Cy3 and Cy5. Recent improvements in the technology  allow hybridization up to four samples simultaneously. The two additional dyes are Alexa488 and Alexa494. The triple-target or four-target technology is very promising, since it allows more flexibility in the design of experiments, an increase in the statistical power when comparing gene expressions induced by different conditions and a scaled down number of slides. However, there have been few methods proposed for statistical analysis of such data. Moreover the lowess correction of the global dye effect is available for only two-color experiments, and even if its application can be derived, it does not allow simultaneous correction of the raw data.Most microarray studies are made using labelling with one or two dyes which allows the hybridization of one or two samples on the same slide. In such experiments, the most frequently used dyes are p differently labelled targets co-hybridized on a same array, for any value of p greater than 2.We propose a two-step normalization procedure for triple-target experiments. First the dye bleeding is evaluated and corrected if necessary. Then the signal in each channel is normalized using a generalized lowess procedure to correct a global dye bias. The normalization procedure is validated using triple-self experiments and by comparing the results of triple-target and two-color experiments. Although the focus is on triple-target microarrays, the proposed method can be used to normalize The proposed normalization procedure is effective: the technical biases are reduced, the number of false positives is under control in the analysis of differentially expressed genes, and the triple-target experiments are more powerful than the corresponding two-color experiments. There is room for improving the microarray experiments by simultaneously hybridizing more than two samples. Cy3 and Cy5 are used to label the two RNA samples co-hybridized on a same array. Recently two more dyes have been proposed (Alexa 488 and Alexa 594) allowing the simultaneous hybridization of three or four samples. Forster et al. [Alexa 594 is best suited as a third dye and that Alexa 488 can be applied as a fourth dye on some microarray types. These extensions of the microarray technology are promising because they increase throughput, minimize costs and allow more powerful design of experiments. Despite these advantages, triple-target microarrays are only sparsely used [Cy3 and Cy5 signals, but seems to be important between Alexa594 and Cy3 signals," therefore signal cross-talk cannot be neglected.DNA microarray technology is a high throughput technique by which the expression of the whole genome is studied in a single experiment. In dual label experiments the fluorescent dyes r et al.  have evar et al.  have invr et al. , but theely used . The lacely used , the lowely used  have conArabidopsis thaliana microarrays are used to check if the proposed normalization is effective for correcting the dye bias. Moreover the comparison of the statistical power of the triple-target experiment versus the usual two-color experiment is performed. All programs and data produced for this project are available under request.We propose in this paper a normalization method for triple-target microarray experiments. First we quantify and correct the signal bleeding. Then we correct the global dye bias using a generalized lowess procedure. Triple-target experiments with blank channel when no material is hybridized for the associated dye. In theory, this blank channel should produce no signal values, and deviations from this show a bleeding phenomena from one dye-label to another. Signal bleeding from one dye-labelled sample to another is a potential source of bias. Indeed, bleeding artificially increases the signal in other channels of the same spot when the signal is high in one channel. Assume that a gene is highly expressed in condition A and weakly expressed in condition B. The difference between the two conditions is decreased by the bleeding. Therefore bleeding may induce a lowering in the statistical power for detecting differentially expressed genes. Another possibility is that the bleeding effect induces a difference between two channels for the same gene: assume that a gene is highly expressed in condition A and equally expressed in conditions B and C; if the bleeding between the channel corresponding to condition A and the channel corresponding to condition B is higher than the bleeding between A and C, then a difference between signals B and C will appear, which is a technical artifact.Using the vocabulary of , we callIn order to investigate bleeding, we have made a "single target hybridization microarray experiment" where only one dye-labelled sample is hybridized (see the dataset URGV1 in the Methods Section). We also analyzed the single target hybridization data set from Forster .Cy3 → Cy5 is negligible but the bleeding bias Alexa594 →  exists. The plots from the Forster single target hybridization experiment and URGV1 datasets present the same patterns with a greater variance for the first set. As the bleeding effect seems to apply on a linear scale, we consider only raw (and not log-transformed) data in this section. Table Cy5 and Cy3 are low, but the dye Alexa594 emits and receives significant cross-talk from the other two dyes.Figure Alexa594 and Cy5 and Cy3 are the blank channels, the following models are used: Gi = α1 + β1Yi + εi and Ri = α2 + β2Yi + G, Y and R stand respectively for green, yellow and red signals and i denotes the spot index. Similar models are used with swapped dyes. Estimation is performed using a robust method . The weakness of the quantitative influence of bleeding is confirmed by the values of the standard error of the signal in the different channels: the values for the empty channels are between 6 and 200 times lower than the corresponding values for hybridized targets , j = 1, ⋯, p the channel index and, yij the log2 transformed intensity measure of gene i along the channel j. Let mean channel raw data for gene i on the log scale, and j and the mean channel for gene i. We generalize the lowess method by modelling Dij as followsLet fj via a lowess. The value of the channel j after normalization of intensity dye-bias is defined by:and by estimating x-axis coordinate is the mean intensity of the three channels y-axis coordinate is the difference between intensity of channel j and the mean intensity, We point out that if this normalization procedure is applied on a two-color microarray, it leads back to the usual lowess method. Figures The normalization procedure has to be validated on two points: first it must suppress or at least cut the technical bias and second it must not reduce the difference of expression between genes. We have used different experiments to check both points. We first use an analysis of variance (Anova) approach, and then a count of the number of differentially expressed genes.Kerr et al.  proposedvarmixt 2. 6 two-color microarrays (see URGV4 in the Methods Section), a dye-swap for each comparison between two of the three conditions.Table The proposed normalization procedure is effective: the number of false positives is under control, and the triple-target microarray experiments are more powerful than the corresponding two-color experiments.p > 2, so that it could be tested for four-target microarrays or used to evaluate the bleeding of Alexa 488.There is thus room for improving the routine two-color microarray experiments. The normalization procedure proposed could be used for any number of channels p(p - 1) bleeding coefficients and correct the raw data using the following expression:As the bleeding seems to work on a linear scale, a natural idea is to estimate Xij is the raw measure of expression of gene i on channel j, βlj is the coefficient of bleeding from channel l to channel j. This bleeding correction works under two assumptions:where 1. the bleeding coefficients do not depend on the intensity of the bleeding channel,2. the effects of the bleeding from several channels are additive on a linear scale.βlj are possible:The first assumption is confirmed by the preceding analysis (see Results Section) and the second one seems realistic. Two ways for estimating the coefficients p slides single-target hybridization,1. use preliminary experiment with p-target hybridization slides.2. use the current data set, with all the p-target experiments is the following:The model framework for estimating the bleeding coefficients in a is the array index, Xija is the measure of expression for gene i, channel j and array a, c is the condition associated with channel j and slide a, αi is the gene effect, γj the dye effect, ηij the interaction between gene i and dye j, ζa the effect of array a, τja the interaction between dye j and array a, δc the condition c effect, θic the interaction gene-condition and βlja is the bleeding coefficient from channel l to channel j for array a. Note that the global condition effect δc is included in the interaction τja. This is a standard linear model. However the size of the design matrix is huge (more than 2np) so the computation is not routinely feasible. Even if the computation were feasible, simulations show that there are many confounding effects in this statistical model and consequently the estimates of the βlja are not reliable (data not shown).where p-target experiments. The bleeding effect may depend on the platform and the technology . This implies that the machine-tuning parameters are not modified during the experiment. For the bleeding correction of the URGV data sets we have used Equation (2) with the coefficients of Table Cy5 to Alexa594, from Cy3 to Alexa594 and from Alexa594 to Cy3.Therefore the only possible procedure is to estimate the bleeding coefficients on preliminary one-target slides. This procedure assumes that the coefficients do not depend on the microarray and that the bleeding coefficients of the preliminary single-target experiments are the same as in real Equation  with theArabidopsis thaliana. Total RNA was extracted from each sample using TRIzol extraction  followed by two ethanol precipitations, then checked for RNA integrity with the Bioanalyzer from Agilent . cRNAs were produced from 1 μg of total RNA from each sample with the "Message Amp aRNA" kit . Then 5 μg of cRNAs were reverse transcribed in the presence of 200 u of SuperScript II , in presence of Amino-allyl-dUTP . The samples are then labelled by coupling with Cy3 or Cy5 monoreactive dyes  or Alexa Fluor 594 . Labelled samples were purified and concentrated with Qiaquick columns . Slides were pre-hybridized for 1 h and hybridized overnight at 42°C in 25% formamide. Slides were washed in 2 × SSC+ 0.1% SDS 4', 1 × SSC 4', 0.2× SSC 4', 0.05 × SSC1' and dried by centrifugation. The slides were scanned on a Genepix Professionnal 4200A scanner  and images were analysed by Genepix Pro 6.0 .Microarray analysis was carried out at the Unité de Recherche en Génomique Végétale , using the CATMA array , containing 24 576 gene-specific tags from Total RNA sample from Arabidopsis thaliana flowers was reverse-transcribed and labelled in a one-dye fashion either with cy3, cy5 or Alexa Fluor 594 and hybridized separately on two slides each (i.e. six hybridizations).One pool of total RNA from Arabidopsis thaliana roots, leaves and flowers was separated in three aliquots and reverse-transcribed and labelled with the three fluorochromes, then melted and hybridized on the same slides in three technical replicates (i.e. three hybridizations).Total RNA from Arabidopsis thaliana roots, leaves and flowers were labelled independently with the three fluorochromes in a one-dye fashion either with cy3, cy5 or Alexa Fluor 594. Then the three samples were hybridized on the same slide, each being labelled with a different fluorochrome, in three technical replicates with fluorochrome switch (i.e. three hybridizations).Total RNA from Arabidopsis thaliana roots; leaves and flowers were labelled independently with the three fluorochromes in a one-dye fashion either with cy3, cy5 or Alexa 594. Then two samples were hybridized on the same slide, each being labelled with a different fluorochrome. Each comparison was performed with a technical replicate with fluorochrome switch: regular dye-swap (i.e. six hybridizations).MLMM, JA, ABH and JJD designed the method. MLMM, JA and JJD wrote the manuscript. JA implemented part of the software and performed the statistical analysis. SE made the URGV experiments under the direction of JPR. FM implemented part of the software. All authors contributed to the discussion and have approved the final manuscript.


Several recent studies have demonstrated the effectiveness of deep sequencing for transcriptome analysis (RNA-seq) in mammals. As RNA-seq becomes more affordable, whole genome transcriptional profiling is likely to become the platform of choice for species with good genomic sequences. As yet, a rigorous analysis methodology has not been developed and we are still in the stages of exploring the features of the data.We investigated the effect of transcript length bias in RNA-seq data using three different published data sets. For standard analyses using aggregated tag counts for each gene, the ability to call differentially expressed genes between samples is strongly associated with the length of the transcript.Transcript length bias for calling differentially expressed genes is a general feature of current protocols for RNA-seq technology. This has implications for the ranking of differentially expressed genes, and in particular may introduce bias in gene set testing for pathway analysis and other multi-gene systems biology analyses.This article was reviewed by Rohan Williams (nominated by Gavin Huttley), Nicole Cloonan (nominated by Mark Ragan) and James Bullard (nominated by Sandrine Dudoit). High throughput sequencing is likely to become the platform of choice for transcriptome analysis. The ability of sequencing platforms to interrogate the whole transcriptional landscape provides new insights into the levels of transcriptional complexity in biological systems. Transcript sequencing also provides novel opportunities such as quantitatively measuring splicing variants  and singDifferent technologies display different data features and will therefore have different strengths and weaknesses. Investigation of the technical and statistical attributes of the data will reveal the advantages and disadvantages of each technology. We hypothesize, that using statistical methods to detect differential expression between samples is biased by transcript length and that this bias is inherent to the standard RNA-seq process.Current RNA-seq protocols use an mRNA fragmentation approach prior to sequencing to gain sequence coverage of the whole transcript. This means, in simple terms, that the total number of reads for a given transcript is proportional to the expression level of the transcript multiplied by the length of the transcript. In other words a long transcript will have more reads mapping to it compared to a short gene of similar expression. Since the power of an experiment is proportional to the sampling size, there is more power to detect differential expression for longer genes. This is an inherent property of the data and will not be altered by any process involving sequencing of full length transcripts with fragments shorter than the transcript. By contrast, intensity measurements from microarrays are proportional only to the expression level of the transcript plus any features intrinsic to the probe itself such as GC content ,3. The fHere we explore several previously published datasets of high throughput RNA sequencing and show that the most widely used protocols for RNA-seq can detect more differential expression in longer transcripts compared to shorter ones. This bias does not exist for microarray platforms, which uses a single or set of diagnostic probes to assess expression levels. We also demonstrate that the inherent biases presented here are shown to exist in all experiments analyzed so far independent of the specific samples, platforms or statistical analysis.Based on a simplified calculation, we can show that the ability to detect differential expression using a very simple testing procedure depends on the length of the transcript (see methods). To empirically investigate the effect of transcript length bias in RNA-seq and microarrays we used three different published data sets that sequence full length transcripts. The first data set compares sequencing from the Illumina Genome Analyzer with Illumina microarrays and looks for differential expression between a human embryonic kidney and B cell line . The secFor each platform we first binned all genes into equal gene number bins based on their transcript length. Next we designated genes as differentially expressed (DE) based on a cut-off from the statistical procedure defined in the relevant publication. We found the specific statistical cut-off used made no qualitative difference to the results. We then calculated the percentage of DE genes for each bin. Figure To further investigate the transcript length bias we used data from Marioni et al (2008) and looked at transcripts that appear in both the RNA-seq and microarray data. We divided the genes into three equal groups based on the average intensity level measured on the microarrays. We then calculated %DE as a function of length for the high and low expression groups Figure  and 1g. Many of the current statistical methods use a measure of expression level normalized by the length of the gene. This gives an unbiased measure of the expression level but also affects the variance of the data in a length dependent manner resulting in the same bias to differential expression estimation (see methods for a toy example). To demonstrate this effect we show the sample mean and variance of each gene calculated from the replicate lanes of the Marioni et al. data. In figure The consequences of transcript length bias in RNA-seq data becomes most problematic when comparing between genes or sets of genes with different lengths. This is most likely to occur when doing gene set testing in systems biology, where specific gene sets have a length bias compared to other sets of genes. If a set contains genes shorter than average it will appear under-represented in differential expression whereas if the set contains genes longer than average the category is more likely to be over-represent in differential expression. To demonstrate this effect we looked for over-represented KEGG pathways using the Marioni et al RNA-seq and microarray data. In each analysis we only used genes found on both platforms and then performed a pathway analysis using the DAVID software ,8. We foTranscript length bias in RNA-seq data is a predictable consequence of the sampling process and cannot be corrected by dividing by length of the transcript  or Sultan et al. (2008)). Length bias is expected for a Poisson random variable, where the expected read count of a gene is proportional to the length as well as expression level of a transcript. In other words, the sampling is higher for long genes compared to short genes and therefore there is more power to detect differential expression at a given statistical significance regardless of the specific test used. Statistical tests to detect differential expression between samples require estimates of the mean and the variance of the samples. Dividing the mean by the length of the transcript removes the bias from that measure but subsequently introduces a length bias into the variance and the problem still persists. Similarly, as with microarrays, there is more power to detect differential expression for genes with higher expression levels. However, we have not focused on this phenomenon here as we feel that expression level is a biologically meaningful quantity compared to transcript length bias, which is technical in nature.Different processing methods can be used with high-throughput sequencing to determine the levels of transcription such as massively parallel signature sequencing (MPSS), serial analysis of gene expression (SAGE), cap analysis of gene expression (CAGE). These methods only count one sequence per transcript hence will not suffer from transcript length effects. However, in many cases researchers will want to examine the full complexity of the transcriptome by sequencing the entire RNA repertoire so we speculate that these unbiased methods will make up a small fraction of RNA-seq experiments. As fragment size is the basis of transcript length bias rather than the number of bases read, improvements in read length on the current platforms will not alter transcript length bias.2 length is similar . This implies that the number of genes doubling in length is similar to the number of exons doubling in length meaning the bias is just as strong. Extending the sequencing depth will increase the ability to detect differential expression. However as transcript length bias is a relative measure between genes it will not affect the presented results.Using exon level analysis as a way to reduce the range in transcript lengths may not reduce the bias significantly. Although the lengths of genes are obviously longer than exons when looking at the length of genes and exons in the human genome the interquartile range (IQR) in logCurrently the only method we suggest to account for length bias between genes would be to use a fixed length window approach, with a window size smaller than the smallest gene. In this method aggregated tag counts for each window could be calculated and assessed for differential expression. A further extension could combine multiple windows per transcript into a single measure in a way similar to combining multiple probes in the RMA microarray algorithm  but thisIt is important to understand that using a statistical cut-off to generate a list of DE genes from aggregated tag counts inevitably will be more sensitive to genes with longer transcripts. Hence gene sets and ontology classes containing genes with different length distributions may look spuriously under or over represented in gene set testing. As gene set testing is an integral part of many systems biology experiments and large biomedical projects such as the International Cancer Genome Consortium, the length bias will significantly impact many applications where RNA-seq is currently being utilized. Sophisticated statistical methodology will be required to develop a new analysis framework that gives similar false discovery rates for different transcript lengths.The different strengths of the currently available RNA-seq and microarray technologies make these platforms complementary for comprehensive analysis of the transcriptome. A technical feature of using high-throughput sequencing to interrogate full length transcripts is that longer transcripts produce more reads relative to short transcripts of similar expression. This higher sampling means that there is more statistical power to detect differential expression for long transcripts compared to short ones. Using three published RNA-seq data sets we have demonstrated that, as we hypothesized, longer transcripts have more power to detect differential expression. These data sets each use different samples, platforms and statistical methods. This bias does not exist in microarray data. If unaccounted for, transcript length bias in the ability to detect differential expression can confound system biology and gene set testing approaches. Understanding technical issues in new technologies will lead to the development of more sophisticated analysis methodologies.X be the measured number of reads in a library mapping to a specific transcript. The expected value of X is proportional to the total number of transcripts N times the length of the gene LHere we show, under some very simple assumptions, that when testing for differences between two samples for a given expression level, a longer gene will be more significant that a shorter gene. Let where c is a proportionality constant. Assuming the data is distributed as a Poisson random variable, the variance is equal to the mean.As an example, we could test if the difference in counts from a particular gene between two samples of the same library size is significantly different from zero using a t-testD is the difference in the observed means from two samples and S.E.(D) is the standard error of D.where E(D)/S.E.(D) = δ which is essentially the non-centrality parameter of the t-distributionThe power of the t-test depends on It can be seen that this is proportional to the square root of the length. Therefore for a given expression level the test becomes more significant for longer transcript lengths.Given the simple set-up above we can see the effect of dividing expression levels by the length of the gene. HereandX was distributed as Poisson random variable we can see that once we divide by length the distribution is no longer Poisson and μ' ≠ Var(μ').Given that we assumed that Using the same format of the t-test above we see that we recover the same length dependence as in equation 1.whereE(D)/S.E.(D) = δ whereAgain the power of the t-test depends on L dependence.We end up with the same test result as equation 1 which still has a square root 2-transformed. Differential expression was then assessed using an empirical Bayes moderated t-test [Processed data was downloaded for each of the three studies presented. Each data set contained a gene ID and the results of a statistical test for differential expression between samples. Each data set uses a different statistical test. Briefly: Marioni et al.  modeled d t-test . Illumind t-test  Aggregatd t-test  to deter2 expression level of the six microarrays in their study. The high expression and low expression groups were used in figures Transcript lengths for all human transcripts and mouse transcripts were downloaded using BioMart and the length of a gene was calculated as the median length of all transcripts relating to that gene. Each gene was then defined to be either differentially expressed or not differentially expressed based on an arbitrary statistical cut-off. Varying this cut-off made no qualitative difference to the results. For the data from Marioni et al (2008) genes were matched between the RNA-seq platform and the microarray platform. These genes were then divided into three equally sized groups based on the average logDE: Differential expression; IQR: interquartile range.The authors declare that they have no competing interests.AO and MJW conceived of the idea and wrote the paper. AO analyzed the data.RNA-Seq and related high-throughput sequencing are receiving intense attention due to their potential to survey the transcriptome in an unbiased, global fashion. While it is likely that these sequencing based approaches will permit a major advance on microarray based technologies, it is also highly likely that unanticipated systematic errors will be present in these data and will need to be corrected in order to permit appropriate application. While expression microarrays and tiling arrays are known to be subject to a number of such effects, to date there has been little investigation of issues in the emerging RNA-Seq literature. Oshlack and Wakefield now present a re-analysis of data from several recent RNA-Seq studies to show that identification of differential expression is positively biased towards longer transcripts . Although it is recognised that tag count will be proportional to the product of expression level and transcript length, adjusting for transcript length does not remove this effect: the authors show the effect arises from increased variance for shorted transcripts. They further argue that this effect is unlikely to be removed by exon-level analysis. Interestingly, this effect is not observable in microarray expression platforms. This paper represents an important contribution to the ongoing development of analysis methodology for RNA-Seq and I recommend it for publication in Biology Direct.In this paper, the authors describe "transcript length bias" in RNAseq data, which is the reduced statistical power to detect differential gene expression of short mRNAs when compared to long mRNAs using a "shotgun sequencing" approach. As randomly fragmented mRNA molecules will generate less short-read tags for a short transcript than for a longer transcript, changes in expression between two (relatively) poorly sampled transcripts are less discernible from sampling noise. The authors examine three published shotgun sequencing-based studies to show this bias exists in the sequencing data, but not in the corresponding microarray data from the same samples. This bias against short transcripts could lead to a general under-representation in gene set testing for functional categories enriched in short genes . This is an important finding that the RNA sequencing community needs to be aware of.The manuscript is generally well written, and the authors have done well to create a manuscript understandable to a biological audience without specialized mathematical or statistical training. As all of my  concerns with this manuscript have been adequately addressed, I recommend this manuscript for publication.In Oshlack and Wakefield the authors demonstrate a relationship between gene-length and observed significance of a statistical test in three published studies . The authors demonstrate that this observed tendency is not present in the analysis of the same samples in the Marioni study when microarrays are used. This "bias" is due to the dependence of the variance on the intensity of the read-process which is proportional to the length of the transcript sequenced.The reviewer recommends the article for publication as the issues presented are both relevantand important. In particular, the issues presented are quite pertinent with the advent of numerous high-throughput sequencing studies. The reviewer believes that in its current form the article would benefit from some revisions to either more rigorously present the mathematics or simply present the statistics described in the offending studies.Background: paragraph 2, "We hypothesize ..." Why are you hypothesizing? I think that this sentence needs reference to a particular test-statistic, then you really don't need to hypothesize anything.Author's response: We believe the statement in the article relates to all statistical analysis methods under the assumptions we have stated however we have not and really cannot test all possible methods. Therefore we have used the word hypothesize but we have also given an example in the methods section.Background: paragraph 3, "All methods for detection of ..." Doesn't this sentence appear a bit strong?Author's response: We amended this to "Most statistical methods..."Results: paragraph 2, Can you comment why the "length bias" is stronger for more lowly expressed genes? Also, I think it is better to present all of the data on the plots, rather than excluding the middle bin.Author's response: We have added the sentence: "We believe the slope is lower in highly expressed genes due to the observation that nearly all of these genes have enough power to be called differentially expressed in this data set even though the p-values are higher for shorter genes."Results: paragraph 3, In the mean-variance plots how do you compute the variance? Is this just the sample variance? What about the different numbers of counts across lanes? As for panel (2), After we divide by length we don't have a Poisson so the mean-variance plot is not correct or at least the proper interpretation of it is non-obvious Author's response: Yes this is exactly the point we are trying to make. This plot is meant to be more heuristic in nature rather than any rigorous proof that dividing by length doesn't remove the length bias. Therefore we have just used the sample variance without taking into account the different number of counts across lanes as a visual demonstration. To clarify we have also added the sentence: "However, when the mean is divided by the length of the transcript the relationship becomes more complex and the data is obviously no longer Poisson"Results: paragraph 4, A potentially "better" plot would be boxplots (of gene-length) ordered from largest to smallest KEGG p-value; both for microarray and sequencing data.Author's response: Thank you for the suggestion. We felt that the plot you suggested was a little bit more tricky to interpret.t is defined to be one thing (random variables on the rhs of equation (1)) and then redefined to be another thing (parameters on rhs of following definition). 2.) Methods: paragraph 2, μ' is a parameter then you do the Var(μ') which is incorrect, you probably want to dene an X' instead, then you can take variances.Methods: paragraph 1, The math is a little sloppy. In general, there is confusion between random variables and parameters. Specifically, I note two obvious errors: 1.) Author's response: Thanks for pointing this out. We have modified and tidied up the math.From your treatment it appears that I can just divide t by √ L to remove the dependence on L in the test-statistic is this correct?Author's response: No, I don't think this is possible. A t-test is like a signal to noise ratio and therefore has a specific relationship between the estimate of the mean and the standard error of the estimate. I don't believe this should be broken by essentially dividing the estimate of the mean by √ L.


Over the last decade, DNA microarray technology has provided a great contribution to the life sciences. The MicroArray Quality Control (MAQC) project demonstrated the way to analyze the expression microarray. Recently, microarray technology has been utilized to analyze a comprehensive microRNA expression profiling. Currently, several platforms of microRNA microarray chips are commercially available. Thus, we compared repeatability and comparability of five different microRNA microarray platforms  using 309 microRNAs probes, and the Taqman microRNA system using 142 microRNA probes. This study demonstrated that microRNA microarray has high intra-platform repeatability and comparability to quantitative RT-PCR of microRNA. Among the five platforms, Agilent and Toray array showed relatively better performances than the others. However, the current lineup of commercially available microRNA microarray systems fails to show good inter-platform concordance, probably because of lack of an adequate normalization method and severe divergence in stringency of detection call criteria between different platforms. This study provided the basic information about the performance and the problems specific to the current microRNA microarray systems. Thus, microarray-based transcriptome devices started to be utilized to stratify patients for personalized medicine. For the quality control and standardization of microarray chips, the US FDA initiated the MicroArray Quality Control project (MAQC) in 2005. A series of reports regarding the first phase of the MAQC project was published in 2006 Since the first DNA microarray paper demonstrated that microarray technology can monitor multiple gene expression profile in 1995 MicroRNAs are a class of small non-coding RNAs [19–23 nucleotides (nt)] that have been found in animal and plant cells. As of today, 718 human microRNAs are registered in the miRBase database  Recently, microarray technology has been utilized to analyze a comprehensive microRNA expression profiling. Currently, several platforms of microRNA microarray chips are commercially available. As mentioned above, the MAQC Project is currently underway for quality control and standardization of mRNA expression microarray. However, no comparative and quality control study of microRNA microarray platforms has been reported yet. Therefore, we compared repeatability and comparability of microRNA microarray using five different platforms . In addition, we compared quantitivity of microarray data generated from five different platforms with that of quantitative RT-PCR (Taqman) method, which is the golden standard method of microRNA measurement.This project repeatedly assayed two RNA sample types on a variety of microRNA expression platforms at one laboratory. Our preliminary experiments showed that the amount of microRNA obtained from the same amount of total RNA depends on the tissue types of the samples (data not shown). This finding suggested that repeatability or comparability of microRNA microarray analysis might depend on the amount of microRNA contained in total RNA. To assess the reproducibility of microRNA microarray data using the different tissue types, we chose both tissue samples, which contain relatively small and large amounts of microRNA. Our preliminary data shows that mouse liver tissue contains relatively small amounts of microRNAs. Therefore, we used two types of total RNA, FirstChoice® Human Liver Total RNA  and FirstChoice® Human Prostate Total RNA , in this study. In fact, the amount of microRNAs in Human Liver Total RNA was smaller than that of Human Prostate Total RNA .Five commercially available microRNA microarray platforms were tested: Agilent Technologies (AGL); Ambion Inc. (AMB); Exiqon (EXQ); Invitrogen (IVG) and Toray Industries Inc. (TRY) . Four ofAgilent and Toray used its own method or software to generate a quantitative signal value and a qualitative detection call for each probe on the microarray, whereas Ambion, Exiqon, and Invitrogen did not specify the scanner or software to quantify the signals of probes in the manufacturer's protocol booklet. To generate a qualitative call for probes, we asked the technical support centers of Ambion, Exiqon, and Invitrogen about the method of detection call. We followed the methods recommended by their technical support center.The MAQC project had a probe mapping problem in that each gene was detected by a differently designed probe between the different microarray platforms It will be important to know whether all data follows a specific distribution, e.g. Gaussian or not. Thus, we checked the distribution profile of data used in this study . MicroRNp = 0.0005). It is reasonable that Rs values of liver were lower than those of prostate, because higher signals in microarray data tend to have smaller data variability in general.We examined microarray data for consistency within each platform by reviewing the repeatability at two levels: the quantitative signal values and the qualitative microRNA list agreement. To assess the data consistency of quantitative signal values, rank-correlation analysis and coefficient of variation (CV) analysis were performed. In this analysis, only data of microRNAs with positive detection call were used. Representative scatter plots of microarray platforms and the Taqman system are displayed in p<0.0001), because these platforms include microRNAs with near-zero values. In addition, the CV values of microRNA microarray platforms ranged in equivalent level to those of the Taqman assay.The distribution of CV for each platform was displayed in Next, we assessed the variation in log-ratio measurement. For each platform, we performed triplicate experiments using human liver and prostate samples. Thus, we can generate 9 ( = 3×3) log-ratios (prostate/liver) for each microRNA. Then, we calculated the Spearman's correlation coefficients (Rs) between 9 sets of log-ratios for the detected microRNAs, and visualized these Rs values inside of green squares in a blue-white heat map . The meap = 0.0003, and Wilcoxon's signed rank test: p = 0.0005). In the current study, we used criteria of detection call of microRNAs that the manufacturers recommended. However, the stringency of these detection call criteria was very different. For AMB and EXQ array, all microRNAs with positive signal were handled as detected microRNAs, whereas other manufacturers provided their own formula as detection call criteria. This difference in the detection call stringency may result in the divergence of detected microRNA percentage. Thus, detected microRNA percentage of AMB and EXQ array were less stable in three replicates  than the others.To assess variation in the qualitative measures, the percentage of 309 microRNAs with concordant detection calls between replicates of the same sample type was calculated on each platform  was selected as a validation method of microarray data. In the microRNA research field, several different types of quantitative RT-PCR (qRT-PCR) methods are in use, such as qRT-PCR using stem-loop shaped RT-primer, Taqman system, Applied Biosystems) The results of the current study provide information about the potential advantages and problems of microRNA microarray technologies as a tool providing microRNA expression data for research and future clinical purposes.vs. cancer In the original MAQC paper, the median values of CV for gene expression microarray ranged from 5 to 20%, whereas those of CV in this study ranged from 20 to 90%, approximately. We wondered why the CV values in this study were much higher than those in the MAQC paper, although the Rs values in this study were similar to those in the MAQC papers. One possible explanation was that the data distributions of the replicated data sets were not well centered due to a lack of data normalization. For the gene expression microarray analysis, data are generally normalized under an assumption that the total amount of mRNA is constant between different samples. However, microRNA microarray data generated from the same amount of total RNA were not normalized in general, because we know that the amount of microRNA varies depending upon cell types, such as normal tissue This normalization problem is associated not only with microRNA microarrays, but also with the Taqman assay. Because we have not discovered reliable housekeeping microRNAs, the Taqman assay measures just Ct values without normalization, which are obtained using the same amount of total RNA. This fact may result in the relatively high CV values that are ranging in the equivalent level to microRNA microarray platforms. In other words, a similar level of repeatability would be a relative advantage of microarray platforms to the Taqman system.Another problem with microRNA microarray platforms is a divergence in the stringency of the detection call criteria. The detection call criteria should be adjusted to each platform, in order to obtain reliable and repeatable data. However, too much divergence in the percentages of detected microRNAs would result in the disagreement in the results of further analyses, which may induce underestimated impressions and reputations of microRNA microarray technology. Therefore, this report emphasizes the necessity of a larger project that will solve specific microRNA problems, such as normalization and detection call stringency, and that builds a consensus in all aspects of the microRNA microarray analysis.In the MAQC project, seven different platforms  were compared The updated version of the miRBase list will include newly registered microRNAs. These newly added microRNAs are expected to be expressed at relatively low levels. Therefore, adding new microRNAs with low expression would cause poorer performance in repeatability or reproducibility, even when using the same platforms. It suggests that a standard set of microRNAs would be needed to compare the performances between microRNA microarray platforms designed according to different miRBase versions.In this study, we assessed the repeatability and comparability of microRNA microarray among several commercially available platforms. Different from mRNA expression microarray, microRNA microarray requires another important characteristic in the assay. For mRNA microarray, the probe(s) for each gene can be designed at the unique DNA sequence site on the gene to avoid the cross-hybridization. However, the short length of microRNAs (18–23 nucleotides) restricts the flexibility of probe design. Moreover, there are many same-family microRNAs which have high sequence homology. Thus, microRNA microarray requires an ability to distinguish these same-family microRNAs with high specificity. In this study, we did not assess the sequence specificity of the microRNA microarray. This issue should be addressed in future studies.http://www.fda.gov/cdrh/oivd/guidance/1610.pdf). Actually, several microarray platforms for mRNA expression have already been approved and utilized in the clinical field as IVDMIAs, e.c., MammaPrint® for breast cancer, and Pathwork® Tissue of Origin Test for unknown origin tumor. Regarding the microRNA microarray, this study demonstrated that some platforms of microRNA microarray have intra-platform repeatability as high as that of the mRNA expression microarray demonstrated in the MAQC papers. Thus, our finding indicated that the microRNA microarray may have high potential as a clinical diagnostic tool when good diagnostic microRNA markers are available. To date, at the research level, many papers have described the physiological and pathological significance of microRNAs, and reported potential biomarker microRNAs. However, the reproducibility of the microRNA microarray has not been assessed by a multi-center study, such as the MAQC project. Furthermore, there are some microRNA-specific problems to be solved, such as building consensus on normalization of the microRNA expression data and the specificity of microRNA detection to distinguish microRNAs with high sequence homology. Thus, a large-scale multi-center quality control project specific to the microRNA microarray is required before its clinical application.Currently, the MAQC project is on-going in the second phase, and the US FDA released the second version of draft guidelines for IVDMIA in July, 2007 , Ambion , Exiqon , Invitrogen , and Toray  were tested in this study. In all assays, we performed microRNA microarray assays according to the manufacturer's protocols available in April, 2008.Our preliminary experiments showed that the amount of microRNA obtained from the same amount of total RNA depends on the tissue types of the samples (data not shown). This finding suggested that reproducibility or detection call rate of microRNA microarray analysis might vary depending on the amount of microRNA contained in the total RNA. To assess the reproducibility of microRNA microarray data using the different tissue types, we chose both tissue samples, which contain relatively small and large amount of microRNA. Our preliminary data shows that mouse liver tissue contains A relatively small amount of microRNAs. Therefore, we used two types of total RNA, FirstChoice® Human Liver Total RNA  and FirstChoice® Human Prostate Total RNA , in this study. As shown in For the Invitrogen microarray, RNA samples were labeled using a two-color and dye-swapping protocol. For other microarrays, a one-color protocol was used. Three replicate assays for each sample were independently processed. In the two-color protocol, two RNA samples differently labeled by Alexa 532 and Alexa 645 were simultaneously hybridized on the same microarray chip. In addition, to normalize the dye-specific bias, RNA samples were labeled by switched dye combination. The microarray data from these two sets of two color scanning image were integrated. On the other hand, in a one-color protocol, each RNA sample was labeled using a single dye, and two RNA samples were hybridized separately on two microarray chips. All target labeling and hybridizations were performed in triplicate, according to the manufacturer's protocols. Microarray slides were scanned using an Agilent microarray scanner G2505B (Agilent technology) and microarray images were automatically analyzed using Feature extraction™ software, version 9.5.1.1 (Agilent technology). In this study, the gTotalGeneSignal values were used as the feature intensities, according to the procedures recommended by Agilent.Microarray slides were scanned USING a ProScanArray™ microarray scanner . For each scanning, a photomultiplier setting of the red channel was manually adjusted to 55, and the obtained microarray images were analyzed using the Genepix Pro™ 4.0 software . Spots that might be associated with artifacts were eliminated using software- and visual-guided flags. In this study, the median values of the foreground signal minus the local background were represented as feature intensities.Microarray slides were scanned using Agilent microarray scanner G2505B and the obtained microarray images were analyzed using the Genepix Pro™ 4.0 software. Artifact-associated spots were eliminated both by software- and visual-guided flags. In this study, the median values of the foreground signal minus the local background were represented as feature intensities.Microarray slides were scanned using Agilent microarray scanner G2505B and the microarray image was analyzed using the Genepix Pro™ 4.0 software. Spots that might be associated with artifacts were eliminated using by software- and visual-guided flags. In this study, the median values of the foreground signal minus the local background were represented as feature intensities.Microarray slides were scanned using ProScanArray™ microarray scanner where the photomultiplier settings of the red channel were manually adjusted according to the procedures recommended by the manufacturer. Each microarray was scanned three times, then merged into one data, and the merged data were analyzed using the Genepix Pro™ 4.0 software. Spots that might be associated with artifacts were eliminated using software- and visual-guided flags. In this study, the median values of the foreground signal minus the local background were represented as feature intensities.http://www.ncbi.nlm.nih.gov/projects/geo/). The accession numbers ARE listed in In ordinary mRNA expression microarray, it is a standard data processing procedure to normalize the microarray data with an assumption that the whole mRNA expression signal is constant among the samples. However, in the microRNA analysis, the amount of microRNA contained in the same amount of total RNA varied depending on the tissue or cell types. All microarray data were registered into NCBI's Gene Expression Omnibus (GEO) database . For the Agilent array, gIsGeneDetected values in output data sheet were used for detection call. For Ambion and Exiqon arrays, customer service offices of both manufacturers recommended handling all spots with over 0 intensities as detected spots. For the Invitrogen array, the lower limit of detection is eight times the median local background of all array features. For the Toray array, positive detection call was defined as spots in which signal intensities showed greater than the upper limits of 95% confidence interval of all blank spots' signal intensities.(40-Ct) represents the expression level of the target microRNA.To validate the microRNA expression in each sample, we measured the expression of 171 microRNAs by using a qRT-PCR platform: TaqMan microRNA Assays (Applied Biosystems Inc.) and ABI 7300 Sequence Detector™. This qRT-PCR method detects specifically mature microRNAs, but not precursor microRNAs. To perform this TaqMan assay, we used the same amount of total RNA, and Ct-values were recorded. Then, the value of 2http://microrna.sanger.ac.uk/) is being updated frequently. The release version of the official database was 11.0 when this study started. However, the microRNA annotation version used for the microarray probe design was different among the five different microarray platforms in April 2008. Agilent and Toray are based on the Sanger miRBase Database, release 10.1 and 11.0, respectively. Exiqon and Invitrogen are based on the release 10.0, and Ambion is based on the release 9.2. To analyze the different formats of microarray data, we extracted microRNA expression data exactly matched to release 10.1. To compare the microRNA profile between two different microarray platforms, we used all overlapped microRNAs available in both microarray platforms. The number of human microRNAs common among all five microarrays was 310. To validate microRNA microarray data, randomly selected 146 human microRNAs were measured by the Taqman qRT-PCR system.The probe annotations for all microarray platforms and qRT-PCR were provided by the manufacturer. The official annotation of microRNAs in the miRBase Database  possible combination of data sets .To compare the similarity of the log ratio for each microRNA between each microarray platform, we determined the slope and intercept of the orthogonal regression between pairs of the log ratio in each microarray platform. The log ratio of each microRNA was calculated as the average of log signals in the liver sample minus the average of log signals in the prostate sample. The slope and intercept are determined by the formula y = ax+b, where “y” is the log ratio from platform Y, “x” is the log ratio from platform X, and the ideal slope is 1. For the slope, the difference from the ideal slope (a = 1) indicates the compression or expansion of the log ratios in one platform relative to the other. For the intercept, the distance of zero means the platform-specific bias between two microarray platforms.Comparability between a pair of each platform was also examined using Spearman's rank correlations of the log ratios. This value compares the relative position of a microRNA in the platform X rank order of the log ratio (fold change) values against its position in the platform Y rank order.The percentage of overlapping microRNAs between each microarray platform and qRT-PCR was a measure of the reproducibility of lists of differentially expressed microRNAs. We considered that the agreement of detected microRNAs in each sample and differentially expressed microRNAs between two samples for each microarray platform. For each platform, microRNA expression profiles in the liver and the prostate were measured three times by independent microarray chips. Therefore, 9 ( = 3×3) combinations of log-ratios (liver/prostate) for each microRNA was calculated. The microRNAs that had consistently greater than or equal to two-fold difference in these 9 ratios were assigned as differentially expressed microRNAs for each platform. Because we considered that qRT-PCR was true, true positive (TP) was detected in both of microarray and qRT-PCR, true negative (TN) was not-detected in both of microarray and qRT-PCR, false positive (FP) was only detected in microarray, and false negative (FN) was only detected in qRT-PCR. The formula for accuracy is (TP+TN)/(TP+TN+FP+FN).Figure S121).Distribution profile of microRNA microarray data Histograms of microRNA microarray data. All 309 microRNA data of each microarray platform were plotted in a histogram. In addition, 142 microRNA data of Taqman RT-PCR data were displayed in the same format. Negative log2 values were handled as 0 (0 = log(4.66 MB TIF)Click here for additional data file.Figure S2Probability plots of microRNA microarray data distribution. To show the normality of the distribution of non-zero data, The probability plot of each data set was generated using non-zero log2 values, excluding 2.5% of values in both sides. If distribution of the data is normal, this probability plot would be a line. In most of cases, kurtosis of the data distribution was around 2.(8.06 MB TIF)Click here for additional data file.Figure S3Probability plots of the distribution of log-ratio values. To demonstrate the normality of the distribution of log-ratio values, probability plots of log-ratio data were generated using 95% of middle log-ratio data. Lilliefor's test showed that the null hypothesis was not rejected in EXQ and TRY, which means that the distribution of log-ratio data in EXQ and TRY array were quite similar to normal distribution. p: p-values of Lilliefor's test.(3.62 MB TIF)Click here for additional data file.Figure S4Scatter plots showing correlations between the same replicates. Red and blue lines indicate the ideal Y = X line, and linear regressed line of scattered dots. S1A∼D: For one-color platforms, representative signal values of microRNA were plotted. S1E: For two-color platform (Invitrogen), log2-ratios (liver/prostate) of microRNA were plotted.(9.50 MB TIF)Click here for additional data file.Figure S5Effect of normalization on the rank-correlation of microRNA microarray. At first, we performed quantile normalization within the same replicates using one-color platform data. Then, the Spearman's correlation coefficients (Rs) were calculated. Because the quantile normalization changes values of microRNAs but not rank of microRNAs, Rs values in (8.12 MB TIF)Click here for additional data file.Figure S6Effect of normalization on the coefficient of variation In contrast to Spearman's correlation coefficients in (5.07 MB TIF)Click here for additional data file.Figure S7Correlation of log-ratios between intra- and inter-platform replications. Heatmaps of Pearson's correlation coefficients and Kendall's rank correlation coefficients. Both heatmaps had a similar pattern to heatmaps using Spearman's correlation coefficients in (6.08 MB TIF)Click here for additional data file.Table S1Skewness and Kurtosis of microRNA microarray data distribution Skewness and kurtosis of each data set was calculated using all expression data or non-zero log2 data of 309 microRNAs. A symmetric distribution has 0 skewness. A distribution with positive skew has a longer right tail, while a distribution with negative skew has a longer left tail. The kurtosis of the normal distribution is 3. A high kurtosis distribution has a sharper peak and longer, fatter tails, while a low kurtosis distribution has a more rounded peak and shorter thinner tails. This table demonstrated that microRNA microarray data tend to have a positive skewness.(0.03 MB DOC)Click here for additional data file.Table S2Rank correlation coefficients of log-ratios between intra- and inter-platforms of microRNA microarray. For rank correlation calculation, we used data of detected microRNAs that meet the detection criteria of each manufacturer. Both prostate and liver samples have triplicated data sets. Thus, 9 ( = 3×3) sets of log-ratios (prostate/liver) of microRNAs were generated. For intra-platform correlation, rank correlation coefficients of 36 ( = 9×8÷2) combinations were calculated, whereas, 81 ( = 9×9) coefficients were calculated for inter-platform correlation. Upper values: Spearman's correlation coefficients, Lower values: 95% confidence intervals.(0.03 MB DOC)Click here for additional data file.Table S3List agreement of differentially expressed microRNA This table showed percentage of concordance in detecting differentially expressed microRNAs. The values in the upper portion of cells reflects the mean percent overlap of microRNAs on the list for the platform X (listed in column) that are also present of the list for the platform Y (listed in row), whereas the values in the lower portion were 95% confidence intervals of the mean percentage. A higher value indicates a high percent overlap between the microRNA lists at both platforms. A lower value indicates a low percent overlap, suggesting that most microRNAs identified in platform X were not identified in platform Y. Therefore, the table is asymmetric and not complementary.(0.03 MB DOC)Click here for additional data file.Table S4http://www.ncbi.nlm.nih.gov/projects/geo/). All data were available to public on March 30, 2009.Accession numbers of microarray data All microarray data were registered into NCBI's Gene Expression Omunibus (GEO) database ((0.04 MB DOC)Click here for additional data file.


A developmental expression atlas, At-TAX, based on whole-genome tiling arrays, is presented along with associated analysis methods. Arabidopsis thaliana, have typically been created using gene-centric expression arrays. Here, we describe a comprehensive expression atlas, Arabidopsis thaliana Tiling Array Express (At-TAX), which is based on whole-genome tiling arrays. We demonstrate that tiling arrays are accurate tools for gene expression analysis and identified more than 1,000 unannotated transcribed regions. Visualizations of gene expression estimates, transcribed regions, and tiling probe measurements are accessible online at the At-TAX homepage.Gene expression maps for model organisms, including  Arabidopsis thaliana yielded important insights into transcriptional control of development, with genome-wide expression maps having become an indispensable tool for the research community. Specific gene expression profiles for various plant organs, developmental stages, growth conditions, treatments, mutants, or even single cell types are available (for example  RNA) or random primers (which target both polyA [+]and polyA [-] RNAs). After conversion to dsDNA, samples were hybridized to tiling arrays. For both tissues analyzed, there was a good correlation between polyA(+) samples and polyA(±)samples . In Arabidopsis the overwhelming majority of known protein coding transcripts possess a polyA tail.Only a small proportion of annotated genes produced a much higher polyA(±) signal compared with the polyA(+) fraction Table . Large dC. elegans tiling array studies using the transfrag method [Arabidopsis than in C. elegans and humans [We also applied the above described mSTAD algorithm to the two polyA(±) samples, to detect transcription from unannotated regions. When we subtracted high-confidence segments found in at least one polyA(+) sample from the segments found in both polyA(±) samples, segments totaling less than 100 kb were identified as potential polyA(-) transcripts Figure . These rg method . To ruleg method . This med humans ,47.To make our results easily accessible to the research community, we created an online resource that consists of two parts: a web-tool that reports expression values for user-specified genes, and a customized generic genome browser .Arabidopsis Information Resource) locus IDs [The At-TAX gene expression visualization tool can be fed with TAIR  contained 1,274 pseudogenes, whereas the recent TAIR7 annotation includes 3,889 pseudogenes [Compared with the ATH1 array, a disproportionately high number of genes that are represented only on the tiling array produced very low hybridization signals. This is not unexpected because the genes selected for the ATH1 array were supported by cDNAs and ESTs, whereas the tiling array includes hypothetical genes that lack any experimental evidence of expression. In addition, the number of annotated pseudogenes in udogenes .Arabidposis than in some other organisms [Arabidopsis mutants impaired in DNA methylation or RNA quality control has revealed more than 200 noncoding transcripts that are normally transcriptionally silenced, indicating that the Arabidopsis genome has at least the potential to generate a large number of transcripts from intergenic regions [Identification of unannotated transcribed regions is a major motivation for tiling array experiments. That our segmentation algorithm generated highly reliable predictions is evident from the observation that there was very good overlap with annotated genes as well as high success rates for RT-PCR validation experiments. Despite extensive cDNA cloning and previous use of tiling arrays , we courganisms . Interes regions ,32.C. elegans indicated that about half of all transcripts are not polyadenylated [Arabidopsis transcriptome. It is already known that specific classes of plants transcripts are generated in a different manner than in animals. For example, some human microRNA precursors are transcribed by RNA polymerase III and hence are not polyadenylated, whereas Arabidopsis microRNA precursors feature characteristics of RNA polymerase II generated transcripts [Tiling array studies of human and enylated . In contnscripts ,53. Anotnscripts -58.Arabidopsis are derived from pseudogenes and transposons. Several examples of actively transcribed pseudogenes have been reported [We found that many nonpolyadenylated RNAs in reported , and manreported . Known mreported ,61. Intereported ,63. Ther® Arabidopsis Tiling 1.0R Array for routine expression analyses does not have any apparent disadvantages compared with the ATH1 array. Rather, it has many advantages, including the ability to provide information on genes that are not represented on ATH1, as well as the ability to analyze additional aspects of gene expression, such as alternative transcript initiation and 3' end formation or splicing, all of which might be under physiological or developmental control [We have demonstrated that the use of the GeneChip control ,65. Tili control ).clv3-7 plants [Wild-type Col-0 and 7 plants  were gro® WT Double-Stranded cDNA Synthesis Kit; Affymetrix Inc.) and dsDNA was purified using the MinElute Reaction Cleanup Kit (Qiagen). A total of 7.5 μg dsDNA was fragmented and labeled using the GeneChip® WT Double-Stranded DNA Terminal Labeling Kit (Affymetrix Inc.). Targets were hybridized to ATH1 and Arabidopsis Tiling 1.0R arrays for 14 hours at 42°C, washed  and scanned using a GeneChip® Scanner 3000 7 G.For synthesis of probes (targets) for ATH1 and tiling arrays, 1 μg of total RNA was used as template for generation of cRNA using the MessageAmp II-Biotin Enhanced Kit . We followed the manufacturer's instructions with one exception; for tiling arrays, biotinylated NTPs were replaced by unmodified NTPs (stock solution 25 mmol/l each). Sixteen micrograms of biotinylated cRNA (for ATH1 arrays) was fragmented using 5× Fragmentation Buffer. Seven micrograms of unmodified cRNA (for tiling arrays) was converted into dsDNA  with an oligo-dT-T7 primer (MessageAmp II-Biotin Enhanced Kit) or a random-T7 primer . All subsequent steps were performed exactly as described above.For comparison of polyA(+) and polyA(±), rRNA was depleted from 10 μg total RNA using RiboMinus™ Yeast Transcriptome Isolation Kit (Invitrogen) and an A. thaliana genome. To this end we applied a method proposed previously [To assess the potential of each 25 mer oligonucleotide probe on the tiling array to crosshybridize to transcripts from different locations, we determined whether its sequence occurred more than once in the eviously , which aArabidopsis gene models as per TAIR7 annotation [In order to analyze annotated genes, we mapped tiling probes to notation . Probe sA. thaliana CDF version 10 provided by the Microarray Lab at the Molecular and Behavioral Neurobiology Institute of the University of Michigan [For the ATH1 array, probe sets were defined according to the Michigan .In order to minimize artificial expression level differences between platforms only resulting from differences in the computational analyses procedures, the RMA method was applied to hybridization data from both platforms . RMA proP < 0.05. The P values were also used to assess platform concordance by CAT analysis, in which gene lists ordered by P value were compared between platforms. The proportion of most significant genes in common between platforms was plotted as a function of list sizes increasing in steps of ten [We applied the Rank Product method (Bioconductor package RankProduct) ,73 to ids of ten . As a meWe preprocessed the hybridization signal to reduce a bias due to divergent probe sequences using a transcript normalization method ,74 and sFor each sample, we trained mSTAD separately on mean intensities across replicates and used the trained instance only for prediction of array data from the same sample. To obtain unbiased whole-genome predictions we employed cross-validation. After splitting the genome between pairs of neighboring genes, one instance of mSTAD was trained on 500 of these genic regions and hyper-parameters were tuned on another 500 genic regions. We trained and tuned a second instance of mSTAD on two further disjoint sets of 500 genes each. For region-wise whole-genome predictions, we chose the mSTAD instance that had not seen the particular region during training and hyperparameter tuning (or a random instance if neither of them had). From the predicted labeling of tiling probes we extracted exon segments by assigning the genomic coordinates corresponding to the start of the first and the end of the last probe of a run of consecutive exon labels. The resulting segmentations are available as gff files and visualized in the At-TAX Generic Genome Browser.Prediction accuracy was determined on genomic regions that had not been used for training or parameter tuning of the mSTAD instance evaluated. Sensitivity and specificity were assessed in comparison to annotated genes on a per-probe level as well as for the overlap between annotated and predicted exons. Figure To determine overlap with annotated regions, we used the TAIR7 annotation  and direOne microgram of RNA from seedlings and young leaves was treated with DNaseI  and converted into cDNA using the RevertAid™ First Strand cDNA Synthesis Kit (MBI Fermentas). One microliter of the resulting cDNA solution was used as a template in a PCR reaction with primers lying within the predicted transcribed region. The sizes of PCR products ranged from about 150 to 300 bp. A complete list of all used primers is available in Additional data file 3.As an independent method to compare transcriptional activity between polyA(+) and polyA(±) samples, we computed transfrags as described previously  and implAt-TAX, Arabidopsis thaliana Tiling Array Express; bp, base pair; CAT, correspondence at the top; dsDNA, double-stranded DNA; EST, expressed sequence tag; kb, kilobases; LNA, locked nucleic acid; mSTAD, margin-based segmentation of tiling array data; PCC, Pearson correlation coefficient; RDR, RNA-dependent-RNA-polymerase; RMA, robust multichip analysis; RT-PCR, reverse transcription polymerase chain reaction; TAIR, The Arabidopsis Information Resource.SL, GZ, MV, BS, GR, and DW designed the study. SL carried out target preparation and array hybridization. GZ, SRH, TS and NN developed tools for tiling array analysis. GZ, SRH, SL, and TS analyzed the data. TS and CKW developed online visualization tools. SL, GZ, GR, and DW wrote the manuscript. All authors read and approved the final manuscript.Arabidopsis chromosomes. Additional data file The following additional data are available with the online version of this paper. Additional data file Listed are all analyzed samples, including growth conditions and plant age.Click here for fileShown is the segmentation accuracy of mSTAD.Click here for fileShown are the oligonucleotide primers that were used for RT-PCR validation of new transcripts.Click here for fileShown is the correlation between platform concordances and probe numbers on the ATH1 array.Click here for fileArabidopsis chromosomes.Shown is the segmentation accuracy achieved by mSTAD along the five Click here for filePresented are the results of all RT-PCR validation experiments.Click here for filePresented is a comparison of mean hybridization intensities in random-primed and oligo-dT-primed samples.Click here for filePresented is a comparison of segmentation accuracy for mSTAD and the transfrag method.Click here for filePresented are gene identifiers with corresponding expression values and z-scores in all samples we analyzed.Click here for file


There is an urgent need for new prognostic markers of breast cancer metastases to ensure that newly diagnosed patients receive appropriate therapy. Recent studies have demonstrated the potential value of gene expression signatures in assessing the risk of developing distant metastases. However, due to the small sample sizes of individual studies, the overlap among signatures is almost zero and their predictive power is often limited. Integrating microarray data from multiple studies in order to increase sample size is therefore a promising approach to the development of more robust prognostic tests.In this study, by using a highly stable data aggregation procedure based on expression comparisons, we have integrated three independent microarray gene expression data sets for breast cancer and identified a structured prognostic signature consisting of 112 genes organized into 80 pair-wise expression comparisons. A classical likelihood ratio test based on these comparisons, essentially weighted voting, achieves 88.6% sensitivity and 54.6% specificity in an independent external test set of 154 samples. The test is highly informative in assessing the risk of developing distant metastases within five years (hazard ratio 9.3 with 95% CI 2.9–29.9).Rank-based features provide a stable way to integrate patient data from separate microarray studies due to invariance to data normalization, and such features can be combined into a useful predictor of distant metastases in breast cancer within a statistical modeling framework which begins to capture gene-gene interactions. Upon further confirmation on large-scale independent data, such prognostic signatures and tests could provide a powerful tool to guide adjuvant systemic treatment that could greatly reduce the cost of breast cancer treatment, both in terms of toxic side effects and health care expenditures. Breast cancer is the most common form of cancer and the second leading cause of cancer death among women in the United States, with an estimated ~213,000 new cases and ~41,000 deaths in 2006 , g = 1, 2, ..., G and n = 1, 2, ..., N, where G is the number of genes in each profile and N is the number of samples (profiles) in the integrated data set. Each column n represents a gene expression profile of G genes with a class label yn = 1 (poor-outcome) or yn = 2 (good-outcome) for the two-class problem in our study. Among the N samples, there are N1  samples labeled as class 1  with N = N1 + N2.Consider i, j), where i, j = 1, 2, ..., G, i ≠ j, let P(Xi <Xj|Y = k), k = 1,2, denote the conditional probability of the event {Xi <Xj} given Y = k. We define a score byFor each pair of genes  based on the training set x byand estimate the score of pair , each of which assumes scalar values, to a new ordered feature vector Z = {Z1, Z2, ..., ZM}, each of which assumes only two values.In other words, the estimated score is simply the absolute difference between the fraction of poor-outcome patients for which gene Zm, m = 1, 2, ..., M, corresponds to the gene pair {i, j}. For convenience, the ordering  will signify which probability in Equation (1) is larger. The reason for this is to facilitate interpretation of the results, as will become apparent. If P(Xi <Xj|Y = 1) ≥ P(Xi <Xj|Y = 2), as estimated by the fractions in (2), we will write  and if P(Xi <Xj|Y = 1) <P(Xi <Xj|Y = 2) we will denote the pair by . The value assumed by Zm is then set to be 1 if we observe Xi <Xj and set to 0 otherwise, i.e., if we observe Xi ≥ Xj. Of course the same definition is applied to each feature in the training set. In this way, observing Zm = 1  represents an indicator of the poor outcome  class in the sense that pm = P(Zm = 1|Y = 1) ≥ qm = P(Zm = 1|Y = 2). For all the features selected in our signature we in fact have pm > 1/2 > qm.Suppose G × N data matrix is reduced to an M × N data matrix. The number of distinct genes in a prognostic signature is obviously fewer than 2M. In our practice, there are always more than M distinct genes among the top M gene pairs. Given that the numbers of genes in published breast cancer prognostic signatures are mostly fewer than 100, we fix M = 200 in this study to maker sure we can identify a prognostic feature signature based on a reasonable number of genes.After this procedure, the original Z = {Z1, Z2, ..., ZM}. In our case the variables Zm are the simple, binary functions of the gene expression profile defined above.The classical likelihood ratio test (LRT) is a statistical procedure for distinguishing between two hypotheses, each constraining the distribution of a random vector The LRT is based on the likelihood ratioz = {z1, z2, ..., zM}are the observed values in a new sample. Notice that z assumes values in {0, 1}M, the set of binary strings of length M. The LRT chooses hypothesis Y = 1 if LR(z) > t and chooses Y = 2 otherwise, i.e., if LR(z) ≤ t. The threshold t is adjusted to provide a desired tradeoff between type I error and type II error, i.e., between sensitivity and specificity. Choosing t small provides high sensitivity at the expense of specificity and choosing t large promotes the opposite effect.where Z1, ..., ZM are binary (as here) and are assumed to be conditionally independent given Y, the LRT has a particularly simple form. It reduces to comparing a linear combination of the variables to a threshold. Recall that pm = P(Zm = 1|Y = 1) and qm = P(Zm = 1|Y = 2), m = 1, 2, ..., M. In this case,In the special case in which the random variables P(z|Y = 2) with pm replaced by qm.and a similar expression holds for It follows thatand consequentlyY = 1 ifThe LRT then reduces to the form: Choose Y = 2 otherwise, whereand choose pm > qm, all these coefficients in Equation (4) are positive and the decision rule in Equation (3) reduces to weighted voting among the pair-wise comparisons: every observed instance of zm = 1 is a vote for the poor outcome class with weight λm. Moreover, under the two assumptions of i) conditional independence and ii) equal a priori class probabilities  = P(Y = 2)), this is in fact the Bayes classifier  for the threshold t = 0.Since t = 0. Since we want a very high likelihood of detecting the poor-outcome class, we choose the threshold t to achieve high sensitivity, defined to be above 90%. Let tα denote the (largest) threshold achieving sensitivity 1 - α. That is, supposeSince our interest lies in high sensitivity at the expense of specificity if necessary, we do not choose tα from the training data in the next sections.) Then, from the Neyman-Pearson lemma, we know that our decision rule achieves the maximum possible specificity at this level of sensitivity. More precisely, this threshold maximizes.Of course this is only a theoretical guarantee and depends very strongly on the conditional independence assumption which is surely violated in practice; indeed, some genes are common to several of the variables t is to maintain high sensitivity (~90%); the specificity is then determined by the sample size and the information content in the features. In order to meet these criteria, we employ k-fold cross-validation to estimate the threshold which maximizes specificity at ~90% sensitivity for each signature size for our likelihood ratio test.In clinical practice, when selecting breast cancer patients for adjuvant systemic therapy, it is of evident importance to limit the number of poor-outcome patients assigned to the good-outcome category. The conventional guidelines  for breast cancer treatment usually call for at least 90% sensitivity and 10–30% specificity. Therefore our objective in selecting the threshold k-fold cross validation to estimate the sensitivity and specificity for each possible value of m = 5, 10, 15, ..., 200, (the number of features in the LRT) and t = 1, 2, ..., 200 (the threshold in Equation (3)). For each such m we then compute the specificity at the largest threshold t(m) achieving at least 90% sensitivity; this function is plotted in Figure mopt which (approximately) maximizes specificity; the threshold is then topt = t(mopt). From Figure mopt = 80.The idea is to use k disjoint subsets of approximately equal sample size; 2) Leave out one subset and combine the other k-1 subsets to form a training set; 3) Generate a feature list of M gene pairs ranked from most to least discriminating according the score defined in Equation (1) and compute the corresponding binary feature vector of length M for every training sample and every left-out sample; 4) Starting from the top five features, sequentially add five features at a time from the ranked list, generating series of 40 feature signatures of sizes m = 5, 10, ..., 200; 5) For each signature in 4), classify the left-out samples using the LRT in Equation (3) for each possible integer threshold t = 1, 2, ..., 200 and record the numbers of misclassified poor-outcome and misclassified good-outcome samples; 6) Repeat steps 1)-5) exhaustively for all k divisions into training and testing in step 1); 7) Calculate the sensitivity and specificity for the prognostic LRT test for each of the 40 signatures, and keep only the largest threshold for which the sensitivity exceeds 90%. The optimal number of features, mopt, is the smallest number which effectively maximizes specificity.Specifically, the steps are as follows: 1) Divide the integrated training data set into mopt top-ranked features (gene pairs) generated from the original integrated training set. The final prognostic test is the LRT with these features and the corresponding threshold topt = t(mopt); this is the classifier which is applied to the validation set and yields the error rates reported in 'Results'.The final prognostic signature is the p-values associated with odds ratios are calculated by Fisher's exact text. We also plot the Kaplan-Meier curve of the signature on the independent test data with p-values calculated by log-rank test. The Mantel-Cox estimation of hazard ratio of distant metastases within five years for the signature is also reported. All the statistical analyses are performed using MATLAB.We compute the odds ratio of our prognostic test for developing distant metastases within five years between the patients in the poor-outcome group and good-outcome group as determined by LRT classifier. The LX, under the supervision of RLW and DG, collected the microarray data sets and implemented the algorithms; all authors developed the methodology and contributed to the final manuscript.Clustering of the training data. Shown is the heat map of the two-group (good- and poor-outcome) supervised clusters of the integrated training data for the 112 signature genes. Those genes which appear in multiple pairs among the 80 gene pairs in the signature will appear multiple times in the heat map. The total number of the rows is 160.Click here for fileClustering of the test data. Shown is the heat map of the two-group (good- and poor-outcome) supervised clusters of the test data (Pawitan) for the 112 signature genes. Those genes which appear in multiple pairs among the 80 gene pairs in the signature will appear multiple times in the heat map. The total number of the rows is 160.Click here for fileThe cell cycle pathway. Our signature genes which appear in the cell cycle pathway are shown in red.Click here for file