- Research article
- Open Access
Accuracy of cDNA microarray methods to detect small gene expression changes induced by neuregulin on breast epithelial cells
BMC Bioinformatics volume 5, Article number: 99 (2004)
cDNA microarrays are a powerful means to screen for biologically relevant gene expression changes, but are often limited by their ability to detect small changes accurately due to "noise" from random and systematic errors. While experimental designs and statistical analysis methods have been proposed to reduce these errors, few studies have tested their accuracy and ability to identify small, but biologically important, changes. Here, we have compared two cDNA microarray experimental design methods with northern blot confirmation to reveal changes in gene expression that could contribute to the early antiproliferative effects of neuregulin on MCF10AT human breast epithelial cells.
We performed parallel experiments on identical samples using a dye-swap design with ANOVA and an experimental design that excludes systematic biases by "correcting" experimental/control hybridization ratios with control/control hybridizations on a spot-by-spot basis. We refer to this approach as the "control correction method" (CCM). Using replicate arrays, we identified a decrease in proliferation genes and an increase in differentiation genes. Using an arbitrary cut-off of 1.7-fold and p values <0.05, we identified a total of 32 differentially expressed genes, 9 with the dye-swap method, 18 with the CCM, and 5 genes with both methods. 23 of these 32 genes were subsequently verified by northern blotting. Most of these were <2-fold changes. While the dye-swap method (using either ANOVA or Bayesian analysis) detected a smaller number of genes (14–16) compared to the CCM (46), it was more accurate (89–92% vs. 75%). Compared to the northern blot results, for most genes, the microarray results underestimated the fold change, implicating the importance of detecting these small changes.
We validated two experimental design paradigms for cDNA microarray experiments capable of detecting small (<2-fold) changes in gene expression with excellent fidelity that revealed potentially important genes associated with the anti-proliferative effects of neuregulin on MCF10AT breast epithelial cells.
Spotted cDNA microarrays are used in high-throughput experiments that interrogate the relative expression of thousands of genes simultaneously for many biological processes with wide applications in biological and medical research. Typically in a two-dye spotted cDNA microarray experiment, two mRNA samples are transcribed into cDNAs, labeled with two different fluorescent dyes, commonly Cy3 and Cy5, and hybridized on the same slide. The relative gene expression level is then measured as a ratio of the intensities of the fluorescent dyes. However, the signal intensity of the dye, which indirectly represents the gene expression level, can be affected by many other sources of error such as dye efficiency, sample preparation, and the variability of the biological samples [1, 2].
An important question is how to identify differentially expressed genes, some of which change only minimally (<2-fold), given many known and potentially unknown sources of variance in the microarray experiment. In order to reduce false positive rates, many published experiments use a cut-off of 2- to 3-fold [3–5]. This limits the ability of the microarray experiment to detect small, but biologically important changes. In fact, recent reports have shown that microarrays can significantly underestimate gene expression changes and therefore a high cut-off will miss important changes . Although more sophisticated statistical methods have been proposed for single slide analysis [7–13], it is becoming clear that in order to reduce random variance, replication becomes more and more important in microarray experimental design by greatly increasing the power of the experiment to measure small gene expression changes [2, 13–17]. As a relatively new technique, many new theories have been developed for data analysis and experimental design, but few of these theories have been rigorously tested against a well-established standard method such as the Northern blot.
In this paper we compared two experimental design and analysis methods performed on quadruplicate arrays that include a dye-swap design [18, 19] and a modified reference design method that uses a control-control hybridization to correct for systematic experimental errors, that we refer to as the "control correction method" (CCM). We demonstrate that both experimental designs accurately identified small (<2-fold) gene expression changes after a 24-hour treatment of MCF10AT breast epithelial cells with the growth and differentiation factor neuregulin. These changes correlate well with the anti-proliferative effects of neuregulin resulting in a relative decrease in proliferative genes and increase in anti-proliferative genes that will be important for future investigations.
The results presented in this paper demonstrate two, complementary cDNA microarray methods capable of reliably revealing small changes in gene expression in transformed human breast epithelial MCF10AT cells after treatment with neuregulin. Since, as shown in Fig. 1, treatment of these cells with neuregulin significantly slows their growth rate, identifying early gene expression changes in this process will be important in understanding how neuregulin regulates cell growth in both normal and malignant breast epithelium, and will also provide both biological markers and potential targets in breast cancer. Large quantities of highly purified total RNA were isolated from MCF10AT cells treated with or without neuregulin for 24 hours and used both for microarray experiments and northern blot confirmation studies.
Experimental designs to address systematic errors
As with most experimental methods, replicate measurements can reduce random errors. Equally important are systematic errors. Systematic errors result from a constant tendency to over- and under-estimate true values and cannot be eliminated by replicate analysis, since they are often highly reproducible. An example of such a systematic error is a gene-specific dye effect, also called "dye–gene" interaction , and is shown in Fig. 2A. For a given gene spotted in duplicate (arrows), the red signal labeling the treated sample (T) is much brighter than the green signal for the control sample (C). This was highly reproducible for both spots on the same array and between multiple arrays. One way to determine whether the apparent up-regulation of this gene is true, is to use the same control sample labeled with both red and green dyes and perform a control/control (C/C) hybridization. Fig. 2A shows that the same intense red signal is seen in the C/C hybridization as was seen in the treated/control (T/C), demonstrating that this signal is a systematic error producing a false positive gene expression change.
Given the unavoidable presence of these systematic errors, methods to correct these errors are needed. One way to correct for systematic errors in microarray experiments is to take advantage of C/C hybridizations to correct the T/C hybridizations. This requires a modified reference design, which we refer to as a "control correction" design. This is different from a common reference design used previously [19, 20]. Here, each spot of the T/C hybridization is "corrected" by the same spot from the C/C hybridization for systematic errors. A second method that will also correct for systematic errors is a "dye-swap" design [16, 17, 19]. The dye-swap design uses an ANOVA to calculate gene expression changes from replicate cDNA microarrays probed with T/C hybridizations performed where the dye color is swapped. Included in the ANOVA are factors to correct for systematic errors such as dye and dye-gene interactions. The "control correction" and the dye-swap designs are compared in Fig. 2B. Each of these experimental designs was performed on quadruplicate arrays. Each of these two designs required its own analysis method. While we used an analysis method that utilizes individual t-tests for each spot for the CCM, we compared both ANOVA and Bayesian analysis methods for the dye-swap design.
Control correction method experimental design and results
A flow chart for the control correction method is shown in Fig. 2C. All microarrays used in this study were from the same lot of 3333 gene spotted cDNA slides (similar to the commercially available NEN MicroMax 2400 slides with 933 additional genes (Alphagene Inc., Woburn, MA) where each gene was spotted in duplicate, and hybridized using an optimized, two-step hybridization protocol with either Cy3 or Cy5-labeled dendrimer complexes (Genisphere, Hatfield, PA). A key advantage of the Genisphere Dendrimer system is the need for only 3 μg of total RNA per array without the need for a potentially non-linear amplification step to boost the signal. After scanning and spot-wise local background correction (Imagene Software, Biodiscovery, CA), a log Cy5/Cy3 ratio versus log signal intensity MA plot was prepared and shown in Fig 3A. Without any correction, the ratio vs. intensity plot shows a banana shape as ratios trend downward in the low intensity range. This suggests an intensity-dependent dye effect. In order to correct this and to normalize data sets between different slides, an intensity-dependent normalization procedure was performed that fits the data to a lowess curve as a function of signal intensity . After normalization, the log ratios became more evenly distributed around zero (Fig. 3B).
However, despite this relatively even distribution, histograms of normalized log ratios for T/C and C/C display long tails to the left as shown in the histograms in Fig. 4A and the quantile-qauntile plots in Fig. 4B. Since there should be no treatment effects on the C/C slides, a symmetric, normal distribution would have been expected. The skewed appearances of the normalized distributions indicate additional, uncorrected systematic errors in both T/C and C/C hybridizations. "Correction" of each spot by subtracting the log (C/C) ratios from the log (T/C) ratios produces an approximately normal distribution of the log (T/C) ratios (shown on the bottom of Figs. 4A and 4B). In addition to the systematic errors that occur on a spot-by-spot basis shown in Fig. 2A, systematic errors were found as a function of slide location, particularly at the edge of the arrays. These errors were also corrected by this method (data not shown). Yang and Dudoit proposed a within slide normalization for this type of spatial effect , however, one concern for within slide normalization is that if the number of genes is small in each spatial group, the assumption that there will be an equal proportion of up- and down-regulated genes may be untrue.
As a final step, a t-test was performed to compare the normalized log ratios of T/C and C/C for each gene. This yields p values for each control-corrected fold change calculated as log (T/C)-log (C/C). In Fig. 5, the average and standard deviation of gene expression ratios for the log (T/C) and log (C/C) are plotted for the genes using 1.7-fold and p < 0.05 cut-offs. This clearly demonstrates the importance of correcting each log (T/C) value with the corresponding log (C/C) control value. For example, while some log (T/C) ratios are close to zero, by using the log (C/C) as baseline, true gene expression changes above or below this were identified that would otherwise have been missed. The 1.7-fold cutoff was chosen to be within the detection range of northern blot analyses, which we felt would be the most sensitive method to confirm these small changes. A volcano plot, shown in Fig. 6A, summaries 46 differentially-regulated genes that met these criteria for the CCM.
Comparison of the control correction to the dye-swap design
Many have proposed that a dye-swap experimental design combined with an ANOVA will correct for systematic errors [17–19]. To verify this and compare the dye-swap design to the control correction design, a dye-swap experiment was performed on quadruplicate arrays using the same RNA samples and the two interconnect ANOVA model of Wolfinger et al [22, 23]. Using this experimental design with the same cut-off values, 14 differentially expressed genes were identified and are presented as a volcano plot alongside that of the CCM (Fig. 6B).
Table 1 lists those genes that met our selection criteria, together with their fold-change, p values, and functional classifications. Only 5 genes were found in common for both methods. The genes have been broadly grouped into proliferation, differentiation, and unclassified genes in order to observe trends in the neuregulin-induced gene expression changes that could be important in regulating cell growth. A general trend showing a down-regulation of proliferation genes and up-regulation of differentiation genes was observed. This includes several oncogenes, cell cycle control and cell proliferation genes that were all down-regulated; and tumor suppressor genes, growth inhibition and differentiation genes were up-regulated. This pattern is consistent with the anti-proliferative/differentiation effects of neuregulin on MCF10AT human breast epithelial cells.
Verification of microarray accuracy by northern blot analysis
To confirm these gene expression changes and to determine the accuracy of each experimental method, we selected 23 genes for verification by northern blot. We chose all 5 genes detected by both methods, 6 up-regulated and 5 down-regulated genes from the control correction design, and 7 genes from the dye-swap experiment. The selection of genes was not random, as we selected a balanced complement of genes of variable intensity that were both up- and down-regulated. The probes used for northern blots were generated by PCR from clones used to spot the arrays. Each blot contained triplicate control and treated samples and was re-probed multiple times. Fig. 7 summarizes the northern blot results for these 23 genes. The band intensities were quantified, normalized to total ribosomal RNA for each gel, and averaged to produce a fold change that was compared directly to the fold change from the microarrays. In general, differential gene expression was confirmed by the northern blots for both array design methods. For the dye-swap method only 1 of 12 genes was a false positive, while 4 out of 16 genes were false positives in the control correction method. Down-regulated genes were verified more reliably in the control correction method (10/10) than up-regulated genes (2/6). All differentially expressed genes common to both methods were confirmed..
Since the ANOVA method we used can sometimes underestimate the variance, we re-analyzed our dye-swap data with a Bayesian method using a regularized t-test as implemented in Cyber-T . This analysis revealed 16 differentially expressed genes using the same cut-offs, 10 of which were in common with the ANOVA method (Table 1). A greater number of genes were identified using the regularized t-test, and the corresponding p values for these genes were lower. Based on the previous northern blot data, 8/9 (89%) of these were confirmed.
Gene expression changes in MCF10AT cells suggest a rapid anti-proliferative effect of neuregulin
MCF10AT cells are a human breast epithelial cell line stably transfected with a mutant ras oncogene. These cells are pre-malignant, but can progress to invasive carcinoma [25, 26]. Given that neuregulin can differentially affect the growth properties of different cell lines, we used the MCF10AT cell line as model system to identify genes that may be down-stream from neuregulin activation and could thus be studied further for their roles in breast cancer cells that respond differentially to neuregulin. Combining two cDNA microarray experimental design methods, we have identified genes differentially expressed by neuregulin treatment that correlated with a significant decrease in their growth rate. The pattern of expression clearly shows an anti-proliferative effect of neuregulin on the MCF10AT cells with a reduction in genes associated with proliferation such as heat shock proteins, oncogenes, cell cycle control genes, genes involved in fatty acid and sugar synthesis, transcription and translation together with an increase in differentiation genes including tumor suppressor genes, DNA damage repair genes, growth inhibition genes and differentiation genes. We further showed that these effects are biologically consistent with the rapid, anti-proliferative effects of neuregulin on cell number. Additional experiments have shown that these genes are important biological markers for the degree of malignancy in other breast epithelial cell lines that have differential proliferation responses to neuregulin (Li Q, Ahmed S, and Loeb JA, unpublished results).
Both experimental designs demonstrate a high confirmation rate for small changes in gene expression
One of the important tasks in microarray technology is to design experiments and develop statistical tools to obtain data efficiently and accurately to answer fundamental questions in biology. In many experiments, this requires the ability to detect small changes in gene expression with high fidelity. In this study we compared two common experimental design paradigms for cDNA microarrays and determined their accuracy by northern blot. Both methods identified small expression changes with considerable accuracy. In the control correction design, we used control hybridizations to correct for systematic errors on a spot-by-spot basis. The method is based on an assumption that systematic errors from slides made from the same lot and processed identically do not vary significantly. To minimize the possible variance of systematic errors in T/C slides and C/C slides we maintained strict experimental conditions, such as same-day sample preparation and same-day hybridization. We also used the same control samples for both the T/C and C/C hybridizations instead of using an arbitrary control sample that might be quite different in mRNA composition . This results in similar spot intensities for each gene both in the treatment and the control and will minimize any differences that could be caused by the different mRNA compositions from different samples. This spot-by-spot control correction can eliminate systematic errors that cannot be corrected with slide-wise normalization. Similarly, in the dye-swap design, two different dyes are used to label the same sample, which enables the correction of dye-gene interactions in the ANOVA model.
A summary of the results from this study are shown in the Venn diagrams in Fig. 8. Using the 1.7-fold and p < 0.05 cut-offs, the overall verification rate was 75% for the CCM and 92% for the dye-swap method using ANOVA. Among the 18 confirmed expression changes, all were below 3-fold and only six were above 2-fold. Many of the expression changes below 2-fold on the microarrays underestimated the fold-change measured by northern blotting. The accuracy was not dependent on microarray spot intensity as genes with both low and high signal intensities had similar verification rates (data not shown). The confirmation rates for both methods are comparable to methods reported by Mutch (87.5%)  and Tusher (92%) . Of particular importance in this study is our high confirmation rates for genes differentially expressed by 2-fold or less.
The t-test used for the CCM and ANOVA for the dye-swap method depend on assumptions of Gaussian distributions that may or may not be present in a microarray experiment with a small number of replicates. Some efforts have been made to develop Bayesian frameworks that incorporate prior distributions in order to estimate the noise [24, 29, 30]. We therefore re-analyzed our dye-swap data using a "regularized" t-test . Using this, we identified 16 genes that met our cut-off criteria, 10 of which were in common with the ANOVA analysis. Of those genes that we measured by northern blot analysis, 8/9 or 89% were verified. In summary, the regularized t-test revealed more genes than the ANOVA method with generally lower p values. If we eliminate the 1.7-fold cut-off, but maintain the p value <0.05, the CCM identified 493 genes, the ANOVA identified 499 genes, and the regularized t-test identified 729 differentially expressed genes (Fig. 8B). Among these, 399 were in common between the regularized t-test and ANOVA, 248 in common between the CCM and the regularized t-test, and 188 in common between the CCM and the ANOVA. These results demonstrate that if the false-positive rate remains the same, the regularized t-test is more sensitive than the traditional ANOVA and has extensive overlap, while the CCM has the least overlap between the other methods, but identifies different genes with slightly less specificity.
In our analysis, we selected genes based on their p values obtained from replicates of individual spots and did not adjust these p-values for multiple comparisons. This may be a major cause for the higher false positive rates for both of our experimental designs. For the CCM, if we apply Bonferroni correction, while we can eliminate all false positives, we would also miss a majority of the differentially expressed genes verified by Northern blotting. Therefore, if accuracy is the main purpose of a study, multiple comparison corrections should be used, while if sensitivity is the main purpose, then it should not be used with the understanding that the accuracy will be lower.
Comparison of a dye-swap versus a control correction method experimental design
For our experimental design, the dye-swap method had a higher confirmation rate than the control correction method. This is, in part, due to the smaller variance that results from an effective doubling of the number of treated samples in the dye-swap method compared to the control correction method. Despite the higher degree of accuracy, the dye-swap design identified fewer genes and only detected down-regulated genes, whereas the control correction identified 3-times the number of genes that were both up- and down-regulated. However the control correction method was less specific for up-regulated genes. These differences may not solely reflect methodological differences, but likely result from experimental variability produced by performing the experiments independently on different days. Nonetheless, the results presented here suggest that both methods have clear merit in their abilities to show true gene expression changes, particularly for expression changes of 2-fold or less, and for genes with low signal intensities and/or low abundance.
The final decision as to which method is preferred depends on the experimental design. For example, the amount of sample and number of replicates required are important considerations both in terms of how difficult the RNA is to obtain and the number of samples that need to be compared. This also translates into the cost to perform the experiment. For instance, the dye-swap method generates a larger sample size for the same number of slides, thus producing greater significance when comparing gene expression between two samples. However this method requires a minimum of two slides and two different labeling reactions per sample. If the amount of sample is limited or population level replication is more desirable than individual sample replication, the control correction is more efficient since individual replicates for reverse dye labeling are not required and each sample can be run with only one slide. For example, to compare 6 treatment samples with a single control sample would require a minimum of 12 microarrays using the dye-swap method, whereas the minimum number of 8 arrays is possible using the control correction method; 6 for treatment samples and 2 for controls.
Another common experimental design used for time course or dose response studies is the reference design. In fact, the control correction method described here is essentially a modified reference design method where the zero time or dose point is the control-control comparison. As discussed above, using a very similar control sample to correct the series will give less false positives and negatives and a more accurate absolute value of the observed change than a dissimilar, pooled reference sample.
Under-estimation of fold changes by cDNA microarrrays
Although our cDNA microarray results were accurate, the measured changes generally underestimated the actual changes measured by northern blots. Yuen et al.  similarly found that both oligonucleotide arrays (GeneChips by Affymetrix) and cDNA arrays underestimate fold changes compared to quantitative RT-PCR. The cause for this underestimation is not clear, however, it may be due to the limited dynamic range of dye signal or non-specific binding of the dye. Nonetheless, the limitations in accuracy and fold change estimation are far outweighed by the ability of microarrays to identify biologically important gene expression changes.
This study demonstrated that dye-swap and control correction experimental design paradigms for cDNA microarray experiments are capable of detecting small, biologically important changes in gene expression with excellent fidelity while revealing important down-stream anti-proliferative effects of neuregulin on breast epithelial cells for future studies.
Human cDNA glass microarrays, called the Alphamax Genechip, were obtained from Alphagene Inc. (Woburn, MA) containing 3333 cDNAs spotted in duplicate. The cDNAs used are identical to commercially available Micromax 2400 slides from Perkin Elmer Life Sciences (Boston, MA), most of which were derived from a human fetal brain cDNA library, with an additional 933 genes (gene list available upon request).
MCF10AT cell culture – MCF10AT cells were from Dr. Robert Pauley at the Karmanos Cancer Institute (Detroit, MI). The cells were cultured in DMEM/F12 media (Invitrogen) supplemented with 5% horse serum (Invitrogen), 10 mM HEPES buffer (Invitrogen), 10 μg/ml insulin (Sigma), 20 ng/ml EGF (Upstate Biotechnology), 100 ng/ml cholera enterotoxin (CalBiochem) and 0.5 μg/ml hydrocortisone (Sigma) at 37°C in 5% CO2 incubator.
Neuregulin treatment and RNA extraction – A recombinant human NRG β1 polypeptide (amino acids 14–246) was generously provided by AMGEN (Thousand Oaks, CA). After 3 days of culture, MCF10AT cells were treated with human recombinant neuregulin β1 form for 24 hours. MCF10AT cells grown under similar conditions without neuregulin treatment were used as a control. The cells were then harvested and total RNA was extracted using Ultraspec (Biotecx laboratories). The total RNA was cleaned up by Rneasy kit (Qiagen) and quantified using a fluorescent dye binding assay, Ribogreen (Molecular Probes). RNA purity was assessed by agarose gel electrophoresis. Proliferation assays were performed by counting quadruplicate cultures plated at 5000 cells/well using a hemocytometer.
Microarray hybridization – cDNA microarrays were used in a 2-step hybridization protocol that was optimized for the Genisphere dendrimer labeling method. Total RNA was reverse transcribed into cDNA containing a unique 5' primer tag, using the Genisphere 3DNA expression array detection kit. In brief, for each reaction, 3 μg of total RNA was reverse transcribed using 0.2 μM oligo-dT-Genisphere capture primer, 0.5 mM dNTP, 200 U Superscript II (Invitrogen) in 1X first strand Superscript II buffer at 42°C for 2 h. The RNA from the DNA/RNA hybrids was denatured with 0.5 M NaOH / 50 mM EDTA at 65°C for 10 min. The reaction was neutralized using 1 M Tris-HCl ph 7.5. The contents of the tube containing the NRG-treated and control cDNA were then mixed together and 3 μl linear acrylamide (Ambion) and 250 μl of 3 M ammonium acetate were added to them. cDNA was precipitated by adding 100% Ethanol and incubating at -20°C for 30 min. The cDNA was collected in a pellet by centrifugation at 13000 rpm for 15 min in a microcentrifuge. and resuspended in Alternate (formamide-containing) Hybridization buffer (Genisphere) at 65°C for 10 min and modified LNA blocker (Genisphere) with denatured Cot 1 DNA.
The entire mixture was added to the pre-hybridized array (Alphamax) for hybridization at 55°C for at 36 hr. A clear increase in signal was obtained with a 36 h hybridization compared to 16 h. After hybridization, the arrays were washed with 2X SSC and 0.2% SDS at 60°C for 15 min, followed by a wash with 2X SSC and another with 0.2X SSC at room temperature. For fluorescence detection, a second hybridization with the dendrimer was optimal. 2.5 μl each of the Cy3 and Cy5 dendrimer in Hybridization Buffer (Vial 6, Genisphere kit) were mixed with denatured Cot1 DNA and differential expander and the mixture was added to the pre-hybridized slides for hybridization at 60°C for 2 hrs. The slides were washed again as described above.
Microarray data analysis method
Analysis of CCM experiment
Arrays were scanned with a GenePix 4000 A scanner (Axon Instruments, Inc., Union City, CA). Images were quantified using ImaGene Software (Biodiscovery, Inc. Marina del Rey, CA) that uses a local background subtracted from the signal. Signals not consistently detectable (background corrected signal lower than 2 times of background standard deviation) were eliminated.
We fitted loess curve to the log transformed data using the "loess" function in SAS software (SAS Institute Inc., NC) for intensity dependent normalization followed by a t-test to compare T/C with C/C ratio, gene by gene. The t-test was performed on the normalized log ratio with Welch correction for unequal variance. The control corrected fold change was calculated as:
log (fold) = log(T/C)-log(C/C)
Analysis of dye-swap experiment
For the dye-swap method we performed the same background correction and data filtering for absent genes and log transformations. We then used a two interconnect ANOVA model [22, 23] and Mixed Model Analysis of Microarray Data (MANMADA) http://statgen.ncsu.edu/ggibson/Manual.htm to identify differentially expressed genes. First we use a normalization model for log-transformed intensity measurements:
y ij = μ + A i + D j + AD ij + ε ij
Where μ is the sample mean, A i is the effect of i th array, D j is the effect of dye cy3 or cy5, AD ij is array dye interaction and ε ij is random error. The residue from normalization model is then used in following gene model to find treatment effects on each gene:
r ijkg = A ig + D jg + T kg
Where r ijkg is the residual of each gene from the normalization model, T kg is the treatment effect (control or treated), and A ig and D jg are the array and dye effects, respectively. The expression change for each gene is thus:
log (fold) = Ttreated-Tcontrol
5 μg total RNA isolated from MCF10AT cells was run on a 1.3% Agarose/2.2M Formaldehyde gel as described previously . Probes were prepared by PCR from the same clones used to spot the slides provided by Alphagene Inc except for AJ224442, X86779 and U62739, where clones BC011696, BI754516 and BG763631, with of over 99% identity, were used as substitutes. Probes were generated by random priming using PrimiT II kit (Stratagene) radiolabeled probes. The auto-radiographs within the linear range of the film were scanned with a flatbed scanner with transparency adapter and quantified using MetaMorph (Universal Imaging) analysis software as described previously . For time course measurements, the amount of signal normalized for loading with either 18S RNA or GAPDH were plotted together after first setting 100% to the intensity of the control measurement at 48 hours and setting the lowest intensity value to 0%.
Nadon R, Shoemaker J: Statistical issues with microarrays: processing and analysis. Trends Genet 2002, 18(5):265–271. 10.1016/S0168-9525(02)02665-3
Draghici S, Kuklin A, Hoff B, Shams S: Experimental design, analysis of variance and slide quality assessment in gene expression arrays. Curr Opin Drug Discov Devel 2001, 4(3):332–337.
Schena M, Shalon D, Heller R, Chai A, Brown PO, Davis RW: Parallel human genome analysis: microarray-based expression monitoring of 1000 genes. Proc Natl Acad Sci U S A 1996, 93(20):10614–10619. 10.1073/pnas.93.20.10614
Brudler R, Hitomi K, Daiyasu H, Toh H, Kucho K, Ishiura M, Kanehisa M, Roberts VA, Todo T, Tainer JA, Getzoff ED: Identification of a new cryptochrome class. Structure, function, and evolution. Mol Cell 2003, 11(1):59–67. 10.1016/S1097-2765(03)00008-X
DeRisi JL, Iyer VR, Brown PO: Exploring the metabolic and genetic control of gene expression on a genomic scale. Science 1997, 278(5338):680–686. 10.1126/science.278.5338.680
Yuen T, Wurmbach E, Pfeffer RL, Ebersole BJ, Sealfon SC: Accuracy and calibration of commercial oligonucleotide and custom cDNA microarrays. Nucleic Acids Res 2002, 30(10):e48. 10.1093/nar/30.10.e48
Newton MA, Kendziorski CM, Richmond CS, Blattner FR, Tsui KW: On differential variability of expression ratios: improving statistical inference about gene expression changes from microarray data. J Comput Biol 2001, 8(1):37–52. 10.1089/106652701300099074
Chen Y, Kamat V, Dougherty ER, Bittner ML, Meltzer PS, Trent JM: Ratio statistics of gene expression levels and applications to microarray data analysis. Bioinformatics 2002, 18(9):1207–1215. 10.1093/bioinformatics/18.9.1207
Efron B, Tibshirani R: Empirical bayes methods and false discovery rates for microarrays. Genet Epidemiol 2002, 23(1):70–86. 10.1002/gepi.1124
Hughes TR, Marton MJ, Jones AR, Roberts CJ, Stoughton R, Armour CD, Bennett HA, Coffey E, Dai H, He YD, Kidd MJ, King AM, Meyer MR, Slade D, Lum PY, Stepaniants SB, Shoemaker DD, Gachotte D, Chakraburtty K, Simon J, Bard M, Friend SH: Functional discovery via a compendium of expression profiles. Cell 2000, 102(1):109–126. 10.1016/S0092-8674(00)00015-5
Sapir M, Churchill G: Estimating the posterior probability of differential gene expression from microarray data. 2000.
Draghici S: Statistical intelligence: effective analysis of high-density microarray data. Drug Discov Today 2002, 7(11):S55–63. 10.1016/S1359-6446(02)02292-4
Draghici S: Data Analysis Tools for DNA Microarrays. Chapman and Hall/CRC Press 2003.
Townsend JP: Resolution of large and small differences in gene expression using models for the Bayesian analysis of gene expression levels and spotted DNA microarrays. BMC Bioinformatics 2004, 5(1):54. 10.1186/1471-2105-5-54
Lee ML, Kuo FC, Whitmore GA, Sklar J: Importance of replication in microarray gene expression studies: statistical methods and evidence from repetitive cDNA hybridizations. Proc Natl Acad Sci U S A 2000, 97(18):9834–9839. 10.1073/pnas.97.18.9834
Yang YH, Speed T: Design issues for cDNA microarray experiments. Nat Rev Genet 2002, 3(8):579–588.
Churchill GA: Fundamentals of experimental design for cDNA microarrays. Nat Genet 2002, 32(Suppl):490–495. 10.1038/ng1031
Kerr MK, Churchill GA: Statistical design and the analysis of gene expression microarray data. Genet Res 2001, 77(2):123–128. 10.1017/S0016672301005055
Kerr MK, Martin M, Churchill GA: Analysis of variance for gene expression microarray data. J Comput Biol 2000, 7(6):819–837. 10.1089/10665270050514954
Dudoit S, Yang Y, Callow M, Speed T: Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments. 2000.
Yang YH, Dudoit S, Luu P, Lin DM, Peng V, Ngai J, Speed TP: Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Res 2002, 30(4):e15. 10.1093/nar/30.4.e15
Jin W, Riley RM, Wolfinger RD, White KP, Passador-Gurgel G, Gibson G: The contributions of sex, genotype and age to transcriptional variance in Drosophila melanogaster. Nat Genet 2001, 29(4):389–395. 10.1038/ng766
Wolfinger RD, Gibson G, Wolfinger ED, Bennett L, Hamadeh H, Bushel P, Afshari C, Paules RS: Assessing gene significance from cDNA microarray expression data via mixed models. J Comput Biol 2001, 8(6):625–637. 10.1089/106652701753307520
Baldi P, Long AD: A Bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes. Bioinformatics 2001, 17(6):509–519. 10.1093/bioinformatics/17.6.509
Shekhar PV, Chen ML, Werdell J, Heppner GH, Miller FR, Christman JK: Transcriptional activation of functional endogenous estrogen receptor gene expression in MCF10AT cells: a model for early breast cancer. Int J Oncol 1998, 13(5):907–915.
Strickland LB, Dawson PJ, Santner SJ, Miller FR: Progression of premalignant MCF10AT generates heterogeneous malignant variants with characteristic histologic types and immunohistochemical markers. Breast Cancer Res Treat 2000, 64(3):235–240. 10.1023/A:1026562720218
Mutch DM, Berger A, Mansourian R, Rytz A, Roberts MA: The limit fold change model: A practical approach for selecting differentially expressed genes from microarray data. BMC Bioinformatics 2002, 3(1):17. 10.1186/1471-2105-3-17
Tusher VG, Tibshirani R, Chu G: Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci U S A 2001, 98(9):5116–5121. 10.1073/pnas.091062498
Townsend JP, Hartl DL: Bayesian analysis of gene expression levels: statistical quantification of relative mRNA level across multiple strains or treatments. Genome Biol 2002, 3(12):RESEARCH0071. 10.1186/gb-2002-3-12-research0071
Tseng GC, Oh MK, Rohlin L, Liao JC, Wong WH: Issues in cDNA microarray analysis: quality filtering, channel normalization, models of variations and assessment of gene effects. Nucleic Acids Res 2001, 29(12):2549–2557. 10.1093/nar/29.12.2549
Loeb JA, Fischbach GD: Neurotrophic factors increase neuregulin expression in embryonic ventral spinal cord neurons. J Neurosci 1997, 17(4):1416–1424.
Li Q, Loeb JA: Neuregulin-heparan-sulfate proteoglycan interactions produce sustained erbB receptor activation required for the induction of acetylcholine receptors in muscle. J Biol Chem 2001, 276(41):38068–38075.
This work was supported by the Ralph C. Wilson, Sr. and Ralph C. Wilson, Jr. Medical Research Foundation (JAL), NINDS (NIH) R01 NS45207 (JAL), and the American Cancer Society 85-003-14 (JAL). SNR was supported by a pre-doctoral fellowship from the Epilepsy Foundation of America. We thank Robert Getts from Genisphere for helpful discussions on optimizing the dendrimer labeling system and Thomas Beaumont for helpful comments on the manuscript.
BY analyzed microarray data. SR carried out microarray experiment. QL conducted MCF10AT cell culture and mRNA extraction. SA carried out northern blots experiments. RK provide input on microarray experiments. SD contributed ideas to data analysis. JAL conceived and design the experiment.
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Yao, B., Rakhade, S.N., Li, Q. et al. Accuracy of cDNA microarray methods to detect small gene expression changes induced by neuregulin on breast epithelial cells. BMC Bioinformatics 5, 99 (2004). https://doi.org/10.1186/1471-2105-5-99
- Northern Blot
- Gene Expression Change
- Breast Epithelial Cell
- Breast Epithelial Cell Line
- Control Correction