Preferential Expression Measure

We devised a Preferential Expression Measure (PEM) to score differential expression of genes in tissues. PEM describes the expression of a gene in a given tissue in relation to its expression in all tissues. Therefore, PEM controls for the fact that some genes are highly expressed across many tissues (housekeeping genes), and has the virtue of reporting a negative value for under-expressed genes, and a positive value for over-expressed genes. Large positive PEM scores for a gene in a particular tissue indicate that the gene is unusually highly expressed in that tissue, relative to its expression in other tissues. Large negative PEM scores indicate repression of a gene in a tissue.

For SAGEmap and TissueInfo we define PEM as log₁₀(o/e), where o is the observed SAGE or EST tag count for a gene in a given tissue, and e the expected tag count under the null hypothesis of uniform expression in all tissues. e is calculated as (G * N/T) where G is the total number of tags ascribed to the gene, N is the total number of tags for the tissue, and T is the total number of tags in the dataset.

For example, carbonic anhydrase XI (UniGene cluster Hs.22777) is linked to a SAGE tag GTCGCTGAGA. This tag occurs 132 times in all normal tissue SAGEmap libraries. The total number of SAGE tags in the normal brain tissue category is 326,481, and the total number of tags in all normal tissue categories is 1,077,231. Therefore, if the distribution of this tag was uniform across all libraries, it would be expected to occur ~40 times (132 * 326,481/1,077,231) in brain libraries (expected count). The actual count in brain libraries is 124 (observed count) bringing the PEM value for this tag in brain to log₁₀(o/e) = log₁₀(132/40) = 0.49.

For microarray experiments, where the raw data is a continuous variable (a relative intensity of a gene-specific fluorescent signal) as opposed to a tag count, we defined PEM as log₁₀(S/A), where S is the specific tissue signal for a gene and A is the arithmetic mean signal for the gene across all tissues.

Reproducibility and internal consistency

A major concern with all types of high-throughput expression data is reproducibility and internal consistency. To investigate this, for each of the three databases, we compared replicate experiments (where available) or equivalent tissues and measured the Pearson correlation coefficient for relative fluorescent signal intensity values assigned to the same probe in a pair of experiments. Gene Expression Atlas oligonucleotide microarray data had good correlation between repeat hybridizations of the same RNA sample (mean r² = 0.94 ± 0.04; N = 45 pairwise comparisons) and, as expected, slightly lower values for repeat hybridizations of different RNA preparations from the same tissue type (mean r² = 0.87 ± 0.06; N = 17 pairwise comparisons).

Internal correlations for SAGEmap and TissueInfo were lower. Counts of identical tags were correlated in pairwise SAGEmap library comparisons yielding mean r² values of 0.51 ± 0.25, N = 15 for brain; 0.26 ± 0.08, N = 6 for prostate; 0.89, N = 2 for vascular endothelium; 0.27, N = 2 for ovary; 0.83, N = 2 for kidney; and 0.97, N = 2 for pancreas.

For TissueInfo, we included tumour tissues to maximize the size of datasets and, therefore, minimize the sampling error. Counts of EST tags linked to the same UniGene cluster in pools of libraries were correlated. Comparison of brain TissueInfo libraries comprising 154,214 EST tags from normal tissue and 111,317 from tumour tissue, revealed an r² of only 0.27.

Correlations among tissue profiles from Gene Expression Atlas, SAGEmap and TissueInfo

To compare different databases we grouped libraries into higher-level tissue categories, and calculated Pearson correlation coefficients for PEM scores for categories that were represented in all three databases. Tumour tissue libraries were not used in any of the interdatabase comparisons. UniGene was used as the common gene index to link entries from the three databases. There were six tissues available for comparison (Table 1). Significant positive correlations were found for brain, prostate and vascular endothelium (Figure 1) but not for ovary, kidney, and pancreas.

	Tissue	TissueInfo vs. SAGEmap				GEA vs. SAGEmap				GEA vs. TissueInfo
	Genesa	r 2	Pb	Signsc	Genesa	r 2	Pb	Signsc	Genesa	r 2	Pb	Signsc
Brain	346	0.59	<0.01	92%	479	0.49	<0.01	84%	1431	0.43	<0.01	77%
Prostate	58	0.36	<0.01	76%	237	0.09	<0.01	59%	266	0.19	<0.01	63%
Vascular endothelium	13	0.29	0.6	92%	150	0.37	<0.01	73%	23	0.05	0.29	39%
Ovary	10	0.11	0.35	30%	175	0.03	0.2	49%	36	0.03	0.28	69%
Kidney	103	0.01	0.36	34%	140	0.00	0.78	50%	508	0.11	<0.01	55%
Pancreas	21	0.02	0.55	62%	141	0.00	0.59	48%	82	0.02	0.19	55%

^a Number of genes compared. SAGEmap and TissueInfo data were only used for genes whose PEM scores were significant by χ² test (P = 0.05), and only tags that could be mapped unambiguously to UniGene were used. ^b Two-tailed significance values for Pearson correlation coefficients. ^c Percentage of genes whose PEM scores have the same sign for the two methods being compared.

We suggest that the relative complexity of a tissue is another important factor for comparison of high-throughput expression profiles deposited in public databases. Complex tissues consist of many cell types that may be mixed in different proportions in different samples. Expression profiles obtained from these tissues will be heterogeneous and consequently require a large number of tags and repeated hybridizations to detect signal in the noise. This is shown by the relatively strong correlations seen in SAGEmap/TissueInfo and SAGEmap/microarray correlations for vascular endothelium despite the small number of tags available (in comparison to brain, about 3-fold fewer SAGE tags and 18-fold fewer EST tags). SAGEmap and dbEST/TissueInfo vascular endothelium libraries are derived from in vitro cultures or homogenous primary isolates of vascular endothelial cells [13].

Correlations of PEM scores obtained from the three databases for kidney, pancreas, and ovary had very low r² values (Table 1). However, measuring the correlation coefficient of PEM scores is a very rigorous test of congruence, because it not only requires that two datasets being compared identify the same sets of genes as being over- or under-expressed in the tissue, but also that the genes deviate from uniform expression to a similar degree. This may be an unrealistic expectation; for example, it is apparent from Figure 1 that PEM scores for SAGEmap in brain never exceed +1.19 (i.e., approximately 15-fold over-representation), which is probably due to the limited size of the SAGE database. A simpler test is to divide the plots into quadrants and measure the fraction of genes for which the two methods being compared give PEM scores with the same sign. For example, for brain tissue the PEM scores from SAGEmap and TissueInfo data are both positive for 134 genes, both negative for 184 genes, and in disagreement for only 28 genes (i.e., one method identifies the gene as significantly over-expressed in brain, and the other identifies it as significantly under-expressed) (Figure 1). Across all six tissues and three datasets, the PEM signs are in agreement for more than 50% of genes (the null expectation) in 13 of the 18 comparisons made (Table 1).

Tissue-specific genes

Gene Expression Atlas

Affymetrix U95A oligonucleotide microarray [15] data for 12,587 consensus probes in 101 human tissue samples were downloaded from the Gene Expression Atlas website http://expression.gnf.org/data_public_U95.gz. Image analysis and normalization has been performed using Genechip 3.2 software (Affymetrix, Santa Clara, CA) by Su et al. [11]. The libraries were grouped into 47 higher-level tissue categories (averaging across duplicates and triplicates). Tumour libraries were grouped as separate to normal tissues and not used for further analysis.

SAGEmap

Tag frequencies for 132 SAGEmap libraries [1] were obtained from the project ftp site ftp://ftp.ncbi.nih.gov/pub/sage. After downloading, the libraries were grouped into 15 higher-level tissue categories (brain, prostate, kidney, colon, pancreas, ovary, breast, skin, peritoneum, stomach, blood, lung, liver, heart and vascular endothelium) and annotated according to their disease status (91 tumour tissues, 37 normal tissue libraries and four immortalised cell lines). Unless stated otherwise, only normal tissue libraries (total of 1,077,231 tags) were used for further analysis.

TissueInfo

The TissueInfo [12] database links ESTs from dbEST to the tissue where there are expressed by assigning a tissue-key to each EST entry. The dataset was downloaded from the TissueInfo website http://icb.mssm.edu/tissueinfo/local-inst.xml, which is updated daily, on 27 February 2002. Our aim was to group EST into major tissues with sufficient numbers of ESTs for statistically meaningful analysis. Therefore, some TissueInfo categories were grouped together into higher-level tissue categories. For example hypothalamus was also annotated as brain. We excluded ESTs that were annotated as mitochondrial libraries, tumour libraries, or multiple tissues. This procedure resulted in 48 higher-level tissue categories, 27 of which had EST counts in excess of 10,000 (the number of ESTs we accepted as the threshold for dataset size). We also included vascular endothelium (8,435 ESTs) because of the high number of SAGE tags available for this category (110,460) and its simple cellular composition.

Mapping

Six higher-level tissue categories were available for comparison between Gene Expression Atlas, SAGEmap, and TissueInfo (Table 1). An integrated MySQL database was set up and gene identifiers from the UniGene gene index were used to link expression data from the three datasets. Only UniGene entries with more than one EST (62,008 entries) were considered as valid clusters. UniGene mapping was provided as part of both SAGEmap and TissueInfo datasets. To map oligonucleotide microarray probes, probe consensus sequences were searched against longest representative sequence of each UniGene cluster using BLASTN with the cutoff parameter E = 10e-20. Alignments were accepted if the percentage identity was higher than 94% and length was at least 99 bp or 90% of the length of the query, or when percentage identity was 100% and length more than 49 bp [11].

When linking UniGene clusters to SAGEmap data, UniGenes mapping to more than SAGE tag sequence were excluded (25% of clusters). The size of the Unigene dataset linked to SAGEmap was, therefore, reduced from 21,224 to 15,872 clusters. Similarly, Unigene clusters mapping to more than one Affymetrix probe were excluded (22% of clusters). The size of the Unigene dataset linked to Gene Expression Atlas data was, therefore, reduced from 9,402 to 7,375 clusters. The overlaps between UniGenes linked to GEA and SAGEmap, GEA and TissueInfo, and SAGEmap and TissueInfo were 2742, 6758 and 15872 clusters respectively. Therefore:

- 63% and 8% of UniGene clusters linked to GEA could not be linked to SAGEmap and TissueInfo respectively;

- 83% and 0% of UniGene clusters linked to SAGEmap could not be linked to GEA and TissueInfo respectively;

- 74% and 89% of UniGene clusters linked to TissueInfo could not be linked to SAGEmap and GEA respectively.

Statistics

Gene expression can be estimated by counting gene-specific SAGE and EST tags but, as a stochastic process, these estimates are subject to sampling error. The size of the sampled dataset can have a dramatic influence on reliability of the data. Two independent groups have established χ² as the best statistical test to use in tag sampling experiments [16, 17]. We excluded from subsequent comparative analysis all tissue/gene data for which the expected number of tags (e) was less than 5, which is the lower limit of reliability for the χ² statistic. We used χ² to filter the PEM score for both tag-based methods: unless significantly different from expectation (with P = 0.05) we concluded that we had no confidence in either the sign or the magnitude of the apparent deviation from uniform expression. A relatively low significance threshold of 0.05 was used to maximize the number of values available for inter-database comparison. Because χ² test is only used on a case-by-case basis for preliminary selection of informative datapoints, a multiple testing correction such as Bonferroni correction was not applicable.

To compare the results from any two databases for a given tissue, we plotted the PEM scores for all genes for which data was available from the two experiments, but excluding SAGE and EST data with non-significant χ². We calculated the correlation coefficient (r) and report r², which represents the proportion of the variability in the data that is explained by the correlation.