- Research article
- Open Access
Normalization of low-density microarray using external spike-in controls: analysis of macrophage cell lines expression profile
© Fardin et al; licensee BioMed Central Ltd. 2007
- Received: 04 September 2006
- Accepted: 17 January 2007
- Published: 17 January 2007
The normalization of DNA microarrays allows comparison among samples by adjusting for individual hybridization intensities. The approaches most commonly used are global normalization methods that are based on the expression of all genes on the slide and on the modulation of a small proportion of genes. Alternative approaches must be developed for microarrays where the proportion of modulated genes and their distribution are unknown and they may be biased towards up- or down-modulated trends.
The aim of the work is to study the use of spike-in controls to normalize low-density microarrays. Our test-array was designed to analyze gene modulation in response to hypoxia (a condition of low oxygen tension) in a macrophage cell line. RNA was extracted from controls and cells exposed to hypoxia, mixed with spike RNA, labeled and hybridized to our test-array. We used eight bacterial RNAs as source of spikes. The test-array contained the oligonucleotides specific for 178 mouse genes and those specific for the eight spikes. We assessed the quality of the spike signals, the reproducibility of the results and, in general, the nature of the variability. The small values of the coefficients of variation revealed high reproducibility of our platform either in replicated spots or in technical replicates. We demonstrated that the spike-in system was suitable for normalizing our platform and determining the threshold for discriminating the hypoxia modulated genes. We assessed the application of the spike-in normalization method to microarrays in which the distribution of the expression values was symmetric or asymmetric. We found that this system is accurate, reproducible and comparable to other normalization methods when the distribution of the expression values is symmetric. In contrast, we found that the use of the spike-in normalization method is superior and necessary when the distribution of the gene expression is asymmetric and biased towards up-regulated genes.
We demonstrate that spike-in controls based normalization is a reliable and reproducible method that has the major advantage to be applicable also to biased platform where the distribution of the up- and down-regulated genes is asymmetric as it may occur in diagnostic chips.
- Receiver Operating Characteristic Curve
- Macrophage Cell Line
- Spike Concentration
- Spike Signal
- Loess Normalization
Studies on gene expression rely heavily on DNA microarray technology . In a typical microarray experiment, the two RNA samples to be compared are reverse transcribed in cDNA, labeled using two different fluorophores and then hybridized simultaneously to the glass slide to measure the relative gene expression level . Essential to the analysis of microarray data is the normalization process, which allows comparison among samples by adjusting for individual hybridization intensities. There are many approaches to normalize expression levels and the most commonly used, referred to as global normalization methods, apply to experiments in which most of the genes are equally expressed in both channels . The global normalization approach is based on the use of the majority of genes on the slide to normalize microarray experiments and a constant adjustment is used to force the distribution of signal ratios to have the same measure of central tendency, e.g., the same median. These methods can be applied when the elements spotted on the array are representative of a random and large number of genes  and when there is symmetry in the frequency of the up/down-regulated genes . Alternative approaches have to be developed when the majority of the genes represented on the array are coordinately up- or down-modulated as in the case of diagnostic chips [3, 6]. Diagnostic chips are designed as low-density microarrays containing a number of selected genes expected to be concomitantly up- or down-regulated in response to given signals, drugs, or pathological conditions. The advantage of low-density over high-density platforms is the competitiveness in price and the flexibility of design.
We propose the use of external reference RNAs (also known as spike-in controls or spikes) to normalize the data of low-density microarray. Spike RNAs show no sequence similarity to the genome of the studied species and they are added in defined amounts to experimental RNA samples before labeling. The oligonucleotides specific for the spike RNAs are spotted onto the slide. The use of spikes allows not only data normalization but also the evaluation of several parameters of the platform quality, including the sensitivity and specificity of the microarray experiments, the accuracy and reproducibility of the measurements and the assessment of technical variability introduced by labeling procedure, hybridization and image scanning [7, 8].
Our laboratory is involved in the study of the cellular response to hypoxia, a condition of low oxygen tension that characterizes many pathological situations . Hypoxia occurs in cardiovascular, hematological, and pulmonary disorders, inflammatory processes, and fibrosis . Areas of low oxygen concentration are present in solid tumors and are known to contribute to tumor growth, metastasis, and resistance to radio and chemotherapy . We and others have applied microarray technology to define the profile of gene expression associated to hypoxia utilizing the Affymetrix GeneChip  and we are in the process of designing low-density microarrays that will identify the hypoxia-inducible genes in tumor specimens, and may serve as prognostic indicators of the aggressiveness of the disease and of the sensitivity to therapy. A prerequisite for the development of such tool is a correct normalization procedure and a sound analysis of the data that does not require preexisting information on the expected pattern of results and that will be suitable even if the majority of the genes is modulated or the distribution of up- or down-regulated genes is asymmetric.
In this study, we demonstrate that a composite loess normalization [13, 14] based on spike-in controls is the proper way to deal with low-density microarray platforms that applies also to extreme distribution of the data when the global normalization approaches will generate erroneous results.
RNA spikes: length and base composition
Initial experiments were performed to characterize the signals of the spike RNA in the biological setting. Experimental RNA from control and hypoxia treated macrophages were mixed with equal amounts of each spike RNA generating an expected ratio of 1 in the spike signal. The experiments were designed in such a way that each pin printed quadruplicate subarrays containing the entire set of eight oligonucleotides corresponding to the spikes and one fourth of the oligonucleotides specific for the experimental genes.
Reproducibility of replicate spots within array
ANOVA analysis before and after normalization
Source of variationa)
sum of squares
degree of freedom
% of variance
sum of squares
degree of freedom
% of variance
R Squaredh) = 0.958
R Squaredh) = 0.887
Variability of spike after excluding outlier values
In summary, these results clearly show that the information provided by spikes' signal is reproducible, characterized by low variability (that can be further reduced by filtering our outliers values) and suitable to normalize the results of our test-array.
Normalization of the spikes' signal
The relative fluorescence intensity between channels must be normalized to adjust for systematic biases such as differences in RNA levels, dye incorporation and detection efficiencies . The dye balance may vary with spot intensity and with spatial position on the array. Loess and print-tip loess normalization are among the most commonly used methods to remove such variability although the application of this algorithm is not free from potential risk on the interpretation of the results [5, 14]. We utilized, as a routine, the loess normalization procedure, which does not take into consideration the tip-to-tip variability because, under the present experimental conditions, the ANOVA showed that the contribution of the position on the array to the total variability were negligible.
To define the effects of normalization on the variability the spikes' signal data were analyzed by ANOVA (Table 3, "After normalization") and compared to those obtained in the absence of normalization ("Before normalization"). When dissecting such variability into its components we found a major decrease for the spike concentration (from 48.3% to 1%) indicating that any spike concentration within the range from 10 to 1000 pg could be used, including low amounts of spike with a substantial saving of reagents.
The normalization did not affect the variability associated to the array position, and most variability is now explained by the spike characteristics (40.4%) or by their interaction with the spike concentration (47.4%). The little change in the R squared between the two ANOVA models (from 0.958 to 0.887) is due to the change in the relationship between the components and the interaction term and it does not affect the reliability of the model.
Analysis of the experimental platform using the spikes
A second consideration is the strength of the loess curve with respect to the number of replicates. We calculated the minimum number of replicates needed to have a robust loess procedure. The robustness was calculated on the basis of reproducibility of the normalized data considering an increasing number of replicates and we set as a threshold for our system a CV of 10%. In our platform we have 8 spikes each replicated 48 times in each slide for a total of 384 replicates. We applied a Monte Carlo resampling procedure aimed at sampling 100 times the 384 replicates collecting randomly each time a fixed number of replicates (n < 384). Each sample was used for normalization of the array, the mean expression value of the experimental data was computed and the CVs based on each sample were calculated (data not shown). We found that the CV was less than 10% when the number of replicates was equal or greater that 200 and we concluded that a minimum of 200 replicates is needed for an accurate normalization of the platform. In our platform we spotted 48 replicates per spike and, theoretically, five spikes would be sufficient to reach the threshold number of 200 replicates. However, this is the minimum number required and five spikes would not allow, for example, the elimination of the outliers. On the bases of our limited experience, we propose that a platform that uses the spike-in normalization system, as that described here, must adhere to the requirement of a minimum of 200 replicates among all spikes and a set of spikes representative of the range of expression values of the experimental data. In general, the optimal number of spikes/replicates to be used will have to be calculated considering the range of log-intensities covered by the spikes and the reproducibility of the normalized data with an increasing number of replicates.
Normalization and scaling of the experimental platform
list of up-regulated genes
Vascular endothelial growth factor A
Selenium binding protein 1
Guanine nucleotide binding protein, alpha 13
Coagulation factor II (thrombin) receptor
Prolyl 4-hydroxylase, beta polypeptide
BCL2/adenovirus E1B interact protein 3-like
Forkhead box G1
Interferon-inducible protein p204
B-cell translocation gene 1
Extracellular matrix protein 1
Chemokine (C-X-C motif) receptor 4
We describe the use of spike-in external control to normalize a low-density microarray. The spikes are an external reference that allows data normalization independently from the expression of the experimental RNA particularly suitable for situation in which there is asymmetric distribution of modulated genes. This approach does not rely upon the low-density property of the array and, theoretically, it can be applied to high-density arrays. We can not exclude that the source of the experimental RNA may affect the spike performance and some evaluation of the parameters described here may be needed in different experimental conditions. We demonstrate that the application of loess normalization to the spikes' signal decreases significantly the major source of variability. Furthermore we introduce a criterion for the removal of the outliers that is quite useful to further reduce the system variability.
The choice of normalization method is critical for a correct interpretation of the results when the distribution of the expression values in not symmetrical and/or the number of spotted genes is limited. In fact, we show that median normalization method or loess normalization based on all genes spotted are unable to cope with situations in which only up-regulated genes are present on the array. Unfortunately, the nature of the distribution of gene expression is generally unknown and normalization methods that are independent from this variable are desirable. We demonstrate that the spike-in method is as effective as other global normalization methods in dealing with symmetric distribution of the expression values. More important, we show that it can be successfully applied also to situation in which the distribution of the expression values is highly asymmetric. We conclude that the spike-in system is a method of choice for arrays with a potentially biased distribution. A situation in which there is the potential for an asymmetric distribution is represented by low-density diagnostic chip where the choice of the genes spotted may be deliberately biased towards those that are up-regulated in a given pathological condition.
There is no reason to believe that the spike-in normalization method could not be suitable for high-density microarrays. The only possible technical limitation is the inclusion of spike probes in commercially available chips. However high-density microarrays are characterized by a symmetric distribution and commonly used normalization approaches are equally effective or possibly superior as the intensity-dependant normalization procedure. A thorough comparison among normalization methods has been published .
We describe the use of eight spikes for an accurate normalization of the platform and in our experience this conditions allows an accurate normalization in every experiment. On the basis of a Monte Carlo resampling procedure we determined that theoretical minimum number of five spikes each replicated 48 times is needed for the normalization of our platform. However, such number has severe limitations including the impossibility of excluding the outliers. In general, the number of spikes to be used in different applications may vary depending on the stringency of the criteria used, on the desired variability threshold and on the robustness of the platform and it may have to be recalibrated when using other sources of RNA.
Our experimental system consists of RNA from cell line cultured in normoxic condition or exposed to hypoxia, a condition of low oxygen tension that characterizes several pathological conditions. Very often, arbitrary expression cut-offs are set to discriminate between genes that are modulated or not changed. We describe here the successful use of the ROC curves to assess objectively the threshold to identify hypoxia-modulated genes. The expression data obtained with our platform are consistent with previous information generated in our laboratory using other platforms and with the data in the literature relative to the response of macrophages to hypoxia confirming that we could detected hypoxia inducible genes and that our platform is suitable for supporting hypoxia diagnostic chips.
In summary, we present an accurate description and characterization of a normalization procedure of a low-density microarray based on spike in external controls that has the potential of a broad applicability to different types of arrays including those in which there is an asymmetric distribution of the up/down-regulated genes.
C6-amino-linker oligonucleotides (50 nucleotides in length) were obtained from MWG oligoset (MWG-Biotech AG, Ebersberg, Germany), and spike-in controls oligonucleotides were purchased from Ambion ArrayControl Spot (Ambion Inc., Austin TX). Oligonucleotides were printed on e-Surf Activated Slides (Life Line Lab S.r.l., Italy) with a SpottingArray 24 (PerkinElmer, Wellesley, MA) using 4 Stealth Micro Spotting Pins (Telechem International, Inc. Sunnyvale, CA) in 150 mM phosphate buffer pH 8,5 at 40% humidity. E-surf activated slides are obtained by adsorption on glass of a hydrophilic polymer containing N, Nacryloyloxysuccinimide (NAS). Oligonucleotides were printed at a final concentration of 10 pmol/μl. The coupling reaction was performed o/n in a saturated NaCl solution chamber with a 75% relative humidity. All oligonucleotides were printed in quadruplicates over 4 subarrays with a 2 × 2 print head. Spike-in controls oligonucleotides, negative control and buffer were printed in quadruplicates onto each subarray. The scheme is repeated 3 times on the entire slide surface resulting in 12 replicates for each gene element and 48 replicates for each control element.
ANA-1 cell line  was cultured and maintained at 37°C in a humidified incubator containing 20% O2, 5% CO2 and 75% N2. For hypoxic conditions, cells were incubated in a humidified anaerobic workstation incubator (BUG BOX, Ruskinn, UK) flushed with a mixture of 94% N2, 5% CO2 and 1%O2. Total RNA was extracted from ANA-1 cells grown under normoxic or hypoxic conditions for 18 hours, using Trizol (Invitrogen Life technologies, Irvine, CA) according to the manufacturer's protocol. The physical quality control of RNA integrity was carried out by electrophoresis using Agilent Bioanalyzer 2100 (Agilent Technologies, Waldbronn, Germany) and quantified by NanoDrop (NanoDrop Technologies, Wilmington, Delaware USA). Spike-in controls RNA were purchased from Ambion ArrayControl RNA Spikes. The RNA Spikes are a set of 8 purified RNA transcripts with sequence homology to the corresponding ArrayControl Spot. The ArrayControl sequences were selected from Escherichia coli genes that show no sequence similarity to mammalian genomes.
Sample labeling and microarray hybridization
15 μg of total RNA were converted in either Cy3- or Cy5-labeled cDNA probe using the Superscript indirect cDNA labelling kit (Invitrogen Life technologies, Irvine, CA). Spike RNA were added in appropriately diluted 2 μl mixture to total RNA and to oligodT primer, RNase-free water was used to bring the volume to 18 μl, and the reaction was denatured at 70° for 5 min and then chilled on ice. Amminoallyl-modified cDNA was generated in the presence of 5× first-strand buffer, 0.1 M DTT, dNTP mix (including amino-modified nucleotides), RNaseOUT™ (40 U/μl), SuperScript™ III RT (400 U/μl) in a final volume of 30 μl at 46°C for 3 hours. RNA template was hydrolyzed by the addition of 15 μl of 1 N NaOH followed by heating at 70°C for 10 min. Reactions were neutralized with 15 μl of 1 N HCl, and cDNA was purified on S.N.A.P. columns according to the manufacturer's instructions followed by ethanol precipitation. cDNA was lyophilized to dryness and resuspended in 5 μl of 2× coupling buffer. NHS ester of Cy3 or Cy5 dye (Amersham Pharmacia, GE Healthcare Little Chalfont, UK) in DMSO (dye from one tube was dissolved in 5 μl of DMSO) were added and reactions were incubated at room temperature in the dark for 1 h. Coupling reactions were quenched by the addition of 20 μl of 3 M sodium acetate pH 5.2, and unincorporated dye was removed using S.N.A.P. columns. The combined Cy3 and Cy5 probes were dried down in a speed-vac and then dissolved in 6 μl of RNase-free water. 10 μg of Cot-1 DNA, 10 μg of poly(A) and 4 μg of yeast tRNA were added, the mixture was denatured at 95°C for 3 min and then cooled down on ice for 1 min. 35% formamide, 3.5× SSC, 0.3% SDS and 2.5× Denhardt's were added to a final volume of 90 μl. Slides were blocked in an appropriate blocking solution, 100 mM ethanolamine, 0.2 M Tris, pH 9.0, at 50°C for 20 min and then washed in 4× SSC, 0.1% SDS for 20 min. Blocked slides were pre-hybridized at 42°C for 45 minutes with a pre-hybridization mixture (35% formamide, 4× SSC, 0.5% SDS, 2.5× Denhardt's, 20 ng/μl Salmon Sperm DNA) in the HS 400 hybridization station (Tecan Austria GmbH, Salzburg, Austria). Hybridizations were carried out at 42°C for 16 h automatically agitated every 5 min, followed by washing in (3 min each): 2× SSC and 0.1% SDS, 1× SSC and 0.5× SSC at room temperature.
Data acquisition, normalization and analysis
Arrays were scanned using a GenePix 4000B dual-color confocal laser scanner (Axon Instruments, Union City, CA) at 10-micron resolution. Images were processed, and signals from spotted arrays were quantitated using GenePix Pro 5.1 software (Axon Instruments). Array images that did not pass minimal quality control were discarded (median signal-to-background >3; median signal-to noise >3; mean of median background signal <200). Technically imperfect spots were removed either automatically by the GenePix software or through manual investigation of the array images. Such spots were flagged as 'absent' in the GenePix results files and they were not included in the analysis. To discard data from weak signals, spots with <50% of pixels >2 SD above median local background signal were flagged 'absent' too. Data from spots that not passed this criterion for one channel but with >95% of the pixels >2 SD above median local background signal in the other channel were kept. GenePix result files, including signal, background, standard deviation, pixel statistics and quality parameters on both channels have been imported in the statistical environment R  using Bioconductor software  for the subsequent normalization process. Background-subtracted fluorescence log-ratios were normalized within each array by using composite loess normalization  available in the Bioconductor package limma [23, 24]. Composite loess normalization corrects the expression log-ratios for intensity-based trends subtracting from each expression log-ratio the corresponding value of the loess curve. The loess curve is constructed by performing a series of local regressions, one local regression for each spike-in control spot on the corresponding MA-plot . Being R and G, the background-corrected red and green intensities for each spot, the expression log-ratio (M-value) corresponding to a spot is M = log2R - log2G, whereas the log-intensity (A-value) of each spot is defined as A = (log2R+log2G)/2, a measure of the overall brightness of the spot. All spike-in control spots (after filtering) have been included in each local estimate of the loess curve (this corresponds to set the parameter span equal to 1 in the implementation of the loess normalization algorithm in the package limma) to avoid a non-reliable representation of the overall trend within the sliding windows used for local regressions due to the low number of genes spotted on the array. Median percentile normalization was performed utilizing the "normalize to median or percentile" option in GeneSpring GX 7.3 (Silicon Genetics, Redwood City, CA).
In some cases, loess normalized M-values have also been scaled across a series of arrays. The need for scaling across arrays has been determined empirically in each instance, according to the experimental evidences on different classes of spots (basically, spike and non-spike genes). We used two different methods for scaling, both implemented in the package limma: the scale method [3, 5, 14], whose basic idea is simply to scale the M-values to have the same median-absolute-deviation (MAD) across arrays; and the quantile method , which ensures that the M-values have the same empirical distribution across arrays and across channels.
Analysis of variance (ANOVA)
Analysis of variance (ANOVA) is a procedure for constructing statistical tests by partitioning the total variance into different sources. ANOVA model consists in a separation of a complex variance term into its components . We create a fixed effect model with interaction terms to evaluate the main effects of the potential sources of variance. To confirm the loess normalization we performed ANOVA among data before and after the normalization process. The ANOVA model is:
logR(g, d, s) = μ+G(g)+D(d)+S(s)+GD(gd)+ ε(g, d, s)
where log R(gds) is the measured log ratio for spike g, concentration d, and array position s; μ is the average log ratio over the whole array, G(g) is main effect for spike characteristics, D(d) is the main effect for the spike RNA amount (concentration), S(s) is main effect for position on the array, GD(gd) is a term accounting for effects of the interaction between the spike characteristics and the concentration and ε(g, d, s) is stochastic error. The error is assumed to be independent and of zero mean. To satisfy these assumptions, the homogeneity of the variances was visually inspected by residual graphic analysis. Statistical analysis was performed with SPSS 13.0 (SSPS Inc., Chicago, IL).
System specificity and sensitivity in detecting differential gene expression were evaluated using receiver operating characteristic (ROC) curves . A ROC curve shows the relationship between the proportion of true positive (Sensitivity) and false positive (1-Specificity) classifications resulting from each possible decision threshold value in a two-class classification task . The area under the curve is a measure of test accuracy , and when applied to a gene expression profile, it provides an estimate of the probability that a gene is up- or down-regulated in a given group. The spike RNAs were added to the hybridization mixture of the arrays at pre-determined specific concentrations ranging from 500 to 1500 pg. Test sensitivity was calculated as the number of regulated genes correctly classified by the test divided by the number of regulated genes. False-positive rate is defined as the number of false positives genes from the group of non-regulated genes divided by the total number of non-regulated genes. Statistical analysis was performed with STATA 8.0 (StataCorp LP, College Station, TX).
Description of experiments
All microarray raw data were provided as additional files. We performed three types of microarray experiments. (i) In dilution experiments all the spike RNAs were added in the same quantity in both channels. We set up four different dilutions: 10 pg (additional file 1), 250 pg, 750 pg (additional file 2) and 1000 pg (additional file 3). The experiment at 250 pg was performed in quadruplicate (additional files 4, 5, 6, 7). Data from these dilution experiments were used for Figure 1, Figure 2, Figure 3, Figure 4, Figure 7, Figure 8, Table 2, Table 3, Table 4 and Table 5. (ii) In range experiments two different mixtures were set up to cover a wide range of signal intensity. Every spike RNA was added in the same quantity in both channels to get a final ratio of 1, but in the same mixture the spikes were present at increasing concentrations. Mix 1 contains spikes at 5 pg, 10 pg, 50 pg, 100 pg, 500 pg and 1000 pg (additional file 8). Mix 2 contains spikes at 250 pg, 500 pg, 1000 pg, 1500 pg, 3000 pg and 5000 pg (additional file 9). Data from range experiments were used for data in Figure 5. (iii) ROC experiments were planned to compare expected with measured signal ratios. The spike RNAs were added in defined quantity to obtain ratios of 1 (500/500 pg), 1.5 (750/500 pg), 2 (1000/500 pg) and 3 (1500/500 pg) (additional file 10). Dye swap was performed to get reverse ratios (additional file 11). Data from ROC experiments were used in Figure 6.
We wish to thank two anonymous referees whose comments were extremely helpful. We also thank the secretarial assistance of Chantal Dabizzi. This work was founded by Fondazione G. Gaslini; Fondazione Italiana per la Lotta al Neuroblastoma; Associazione Italiana Glicogenosi, Italian Association for Cancer Research, the EU project New-Generis, European Union 6th FP (FOOD-CT-2005-016320).
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