The methods used for sample selection and processing can have a strong influence on the expression values obtained through microarray profiling. Laser capture microdissection (LCM) provides higher specificity in the selection of target cells compared to traditional bulk tissue selection methods, but at an increased processing cost. The benefit gained from the higher tissue specificity realized through LCM sampling is evaluated in this study through a comparison of microarray expression profiles obtained from same-samples using bulk and LCM processing.Expression data from ten lung adenocarcinoma samples and six adjacent normal samples were acquired using LCM and bulk sampling methods. Expression values were evaluated for correlation between sample processing methods, as well as for bias introduced by the additional linear amplification required for LCM sample profiling.The direct comparison of expression values obtained from the bulk and LCM sampled datasets reveals a large number of probesets with significantly varied expression. Many of these variations were shown to be related to bias arising from the process of linear amplification, which is required for LCM sample preparation. A comparison of differentially expressed genes  selected in the bulk and LCM datasets also showed substantial differences. There were more than twice as many down-regulated probesets identified in the LCM data than identified in the bulk data. Controlling for the previously identified amplification bias did not have a substantial impact on the differences identified in the differentially expressed probesets found in the bulk and LCM samples.LCM-coupled microarray expression profiling was shown to uniquely identify a large number of differentially expressed probesets not otherwise found using bulk tissue sampling. The information gain realized from the LCM sampling was limited to differential analysis, as the absolute expression values obtained for some probesets using this study's protocol were biased during the second round of amplification. Consequently, LCM may enable investigators to obtain additional information in microarray studies not easily found using bulk tissue samples, but it is of critical importance that potential amplification biases are controlled for. Microarray gene expression profiling is extensively used to study the etiology of disease and identify differential expression between two states. This high-throughput technology simultaneously measures expression levels in thousands of transcripts, providing a snapshot of the molecular makeup of a sample. The resulting data are representative of the cumulative expression of all cell types found in the sample. To increase specificity of the microarray expression signatures, investigators have used laser capture microdissection (LCM) for cell-collection prior to microarray hybridization. LCM is a precise extraction method that targets and extracts single cells from a sample -7. UsingThe perceived benefit of using LCM cell-sampling instead of bulk tissue-sampling for microarray analysis is balanced by the additional time and expense this method requires. LCM enables the precise extraction of target cells from a tissue sample consisting of a heterogeneous mixture of cell types. Bulk tissue sampling is faster and less expensive than LCM, however, sampled tissues often consist of a mixture of target and contaminating cell types. The process of LCM cell selection often generates low yields of RNA. Consequently, LCM-coupled microarray studies will use an additional round of RNA amplification (linear amplification) prior to microarray hybridization ,8-10, whThis study describes the first known comparison of microarray data generated from lung adenocarcinoma tumor cells and adjacent normal cells, acquired by LCM cell-sampling and bulk tissue-sampling. Lung cancer is the leading cause of cancer death in the U.S. in both men and women . Its heaMicroarray expression data were generated from LCM-cell-sampled and bulk tissue-sampled specimens acquired from matched cases, for ten adenocarcinomas and six adjacent-benign samples. To provide a reference to assess potential bias arising from the additional amplification step, RNA from two adenocarcinomas and two benign samples, acquired by bulk tissue-sampling, were linear amplified and hybridized to microarrays. The direct comparison of expression values revealed a substantial number of probesets with significantly altered expression levels between the bulk and LCM datasets. Evaluation of the linear amplified bulk data, however, showed many of the alterations are likely caused by an amplification bias, and not by differences in the cell populations profiled. Conversely, a comparison of differentially expressed probesets  identified in the bulk and LCM datasets, showed a considerably higher number of differentially expressed genes were found in the LCM data. Interpreted in context of the linear amplified bulk data, this observation appears to be predominantly unrelated to the amplification bias and suggests LCM increases the sensitivity of detecting differentially expressed genes in the microarray data.Fresh frozen tissue samples were obtained from the Mayo lung tissue bank for ten patients with stage IIIA or IIIB adenocarcinoma of the lung and with surgical tumor resection. Normal lung tissue adjacent to tumor in six patients were also obtained from the Mayo lung tissue bank. All samples were fast frozen within 30 minutes after resection and stored in -80 °C. All cases were first reviewed by a pathologist (MC) for their diagnosis accuracy and adequacy for a microarray study. Normal and tumor samples were selected if there was sufficient material to microarray profile both laser capture microdissected samples and bulk tissue samples. The use of the tissue for the study was approved by Mayo Foundation Institutional Review Board.For each patient, a pathologist assessed an H&E stained slide from each frozen tumor block . Fifteen sections (10 μm thick) of the chosen tumor and normal blocks were cut in a cryostat and placed on slides by the Tissue and Cellular Molecular Analysis (TACMA) core facility  under Rnase free conditions. Slides were kept at -80°C and immediately stained with cresyl violet using an Ambion LCM staining kit (#1935) protocol. The desired area of tissue was microdissected from the slide within 30 minutes of staining using Arcturus LCM caps and microscope. The cap was placed on a tube that contained 75 μl of digestion buffer (RLT with BME from the Qiagen RNeasy micro kit #74004). The tube was inverted so the buffer covers the cap, vortexed at a medium speed for one minute and incubated at room temperature for 5 minutes. The tube was then centrifuged and the cap discarded. The samples were stored at -80°C until ready for RNA isolation. This procedure was repeated on the next slide for the case until a total of 5000 pulses were captured for that case. Each pulse targeted 3–5 cells. Due to tumor cell pleomorphism, it is difficult to standardize the number of cells captured per pulse. The number of slides used per case varied from 2 to 10, as every case and every tumor block had different sized tumors.RNA is isolated according to the Qiagen RNeasy micro kit protocol  with the following modifications. All of the tubes from the same sample were pooled together and 75 μl of 70% ethanol was added for each tube in the pool. The sample was applied to the column 700 μl at a time and spun according to protocol until the whole sample was applied. The RPE step was repeated to get rid of excess salt that may have accumulated due to multiple applications of sample to the column (depending on how many caps were needed per sample). The column was incubated with elution water for 5 minutes prior to spinning. The amount of RNA in each case was quantitated using the Quant-iT™ RiboGreen kit . The quality of the RNA was assessed using an Agilent 2100 Bioanalyzer in the Advanced Genomics Technology Center (Mayo Foundation). RIN (RNA integrity number) of 7 or greater was required to proceed to the linear amplification step.Ten nanograms of RNA was linear amplified using the Affymetrix protocol  (Affymetrix kit # 900432 and Ambion kit #1334). The cRNA yield ranged from 84 μg to 134 μg, with a mean of 120 μg, per sample. Of each sample, 15 μg was then hybridized to the Affymetrix HU 133 plus 2.0 chips in the Advanced Genomics Technology Center using the same protocol.For the bulk samples, the same blocks had 50 μm of tissue cut by the TACMA lab directly into a tube for tumor cases and 1000 μm of tissue for normal. These samples were kept frozen at -80°C until RLT buffer was added. The samples were homogenized using a mechanical homogenizer for 30 seconds and the RNA isolated using the RNeasy kit protocol. Once the RNA was isolated, it was evaluated using the Agilent and Ribogreen assays described above. The 1.4 μg of sample was then processed and hybridized (15 μg) onto the HU 133 plus 2.0 chip. From four samples, an additional 10 ng of RNA was linear amplified (as described for LCM) and then processed on the microarray chips. The cRNA yields for these four samples ranged from 130 μg to 145 μg, with a mean of 136 μg.The Affymetrix U133 Plus 2.0 gene chips were read using a GeneChip Scanner 3000 7G. Raw expression CEL files were analyzed using the dChip invariant set normalization procedure, with PM-only modeling. Expression values were log2 transformed.a priori thresholds were imposed to restrict the analysis to large changes in expression occurring beyond the scope of background signal. Two selection criteria were used when comparing expression values from A and B:For all comparisons, A, B ≥ log2 2  (denotes a 4× change in expression)(i) Δ2 8  (ii) max ≥ log2 2; max exp value ≥ log2 8). Comparisons were initially conducted on the cases with linear amplified bulk samples, and subsequently performed on the complete dataset.To estimate the effect acquisition methods have on the selection of differentially expressed probesets, concordance between bulk and LCM rank-ordered differential probeset lists was calculated. Concordance was used to measure the agreement in ordered lists based on an absolute threshold, where probesets ranked above the absolute threshold on both lists were considered "in agreement" and the remainder "not in agreement". Analysis was performed over a range of thresholds and the results plotted. Probesets were rank ordered by the magnitude of the difference in log2 expression values, while maintaining the same selection criteria used throughout the study  all probesets with significant alterations in expression between bulk and linear amplified bulk samples. Density plots for the two groups were computed using the R 'density' function, and independence in the distributions tested using the Kolmogorov-Smirnov test.Using Affymetrix's NetAffx Annotation File , the corresponding RefSeq transcripts, where annotated, were identified for all probesets. The sixth probe from each probeset was sequence-compared to its RefSeq transcript. Probesets for which the sixth probe mapped to the corresponding RefSeq transcript with 100% identity, were selected for further analysis. The distance between the 5' end of the sixth probe sequence and the transcript 3' end, minus any terminal poly-A repeat, was computed in bases. Distance values were determined for two groups: (i) all probesets with an average expression greater than logFor these probesets, the corresponding RefSeq transcript was scanned for poly-A repeats of at least six adenines in length at the 3' terminus.The direct comparison of microarray data generated from bulk and LCM-sampled tumor and adjacent benign tissue identified a significant number of probesets with highly variant expression. All comparisons were limited to large changes in expression (4-fold or higher) to minimize type-I error arising from cross-hybridization and platform measurement variability. Results from the matched-sample comparisons between bulk and LCM data, stratified by cancer or normal status, are plotted in Figure The preceding comparisons were repeated using two alternative methods to determine how robust the findings were. First, the average probeset expression value across all six bulk normal samples was compared to the average probeset expression value across all six LCM normal samples, and likewise for the cancer samples, identifying 387 probesets in normal tissue and 500 probesets in cancer tissue with significantly varied expression levels. Second, a Bonferroni corrected T-test (α = 0.05) was used to compare the microarray groups identified 577 probesets in normal tissue and 3616 probesets in cancer tissue, with significantly divergent expression.To estimate whether the second round of RNA amplification, required for LCM sample processing, induced a bias in the expression data, RNA from two normal-bulk and two cancer-bulk tissue samples were linearly amplified. Using the two-sample average probeset expression values from the linear-amplified bulk, bulk, and LCM microarrays, the previous comparisons were repeated. As evident in Figure th percentile p-value of 0.22. Based on the false discovery analysis, the increase in probeset distance from the gene 3' end observed for the probesets with linear amplification bias appears to be real and not an artifact of subset selection.The second round of the linear amplification method used to process the LCM samples includes a random priming step which may induce a bias in the amplified expression level of probesets distant from the target gene's 3' end . To testA common objective of many microarray studies is the selection of differentially expressed genes; genes with significantly altered expression levels between normal and disease states. To determine whether LCM cell-sampling significantly effects the identification of differentially expressed genes, comparisons of up-regulated and down-regulated genes, identified using the bulk and LCM datasets, were carried out. Differential expression was computed using the average probeset expression across all cases. A total of 110 upregulated probesets were identified in the LCM dataset and 122 in the bulk dataset. Of these, 68 probesets were identified in both datasets. There were also 378 down-regulated probesets identified in the LCM dataset and 181 in the bulk dataset. Of these, 158 probesets were identified as down-regulated in both datasets. A list of the differentially expressed probesets identified in the bulk and LCM datasets is provided in Additional file To estimate the effect of the amplification process bias on the selection of differentially expressed probesets, the analysis was repeated using the two-sample average values for the bulk, LCM, and linear amplified samples. In the bulk data, 297 up-regulated probesets were identified, 238 in the linear amplified bulk data, and 217 in the LCM data. As illustrated in Figure To further evaluate the effect amplification bias has on the selection of differentially expressed genes, probesets expressed at significantly different levels in the bulk and linear amplified bulk samples were compared to probesets possessing cancer-to-normal differential expression levels in either the LCM or the bulk samples, but not both. The results are reported in Table The impact LCM cell-sampling has on the relative rankings of differential probesets was also examined, by calculating the concordance between differentially expressed probeset rankings in the bulk and LCM datasets. A sliding threshold was used to select for the top 10, to the top 100 differentially expressed probesets. Probesets previously identified as up- or down-regulated were ranked by the magnitude of expression value change. The ranking concordance values were plotted against the selection thresholds, with independent response curves generated for up-regulated and down-regulated probesets. As illustrated in Figure The impact linear amplification has on the ranking of differentially expressed probesets was also evaluated. Response curves for rank concordance were computed for the differentially expressed probesets identified from the two-case average expression values in bulk, linear amplified bulk and LCM datasets Figure . The higComparative analysis of microarray data generated from LCM-cell-sampled and bulk tissue-sampled matched cases revealed many significant changes in expression levels between the two datasets. The discrepancies identified in these datasets highlight two main observations regarding the LCM process and resulting expression data. First, when the absolute gene expression levels are compared between LCM and bulk microarrays there are many significant differences identified. These variations in gene expression correlate with the second round of linear amplification, and do not appear to be related to the cell populations sampled. Second, substantially more down-regulated genes are uniquely identified in the LCM dataset than identified in the bulk dataset Figure . This obThe direct comparison of expression data from LCM-cell-sampled and bulk tissue-sampled microarrays identified a high number of genes (~650) with significantly different expression levels. To ascertain whether these changes are biologically significant, reflecting the expression patterns of the cell populations sampled, or an artifact arising from the LCM process, the expression of these genes in linear amplified bulk samples were examined. Approximately two-thirds of the variant probesets identified between the bulk and LCM datasets possess clearly attenuated expression levels in both the LCM and linear amplified bulk microarrays. The loss of expression signal in the linear amplified bulk data clearly indicates most of the observed variations in gene expression are a by-product of a bias arising from the amplification process. Only a small number of probesets displayed variant expression between the bulk and LCM microarrays, while displaying consistent expression between the bulk and linear amplified bulk microarrays. These probesets may measure biologically relevant variations of gene expression in the samples acquired by LCM cell-sampling and bulk tissue-sampling. However, these biologically relevant differences in expression are obscured by the process bias and not easily identified when directly comparing amplified (LCM) microarray data to unamplified (bulk) microarray data.in vitro synthesized RNA. Affymetrix constructed the U133 Plus 2.0 microarray with probesets located within 600 base pairs of the 3' end of the transcript . However, by mapping probeset positions to the target RefSeq transcripts, a subset of probesets are found substantially further than 600 base-pairs from the 3' terminus. A density distribution of probeset to transcript 3' terminus distance, for probesets with linear amplification bias and for a control group of significantly expressed probesets, illustrates a statistically significant shift in the distribution curves , many investigators continue to use rankings based on the magnitude of expression variation to prioritize candidate gene lists. By determining the percent agreement between LCM and bulk datasets, at various thresholds, it becomes clear the differences in the differential genes selected are fairly consistent throughout the rankings. As Figure The impact of LCM cell-sampling on the identification of differentially expressed probesets observed in this study has been reported in other cancer studies ,16,17. AAn interesting observation can be made when comparing the results obtained by using different methods to directly compare the bulk and LCM microarray data. When defined thresholds are used to identify changes in expression between matched-sample microarrays, an average of 749 variant probesets were found in the normal samples and 684 in the cancer samples. Similar but an expectedly lower number of changes were found when comparing group-average expression values . However, when the same comparisons were repeated using a Bonferroni corrected T-test, a similar number of variant probesets was identified in the normal tissue (577), but considerably more were found in the cancer tissue (3616). It appears this may be partially explained by the relative sample size of the normal (n = 6) and cancer (n = 10) sample sets. When a random selection of six cancer sample-pairs were analyzed using the Bonferroni-corrected T-test, the number of variant probesets decreased and approached the number found in the normal tissue and by the defined threshold method (data not shown). The sensitivity of the Bonferroni-corrected method to sample size suggests a propensity towards identifying modest changes in expression. With the relatively noisy measurements obtained from microarrays, there is the risk these modest changes in expression may fall within the error bounds of the microarray platform, and be biologically irrelevant. The defined threshold method used in this study was specifically designed with conservative criteria, limiting selection to large, reproducible, changes in expression, which are more likely to be experimentally "real", and subsequently more robust to changes in sample size.The linear amplified bulk microarray data generated in this study may have more universal application to researchers. Throughout these analyses, this data was used to estimate the impact of the T7 amplification bias on the expression values obtained from bulk tissue sampling and LCM cell sampling (with amplification). While this study specifically addressed lung samples, there may be broader applicability of the amplified bulk expression data. Any study using a similar amplification protocol coupled to LCM-based microarray analyses may find this data useful in interpreting the results. The amplification bias described in this manuscript should be tissue-type independent. Therefore, provided the gene under study is expressed in the nascent sample, this data may be used to test whether an apparently down-regulated transcript was reflecting a potential amplification bias or a modified expression state.This study comparatively analyzed expression data generated from lung adenocarcinoma and adjacent normal tissue acquired from the same samples using LCM cell-sampling and bulk tissue-sampling. The results highlight the importance that for any expression profiling study using LCM cell-sampling, steps are taken to evaluate all possible bias arising from the sample processing methods. It is also evident from these analyses that restricting any evaluation of acquisition methods to the comparison of absolute expression level changes between the methods is insufficient. The value of the acquisition method may only be observed when relative changes in expression values are compared. In this study, all comparisons were predicated on large changes in expression values. It is possible more value may be realized in LCM cell-sampling when the dynamic range of expression profiling technologies improve and low level changes can be confidently evaluated.The authors declare that they have no competing interests.EK was involved in the study conception, carried out the bioinformatics analysis, and drafted the manuscript. SE carried out the pathology review of all samples. LT carried out all the molecular biology and assisted in writing the manuscript. FK, ZS, DW, PY, MC were all instrumental in the study design, review of the ongoing study, and critical assessment and review of the manuscript. GV was involved in the study conception, study management, and critical assessment of the manuscript. All authors read and approved the manuscript.The pre-publication history for this paper can be accessed here:In depth discussion of specific examples of differentially expressed probesets. It also contains additional discussions of the probe expression level bias identified in the manuscript.Click here for fileContains a tab-delimited list of differentially expressed probesets identified in this study.Click here for file


The number of methods for pre-processing and analysis of gene expression data continues to increase, often making it difficult to select the most appropriate approach. We present a simple procedure for comparative estimation of a variety of methods for microarray data pre-processing and analysis. Our approach is based on the use of real microarray data in which controlled fold changes are introduced into 20% of the data to provide a metric for comparison with the unmodified data. The data modifications can be easily applied to raw data measured with any technological platform and retains all the complex structures and statistical characteristics of the real-world data. The power of the method is illustrated by its application to the quantitative comparison of different methods of normalization and analysis of microarray data. Our results demonstrate that the method of controlled modifications of real experimental data provides a simple tool for assessing the performance of data preprocessing and analysis methods. The number of methods available for pre-processing and analysis of high-dimensional data continues to increase, making a comparative assessment of the performance of these various methods increasingly important. Such a comparison should be qualitative, as well as time-, computation- and cost effective. Currently, no commonly accepted rules exist for such comparative testing. The performance of each test should be characterized in terms of the test's ability to distinguish true changes from noise that appears to represent a pattern. The receiver operating characteristic (ROC) curve, which represents all possible combinations of the relative frequencies of the various kinds of correct and incorrect decisions, is usually employed as a simple empirical description of these characteristics a priori, and such comparison approaches do not guarantee complete similarity between the structure of the simulated data and complex real-world expression data. The most objective discrimination between false and true changes in a dataset was achieved by using a “spike-in” experimental procedure based on Affimetrix GeneChip technology One promising approach is based on the use of a simulation strategy that constructs more-or-less realistic data models with varying statistical characteristics that reflects the properties of real gene expression data We propose a simple approach to assessing various methods of data analysis that is based on the use of real microarray data. Our assessment can be easily implemented into the design of any microarray experiment and can be performed with minimal training. It is important to note that our approach is applicable to any microarray dataset generated from homogenous groups of samples hybridized to the arrays.The first step in our strategy is the introduction of controlled changes into homogeneous group of microarray data. The group is split into two equal subgroups, one of which remains unchanged and is used as a standard for comparison. The second subgroup is altered by introducing controlled fold changes in the gene expression values. Similar step - introduced changes in the real gene expression data - was used earlier for demonstration of the breakdown of Lowess normalization after “one direction changes” We illustrate the application of this method for an optimal cutoff estimation, intensity-based filtering and for optimal fold differences selection that provides the highest ratio of true to false signals. This method enables us to estimate the degree to which the quality of the analysis depends on the level of gene expression. Our method estimates the minimal necessary number of replicates to achieve an expected sensitivity in the differential gene expression analysis. We also applied our approach to the comparative estimation of the quality of data preprocessing  and the performance of the methods of differential gene expression analysis.We performed gene expression profiling from 20 samples of Epstein-Barr Virus-transformed B cells collected from normal healthy donors. RNA was isolated using Ambion's RNAqueous RNA isolation kit  according to the manufacturer's protocol. After purification, RNA concentration was determined with a Nanodrop scanning spectrophotometer. Illumina Whole Genome Human Ref-8 v2.0 arrays containing ∼24,000 probes were handled according to standard protocols established by the manufacturer. Briefly, 200ng of total RNA from each sample were used to generate biotin-labeled cRNA probes using an Illumina MessageAmp cRNA labeling kit protocol (Ambion). Quality control of the cRNA was performed using an Agilent Bioanalyzer and a Nanodrop. Labeled cRNA probes were hybridized to Illumina arrays and images were obtained on an Illumina Beadchip scanner. The images and raw data from each chip were transferred automatically to the microarray database using one of the specified microarray core servers. The raw data is available on GEO Our methods for data normalization and analysis are based on the use of “internal standards” The first step is determination of the parameters of a background of an array – average (Av) and standard deviation (SD) – is performed using a special iteration procedure. Data in each array are transformed to make these parameters equal to 0 and 1, correspondingly. After this transformation gene expression data are presented in the units of standard deviation of the background. We accept the threshold of 3 SD above the mean of background distribution as the preliminary criterion for distinguishing between expressed and non-expressed genes. Only genes expressed above background are used for the second step.not associated with this internal standard of equality of expression.The second step is the adjustment of normalized profiles to each other by robust linear regression. This procedure is based on the selection of equally expressed genes as a homogenous family of genes with normally distributed residuals defined as deviations from the regression line. The parameters of this distribution are obtained by the iterative procedure similar to one used for the selection of normally distributed background noise. Outliers are thereafter determined as having deviations The differential gene expression analysis – Associative analysis Construction of the ‘reference group’ by identifying a group of genes expressed above background with inherently low variability as determined by an F-test. The ‘reference group’ presents an internal standard of equal expression. As such, the ‘reference group’ is used to assess the inherent variability resulting from technical factors alone . By creating an estimate of the technological variation we are able to select a group of biologically stable genes.Selection of replicates using the commonly accepted significance threshold of p<0.05 with a Student T-test. This selection maintains the commonly accepted sensitivity level; however, a significant proportion of genes identified as differentially expressed at this threshold will represent false positive determinations.Ho hypothesis is checked to determine whether the levels of gene expression in the experimental group presented as replicated residuals (deviations from the averaged control group profile) is associated with a highly representative  normally distributed set of residuals of gene expression values in the reference group. The significance threshold is then corrected to render the appearance of false positive determinations improbable. Only genes that pass both tests are presented in the final selections.An Associative T-test in which the replicated residuals for each gene from the experimental group are compared with the entire set of residuals from the reference group defined above. The diffGeneAnalysis, available as a part of Bioconductor packages . Other Bioconductor packages  were used for Quantile, Lowess and VSN data normalization and for Limma analysis. SAM: Significance Analysis of Microarrays was used as an Excel add-on, downloaded from http://www-stat.stanford.edu/~tibs/SAM/.The two-step normalization procedure and the Associative analysis functions are implemented in MatLab  and available from authors upon request. These algorithms are also obtainable from an R package In this study, we used gene expression data from 20 total RNA samples from Epstein-Barr Virus-transformed B cells collected from normal healthy donors. This presumably homogenous group of samples was sorted by average expression level and split into two equal subgroups, designates as “control” and “experimental”. The data was then split into blocks of 1,000 genes each. Controlled balanced (+/−) changes were introduced into 20% of the data in the experimental subgroup. One hundred genes in each 1000 gene block were modified by a positive change (multiplied by fold change) and another hundred genes were modified by a negative change (divided by fold change). The top thousand genes with the highest expression levels were treated slightly differently: they were split into five 200-gene blocks, and within each of them 20 genes were modified by positive change (multiplied by fold change) and 20 genes were modified by negative change (divided by fold change)  in contrast to the remaining genes that were initially not changed . One block of AP/AN genes is shown on However, the use of Specificity expression as presented here was not particularly useful for characterizing the quality of microarray data analyses. The problem with this metric is that in any microarray analysis, the number of AN-genes always exceeds the number AP-genes by at least several fold. As a result, a significant number of false negative selections will produce only a small deviation from 1 in Specificity value. For example, if there are two hundred differentially expressed genes from twenty thousand total genes measured in an array, selection of ten false positives will result in a Sensitivity of 0.95. In contrast, the same number of false negatives will produce a Specificity value of 0.999. This Specificity value does not express the real impact of this number of selected false negatives, which should be more comparable to the impact of false positive selections. To improve this situation, we used instead the Precision parameter, which is a better characteristic for testing exactness or fidelity in cases with significant differences in sizes of positive and negative numbers. Accordingly To estimate stability of the proposed characteristics, our test procedure can be repeated several times with random permutation of the order of samples within the group before each split. The averaged Specificity and Precision parameters from multiple tests, together with their Standard Deviations, are presented in the figures.The data that include controlled balanced changes in the expression levels of a small proportion of the genes in the array can be used for a variety of purposes, including comparison of different methods of data pre-processing and analysis. We begin, however, with a demonstration of the potential of this method for estimation of the effects of different restrictions frequently used in differential gene expression analyses.The step typically following completion of a differential gene expression analysis using strong statistical criteria is to focus on the most prominent changes. To achieve this some additional restrictions are applied, such as minimal level of expression and the fold changes in the level of expression deemed biologically significant. The influence of these pre-processing steps on the quality of analysis has not been extensively studied.Filtering by a minimum expression level obscures the influence of extreme variability of low expressed genes. Additionally, filtering out genes that are expressed only at low levels unambiguously demonstrates that the most important biological changes are usually well represented by genes with high expression level. However, the cost of this restriction is that changes in regulatory genes expressed at low levels, such as transcription factors Statistical analysis of differences in gene expression can identify even minimal changes in levels of gene expression if those changes are extremely stable across replicated experiments. However, the statistical significance of a fold change does not necessarily reflect biological relevance. Genes with low fold change differences should certainly be excluded, at least at the stage of initial examination of the results. Those genes displaying high fold changes in gene expression  potentially represent the most important functions in the biological system. We refer to these as “beacon genes” To estimate the influence of fold change and expression level restrictions on the quality of analysis, we used the data described in the In summary, the maximal level of Sensitivity does not exceeded 0.85 even under the best conditional situations. The controlled changes introduced into real expression data do not necessary make the changed expression really significantly different. The statistical tests for differential expression analysis are based on the difference in the expression level (Average) and its variation in replicates (SD). A proportion of genes with extremely high variation of the expression level is always present even in the very homogeneous group of samples. The introduction of even substantial difference for such genes does not cancel the fact of their extreme variability. Being significantly different in the level of expression (after controlled changes) such genes remain to be extremely variable, and as a result could not be selected as significantly different with any realistic differential analysis. The selection and exclusion from analysis of such genes  is a great advantage of this approach over similar attempts that have used simulated data or artificial models based on the use of simplified/averaged statistical characteristics from real experiments. Introduction of controlled changes into the expression dataset enables us to address many problems associated with pre-processing and analysis of microarray data, including the quality dependence of the analysis from the threshold of minimal gene expression, the influence of fold change restrictions, and other factors. Further, the introduction of controlled changes completely eliminates the problem of non-linearity of probe effects because the changes are introduced into the final expression levels as if they were measured in the linear region of the dose/response dependence curve. We believe that the use our comparative approach will improve the robustness of microarray-based experiments.Homogeneous real gene expression data from any experiment can be used to create a transformed dataset with controlled balanced changes introduced into a portion of the genes measured in the array. Similar microarray data matching the manufacturer, organism, tissue/cells can almost always be found among >400,000 microarray samples stored on Gene Expression Omnibus GEO, , if suchHomogeneity of the initial data set will influence estimations of the performance of the analyses. Estimation of true and false selections are based on the proposition that after arbitrarily chosen split the resulting subgroups have only changes in gene expression produced mainly by the controlled modifications in one of the subgroups. In practice, however, we have the presence of various types of outliers even within most homogeneous biological data. The approach presented here is especially sensitive to the presence of genes having extremely variable expressions To demonstrate the potential of our proposed methodology we performed quantitative comparisons of the efficiencies of different normalization and analysis methods. The presented results enable to compare the performance of all these methods quantitatively in the wide ranges of gene expression levels and estimate stability of the characteristics of the performance (Sensitivity and Precision). This analysis is based on the use of preliminary data. However, if the user is unable to run several microarray experiments to estimate the robustness of specific pre-processing and statistical analysis methods, real data that mimic the experimental conditions can be freely obtained from public microarray data repositories GEO .The question ‘how many replicates is enough?’ is complicated by many potentially confounding factors such as the type of array equipment, laboratory technique, and, most importantly, the quality of the samples. Cost often represents a significant restraint and it is important to know which fold changes can be detected reliably for a given number of replicates. Our analytical approach can be used to objectively estimate the number of replicates of a microarray experiments required to reach any desired quality of analysis and can be completely adjusted to the technological platform and experimental design.In summary, we present an accurate and universal procedure for quantitative and qualitative estimation of the methods of microarray data analysis. Our approach has the potential for broad applicability to different types of arrays, including those with asymmetric distributions of up/down-regulated genes. All programs used for our analysis were written in MATLAB and are available upon request. The microarray data containing balanced 2-fold differences in the levels of expression of 20% of the genes measured are provided as Supplemental material  and may Figure S1Comparison of normalization methods. Two-step normalization vs. Quantile, Lowess, and VSN normalizations. All designations are as in (0.73 MB TIF)Click here for additional data file.Figure S2Comparison of different methods for gene expression analysis. Limma, SAM and Associative analysis performance compared in terms of Sensitivity/Precision. 2-step normalization procedure was used in all cases. The restrictions were Fd/Fa/Em = 2/1.5/20 as before. A) Sensitivity and B) Precision for symmetric changes in 10% of gene expression; C) and D) - the same for 5% changes.(0.58 MB TIF)Click here for additional data file.Table S1Raw data  without introduced changes. In this research we used the group of arrays created from 20 samples from Epstein-Barr Virus (EBV)-transformed B cells collected from normal healthy donors . This presumably homogenous group was split into two equal subgroups. One of the subgroups (columns Q-Z) was used as a control, whereas the artificial changes in gene expressions were introduced in another subgroup . All data were sorted by the average expression in experimental group.(6.30 MB XLSX)Click here for additional data file.Table S2Data with introduced symmetrical 2-fold differences in 5% of the expression in one half of samples. Column AA (Modifications) shows the changed gene expression: 1 - means 2-fold increase of expression, and 2- 2-fold decrease, zero means unmodified data. Data with increase/decrease of expression are highlighted red/blue, respectively.(6.45 MB XLSX)Click here for additional data file.Table S3Data with introduced symmetrical 2-fold differences in 10% of the expression in one half of samples. Column AA (Modifications) shows the changed gene expression: 1 - means 2-fold increase of expression, and 2- 2-fold decrease, zero means unmodified data. Data with increase/decrease of expression are highlighted red/blue, respectively.(6.46 MB XLSX)Click here for additional data file.Table S4Data with introduced symmetrical 2-fold differences in 20% of the expression in one half of samples. Column AA (Modifications) shows the changed gene expression: 1 - means 2-fold increase of expression, and 2- 2-fold decrease, zero means unmodified data. Data with increase/decrease of expression are highlighted red/blue, respectively.(6.41 MB XLSX)Click here for additional data file.Table S5Data with introduced asymmetrical 2-fold increase only in 10% of the expression in one half of samples. Column AA (Modifications) shows the changed gene expression: 1 - means 2-fold increase of expression, zero means unmodified data. Data with increase of expression are highlighted red.(6.45 MB XLSX)Click here for additional data file.Table S6The raw data  with introduced asymmetrical 4-fold increase 10% of the expression and 2-fold decrease in another 10% of the expression in one half of the data. Column AA (Modifications) shows the changed gene expression: 1 - means 4-fold increase of expression, 2 - means 2-fold decrease of expression, zero means unmodified data. Data with increase/decrease of expression are highlighted red/blue, respectively.(6.30 MB XLSX)Click here for additional data file.


Typically, pooling of mRNA samples in microarray experiments implies mixing mRNA from several biological-replicate samples before hybridization onto a microarray chip. Here we describe an alternative smart pooling strategy in which different samples, not necessarily biological replicates, are pooled in an information theoretic efficient way. Further, each sample is tested on multiple chips, but always in pools made up of different samples. The end goal is to exploit the compressibility of microarray data to reduce the number of chips used and increase the robustness to noise in measurements.A theoretical framework to perform smart pooling of mRNA samples in microarray experiments was established and the software implementation of the pooling and decoding algorithms was developed in MATLAB. A proof-of-concept smart pooled experiment was performed using validated biological samples on commercially available gene chips. Differential-expression analysis of the smart pooled data was performed and compared against the unpooled control experiment.The theoretical developments and experimental demonstration in this paper provide a useful starting point to investigate smart pooling of mRNA samples in microarray experiments. Although the smart pooled experiment did not compare favorably with the control, the experiment highlighted important conditions for the successful implementation of smart pooling - linearity of measurements, sparsity in data, and large experiment size. Presently, pooling in microarray experiments refers to the act of mixing messenger RNA (mRNA) collected from several biological-replicate samples, before hybridization onto a microarray chip -6. This In this paper, we describe a different pooling strategy; a smart pooling strategy based on compression algorithms from digital communication theory. The smart pooling strategy is applied to a large number of diverse biological samples, not necessarily biological replicates, which are pooled and tested on several microarray chips based on a pre-specified pooling design. The mathematical properties of smart pooling designs ensure that each sample is tested on multiple chips, but always in pools made up of a different set of samples, such that, data from all the chips taken together capture the same information as the standard one-sample-one-chip approach. Because of the convolution step involved in testing pools of samples on multiple chips, the measurements made from the smart pooling strategy must be decoded to obtain the gene expression value of each gene in every sample. To save cost and to accurately transmit information across digital communication channels, where bandwidth is limited and the channel is noisy, a similar compression and recovery strategy is used. Similarly, smart pooling can achieve an overall savings by using fewer microarray chips than samples being tested. The built-in redundancy of testing each sample on multiple microarray chips can also provide robust expression measurements. The gains of compression and robustness from using smart pooling strategies motivated us to investigate this method. Smart pooling strategies have been used in other high-throughput biological applications such as blood testing , drug scThe pooling and decoding strategy used in this paper we call poolMC - pooled microarray. poolMC is based on theoretical ideas from the field of compressive sensing, which has already demonstrated its utility in signal and image processing applications ,17. CompTo test the poolMC strategy, we have carried out a small pooling experiment using validated biological samples on commercially available gene chips. In the context of this pooled experiment, we explain poolMC's pooling and decoding strategy. The results of this pooled experiment are then compared to the standard one-sample-one-chip method for the same set of samples. Finally, we propose the ideal setting for using poolMC and suggest its potential benefit for large microarray experiments.A key requirement for any smart pooling strategy is that the data must be sparse to allow compression. In this paper, the data describing a single gene's expression pattern across several biological samples is called its expression profile. A gene's expression profile is said to be sparse if, across several samples, there are only a small number of samples in which the gene's expression is significantly different from its median expression value in these samples. In this context "different" is ill defined, so for practical purposes, "different" can be viewed as meaning differential expression. If a gene is differentially expressed in only a small number of samples across many being tested, then it is possible to exploit this sparsity to reduce the number of microarray chips needed to obtain the gene expression profile by pooling or multiplexing samples.Figure Before describing the details of the pooling method, we first identify two cases of microarray experiments that could produce sparse gene expression profiles. A first example of sparsity could be mRNA samples obtained from similar cell samples, but with different, non-overlapping gene knockouts. This example case would bias the expression profiles toward sparsity because many of the genes would have no difference in expression (zero spike) across the various knockout samples. Those genes that do change would likely change only in one genetic background. A second example of a sparse gene expression study is in biomarker discovery where the samples, classified as treatment (or control), would have few genes differentially expressed in the samples within each classification, though they may show great variation when compared across each other.The next sub-section describes the concept of smart pooling and how it exploits sparsity in gene expression profiles.The central idea of smart pooling is to exploit sparsity in a gene's expression across several samples to obtain robust estimates of the gene's expression in all samples, while using fewer chips. This concept differs significantly from earlier microarray pooling attempts by not focusing on reducing the number of microarray chips used or increasing statistical power by estimating a single average expression value for each gene across multiple biological replicate samples. The goal of smart pooling is to recover the unique expression value of each gene in each of the samples being tested. To achieve this goal, samples are pooled in a systematic way across several chips while keeping the number of chips used lower than the number of samples tested. The pooling design ensures that each sample is tested on multiple chips, always in pools with different samples. The mathematical properties of the pooling design and decoding algorithm, discussed in more detail in the Methods section, guarantee that the expression of each gene in all samples is decoded accurately. For this strategy to succeed, two principle requirements need to be satisfied. First, as described in the previous sub-section, gene expression profiles need to be sparse. Second, the intensity of pooled measurements should map linearly to the contribution from each sample being pooled in the measurement.If the data are sparse and mixing is additive, the pooling strategy can be reduced to a linear system of equations for each gene. As shown in Figure This section describes the pooling design and decoding algorithm that together form the poolMC smart pooling strategy. The details of the pooling design construction, the laboratory protocol for pooling, and the decoding of the results are described in the context of a small pooling experiment that was performed to test poolMC experimentally.The current one-sample-one-chip microarray experiment is termed the monoplex. The pooled microarray experiment is called the multiplex. Because the data measured by the monoplex experiment are directly used, these data serve as both the monoplex measurements and the monoplex results. However, the multiplex experiment has two parts. The multiplex measurements refer to the data obtained from pooling samples on microarray chips, hence fewer measurements than the monoplex, and multiplex results refer to the data obtained from decoding the multiplex measurements. The multiplex results can be directly compared with the monoplex results. A synthetic mutliplex was performed using the monoplex results and multiplying them with the pooling design for each gene in the system. This produced synthetic measurements that were comparable to the multiplex measurement. The synthetic results, comparable to the monoplex and multiplex results, were obtained by decoding the synthetic measurements. The synthetic multiplex helped investigate the linearity assumption, by comparing multiplex and synthetic measurements, and the sparsity assumption by comparing the synthetic and monoplex results.A proof of concept test was carried out using 15 mRNA samples obtained from the root epidermis of the plant Arabidopsis thaliana. These samples included 4 pairs of biological replicates, hence 8 samples, and 7 independent knockout samples. The monoplex measurements were performed at the usual concentration using 15 microarray chips. The multiplex measurements were obtained by mixing fractions of individual samples and using 12 microarray chips, employing the pooling design shown in Figure The mixing of fractions of individual samples was conducted at the mRNA level. Following isolation of mRNA from plant material (using the Qiagen RNeasy kit), the concentration of each sample was determined . For theThe resultant mircoarray hybridization signal data corresponding to monoplex and multiplex measurements were preprocessed separately using RMA  and annoSeveral pooling methods have been discussed in the literature ,23. The The theoretical guarantee  is decoded accurately, as guaranteed by the pooling design used.b A gene with exactly 1 spike but with significant noise in a measurement , is not decoded accurately.c A gene with multiple spikes but low noise is decoded with moderate accuracy, even though the number of spikes exceeds the algorithmic guarantee.d Gene with multiple spikes and low noise is not decoded accurately due to a larger deviation from the sparsity assumption than in (c).Although the four examples are only a small sampling, the patterns shown in Figure Given that the monoplex and multiplex results often produce the same pattern of expression, we next compared the lists of differentially expressed genes obtained from both lists. Because the 15 samples contain 4 pairs of replicate sample measurements , differential expression analysis was performed between pairs of biological replicates using the Significance Analysis of Microarrays (SAM) method  to obtaiIn all six comparisons, the longest significant-gene lists were produced by the monoplex dataset. The multiplex and synthetic datasets identified fewer differentially expressed genes at the 10% q-value cut-off. As expected, the synthetic dataset shows more overlap with the monoplex than the multiplex does. This difference in overlap can be attributed to the decrease in noise in the synthetic dataset. The significant disparity in number of significant genes common to both monoplex and synthetic datasets can be attributed to the lack of sparsity in a large number of genes in the 15 samples chosen for the experiment, resulting in decoding errors.The aim of this work was to determine whether pooled experiments following a compressive sensing inspired design and decoder (poolMC) would be effective for gathering gene expression data. The approach described here is easy to implement with existing chips, as it only requires an intelligent mixing of samples. The analysis results indicate that gene expression measurements are sufficiently additive to be amenable to a linear decoder, and in some cases are sufficiently sparse to be compressed. When the experimental noise was sufficiently low and the expression profile was sparse, we have shown that poolMC can provide experimental compression.k spikes in a experiment of size n is approximately k log(n/k)  experiment size, (2) sparsity of gene expression, and 3) experimental noise. In this study, we carried out a small pilot study with only 12 multiplexed results compared to 15 monoplexed results. At this scale, multiplexing provides relatively few benefits in terms of both compression and robustness. As is shown in Figure  experimeThe second factor was the sparseness of the gene expression data. As is shown in Figure The third factor is experimental noise. Gene expression profiling is a well-standardized method with relatively little noise, however this study shows that even the technical noise present in gene expression profiling can cause artifacts in multiplexed results. The experimental design can be modified to increase the robustness of the predictions to noise, but at the cost of adding additional experiments.1 regression methods such as Lasso [This small experiment suggests that pooling can be carried out successfully within the theoretical guarantees provided by poolMC. In practice, however, there are a number of strong requirements of experiment size, sparseness, and assay noise that need to be considered to make pooling work well for gene expression profiling. Figure as Lasso .In this section, we provide the mathematical statements for the pooling design and decoding strategy that underlie poolMC. The practical implementation of these statements is provided in Additional file A) to a set of nodes on the right (B) with a set of edges (E). The connections are such that each left node has a diverse set of neighbors on the right resulting in high-connectivity. The high-connectivity is useful for pooling as it ensures that no two samples are pooled together often. The graph can be represented as a binary matrix with the left nodes as columns, the right nodes as rows, and the edges as entries of the matrix; a matrix entry of value 1 represents the presence of an edge between the corresponding column and row nodes. This matrix is used as the pooling design. The mathematical definition of an expander graph, provided in [An expander graph is a bipartite graph with high-connectivity. It connects a set of nodes on the left -unbalanced expander is a bipartite simple graph G- with left degree d such that for any X in A with |X| ≤ k, the set of neighbors N(X) of X has size |N(X)| ≥ (1 - ϵ) d|X|, where |·| represents the cardinality of a set.B| (rows of the pooling matrix), d (number of times each sample is pooled), and ϵ  as small as possible. Details of their construction are provided Additional file In constructing such graphs for smart pooling, the goal is to make the right set size |poolMC provides the following decoding guarantee (from ):m × n matrix of an unbalanced  expander. Consider any two vectors x, S is the set of k largest (in magnitude) coefficients of x, then,Let Φ be a x is the "true" expression profile which contains k spikes, then the decoding error, represented as sum of absolute values (l1 norm) of the error in each entry of x, is bounded by the l1 norm of the non-spikes in x  and is scaled by a constant that depends on the properties of the pooling design Φ.Here The two conditions of poolMC uses the MATLAB-based software package l1-magic  to solveRMK framed the original problem, developed the algorithms and software, analyzed the data, and drafted the manuscript. AB and JS designed the experiment. AB performed the experiment. ACG and PJW participated in algorithm development and data analysis. All authors read and approved the final manuscript.Monoplex data. Tab-delimited file containing the monoplex data. The file contains 21,505 rows corresponding to the genes and 15 columns of gene expression data (log-transformed) corresponding to the samples.Click here for fileMultiplex measurement. Tab-delimited file containing the multiplex measurement. The file contains 21,505 rows corresponding to the genes and 12 columns of gene expression data (log-transformed) corresponding to the pooled samples.Click here for fileSupplementary materials. An Adobe PDF file containing mathematical details and illustrative examples of the pooling and decoding strategies underlying poolMC. Also included are complete figures showing the comparison between monoplex, synthetic, and multiplex results for all samples used in the experiment.Click here for filepoolMC design. The MATLAB code implementing the expander graph based pooling design.Click here for filepoolMC decoder. The MATLAB code implementing the decoding procedure.Click here for fileSmart pooling example. The MATLAB code illustrating poolMC's pooling and decoding procedure.Click here for file