rich.media Het_A 1 15.664
Exp032-06.CEL rich.media Het_A 1 19.049
Exp032-07.CEL rich.media Het_A 1 22.604
Exp032-08.CEL rich.media Het_B 2 3.867*
Exp032-09.CEL rich.media Het_B 2 7.115
Exp032-10.CEL rich.media Het_B 2 9.922
Exp032-11.CEL rich.media Het_B 2 13.185
Exp032-12.CEL rich.media Het_B 2 16.553
Exp032-13.CEL rich.media Het_B 2 19.67
Exp032-14.CEL rich.media Het_B 2 23.276
Exp032-15.CEL rich.media Het_B 3 4.155*
Exp032-16.CEL rich.media Het_B 3 7.448
Exp032-17.CEL rich.media Het_B 3 10.175
Exp032-18.CEL rich.media Het_B 3 13.494
Exp032-19.CEL rich.media Het_B 3 16.953
Exp032-20.CEL rich.media Het_B 3 20.282
Exp032-21.CEL rich.media Het_B 3 23.946
Exp033-01.CEL rich.media Het_A 4 3.571*
Exp033-02.CEL rich.media Het_A 4 7.96
Exp033-03.CEL rich.media Het_A 4 11.4
Exp033-04.CEL rich.media Het_A 4 14.711
Exp033-05.CEL rich.media Het_A 4 17.936
Exp033-06.CEL rich.media Het_A 4 21.276
Exp033-07.CEL rich.media Het_A 4 24.725
Exp033-08.CEL rich.media Het_B 5 3.390*
Exp033-09.CEL rich.media Het_B 5 7.843
Exp033-10.CEL rich.media Het_B 5 11.168
Exp033-11.CEL rich.media Het_B 5 14.487
Exp033-12.CEL rich.media Het_B 5 17.889
Exp033-13.CEL rich.media Het_B 5 21.275
Exp033-14.CEL rich.media Het_B 5 24.721
Exp033-15.CEL rich.media Het_B 6 3.122*
Exp033-16.CEL rich.media Het_B 6 7.54
Exp033-17.CEL rich.media Het_B 6 10.811
Exp033-18.CEL rich.media Het_B 6 14.312
Exp033-19.CEL rich.media Het_B 6 17.659
Exp033-20.CEL rich.media Het_B 6 21.006
Exp033-21.CEL rich.media Het_B 6 24.415

[Supplemental Methods2] Table 2: Numbers of deletion strains represented by qualified-tags

TagNumber of strains detectable in Pool A and B
Both tags3,523
One tag: UPTAG or DNTAG811
At least one tag4,334

2) Calculation of regression coefficients from time-course data:

Growth fitness was estimated from regression slopes, which were determined by a linear model corresponding to a multiple-regression model on time (measured in generations and treated as a quantitative predictor) and replicate series (treated as a categorical predictor) simultaneously. This analysis provides estimates of statistical significance using the F-statistic. The results of the ANCOVA were interpreted as a linear regression, where the F-statistic provides P-values. This analysis was performed using an additive linear dummy regression model with interactions in statistical language R for each tag. We added 1 to the tag regression slope to obtain a relative tag fitness where values <1 indicate a fitness defect. Click here more information

3) Calculation of strain fitness:

A tag-intensity for each deletion strain in a given pool was averaged and is referred to as heterozygous Het_YES_A and Het_YES_B. The following criteria were used to identify heterozygous strains with reduced fitness: both Het_YES_A and het_YES_B fitness values had to be < 0.98 and at least one tag for that gene had to be statistically significant (P < 0.05) in both pools as determined by ANCOVA. To calculate the Het_AV (AVerage) fitness measure for each gene, fitness values from the qualified-tags were averaged across both the A and B heterozygous pools. Those fitness scores of Het_YES_A, Het_YES_B and Het_AV are plotted as shown below.


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