1 Center for Bioinformatics, Philadelphia, Pennsylvania 19104
2 Department of Genetics, University of Pennsylvania, Philadelphia, Pennsylvania 19104
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ABSTRACT |
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evaluation of labeling protocols; direct labeling; indirect amino-allyl labeling; dendrimer labeling
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INTRODUCTION |
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We performed a side-by-side comparison of the three methods using a single source of total RNA and multiple replicates to assess the reproducibility and the ability to detect expression of the methods. This study is referred to as "the replicate study." We also performed a smaller study, with five arrays per labeling method, where we varied the amount of total RNA used in one channel (Cy3), hence, the ratio of Cy3-labeled RNA to Cy5-labeled RNA, to assess one aspect of predictive ability of each of the methods. This study is referred to as "the dilution series study."
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MATERIALS AND METHODS |
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The microarray used for all experiments in the replicate study was the Pancreas 2.1 Array containing 3,840 spots. The list of clones arrayed is available at http://www.cbil.upenn.edu/EPConDB. Sixteen print-tips were used, resulting in a 4x4 grid layout, with 15x16 spots per grid (print-tip group). Of these 3,840 spots, 155 were left blank, 16 represent yeast negative controls (8 yeast controls, from Incyte Genomics, spotted in duplicate), 3 represent cDNA controls, 8 represent anchors (Cy3 end-labeled random 70-mers), and the remaining 3,658 represent primarily mouse genes expressed in pancreas as described above. Of the 3,840 spots on the array, 10 (the 8 anchors and 2 of the blanks) had no values upon image quantification.
The microarray used for all experiments in the dilution series study was the Pancreas 2.1.1 Array. This was identical to the Pancreas 2.1 Array, except that the eight anchors in the latter were replaced by blanks in the Pancreas 2.1.1 Array.
Preparation of RNA
Six adult CD1 female mice were euthanized; the pancreas of each and the livers from four were immediately homogenized in 10 ml denaturing solution (4 M guanidium thiocyanate, 0.1 M Tris-Cl pH 7.5, 1% ß-mercaptoethanol). Total RNA was extracted using an acid-phenol extraction procedure (1). Approximately 150 µg of total RNA from each individual pancreas sample was pooled, resulting in 900 µg of a pooled pancreas RNA. This pancreas RNA pool was used for both channels of the labeling experiments in the replicate study. For the dilution series study, 800 µg of liver total RNA (200 µg from each liver) were pooled with 1,800 µg of pancreas total RNA (300 µg from each pancreas) to prepare a liver/pancreas RNA pool.
Labeling Methods and Hybridization
Prehybridization was performed for all arrays (2). A coplin jar containing 50 ml of prehybridization buffer (5x SSC, 0.1% SDS, and 1% BSA) was brought to 42°C. The arrays were incubated for 45 min, rinsed five times in deionized water at room temperature, once in isopropanol, and then placed into a 50-ml conical tube and centrifuged 1 min at 1,000 rpm. The prehybridization was done no more than 1 h prior to hybridization.
The direct labeling protocol was adapted from Ref. 2. Briefly, RNA was labeled in a reverse transcription reaction with fluorescently labeled dUTP (Cy3 PA53022 and Cy5 PA55022, Amersham, Pharmacia) and purified. The purified labeled cDNA was precipitated with 1/10 vol sodium acetate and 3 vol ethanol. In the replicate study, 20 µg of pooled pancreas RNA was labeled with both fluorophors. In the dilution series study, 20 µg of the pooled liver and pancreas RNA was labeled with Cy5 for each slide. The amount of RNA labeled with Cy3 was 5, 10, 20, 40, and 80 µg, respectively, so that the ratio of the Cy3-labeled RNA to Cy5-labeled RNA was ,
, 1, 2, and 4, respectively.
Indirect or amino-allyl labeling was adapted from the Brown web site (http://cmgm.stanford.edu/pbrown/protocols). Total RNA and 2.5 µg of oligo dT were brought to 25 µl with sterile, deionized DEPC-treated water and denatured for 5 min at 70°C. The reaction was then cooled to 42°C and an equal volume of RT reaction mix [2x first-strand buffer (InVitrogen, Y02321), 400 U SuperScript II (InVitrogen, Y02226), 1 mM dATP, 1 mM dGTP, 1 mM dCTP, 0.4 mM amino-allyl-dUTP, 0.6 mM TTP, 20 mM DTT, and 20 U RNasin (Promega, product no. 12772505)] was added, and the reaction incubated at 42°C for 2 h. The reaction was stopped by incubating at 70°C for 5 min. RNase H (2.5 U, US Biochemicals product no. 70054Y) was added and the mixture incubated at 37°C for 15 min. The reaction was denatured by bringing it to 0.2 N NaOH, 0.1 M EDTA, and then neutralized by adjusting the reaction to 0.29 M Tris-Cl pH 7.5. The buffer was removed with a Microcon YM-30 (Amicon no. 42410), and the cDNA was dried in a SpeedVac. Monofunctional Cy3 (PA23001) or Cy5 dye (PA25001, Amersham Pharmacia) was coupled to the cDNA in 30% DMSO, 66 mM sodium bicarbonate buffer, pH 9.0, in the dark at room temperature for 1 h. The reaction was quenched with 4.5 µl hydroxylamine (Sigma) for 15 min. The two dye reactions were combined, and the labeled cDNA was purified with a Qia-Quick PCR purification kit (Qiagen). The purified labeled cDNA was precipitated with 1 µl polyacryl carrier (Molecular Research Center, no. PC 152), 1/10 vol of 3 M sodium acetate, pH 5.2, and 3 vol ethanol (-20°C). In the replicate study, 20 µg of pooled pancreas RNA were labeled with both fluorophors. In the dilution series study, 20 µg of the pooled liver and pancreas RNA were labeled with Cy5 for each slide. The amount of RNA labeled with Cy3 was 5, 10, 20, 40, and 80 µg, respectively, so that the ratio of the Cy3- to Cy5-labeled RNA was ,
, 1, 2, and 4, respectively. In preparation for hybridization the labeled cDNA from the direct or the indirect labeling reactions were resuspended in 15 µl of sterile, deionized water with 2.5 µg of oligo dT21 and 2.5 µg of mouse cot1 DNA (500 mg/ml, GIBCO-BRL, no. 1844-016) and denatured for 5 min at 95°C. An equal volume of hybridization buffer (50% formamide, 10x SSC, 0.2% SDS) was added. The cDNA hybridization mix was placed on a prehybridized glass microarray and incubated overnight at 42°C in a Corning hybridization chamber. Both the direct and indirect slides were washed with the same conditions post hybridization: once in 2x SSC, 0.1% SDS to remove coverslip, once in 0.2x SSC, 0.1% SDS at 40°C for 5 min with agitation, and once in 0.2x SSC at room temperature for 5 min with agitation.
3DNA dendrimer (Genisphere) labeling was done with the 3DNA submicro Array kit (Genisphere, Cy3 A100731V12, Cy5 A100741V12) according to the manufacturers protocol and recommendations. Common control total RNA (2.5 µg) and 2 pmol Cy3 capture sequence primer or Cy5 capture sequence primer were brought to 10 µl with DEPC-treated water and incubated for 10 min at 80°C. At 42°C an equal volume of reaction mix [2x first-strand buffer (InVitrogen, Y02321), 1 mM dATP, 1 mM dGTP, 1 mM dCTP, 1 mM TTP, 20 mM DTT, 40 U RNasin (Promega, 12772505), and 200 U Superscript II reverse transcriptase (InVitrogen, Y0226)] was added, and the reaction was incubated 2 h at 42°C. The reaction was terminated by bringing it to 0.074 N NaOH and 7.4 mM EDTA and incubating it at 65°C for 10 min. The reaction was neutralized by bringing it to 0.175 M Tris-Cl, pH 7.5. The Cy3 and Cy5 reactions were then combined and precipitated with 20 µg of linear polyacrylamide (Ambion no. 9520), 1 vol 7.5 M ammonium acetate, and 9 vol ethanol at -20°C for 30 min. Following precipitation the pellet was air dried. In preparation for hybridization the cDNA pellet was resuspended in 5 µl sterile deionized water. Then, 2.5 µl of the Cy3 dendrimer, 2.5 µl of the Cy5 dendrimer, and 1 µl high-end differential enhancer (Genisphere, vial 10) were added to the cDNA. Mouse cot1 DNA, 2.5 µg (1 mg/ml, GIBCO-BRL, no. 1844-016), 2.5 µg oligo dT21 and 1 µl of anti-fade reagent (Genisphere, vial 8) were added to 100 µl of hybridization buffer (40% formamide, 4x SSC, 1% SDS, Genisphere, vial 7), and the hybridization buffer was brought to 45°C. The prepared hybridization buffer, 19 µl, was added to the cDNA/dendrimer mix and incubated at 45°C for 15 min. This hybridization mix was added to a prehybridized glass microarray, covered with a glass coverslip, and incubated in a Corning hybridization chamber containing 10 µl of water in each reservoir overnight at 45°C. The Genisphere labeled arrays were washed for 10 min each, once at 55°C in 2x SSC, 0.2% SDS, once in 2x SSC at room temperature, once in 0.2x SSC at room temperature, and dried by placing them into a 50-ml conical tube and centrifuging them at 1,000 rpm for 3 min. In the replicate study, 2.5 µg of pooled pancreas RNA was labeled with both fluorophors. In the dilution series study, 2.5 µg of the pooled liver and pancreas RNA was labeled with Cy5 for each slide. The amount of RNA labeled with Cy3 was 0.625, 1.25, 2.5, 5, and 10 µg, respectively, so that the ratio of the Cy3- to Cy3-labeled RNA was maintained at ,
, 1, 2, and 4, respectively.
Scanning and Image Analysis
All slides were scanned immediately following hybridization using an Affymetrix (formerly GMS) 418 scanner. The laser power was set to 100%, and the PMT settings varied depending upon the intensity of the array. (Note that in the dilution series study, the PMT settings varied from method to method but were constant across the five arrays of each method.) Our goal was to scan at a setting that would avoid signal saturation in any spots. For the replicate study, 910 hybridizations were performed for each labeling. After scanning the arrays, images were visually inspected, and eight slides with no apparent major flaws selected for each labeling procedure.
The image analysis was performed with ArrayVision 6.0 (Imaging Research). The segmentation adaptive function of the program was enabled, and the local background values were computed from diamond-shaped regions between the spots. The mean measure of pixel intensities was used both for the foreground and for the background at each spot.
After quantification, the data were stored in the relational database RAD (4, 10). Access to the data will be provided through the EPConDB web site (http://www.cbil.upenn.edu/EPConDB). Our data will also be deposited in the public repository ArrayExpress (http://www.ebi.ac.uk/microarray/ArrayExpress/arrayexpress.html).
Preprocessing and Analyses
We used version 1.3.1 of the statistical software package R (3) for all the statistical calculations and plots in this paper. To fit curves to scatter plots, we used the "lowess" function (a robust scatter plot smoother) implemented in R, with f typically set to 0.3 or 0.4.
For the comparisons in the Labeling Method Reproducibility (below, in RESULTS) (using the replicate study), the M values have been normalized via the print-tip group lowess normalization described in Ref. 12 (http://www.stat.Berkeley.EDU/users/terry/zarray/Html/normspie.html) and implemented in the (R) SMA package by those authors. This method provides the means for normalizing log (base 2) ratios of Cy5 signal to Cy3 signal in a way that is not only array dependent, but also intensity and print-tip dependent. The method is applicable to the experiments in the replicate study since they consist of self-to-self hybridizations (of equal amounts of total RNA); therefore, the assumptions underlying this kind of normalization are satisfied.
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RESULTS |
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Thresholds for Useful Signals
Evaluation of sensitivity and reproducibility of microarray data must take into account which signals are likely to be real indicators of expression and which simply reflect autofluorescence or nonspecific binding of labeled material. To investigate this issue, we used as controls the signals obtained in the replicate study from the blank spots and the yeast sequences. One of the yeast sequences (yeast 400) exhibited high signal most likely due to excessive cross-hybridization with a mouse mRNA and was therefore excluded. All other yeast spots consistently gave very low fluorescence signals. Two of the blank spots had missing values after analysis with ArrayVision and could not be used. Therefore, we utilized 167 control spots on the array. For each array, the 90th percentile of the A values for these controls was computed to provide a threshold for useful signals. On each array, spots with A failing to exceed the threshold were flagged. (Blank, yeast controls, and anchor spots were flagged as well.)
In this study, the three different labeling procedures were tested using Cy5 and Cy3 on the same amount and source of total RNA extracted from adult mouse pancreas. Since we used a pancreas array, hybridization of pancreas total RNA should result in useful signals for a large fraction of spots. We considered a signal useful on an array if the spot was not flagged by the procedure described above. For each labeling method and each spot, we counted the number of unflagged replicates for that spot across the eight arrays for that labeling method. Then, for each labeling method, we examined the number of spots with a high number of unflagged replicates (7 or 8) to check sensitivity together with reproducibility. All of the above was done both with and without background subtraction, and the results are reported below. Briefly, both the indirect and the dendrimer methods outperform the direct method in this respect, but they do not differ significantly between each other if the number of spots with at least seven unflagged replicates is considered. If only the number of spots with eight unflagged replicates is considered, then the dendrimer method outperforms the indirect.
It should be noted that the tests above check one of the measures of sensitivity, namely, the ability to detect expression, and they do not establish the sensitivity for each gene. In the Dilution Series Study (below), we use that smaller set of experiments as a first step into the investigation of another aspect of sensitivity, namely, the degree of linear response, with adequate slope, to different dilutions. In what follows, we often use for short the term "predictive ability" to denote the latter, which in reality is just one aspect of predictive ability.
Without background subtraction.
For each labeling method and each spot, we computed the average value of A over the eight replicates for that spot. The ranges of such average A values for the three labeling methods over all spots are reported in Table 1. The A thresholds for each replicate in each labeling method are also reported in this table.
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With background subtraction.
For each labeling method and each spot, we computed the average value of A over the eight replicates for that spot. The ranges of such average A values for the three labeling methods over all spots are reported in Table 2. The A thresholds for each replicate in each labeling method are also reported in Table 2.
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Correlation of Relative Signal Intensities Between Labeling Methods
To have a better sense of how the three labeling methods perform with respect to quantities of interest (e.g., the signal-to-noise ratios, or the Cy5-to-Cy3 ratios), it is useful to examine, for each method, how each such quantity depends on the relative intensity of the signals. To do this across the three methods, it is convenient to first know whether genes are high or low expressers consistently across the three methods. In other words, was the position of a genes intensity in the spectrum of intensities dependent on the labeling method used? To determine this, we utilized the replicate study, and, for each labeling method and each spot, we calculated the average value of A over the eight replicates for that spot and determined which quantile such a value represented over the distribution of average A values across all spots. Then, for each pair of labeling methods, we examined the correlation between such numbers. We did this both with and without background subtraction, figures of the scatter plot matrices in each case are available in the Supplementary Material1
published online at our web site. Correlation coefficients between the indirect and each of the other two methods were greater than 0.9, and between the direct and the dendrimer methods they were around 0.8, regardless of whether background was subtracted. Therefore, there was a good correlation between the relative intensities for a spot between pairs of labeling methods. This justifies the use of the average A value across all 24 arrays (which we refer to as the "grand average A") as a measure of spot intensities common to all three methods, based upon which fair comparisons between methods can be made. The correlation results above show that genes with high grand average A are relatively high expressers in all three methods and similarly for those with low grand average A.
When no background subtraction was performed, the range of the grand average A was [7.2, 14.6] with interquartile range [7.8, 9.9] and 90th percentile 11. When we assigned missing values to all spots flagged as above, in Thresholds for Useful Signals, and then computed the grand average A, the interquartile range was [8.2, 10] and the 90th percentile was again roughly 11. If background subtraction was performed, then the range of grand average A was [1.9, 14.6] with interquartile range [5.2, 9.5] and 90th percentile 10.9. The interquartile range after assigning missing values to all flagged spots was [7, 9.8] and the 90th percentile was roughly 11.
Signal-to-Noise Ratio
One of the measures generated by ArrayVision is the signal-to-noise ratio (S/N). This is calculated, for each spot and each channel on an array, as (foreground intensity-background intensity)/(standard deviation of the background). The higher the S/N for a spot the better. Using the replicate study, we explored how S/N behaved as a function of signal intensity in each of the methods in two different ways.
First, for each labeling method and each spot, we computed the average S/N over all the 16 S/N values (8 replicates x 2 channels) for that spot. We then analyzed the scatter plot of the average S/N vs. the grand average A for each method and fitted a curve to this scatter plot, as described in Preprocessing and Analyses, in METHODS, above (f = 0.3). We did this using A values both with and without background subtraction. The three scatter plots and curves for the no-background subtraction case are shown in Fig. 1. These indicate that the dendrimer method tends to outperform the other two methods for values of grand average A greater than 9, which represents most of the range for useful signals.
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Labeling Method Reproducibility
There are various statistical measures that can be used to assess reproducibility when replicate experiments are available, e.g., the coefficient of variation of a collection of measurements. In our replicate study, a more natural statistic was the root mean square (rms) of M to measure the deviations of M from its ideal value of 0 at every spot on every slide (modulo removal of dye and print-tip/spatial biases). This is justified because the experiments are self-to-self comparisons (with the same amount of labeled material for each channel). We normalized the M values within each array as illustrated in Preprocessing and Analyses (in METHODS, above). For each labeling method and each spot, we then examined the rms of M for that spot over the eight replicates [i.e., the square root of the average M2 for that spot; the smaller the rms(M) for a spot, the better]. After generating scatter plots (as described below) of rms(M) vs. the grand average A and fitting curves to these (using lowess with f = 0.4) for each labeling method, we evaluated the three resulting graphs obtained in each case to compare the rms(M) across the range of intensities. Of the normalization methods proposed in Ref. 12, we chose print-tip group lowess normalization, preferring this to the scaled-print-tip group lowess normalization and to the across slide scale normalization also described in the same paper (one of the reasons for this choice was that this was the method which seemed to perform best according to the studies carried out in Ref. 12). Because of this, we decided to examine the rms(M) vs. grand average A not only across all spots on the array, but also print-tip by print-tip.
Finally, we applied also in this situation a vote casting procedure, this time based on rms(M).
We did all of the above using values both with and without background subtraction. Results are summarized below. All relevant plots are available in the Supplementary Material.
Without background subtraction.
Figure 2 shows the scatter plots and the corresponding lowess curves of rms(M) vs. grand average A for the three labeling methods: rms(M) values are in general very close to 0 in all three methods, and the methods are roughly equivalent in this respect. The dendrimer method slightly outperforms the other two methods over the interquartile range of grand average A. The indirect method slightly outperforms the other two for higher values of grand average A, but one should keep in mind that 11 is roughly the 90th percentile of these values. We repeated the same procedure print-tip by print-tip and obtained analogous results.
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DISCUSSION |
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The choice of image analysis software is clearly critical in the production of reliable intensity values. In this study, a single software package (ArrayVision) was used to quantify the results from the three labeling methods under evaluation. A (growing) number of image quantification software packages are available with varying abilities to accurately identify a spot and to measure foreground and background signals. Yang et al. (11) have carried out a comparison of several of these packages. A relevant conclusion drawn from that study is that the method for determining background varies between these packages and can significantly affect the final signals used. There is some debate on whether it is best to subtract background when this has been obtained with local background techniques (here background subtraction typically has a strong impact on low-intensity values), as was the case with the package we used. This prompted us to carry out our comparisons both with and without subtracting background.
In each of our two studies (the replicate study and the dilution series study), we have utilized a single RNA source and a single print run of microarrays to evaluate three different labeling methods. Yue et al. (14) utilized a series of replicate self-to-self hybridizations with one particular array technology and protocol to assess reproducibility of this method as well as performance in terms of detecting differential expression. In our studies, the focus was on the comparison of three different labeling protocols in terms of reproducibility and sensitivity.
For reproducibility evaluation in the replicate study, we opted for the rms(M) instead of the coefficient of variation (cv) of Cy5-to-Cy3 ratios, as the former is a more natural statistical measure to evaluate deviations in self-to-self comparisons (the cv of Cy5/Cy3 would be measuring how the variance of these ratios compares to their mean and could be small even in cases where the mean is very different from 1, the ideal value). We also found it useful to examine this measure as a function of signal intensity.
As for sensitivity, we used our replicate study to investigate the ability of the methods to generate sufficient signal to detect expression from most of the genes on our pancreas-specific microarray, as pancreas RNA was hybridized to it. We also carried out a smaller study, consisting of three five-point dilution series (one per labeling method) as a first step investigation into predictive ability.
In terms of the parameters we have investigated in this study, the Genisphere dendrimer method performed at least as well as the other two and often slightly better as it pertains to reproducibility and detection of expression, but not as well as it pertains to response to different dilutions. Using dendrimers to label cDNA for microarrays was first described as a means of amplifying the fluorescent signal by Stears et al. (9), who concluded that the dendrimer method has many desirable properties. In that paper they compared the dendrimer labeling with direct labeling, as we have done. In their comparison of methods they focused on two main issues: the amount of RNA required to obtain comparable signal strengths, and the levels of background as a function of the amount of RNA. They found that the dendrimer method gave a signal strength with 2.5 µg that was comparable to the direct method when 40 µg were used. With the direct labeling method they showed that the background signal increased significantly with amount of total RNA, whereas with the dendrimer method the background signal remained nearly constant. They did not, however, compare the methods in terms of the signal to noise or signal variation. We have done this, and further investigated how such comparisons depend on the intensity level of the signal. In Ref. 9 there is also a claim that with the dendrimer method signal strength was proportional to the amount of RNA probe (this was apparently done using four hybridizations with total RNA amounts ranging from 1 to 20 µg, see figure 2A of Ref. 9), albeit some signal saturation was observed for the most highly expressed genes. We carried out a first pass comparison of responses to different total RNA amounts between the three labeling methods in our dilution series study. We, too, observed some saturation in the dendrimer method (in our case at the highest dilution point), but from our results we also observed that in general the dendrimer method did not seem to perform as well as the others in terms of response to different dilutions, with compression at both ends of the spectrum.
The results from our two studies do not contradict each other, as these studies examine different aspects of the performance of the three labeling methods providing a more rounded view of the latter. On one hand, from our replicate study, the dendrimer method appeared to perform well in terms of ability to detect expression, in terms of S/N, and in terms of reproducibility of log ratios in self-to-self comparisons, which is concordant with some of the results of Ref. 13. On the other hand, the compression observed in our dilution series study at both ends of the spectrum in the dendrimer data raises some concerns.
The dendrimer method has the advantage of requiring less starting material than the other two methods. When that is a limiting factor and when the study of interest focuses on screening for genes that are expressed in a given sample, our analysis supports its use (at least with an experimental design that utilizes this sample in self-to-self hybridizations to find A values above a certain threshold). Otherwise, for the moment we will continue our use of the indirect method, which over all the parameters we have investigated in our two studies performed relatively well (and was consistently not the worst).
We realize that our dilution series study was a relatively small one (15 hybridizations in total) with no replicates, and replicates are always desirable. We have also just learned that Genisphere is now releasing a new set of protocol recommendations. The latter might show a different performance in terms of response to different dilutions. Moreover, other types of studies could be carried out to investigate this issue. For example, instead of varying the total amount of RNA used in one channel, this could be left constant and the amount of labeled RNA for this channel could be varied, or spiking studies could be used. Response to different dilutions is only one aspect of predictive ability. Ultimately it is the ability to identify real differences in gene expression levels that is desired, so an interesting direction for future work is to carefully design and carry out an appropriate set of experiments to investigate how the three methods compare in a scenario where the Cy5-to-Cy3 ratios might vary from gene to gene (the availability of our replicate study data should provide a better understanding of microarray data under the null hypothesis of no change). Thus, for a fuller investigation of predictive ability, a combination of different types of studies, each involving replicate hybridizations, is called for and is a direction for future work.
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ACKNOWLEDGMENTS |
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We gratefully acknowledge support through National Institute of Diabetes and Digestive and Kidney Diseases Grant DK-56947 (to K. H. Kaestner). At the time this paper was written, G. R. Grant was under the support of National Institutes of Health (NIH) Award K25-HG-00052 and E. Manduchi was under the support of NIH Award K25-HG-02296.
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FOOTNOTES |
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Address for reprint requests and other correspondence: E. Manduchi, Center for Bioinformatics, Univ. of Pennsylvania, 1428 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104-6021 (E-mail: manduchi{at}pcbi.upenn.edu).
10.1152/physiolgenomics.00120.2001.
1 Supplementary material (all additional figures relative to this work) is available at http://www.cbil.upenn.edu/EPConDB/labeling_method_comparisons. Moreover, any future feedback or possible errata will be posted at this web site.
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REFERENCES |
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