Microarray data was collected from a variety of sources to create our compendium, including NCBI's Gene Expression Omnibus, EBI's ArrayExpress, the Stanford Microarray Database, and several other publication and laboratory web pages. These data from 81 publications, totaling 2394 array hybridizations, were broken down into their smallest logical groupings of conditions. For example, the stress response dataset from (Gasch et al., 2000) originally consisted of 142 hybridizations corresponding to several different types of induced stress and growth phases. We have separated this dataset in a manner similar to the authors' analyses, resulting in 21 logical datasets such as "hydrogen peroxide exposure," "osmotic shock," and "heat shock from 25° to 37°."

In order to make valid comparisons between the datasets collected, all data was normalized in a similar manner. First, suspect values were removed (i.e. missing values were inserted) in all data based on the information available in the original publication where possible, or in a manner appropriate to the microarray platform used. After identifying missing values, any genes present in less than 50% of the conditions in a dataset were removed from that dataset. Remaining missing values were imputed using the KNN impute algorithm with K=10 using Euclidean distance to identify nearest neighbors. After the imputation process, technical replicates were averaged together, resulting in data files of complete matrices with one entry per gene appearing in the dataset.

Most of the data collected falls into two main categories: dual-color competitive hybridization data and single-channel data. Dual-color data was typically found in log ratio format or was transformed into this format. Single-channel data was typically from Affymetrix platforms and was log transformed as a final step in normalization. Other types of data were transformed into a format as close as possible to these sources.

The full list of publications and datasets collected for our functional analysis and search engine are available as a tab-delimited text file and in html:
txt
html

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The distribution of Pearson correlations between all pairs of genes within each dataset varies greatly depending on the number of conditions in each dataset, the process targeted, and the array platform used. In order to ensure comparable measures of correlation from one dataset to the next, we employ the Fisher z-transformation as described in the main text. This produces distributions of pair-wise correlations that are approximately normal for all datasets.

Histograms of correlations between all gene pairs
DeRisi et al., 1997	Primig et al., 2000
DeRisi et al., 1997	Primig et al., 2000

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The full table of functional coverage consists of a matrix containing pseudo p-values (based on the z-test for significance) for each combination of dataset and GO biological process examined. These files are available as a tab-demilited text file of p-values, a (very large) image, and a hierarchically clustered version compatible with JavaTreeView for browsing. Note that a p-value of ~10^-10 corresponds to the Bonferroni corrected p-value of 10^-4 which was used for significance testing in Figure 1.
txt
picture
JavaTreeView Compatible

In addition to the z-test results, we have also calculated significance based on the non-parametric two-sample Kolmogorov-Smirnov test. The results of the KS-test show significance for generally the same GO term/dataset pairings, however several more pairs are also found to be significant. This is due to the fact that the KS-test can judge distributions significantly different if the shapes are sufficiently different, while the means are very similar. As the z-test is based on differences in means, it would not consider such distributions to be significantly different. The full results of the KS-test are available here in a tab-delimited text file:
KS results

Supplemental Table 1
Our analysis of the functional coverage of existing gene expression microarray data for S. cerevisiae characterizes both which biological processes are represented in each dataset and which biological processes are represented in existing data as a whole. This table shows a selection of processes that are significant in many datasets (left column), significant in some, but not many datasets (center column), and significant in very few datasets (right column). Of those processes under-represented in the compendium, there are three major explanations identified: (A) non-transcriptionally regulated processes, (B) processes not occurring in many common laboratory strains, and (C) specific processes not yet targeted by existing gene expression microarray data. Biological processes in the later category may be areas that warrant further investigation.

Highly-represented (significant in >15 datasets)	Moderately-represented (significant in <15 but >3 datasets)	Under-represented (significant in <3 datasets)
tricarboxylic acid cycle	response to oxidative stress	MAPKKK cascade (A)
DNA repair	amino acid transport	protein kinase cascade (A)
glycolysis	exocytosis	Ras protein signal transduction (A)
phosphate metabolism	vesicle fusion	mating type switching (B)
chromosome segregation	meiotic recombination	invasive growth (B)
DNA replication	arginine biosynthesis	pseudohyphal growth (B)
electron transport	steroid metabolism	response to salt stress (C)
ubiquitin-dependent protein catabolism	alcohol metabolism	heme biosynthesis (C)
ribosome assembly	double-strand break repair	mitochondrial genome maintenance (C)
amino acid metabolism	filamentous growth	telomerase-dependent telomere maintenance (C)

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We have quantitatively found that our application of SVD to microarray data for the purpose of signal balancing performs much more accurately than the traditional use of SVD for noise reduction. In the traditional use of SVD, low singular values and their corresponding singular vectors are removed from the decomposed matrices (UΣV^T), then the matrices are multipled back together to reconstruct a version of the original data matrix (X). Often, enough singular values are retained to account for some percentage of the variation of the original data. However, in our analysis we find that performance generally degrades when using this traditional application of SVD. Rather, by calculating correlations within the left singular vectors (U) we perform our analysis in a space where the more dominant patterns are dampened and the less dominant patterns are magnified. Note that this process is related to some applications of SVD to microarray data, such as the work by Alter et al. which found that dominant eigengenes are sometimes highly correlated with noise.

The following figure compares our use of SVD for signal balancing with retaining 50% and 90% of data variance and reconstructing the orignal data matrix. The type of analysis is the same as described in the main text.

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In the main text we discuss our choice to limit the influence of negative correlations on our method by disregarding z-scores less than 1 standard deviation away from the mean. We have found that negative correlations tend to not be functionally informative in may cases. As an example of this effect we have examined the precision- recall plot of positive correlations across all microarray data and negative correlations across all data. The following graph was created using the GRIFn system (Myers et al., 2006). Several reference datasets are included for comparison:

An interactive SVG of this analysis is also available (requires the Adobe SVG plugin and a compatible browser to view).

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Precision-recall curves were created by traversing the ordered list of results for each method for each GO term examined and calculating precision, recall pairs at each step. Precision is calculated as the ratio of true positive (TP) predictions to the sum of TP and false positive (FP) predictions. Recall is measured as the number of TPs recovered for individual GO terms, or as the proportion of TPs to the total number of possible TPs (TP + FN [false negatives]) for results averaged over multiple GO terms.

Average precision was used as a summary statistic for comparing the performance of different methods in a more straightforward way. This measure is commonly used in search domains, such as document retrieval. Average precision is calculated for a GO term as:

Where the GO term contains k genes, and rank_i is the rank placement of the i^th gene annotated to the term in the ordered list of results. Note that if all genes annotated to a GO term appear as the first k genes in the ordered result list, the average precision will be 1. Also note that this measure is a quantized version of the area under the precision-recall curve.

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In addition to the summary comparision available in Figure 3 of the main text, here are individual results for all 126 GO terms analyzed.

GOID	Term Name	Results
GO:0000074	regulation of progression through cell cycle
GO:0000160	two-component signal transduction system (phosphor
GO:0000278	mitotic cell cycle
GO:0000279	M phase
GO:0000746	conjugation
GO:0000902	cellular morphogenesis
GO:0001510	RNA methylation
GO:0005975	carbohydrate metabolism
GO:0006056	mannoprotein metabolism
GO:0006066	alcohol metabolism
GO:0006081	aldehyde metabolism
GO:0006082	organic acid metabolism
GO:0006112	energy reserve metabolism
GO:0006113	fermentation
GO:0006118	electron transport
GO:0006260	DNA replication
GO:0006308	DNA catabolism
GO:0006310	DNA recombination
GO:0006323	DNA packaging
GO:0006352	transcription initiation
GO:0006353	transcription termination
GO:0006354	RNA elongation
GO:0006360	transcription from RNA polymerase I promoter
GO:0006366	transcription from RNA polymerase II promoter
GO:0006383	transcription from RNA polymerase III promoter
GO:0006399	tRNA metabolism
GO:0006401	RNA catabolism
GO:0006417	regulation of protein biosynthesis
GO:0006457	protein folding
GO:0006461	protein complex assembly
GO:0006473	protein amino acid acetylation
GO:0006476	protein amino acid deacetylation
GO:0006508	proteolysis
GO:0006512	ubiquitin cycle
GO:0006519	amino acid and derivative metabolism
GO:0006629	lipid metabolism
GO:0006725	aromatic compound metabolism
GO:0006730	one-carbon compound metabolism
GO:0006766	vitamin metabolism
GO:0006790	sulfur metabolism
GO:0006793	phosphorus metabolism
GO:0006800	oxygen and reactive oxygen species metabolism
GO:0006807	nitrogen compound metabolism
GO:0006811	ion transport
GO:0006818	hydrogen transport
GO:0006839	mitochondrial transport
GO:0006869	lipid transport
GO:0006913	nucleocytoplasmic transport
GO:0006914	autophagy
GO:0006944	membrane fusion
GO:0006970	response to osmotic stress
GO:0006974	response to DNA damage stimulus
GO:0006986	response to unfolded protein
GO:0006997	nuclear organization and biogenesis
GO:0007005	mitochondrion organization and biogenesis
GO:0007010	cytoskeleton organization and biogenesis
GO:0007031	peroxisome organization and biogenesis
GO:0007033	vacuole organization and biogenesis
GO:0007034	vacuolar transport
GO:0007046	ribosome biogenesis
GO:0007047	cell wall organization and biogenesis
GO:0007059	chromosome segregation
GO:0007155	cell adhesion
GO:0007166	cell surface receptor linked signal transduction
GO:0007243	protein kinase cascade
GO:0007264	small GTPase mediated signal transduction
GO:0007530	sex determination
GO:0007568	aging
GO:0008213	protein amino acid alkylation
GO:0008219	cell death
GO:0008298	intracellular mRNA localization
GO:0008380	RNA splicing
GO:0008643	carbohydrate transport
GO:0009100	glycoprotein metabolism
GO:0009116	nucleoside metabolism
GO:0009117	nucleotide metabolism
GO:0009266	response to temperature stimulus
GO:0009308	amine metabolism
GO:0009415	response to water
GO:0010035	response to inorganic substance
GO:0015837	amine transport
GO:0015849	organic acid transport
GO:0015893	drug transport
GO:0015931	nucleobase, nucleoside, nucleotide and nucleic aci
GO:0016071	mRNA metabolism
GO:0016072	rRNA metabolism
GO:0016192	vesicle-mediated transport
GO:0016458	gene silencing
GO:0016481	negative regulation of transcription
GO:0016485	protein processing
GO:0018193	peptidyl-amino acid modification
GO:0019236	response to pheromone
GO:0019748	secondary metabolism
GO:0019932	second-messenger-mediated signaling
GO:0019953	sexual reproduction
GO:0019954	asexual reproduction
GO:0030261	chromosome condensation
GO:0030435	sporulation
GO:0030447	filamentous growth
GO:0030705	cytoskeleton-dependent intracellular transport
GO:0031023	microtubule organizing center organization and bio
GO:0031123	RNA 3'-end processing
GO:0040029	regulation of gene expression, epigenetic
GO:0042157	lipoprotein metabolism
GO:0042594	response to starvation
GO:0043094	metabolic compound salvage
GO:0043284	biopolymer biosynthesis
GO:0045184	establishment of protein localization
GO:0045185	maintenance of protein localization
GO:0045333	cellular respiration
GO:0045454	cell redox homeostasis
GO:0045941	positive regulation of transcription
GO:0046483	heterocycle metabolism
GO:0048284	organelle fusion
GO:0048308	organelle inheritance
GO:0050790	regulation of enzyme activity
GO:0050801	ion homeostasis
GO:0051052	regulation of DNA metabolism
GO:0051169	nuclear transport
GO:0051186	cofactor metabolism
GO:0051236	establishment of RNA localization
GO:0051301	cell division
GO:0051321	meiotic cell cycle
GO:0051325	interphase

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SPELL predicts that the un-annotated gene, ARP8 is involved in the following 13 biological processes which break down into 3 main classes:

Supplemental Table 2

Predicted GO term for Arp8	Class
mitotic cell cycle	Cell Cycle
interphase	Cell Cycle
regulation of progression through cell cycle	Cell Cycle
cell division	Cell Cycle
asexual reproduction	Cell Cycle
transcription from RNA polymerase II promoter	Transcription
negative regulation of transcription	Transcription
positive regulation of transcription	Transcription
transcription initiation	Transcription
mRNA metabolism	Transcription
cellular morphogenesis	Morphology
cytoskeleton organization and biogenesis	Morphology
response to osmotic stress	Other

Cell volume was determined using the Z2 automated cell counter (Beckman Coulter, Fullerton, California, United States). Culture was diluted into Isotone II buffer for the measurement. Cell morphology was determined using a 40x objective on a Zeiss Axioskop (Germany). The entire field of view is shown for both wild-type yeast and the arp8 deletion allowing for direct comparison of the images in Figure 6.

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SPELL version 1.0
Hibbs MA, Hess DC, Myers CL, Huttenhower C, Li K, Troyanskaya O. Exploring the functional landscape of gene expression: directed search of large microarray compendia. Submitted for consideration, 2007.
Contact: spell@genomics.princeton.edu