High-throughput transcriptomics has matured into a very well established and widely utilised research tool over the last two decades. Clinical datasets generated on a range of different platforms continue to be deposited in public repositories provide an ever-growing, valuable resource for reanalysis. Cost and tissue availability normally preclude processing samples across multiple technologies, making it challenging to directly evaluate performance and whether data from different platforms can be reliably compared or integrated.This study describes our experiences of nine new and established mRNA profiling techniques including Lexogen QuantSeq, Qiagen QiaSeq, BioSpyder TempO-Seq, Ion AmpliSeq, Nanostring, Affymetrix Clariom S or U133A, Illumina BeadChip and RNA-seq of formalin-fixed paraffin embedded (FFPE) and fresh frozen (FF) sequential patient-matched breast tumour samples.The number of genes represented and reliability varied between the platforms, but overall all methods provided data which were largely comparable. Crucially we found that it is possible to integrate data for combined analyses across FFPE/FF and platforms using established batch correction methods as required to increase cohort sizes. However, some platforms appear to be better suited to FFPE samples, particularly archival material.Overall, we illustrate that technology selection is a balance between required resolution, sample quality, availability and cost. Since their inception microarrays have been adopted as a major tool for the study of clinical samples to improve our understanding of diseases, development of molecular subtyping and prognostic signatures for clinical decision-making . A cruciRNA sequencing (RNAseq) has somewhat supplanted microarrays for transcriptome analysis. However, in translational research when the focus is often restricted to identifying differentially expressed genes and pathways, rather than detecting specific isoforms and splice variants, decisions on which platform to use are often based upon cost, rather than resolution, particularly if this means more samples can be examined to maximise statistical power for a fixed budget. Indeed, RNAseq is not without its limitations, Robert and Watson recently demonstrated that RNAseq is unable to accurately measure expression of hundreds of genes in the human genome .Many high-throughput profiling studies rely on sample availability and cost rather than statistical power . Direct Early microarray studies involving clinical samples were dependent on relatively large amounts of high quality RNA and thus relied heavily on the availability of fresh frozen (FF) tissue. However, collection and storage of FF tissue is costly and can be logistically prohibitive. Protocols and technologies capable of generating high quality whole-genome transcriptomic data from archival formalin fixed paraffin embedded (FFPE) tissues are in demand . FFPE tiAll patients gave informed consent and the study was approved by the local ethics committee . RNA was extracted from primary human breast cancer samples collected over 15 years at the Edinburgh Breast Unit from post-menopausal women with estrogen receptor positive disease, treated with 3-months of neoadjuvant endocrine therapy. Sequential biopsies were taken pre-treatment, early (14-days) on-treatment and at surgery 3–6 months later (late on-treatment) from each patient. Part of the biopsy material collected was snap-frozen in liquid nitrogen and part was fixed in formalin and embedded in paraffin. RNA was extracted from fresh frozen tissue using the Qiagen miRNeasy kit and from 2 × 20 μm FFPE tissue sections using the RNeasy FFPE kit using the manufacturer’s standard protocols for each kit. Agilent RIN values for fresh frozen tissue were > 7 and for FFPE tissue were < 3.www.ncbi.nlm.nih.gov/geo/) under super-series accession GSE130645.Building upon large scale clinical studies to investigate the effects of endocrine therapy on breast cancer using Affymetrix U133A arrays  and Illuhttps://www.bluebee.com) and read-trimming and alignment was performed using the QuantSeq plugin. TempoSeq FASTQ files were sent to BioCalvis (the manufacturer of BioSpyder), who performed the alignment and then generated the raw  gene counts file using their proprietary software. For RNAseq, alignment was performed using STAR74. Transcript abundance estimates for each sample were performed using Salmon, an expectation-maximization algorithm using the UCSC gene definitions. Raw read counts for all RNAseq samples were normalized to a fixed upper quartile.Illumina and Affymetrix data were pre-processed and normalised as described previously . NanoStrAll sequence data were aligned to the human reference hg19 genome. For all data, probes or genes were then mapped to Ensembl gene annotations: Affymetrix datasets were mapped using a chip definition file (CDF)  and all Each of the nine technologies evaluated here have different mRNA input requirements, probe designs Fig. a and proTo evaluate how newer technologies with desirable features such as lower costs or RNA input requirements compared to the more established methodologies, we profiled the same RNA from a subset of samples to directly compare gene expression measurements across the platforms . These comparisons have two purposes; firstly to determine whether the new technology provides similar quality results to the established method. Secondly, to evaluate whether it will be possible to directly integrate datasets generated on the new platform with existing local or publicly available data from another platform, as we have done previously , 4, 6. Ir = 0.4–0.6) were observed between pairs of samples processed on different technologies. Hierarchical clustering showed clearly that gene expression values group by technology and technical artefacts, rather than by genuine biology . However standard batch correction approaches such as ComBat [r > 0.9 for paired samples. Similar low correlations and improved correlations following batch correction were observed for different technologies with FFPE samples and for comparisons of matched FF and FFPE or for the same sample across different platforms (Fig. Clear batch effects were evident when comparing mRNA extracted from FF samples across Illumina HT12, Ion Ampliseq Transcriptome and Affymetrix Clariom S, with low Pearson correlations (s ComBat  minimiseLooking at the samples more closely by multi-dimensional scaling it is clear that whilst they cluster by platform before batch correction Fig. a, afterwFor further confirmation of the validity of the batch-corrected data, we ranked samples by expression of 42 proliferation genes, previously reported by us  that chaOverall we find that gene expression data from the newer technologies is largely concordant with that from the more established methods. The newer 3′ sequencing approaches from Lexogen and Qiagen appear highly reliable and cost effective for old FFPE samples, this potentially allows valuable data to be generated from clinical samples that would not have been previously possible. The TempO-Seq method  from BioA general finding of most platform comparison approaches is that although the correlation values between different microarray or sequencing approaches may be poor to moderate, which may relate to differences in dynamic range of the technologies, there is generally very high concordance when considering differentially expressed genes , 6, 21. This single study perhaps considers the widest range of gene expression technologies using FF and FFPE tissues published to date, but we acknowledge that this study documents a translational research group’s experiences, rather than being a definitive, comparison study. Not every sample was tested on every platform and some leading technologies remain to be tested, including Agilent, TaqMan and Fluidigm - due to local availability and opportunities.This study highlights the relative merits and limitations of a range of new and established gene expression profiling platforms and demonstrates that transcriptomic data from FFPE archival samples can be reliably integrated with data from FF samples, even if different measurement platforms are used. Ultimately, the choice of technology will depend upon the required resolution and coverage, throughput, sample quality, availability and budget.Additional file 1 : Table S1. Table demonstrating the directly overlapping samples across the nine gene expression platforms coloured by sample type, Pink = FFPE, yellow = fresh frozen.Additional file 2 : Table S2. List of the 42 proliferation-related genes showing reduction on endocrine treatment [reatment .


Batch effects are technical sources of variation introduced by the necessity of conducting gene expression analyses on different dates due to the large number of biological samples in population-based studies. The aim of this study is to evaluate the performances of linear mixed models (LMM) and Combat in batch effect removal. We also assessed the utility of adding quality control samples in the study design as technical replicates. In order to do so, we simulated gene expression data by adding “treatment” and batch effects to a real gene expression dataset. The performances of LMM and Combat, with and without quality control samples, are assessed in terms of sensitivity and specificity while correcting for the batch effect using a wide range of effect sizes, statistical noise, sample sizes and level of balanced/unbalanced designs. The simulations showed small differences among LMM and Combat. LMM identifies stronger relationships between big effect sizes and gene expression than Combat, while Combat identifies in general more true and false positives than LMM. However, these small differences can still be relevant depending on the research goal. When any of these methods are applied, quality control samples did not reduce the batch effect, showing no added value for including them in the study design. Various OMICS techniques are increasingly being used in human population studies to link dietary factors, environmental exposures or therapeutic use of medication to adverse health outcomes and related molecular changes in numerous tissues. Transcriptomics is the study of RNA transcripts produced by the genome using high-throughput technology. Gene expression analysis provides a snapshot of expressed genes and transcripts responding to environmental changes. Since external environmental conditions can change the expression profiling, transcriptomics has become an emerging and promising tool for biomarker discovery , 2.The expression level of mRNAs can be measured using microarray technology. The technique relies on a series of complex chemical reactions between large amounts of RNA molecules and reagents to convert the RNA to cDNA. The gene expression levels are measured by quantifying hybridized and labelled cDNA. The efficiency of these reactions is highly sensitive to experimental settings such as the physical and chemical conditions in which the experiment took place  and to compliance with the standardized experimental protocol , 4. In rIn mRNA microarray technology, the batch effects mainly originate from the isolation of the mRNA, dye labeling of the samples and hybridization onto the microarray , 11. In In order to facilitate the quantification of the  measurement error across batches, the inclusion of the same and characterized quality control (QC) sample in all batches is a powerful, but sometimes costly, approach.In the current study, we propose to investigate the relative performances of the two main approaches to correct for batch effects, linear mixed models correcting for batch as a random effect and Combat. Both approaches will also be investigated in conjunction with the use of QC samples to assess whether these technical replicates, used for calibration purposes, actually improve the models’ performances. For the sake of comparison we also included linear models correcting for batch as a fixed effect. This third approach is equivalent to the genewise one-way ANOVA adjustment performed by some methods like PAMR.We use existing microarray gene expression data from 251 blood samples of individuals that belong to the EXPOsOMICS project. Standard numerical summaries on the batch effect are estimated from the existing data and used to generate new data. Therefore, we simulate gene expression data using the existing expression data, these numerical summaries and added effect in order to be able to identify the true positives and negatives. We assess the performances of the main approaches to correct for batch effects in the simulated data and the potential added value of including QC samples. Furthermore, the simulated set of effects and batch effects that are introduced in the existing gene expression dataset follow different scenarios, allowing us to evaluate the impact of the effect size, sample size and additional random error. The aim of the simulation study is to assess (i) the impact of batch effect in terms of statistical performances (sensitivity and specificity), (ii) the absolute and relative ability of the proposed methods to improve the models performances in a linear regression context and (iii) the utility of the QCs.The study population is derived from the EU-funded research project EXPOsOMICS which aims to link environmental exposures with biomarkers of exposure, effect and disease. Gene expression levels were obtained from blood samples collected from subjects with asthma and their matched controls  value > 6 were selected for data analysis. All the QCs are the result of one blood withdrawal from one independent subject whose blood was divided into different tubes, mixed with RNAlater and stored using the same procedure as the study samples. Therefore, each of these QCs is a technical replicate. QCs also follow the same quality criteria as the study samples with respect to the 260/280 ratio and RIN values for the RNA isolation. Together with the study samples, two quality control samples (QCs) or technical replicates per batch of microarray hybridization were included in order to assess the potential variation of these QCs which in that case would be a result of the possible variation in the signal across batches.The original sample set consisted of 291 samples. However, 40 samples belonging to two batches are excluded due to poor quality of the QCs from those batches, resulting in 251 samples and 27 QCs included in the current study. The total number of batches is 14.2 transformation are applied using the quantile method (github.com/BiGCAT-UM/arrayQC_Module). We adopt two normalization approaches: (i) a two-step approach where independent normalizations of the data from each batch separately are performed followed by normalized data merge, and (ii) a single-step procedure where all samples across batches are normalized together.Normalization is performed using Bioconductor in R . Local bAfter normalization, genes with less than 30% flagged bad spots are selected, transcript replicates are merged by calculating their median and missing values are imputed using the k-nearest neighbors   for all In some of the analyses, normalization is complemented by applying a multiplicative correction factor to measured gene expression levels and therefore ensuring optimal consistency in measurements obtained across QC samples.i, measured in batch j, the correction factor QCij is defined by the following ratio:i across the k = 2 measurements (there are two QC samples per patch) in batch j, and the denominator the mean expression levels of gene i measured across all batches . The resulting set of calibration coefficients are subsequently applied to all gene expression measurements.In practice for a given gene Several analyses are performed for comparison purposes in both normalization per batch and merged normalization. For each normalization method, QC correction either is or is not performed. Batch effects are removed by using maximum likelihood for the linear regression methods and by the empirical Bayesian framework for Combat. The statistical methods that we use are linear mixed models (LMM) correcting for batch as a random effect, linear models (LM) correcting for batch as a fixed effect (LMBatch), LM without batch correction as a control and Combat. Thus, there are two methods of normalization, presence or absence of a QC correction and the four modelling methods for a total of 16 different statistical analyses .The variables sex and body-mas index (BMI) are used to find association between these variables (one at a time) and gene expression. In Combat a linear regression model using gene expression corrected for batch as the dependent variable is implemented.Principal components analysis (PCA) is used to identify batch effects by examining patterns in plots of the first two principal components.The statistical models  are fit 2 for the data before and after correcting for batch effect.We compute the association between the first five Principal Components (PCs) and the batch variable using RA Pearson correlation test (R package “stats”) is applied to the QC samples after batch effect removal to determine the level of agreement between the QC sample values. If the batch effect removal approach is successful, the QC sample values should be highly correlated.Examining a single dataset across multiple batch correction methods provides limited information for determining how methods compare. This is mainly due to not knowing if there is a batch effect and the actual magnitude of the true effect when it exists. In simulation experiments the random data is generated from a situation where the magnitude of the batch effect is known, which enables the appropriate evaluation of the correctness and suitability of the methods in handling the batch effect. The simulation experiment conducted here creates gene expression data for numerous individuals that are analyzed in batches with two QCs for each batch without the limitation of not knowing the true positives. A known set of effects or “treatment” effects and batch effects is then used to evaluate how well a method procedure corrects for batch while still identifying the treatment effects. Rather than generating random gene expression datasets from normal distributions , data is generated by resampling from a real gene expression dataset. The known set of effects and the batch effect are simulated at random and added to the expression values. The four models are then fit to each randomly-generated dataset. One-thousand (1000) random datasets are generated for each case in the simulation study.In mathematical terminology, the process is as follows:Ym defines the expression level for sample m, β0 the intercept of the model, X1 the variable of interest (the effect), β1 the regression coefficient associated with the variable of interest and εm the residual error.The general form of the linear models used in the analyses isAm, the batch effect variable from sample m (10). The rest of the parameters are as defined in the LM equation.The general form of the linear mixed model used in the analyses has an additional term:γij and δij represent the additive and multiplicative batch effects of gene i from batch j [The general form of the model used in the Combat analysis is: batch j . The resX to represent the array of gene expression responses X = {X1, X2, … Xw} where Xi is the individual response for a given gene, and define x as the realization of that variable from a specific sample, x = {x1, x2, … xw}. Define the variable associated with the gene expression data for the quality control as Q = {Q1, Q2, … Qw}, with q being a realization of Q from a given sample. There are 14 batches (b) in the dataset we are using to generate data, 251 subjects (m) and 27 QCs (n). The data for an individual gene for a specific person can then be characterized as xijl where i refers to the gene, j refers to the batch and l refers to the individual. Replacing I or j with a dot denotes an analysis is done over the entire subscript. For example, xij• would refer to the values for gene i across all individuals in batch j. Similarly, define qijk where k represents each QC in a batch .Define x = {xijl: i = 1, …w, j = 1, …b, and l = 1, …m} and q = {qijk: i = 1, …w, j = 1, …b, k = 1,2} denote the data from the control dataset. The data are first log-transformed and QC corrected. A mean QC value is then calculated across batches:Simulation parameters are calculated from the control dataset (EXPOsOMICS) using a deconvolution approach. So, For each gene in the dataset in each batch, a mean gene expression value is calculated after QC correction:qij• + τi••” in (7) is equivalent to the previous equation QC factorij in Eq . For each batch, a random batch effect rj = rnorm is generated, where rnorm is the function for generating normally distributed random numbers in R. The vector r = {rj, j = 1, …b} is a simulated realization of the batch effects for each batch.These estimated values are now used to generate new data. The batch correction values are assumed to come from a normal distribution with mean zero and standard deviationγ  or N. The additive effect introduced as treatment is generated as sijl = xijl + qij• − τi•• + rj + ti, where "+qij• − τi••" is the QC correction factor, rj is the randomly-generated batch effect and ti the additive change in the mean that is expected for gene i (note ti = 0 for i>500). The random treatment, which can be seen as a proxy for an effect of exposure, is generated as rnorm for i≤500 and 0 for i>500, with SD = {0.1, 0.2, 0.3, 0.4, 0.5, 1, 3} for the various simulations. These values are chosen to provide a range of treatment effects from very small to large. The same value is used afterwards as the variable of interest for the regression analysis. Alternatively, simulations without the term "+qij• − τi••" are implemented to compare results with and without QC correction.In order to simulate “treatment”, a variable is generated with one value per subject and gene. The simulation is implemented assuming that the treatment affected the first 500 genes with value Once a dataset is simulated, regression analyses are performed in order to identify genes that are significantly impacted by the treatment. P-values are estimated from the linear mixed models (LMM) approach, linear models (LM), linear models correcting for batch as a covariate (LMBatch) and Combat followed by linear models (LMcom). P-values are adjusted using the Benjamini and Hochberg method with threshold at 5%.The script is available at:https://github.com/alespre/Batch_Effect/blob/master/SImulation_batcheffect_QCs.RThe simulations are repeated using an independent experimental dataset  with a Finally, the area under the curve (AUC) is calculated for each simulation in order to quantify the overall performance of the different statistical methods to correct for batch effect (R package “pROC”).eijl) is added to the simulated expression data such that sijl = xijl + qij• − τi•• + rj + ti + eijl, where eijl = rnorm :ϵil• is defined as the residuals for gene i from subject l. Simulations are also run without the QCs correction(qij• = 0).Random error (eijl) are performed with a smaller dataset  in order to assess the influence of the population size. Additional simulations were performed to test the effect of the batch size by designing scenarios where the number of samples per batch increases by three . This test was run using 100 simulations with SD 0.1 and SD 0.5 as effect sizes in the approach with no quality control samples.The same simulations excluding error  in such a way that the first batch contained the highest values, the second batch the second highest, etc. The last batch contained the lowest values. Then, 20% of the samples are randomized, meaning that the variable “treatment” is perfectly sorted according to batch in 80% of the samples and randomly distributed in 20% of the samples.ANOVA tests are run in order to measure the relationship between batches and”treatment” values. The p-values after sorting the data were very close to zero, while before sorting they were non-significant . Additional simulations were used to test the effect of the association between batches and “treatment” by designing scenarios where a certain number of samples are randomly distributed and another number of samples are not randomly distributed. Using the sorted data as input, 10% of the samples were randomly distributed among batches, resulting in 90% of the “treatment” variable unbalanced and 10% randomly distributed. This approach was repeated for the next deciles . This test was run using 100 simulations with SD 0.5 as effect size in the approach with no quality control samples.PCA plots of the gene expression data from the four different approaches demonstrate a clear batch effect . The appPCA plots of the gene expression data from the different approaches after batch effect removal show that the methods efficiently removed the batch effect, even in the cases where batch had a significant impact in the PCA plots from 2 for the pre-processed data and the denoised data after removing batch effect using the three different statistical methods. The correlation values decrease toward zero after correcting for batch effect using any of the three methods, with or without QC correction.The correlation test might not be enough to capture some systematic changes in gene expression caused by batch since all genes contribute to the test (one test per pair of QCs). Some genes can be more susceptible to batch effects than others  and therγ parameter calculated from the real dataset). “Error” specifies if a random error was added on top of the previous simulated values. The next eight columns show the results of the simulation. Each statistical approach  is subdivided into two columns, where the number of expected genes and not expected genes is indicated. Thus, “TP” stands for true positive and “FP” for false positives. The values are calculated from the average of values found from the 1,000 simulations per approach.Very small differences between correcting and not correcting for QCs are observed except for the case of LM where no batch effect removal is applied in the statistical method.In the same way as in Again very small differences are observed between simulations with and without QC correction.Almost no differences are found between simulations with and without QC correction.The additional simulations using a varied sample size per batch were run in order to study the performance of the different methods showed a similar performance across methods, where Combat identified slightly more true positives while also identifies more false negatives  and their level of significance are extracted from the data analysis of whole-genome gene expression, including both simulated transcripts with added treatment or true positives and transcripts with non-added treatment or false positives. In addition, we evaluate the performance of the statistical methods in two population sizes and the impact of simulated random error, with and without QC correction. The three methods implemented in this study  correct efficiently for the introduced batch effects and show similar performance by identifying approximately the same numbers of true and false positives. Nevertheless, small differences in performance are observed depending on the effect size, noise and population size Tables  Tables. LMM and LM show similar results in our study. LMM is based on the maximum likelihood (ML) and restricted maximum likelihood (REML) methods whereas LM uses the analysis of variance (ANOVA) method. Therefore, LM generates optimal estimators only for balanced designs while LMM generates them for both balanced and unbalanced designs. The need to account for non-independence responses that derive from having different responses by the same batch and therefore adjusting for the covariance structure may differ for each specific dataset. Even though LMM does not outperform LM in this analysis, LMM is in principle able to correct for batch effects in a potentially more efficient way than LM in real datasets in view of its enhanced performance on unbalanced designs.Although the number of true and false positives from the three statistical methods did not differ to a large extent, the interpretation of the magnitude of the differences between them may depend on the goal of the research studies. For instance, in the development of diagnostic biomarkers, identification of genes for further validation requires high levels of confidence in order to prevent misclassifications which potentially have serious consequences to individuals. On the other hand, the impact of false negatives in a biomarker signature for risk assessment purposes has relatively limited consequences, as the biomarker profile may still identify potential relationships between environmental exposures and biological signals. In general, a stronger identification of true positives using LMM compared with Combat for big effect sizes is observed in this study. Combat identifies in general more true and false positives for small effect sizes, except for the case of added random error where the performance of the different methods is very similar; particularly in cases where there are small sample sizes, Combat shows smaller true positive/false positive rates compared to LMM. An increase of 0.5 true positives in Combat compared to LMM is observed while the increase of false positives is 10, implying a large occurrence of false positives compared to the identification of true positives when batch correction is applied using the Combat method.On top of the batch effect removal methods available , some study designs include QCs  for additional batch correction. The variation of signals across batches can be controlled by placing these QCs across the batches and calculating a correction factor  that is applied to the rest of the samples from the same batch. However, this can be costly (e.g. the inclusion of two QCs per batch of 24 samples increases the budget of the array experiment by up to 8%). Our study shows no significant differences in the number of true and false positives between approaches with or without QCs. Therefore, if the inclusion of these additional samples goes at the expense of the number of samples from the actual study population it results in a reduction of the statistical power.In this study we evaluate the impact of population size, random measurement error, effect size and level of balanced/unbalanced designs on the number of true and false positives from the different statistical methods. In most epidemiological studies, focusing on the link between environmental exposures and gene expression, the magnitude of the associations to be discovered is relatively small. The participants are usually exposed to relatively low doses of environmental factors and therefore the effect sizes are generally rather modest. In addition, sample size is often a limitation in epidemiological studies due to budget restrictions. These two issues increase the likelihood of having noise  mask the potential relationships of interest. The simulations of this study that included low effect size (low SD), added noise and/or decreased population size is expected to generally mimic real environmental studies examples.The sample size in epidemiological studies should be big enough for the effect of an expected magnitude to become statistically significant. In our study, batch, treatment and error are simulated assuming a Gaussian distribution but this assumption was not implied in the gene expression dataset. This flexibility in the normality assumption leaves open the possibility of assessing these procedures with other types of datasets such as sequencing data. Sources of batch effects from RNA sequencing technology include variability in day-to-day isolation of the mRNA and library preparation, sequencing runs and between different lanes on the flow-cell . In addiIn conclusion, this study shows a comparison of the performance of the most commonly available methods for batch effect removal, LMM correcting for batch as a random effect, LM correcting for batch as a fixed effect and Combat. Small differences among the methods are observed. LMM and LM correcting for batch provide a slightly safer option than Combat by identifying stronger relationships between big effect size and gene expression and better true/false positive rates for small effect size. The study also shows no improvement in the batch correction by adding QCs in the study design when any of the mentioned statistical methods are applied to correct for batch effect.S1 FigPCA plots after batch effect removal using each of the three methods with and without QCs and using batch normalization (A) or merged normalization (B).(DOCX)Click here for additional data file.S2 FigHierarchical clustering of the QCs using batch normalization (A) or merged normalization (B).(DOCX)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 TableNumber of hits from the variables of interest (sex and BMI) in the EXPOsOMICS dataset after Bonferroni correction (cutoff 0.05) for each of the four models with and without QCs and using batch normalization (A) or merged normalization (B).(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S6 TableA) Number of TP and FP found in the different simulations and B) Mean of the FDR values from the TP and FP found in the different simulations.(DOCX)Click here for additional data file.S7 Table(DOCX)Click here for additional data file.S8 Table(DOCX)Click here for additional data file.S9 TableEffect of varied sample size per batch with effect size SD 0.1 A) Number of TP and FP found in the different simulations using different sizes per batch (first column). B) Mean of the FDR values from the TP and FP from the same simulations as A).(DOCX)Click here for additional data file.S10 TableEffect of varied sample size per batch with effect size SD 0.5 A) Number of TP and FP found in the different simulations using different sizes per batch (first column). B) Mean of the FDR values from the TP and FP from the same simulations as A).(DOCX)Click here for additional data file.S11 Table(DOCX)Click here for additional data file.S12 TableA) Number of TP and FP found in the different simulations for the different degrees of association between the “treatment” variable and the batch. B) Mean of the FDR values from the TP and FP found in the different simulations for the different degrees of association between the “treatment” variable and the batch.(DOCX)Click here for additional data file.


The SILVER-seq technology enables sequencing extracellular RNAs (exRNAs) from a single droplet of liquid biopsy. This study revealed strong associations between serum exRNA expression levels and the donor’s sex and age. SILVER-seq detected serum exRNAs from the genes that are only expressed in brain, suggesting the possibility of monitoring brain gene expression from a blood test. Classifiers based on exRNA expression levels were able to separate breast cancer patients from control donors. The exRNA-based classifiers could also distinguish the patients with recurrent cancer from other breast cancer patients. The SILVER-seq technology can therefore lead the way to future in vitro diagnostics trials based on finger prick blood, which is more accessible for screening and frequent monitoring of human diseases. Extracellular RNAs (exRNAs) are present in human serum. It remains unclear to what extent these circulating exRNAs may reflect human physiologic and disease states. Here, we developed SILVER-seq  to efficiently sequence both integral and fragmented exRNAs from a small droplet (5  Liquid biopsy is a rapidly expanding class of in vitro diagnostics (IVD) due to its accessibility . Nearly A variety of exRNAs have been detected in human plasma and serum , 5. Smalts study , we asseSI Appendix, Fig. S1A). Most detected exRNA are within the size range of 20 nucleotides (nt) to 200 nt . These data suggest that the exRNA concentrations are approximately several nanograms per milliliter and are either small RNAs or fragmented long RNAs in human serum.We started by measuring the range of concentrations and sizes of exRNA in human serum. To this end, we analyzed 10 serum samples. To account for technical variability, we purified exRNA with 4 different RNA purification kits, including exoRNeasy, TRIzol LS, NORGEN, and QIAzol, and subsequently quantified them with a bioanalyzer. The measured exRNA concentrations ranged from 0.3 ng/mL to 4.2 ng/mL in these serum samples .We developed the SILVER-seq technique for exRNA sequencing, by adapting the major steps of single-cell RNA sequencing that also dealt with a small amount of input materials , 10. UnlTo test whether SILVER-seq could reliably produce sequencing libraries from microliters of human serum, we split a serum sample into 8 aliquots, with the volumes of 3, 5, 6, and 7 o 300 bp A. This so 300 bp B. These SI Appendix, Fig. S3A) with more detected exRNAs . These data from the 2 additional donors reinforced the idea that SILVER-seq can produce sequencing libraries from microliters of input serum, and suggest 5 To evaluate the impact of input volume on the quality of the sequencing library, we used the sequence mapping rate and the number of mapped exRNAs as 2 metrics to reflect the quality of a sequencing library. While 5 d exRNAs B and C. We compared the exRNA expression profiles obtained using SILVER-seq with those obtained using standard RNA-seq methods. The expected amount of exRNA in 5 gle cell . Given tgle cell , we did SI Appendix, Fig. S4 A and B and Table S1 A and B). A gene detected by either RNA-seq-1 or RNA-seq-2 has a 4.5-fold increase of odds to be detected by SILVER-seq  . Furthermore, a gene detected by both RNA-seq-1 and RNA-seq-2 has a 6.9-fold increase of odds to be detected by SILVER-seq . Therefore, exRNAs detected by standard RNA-seq are more likely to be detected by SILVER-seq than those undetectable by the standard RNA-seq. Furthermore, the exRNAs detected by both standard RNA-seq assays are even more likely to be detected by SILVER-seq.We examined the overlaps of detected exRNAs between 2 experiments. To establish the exRNAs that can be detected by 2 standard RNA-seq experiments, we purified and sequenced RNA from 2 serum samples from the same donor (RNA-seq-1 and RNA-seq-2), which detected 2,379 and 4,500 exRNAs, respectively, with 563 exRNAs in the intersection. Next, we applied SILVER-seq to 7 SI Appendix, Fig. S4C). In comparison, the Pearson correlation was 0.67 between RNA-seq-1 and SILVER-seq, and 0.84 between RNA-seq-2 and SILVER-seq . Thus, the correlation of the measured expression levels between SILVER-seq and a standard RNA-seq was comparable to the correlation between 2 standard RNA-seq methods.Next, we compared the measured exRNA expression levels. As a reference, Pearson correlation between the exRNA expression levels derived from RNA-seq-1 and RNA-seq-2 was 0.68 . An exRNA detected by either SILVER-seq assay exhibited a 6.4- and 5.5-fold increased odds of being detected by standard RNA-seq . An exRNA detected by both SILVER-seq assays exhibited a 6.2-fold increased odds for being detected by standard RNA-seq . In this test, the correlation between 2 SILVER-seq assays was comparable to the correlation between a SILVER-seq and a standard RNA-seq.Next, we compared the measured exRNA expression levels. The Pearson correlation was 0.66 between the 2 SILVER-seq assays, and 0.64 and 0.85 between SILVER-seq and each standard RNA-seq assay . As we sequentially combined SILVER-seq data of additional aliquots, these numbers increased and plateaued at ∼41,000 genes, which is 67.6% of the annotated coding and noncoding genes of the human genome (hg38). These data suggest that not all genes gave rise to exRNAs in serum. Each SILVER-seq based on 7 SI Appendix, Fig. S5).We tested whether the number of detected exRNAs will increase as we combine SILVER-seq data of serum aliquots from the same donor. To this end, we analyzed 2 donors and prepared 15 serum aliquots from each donor. We carried out SILVER-seq from every aliquot. The SILVER-seq of the first aliquot of each donor was mapped to approximately 30,000 genes (Upper). Furthermore, the expression levels as measured by transcripts per million (TPM) were not concentrated near 0 (Lower). Instead, the exRNA abundances (TPM) of tissue-specific genes exhibited unimodal distributions with positive modes , and bone marrow, respectively , 15. WitSI Appendix, Fig. S1), which raises the question of whether different parts of a long RNA had equal chances of being detected as exRNA. We used the KRAS oncogene as a test case for this question. In the 128 serum samples in this study , a total of 6,864 reads were uniquely mapped to KRAS, in which 5,576 reads (81.2%) were derived from the fourth exon  . Next, we checked whether the abundance of Exon 4-derived exRNA was driven by a small number of serum samples. The Exon 4-derived exRNA was detected in the majority (78.1%) of the samples, whereas no other fragments of the KRAS were detected in more than 1/3 of the samples . We plotted the normalized numbers of uniquely mapped SILVER-seq reads to the sex chromosomes of every serum sample , including mRNA- and noncoding RNA-derived exRNAs , as well as hematological, metabolic, and cardiovascular disorders [Benjamini-adjusted P value = 0.10; disease class enrichment analysis by DAVID . The exRNAs with the strongest negative correlations with age included PRRG3, a vitamin K-dependent transmembrane protein, YBX1, a ribonucleoprotein (RNP) involved in microRNA processing and mRNA splicing, and FSTL3, a secreted glycoprotein that binds and inhibits Activin A and BMP2 signals . These top-ranked age-correlated exRNAs were derived from the mRNAs of secreted or transmembrane proteins that conjugate, bind, or modify glycans. Indeed, glycans have been nominated as a biomarker of biological age . Taken together, exRNA age is predictive of chronological age. The correlation of SILVER-seq data and human physiology provided a baseline for us to move on to testing SILVER-seq’s predictive power to disease status.We built a regression model using exRNA expression levels as covariates and age as the outcome. Hereafter, we denote the exRNA predicted age by this regression as exRNA age. The exRNA age exhibited a Pearson correlation of 0.986 with chronological age D. ApproxSI Appendix, Table S4) and 32 serum samples from other donors who did not have self-reported disease  . TPM were calculated for each exRNA and used as the surrogate metric for the expression level of the exRNA. The distributions of TPM exhibited little difference between any 2 cancer samples or between a cancer sample and a normal sample . Thus, every sample contains a similar proportion of highly expressed exRNAs, regardless of the threshold for calling highly expressed exRNAs.We tested whether the overall distributions of exRNAs were different between cancer and normal sera. Our SILVER-seq datasets included 96 serum samples of breast cancer patients (cancer samples) . The upregulation of calcium-dependent protein kinase CAMK2A likely reflects perturbed calcium homeostasis, a hallmark of cancer  for every exRNA B. Regardf cancer . The canf cancer F and G. SI Appendix, Fig. S11A). Cancer and noncancer samples were not distinguishable by the first principal component (PC1), but they exhibited some extent of separation on the second principal component (PC2) . These data suggest not only large sample-to-sample variations, but also the possible separation of cancer and noncancer samples by some subspaces (subsets of genes). This global feature is not sensitive to the number of exRNAs used for PCA analysis .We asked whether different types of RNAs exhibit the same power of differentiating cancer and normal samples. To establish a baseline, we did a principal component analysis (PCA) using exRNAs of all known genes  . The second class exhibited some differentiation capability in PC2 but not in PC1 . This class, which included protein-coding transcripts, processed pseudogenes, lincRNAs, and others, reflects the baseline  in that, although cancer-normal differences contributed to explain sample difference, it was not the major contributor to sample variations (PC1). The third class was able to differentiate cancer and normal samples in both PC1 and PC2. This class included miRNA, mitochondrial transfer RNA (Mt_tRNA), ribosomal RNA (rRNA), and other noncoding RNA (misc_RNA). With the third class, the major contributor to sample variation is the cancer/noncancer status. Taken together, cancer and noncancer samples are well separated in some subspaces, including the subspaces defined by the miRNAs and Mt_tRNAs.We proceeded to test whether the degrees of cancer-normal separation are similar across different types of RNAs. To this end, we did a PCA analysis with each type of RNA. Three classes of RNA types emerged based on the capacity of their principal components to explain cancer-normal differences. The first class failed to separate cancer and normal samples by either PC1 or PC2  and SVM . The different RNA types exhibited different classification performances. Several transcript categories, including small Cajal body-specific RNA (scaRNA) and polymorphic pseudogene, failed to classify cancer and normal samples . On the other hand, using lincRNA and miRNA as feature sets improved classification performances  into protein coding, pseudogene, and noncoding genes, which were further categorized into 17 subtypes, including antisense, lincRNA, miRNA, and small nuclear RNA (snRNA) . We usedormances J and K. ormances K. These We tested whether there is any difference in exRNA expression that may correspond to cancer recurrence. The 96 analyzed serum samples were collected from breast cancer patients during a 5-y follow-up starting from their chemotherapy start date. Among them, 28 and 68 samples were collected from patients who developed and did not develop recurring cancer, respectively, in the 5-y follow-up; these 2 groups of serum samples will be referred to as recurrence and nonrecurrence samples, respectively. No exRNA was called as differentially expressed at the significance level of FDR = 0.1, suggesting that the difference between recurrence and nonrecurrence samples is more obscure than the difference between cancer and normal serum samples. Nevertheless, based on 2,230 exRNAs that exhibited fold changes of 2 or greater, recurrence and nonrecurrence samples could be accurately classified .SI Appendix, Figs. S15 and S16), consistent with the idea that recurrence and nonrecurrence samples were less separated than cancer and normal samples. Nevertheless, classifications based on several RNA biotypes, including unprocessed pseudogene . This mixed group of genes were associated with breast cancer in many ways, including genotype–disease association and the associations of gene expression in cancer biopsy and cancer subtypes, grades, or prognoses. When this mixed bag of literature-derived genes was used as the feature set, an SVM classifier was able to better distinguish recurrence and nonrecurrence samples (average AUC = 0.720) than using the genes of any biotype (maximum average AUC = 0.696) . These data suggest that the exRNA expression of the prior-association genes was correlated with recurrence status, even though the prior association was not discovered using exRNA expression.Next, we compiled a list of 750 genes that were associated with breast cancer by prior literature (prior-association genes)  serum as input, large measurement variations of exRNA from sequencing have been reported , 25. DesWe suspect that the biological variation in exRNA content between 2 serum droplets is primarily attributable to the different composition of exRNA carriers. These carriers include extracellular vesicles (EVs), RNPs, lipoprotein complexes , nonmembMost previous analyses focused on small RNAs , 22, 25.We hypothesized exRNA in serum reflects differences based on a donor’s age and sex. However, a recent analysis reported a counterintuitive observation that the sex-associated exRNAs in human serum were not expressed from the sex chromosomes . There iThis study only analyzed donors between 18 y and 48 y old. The identified age-associated exRNAs are probably specific to this age group and cannot be extrapolated to older ages. For example, the genes involved in substance dependence and psychological disorders C are pri27A common practice to avoid overfitting is to subject the biomarkers developed from one patient cohort to validation in another cohort. However, there is only one cohort in this study. To minimize overfitting in this scenario, we did not use the common practice of using differentially expressed exRNAs as features for classification. Instead, we used the entire list of genes of each gene category  as a feature set to classification. This approach tested whether the exRNAs of each gene category as a whole contain any information on the disease status. Interestingly, miRNAs and Mt_tRNAs exhibited the largest differentiating powers to classify breast cancer patients and normal donors. These data expanded the previously reported clinical variables that correlate with serum/plasma miRNAs , 7. ThesSI Appendix, Table S4). The top 100 exRNAs that were most correlated with subtype differences  included ODC1 . Thus, exRNA expression may reflect the differences between different subtypes of breast cancer. However, the small number of samples in each subtype is insufficient to assess the significance of such correlations.Breast cancers include several molecular subtypes. This study included 10, 48, 12, and 26 samples from Her2-enriched, luminal A or normal-like, luminal B, and triple-negative subtypes, respectively . Thus, this study cannot separate chemotherapy-induced changes from cancer-induced changes. However, the consistent upregulation of RAC2 and KRAS exRNAs in serum and mRNAs in tissue in breast cancer patients as compared to normal donors, together with the known roles of these 2 members of the Ras proto-oncogene superfamily in breast cancer etiology, suggest that a subset of the observed serum exRNA expression changes relate to the disease rather than the treatments. Future studies that control for treatment status and cancer subtypes are needed, preferably as double-blind prospective trials.This study did not rule out all possible confounding factors that may contribute the separation of cancer and normal samples. Most of the serum samples from cancer patients were collected during or after chemotherapy . RNA of each sample was purified by one of the 3 kits, namely, exoRNeasy Serum/Plasma Midi Kit (QIAGEN), TRIzol LS Reagent (Invitrogen), or Plasma/Serum RNA Purification Kit (NORGEN). The RNA extracted with the NORGEN kit was treated with RNase-Free DNase I (QIAGEN) and RNeasy MinElute Cleanup Kit (QIAGEN) according to manufacturer’s instruction. Another serum sample of 200-SI Appendix, Fig. S1). RNA from this sample was purified with the QIAzol (QIAGEN) kit. Extracted RNA was stored at −80 °C until use. RNA sizes were analyzed by the bioanalyzer RNA pico chip (Agilent).A total of 9 serum samples of 1-mL volume were analyzed  (https://www.genemo.com/technology/silver-seq). The resulting material from the previous step was incubated with a mix of random hexamer and oligo-dT primers at 70 °C for 2 min, and incubated with temperature-sensitive double-strand DNase (HL-dsDNase) at 37 °C for 10 min, then at 65 °C for 5 min for enzyme deactivation, and subsequently incubated with reverse transcriptase at 25 °C for 5 min followed by 40 °C for 30 min and 70 °C for 10 min. The resulting material was incubated with DNA polymerase and template-switching oligo at 25 °C for 15 min, at 37 °C for 15 min, and then 70 °C for 10 min and subjected to end repair, adaptor ligation, size selection, amplification, and rRNA sequence depletion (https://www.genemo.com/technology/silver-seq). The product library was quantified with Qubit (Invitrogen), and measured by Bioanalyzer (Agilent) for size distribution.The starting volume of each serum sample was between 3 STAR  was used to align SILVER-seq and RNA-seq reads to the reference genome (hg38). Uniquely aligned reads were used together with the gene annotation file (Hg38/Ensembl) as input files to HTSeq-count (version 0.9.1) to count the number of reads per gene, which was subsequently transformed in TPM.P values were provided to the R package {qvalue} to calculate q values. The chronological age and the top 500 exRNAs with the largest Pearson correlation with age were given to the R package {glmnet} to fit a linear regression with elastic net regularization.F test was used to test the correlation of the TPM of every exRNA with chronological age. The F test-derived An exRNA is called detected in a sample at the threshold of TPM > 5. The frequency of detecting an exRNA among the samples was calculated as the proportion of samples in which this exRNA was detected.The gene categories as defined by Ensembl were used in PCA and classification analyses. Ensembl categorized genes by their RNA types, also called RNA biotypes. A total of 23 gene categories contained at least 10 genes per category, which included protein coding, lincRNA, miRNA, snRNA, and other biotypes.Classification of cancer samples including both recurrence and nonrecurrence samples and noncancer samples was carried out with both random forest and linear kernel SVM using R package {mlr} . Each feSI Appendix, Table S5) as a feature set.Classification of recurrence and nonrecurrence cancer samples were carried out using the same procedure as that used for classification of cancer and noncancer samples. In addition, all analyses were repeated using the prior-association genes (Supplementary File


The Androgen Receptor (AR) is the key-driving transcription factor in prostate cancer, tightly controlled by epigenetic regulation. To date, most epigenetic profiling has been performed in cell lines or limited tissue samples. Here, to comprehensively study the epigenetic landscape, we perform RNA-seq with ChIP-seq for AR and histone modification marks  in 100 primary prostate carcinomas. Integrative molecular subtyping of the five data streams revealed three major subtypes of which two were clearly TMPRSS2-ERG dictated. Importantly, we identify a third subtype with low chromatin binding and activity of AR, but with high activity of FGF and WNT signaling. While positive for neuroendocrine-hallmark genes, these tumors were copy number-neutral with low mutational burden, significantly depleted for genes characteristic of poor-outcome associated luminal B-subtype. We present a unique resource on transcriptional and epigenetic control in prostate cancer, revealing tight control of gene regulation differentially dictated by AR over three subtypes. The Androgen Receptor (AR) is the main driver of prostate cancer and functions in conjunction with chromatin modifications. Here, the authors comprehensively profile 100 primary prostate carcinomas by sequencing RNA transcripts in combination with ChIP-sequencing for AR and the active histone marks H3K27ac, H3K4me3 and repressive mark H3K27me3. Most men present with organ-confined prostate cancer which can potentially be cured through local therapy such as radical prostatectomy, radiation therapy, and/or brachytherapy2. However, approximately one-third of these patients will experience a rise in serum prostate-specific antigen (PSA), indicating cancer relapse which is referred to as biochemical recurrence3. Furthering our understanding of molecular alterations in primary prostate cancer might be helpful in determining why some patients develop a recurrence while others do not. To date, studies on primary prostate cancer have provided insight into the drivers of the disease, mostly focusing on Androgen Receptor (AR) function, mRNA expression, DNA copy number and mutations, as well as deviations in protein expression10. However, characterization of the epigenome in prostate cancer tissue is less well explored.Prostate cancer is the second most common cancer in men worldwide, with over 1 million newly diagnosed cases each yearERG, ETV1, and FLI1) with 5′ regions of androgen-responsive genes12. In particular, the TMPRSS2-ERG gene fusion is found in ~50% of the tumors, leading to overexpression of the oncogene ERG. Recently, it was shown that TMPRSS2-ERG fusion-positive tumors have a distinct H3K27ac chromatin landscape, facilitating ERG binding and recruitment of master transcription factors such as AR, FOXA1, and HOXB13 affecting the transcriptional program13. This reveals how genetic and epigenetic changes co-participate in regulating gene expression in TMPRSS2-ERG fusion-positive tumors. In addition to tumors with ETS fusions, the Cancer Genome Atlas (TCGA) network further classified primary prostate tumors in three additional molecular subtypes on the basis of mutations in SPOP, FOXA1, and IDH1. Both SPOP and FOXA1 are key regulators of the AR, and tumors carrying mutations in SPOP and FOXA1 possess enhanced AR transcriptional activity. However, the clinical significance of these mutations has not been thoroughly investigated. Others used gene sets associated with luminal and basal cell features to classify prostate cancers on outcome15. In the latter study14, it was shown that luminal B classified tumors have the poorest clinical outcome but a better response to postoperative androgen deprivation therapy (ADT) than non-luminal B tumors.The most common genetic alteration in prostate adenocarcinomas is the fusion of members of the ETS transcription factor family , AR ChIP-seq (n = 88), H3K27ac ChIP-seq (n = 92), H3K4me3 ChIP-seq (n = 56), and H3K27me3 ChIP-seq (n = 76). For 42 samples, data was available for all genomic datastreams 19 and H3K27ac ChIP-seq (n = 19)13 on primary prostate cancers to compare normalized strand cross-correlation (NSC) and relative strand cross-correlation (RSC) values. Overall similar RSC and NSC values were observed among the datasets, with all NSC values from the ChIP-seq samples higher than input samples . For AR binding sites, DNA sequence motif analysis revealed, as expected, androgen response elements as well as forkhead motifs , while no differences in Gleason score were observed for all ChIP-seq based clusters. Samples were further classified into luminal and basal-like subtype using the PAM50 classifier, which was recently applied successfully in prostate cancer14 . However, the PAM50 subtypes distributed almost evenly between all clusters in the ChIP-seq datasets. Next, we analyzed AR pathway activation within the tumors using a previously reported AR activity signature24, which was also implemented in the TCGA report on primary prostate tumors8. The AR activity score was highly variable among tumors (range: −23.5 to 19.0). Interestingly, AR activity score is associated with RNA-seq, H3K4me3- as well as H3K27me3-derived clustering   and 1 tumor with an ETV5 fusion transcript (1%). Transcriptome-wide analysis between low and high ERG expressing tumors revealed differential expression of previously identified ERG target genes  Fig. . Given tIndividual ChIP-seq data clustering shows similarities to transcriptomic clustering Fig. , which lk = 2 up to k = 5. Consensus scores for different k’s suggested robust assignment of the samples into two or three clusters , Cl2 (n = 28), Cl3 (n = 29) . This computational approach allows for the use of incomplete data, so that all samples (including those for which one or more datastream are missing) could be included in the analyses. To find the number of clusters that provide a stable solution, we applied MIV-NMF multiple times with random initialization using varying number of clusters from ers Fig. . For k =29) Fig. .Fig. 4InWe assessed the contribution of each data type in integrative clustering analysis by comparing consensus factor matrix to individual data factor matrix derived from MIV-NMF analysis. RNA-seq data achieved the highest Pearson’s correlation coefficient of 0.63, while H3K4me3 contributed least (Pearson’s correlation coefficient = 0.11) Fig. . The intTo explore reproducibility of the three prostate cancer subtypes, we downloaded gene expression data for 497 primary prostate cancers from the TCGA database. The top 100 most differentially expressed genes in each of the three clusters in Porto data . As expected from individual data cluster analysis, the three clusters were strongly associated with ERG expression status in both cohorts . Tumors in cluster 1 are characterized by high ERG expression, while tumors in cluster 2 express low ERG levels. Interestingly, ERG levels were not the sole-driving factor, as cluster 3 contains both tumors with low and high ERG expression level. Assessment of PAM50 subtype classification for the three newly-identified clusters in both cohorts, revealed that luminal B tumors were less frequently classified into cluster 3, while basal-like tumors were less often grouped into cluster 1. Also, AR activity score was associated with the three clusters , revealing high AR activity score in cluster 2 and a low score in cluster 3. Though AR mRNA expression levels were slightly lower in cluster 3  classifier26. Intriguingly, tumors in clusters 1 and 2 scored low on NEPC-likeness, while cluster 3 tumors showed a more NEPC-like expression profile  are not represented in the neuroendocrine gene expression signature, these findings would be an independent validation of NEPC-status in these tumors. However, most cases have less than 1% of cells expressing chromogranin and/or synaptophysin  and 17p (TP53), previously shown to be associated with TMPRSS2-ERG fusions5.We next sought to relate the three clusters to copy number alterations. In the Porto cohort, we determined copy number alterations by using CopywriteRain Fig. . Notablyain Fig. . The samain Fig. , but thiain Fig. . As clusSPOP, KMT2D, FOXA1, CSDM3, KDM6A, FBN3, and NOTCH2 are enriched in cluster 2. Among the mutated genes in cluster 2, FOXA1 and SPOP mutations have been previously shown to be mutually exclusive with ERG fusions possessing high AR activity29. Importantly, none of the mutations are enriched in cluster 3.The samples in the TCGA cohort have been previously characterized for mutations in coding regions. A low mutational burden was observed for tumors classified in cluster 3 as compared to tumors in clusters 1 and 2 Fig. . Mutatio30. HotNet2 identified eight large networks including those indicating activated fibroblast growth factor (FGF) signaling, WNT signaling, and nerve growth factor (NGF) signaling  between the Porto and TCGA cohort correlated well. In cluster 1, comprising ERG fusion-positive tumors, only one gene set related to ERG fusion passed FDR threshold < 0.2 Fig. . In clusCumulatively, these data indicate that by integrative analyses, we revealed a subclass of prostate tumors, hallmarked by low AR activity, neuroendocrine-like gene expression, minimal copy number alterations, and few mutations.Here, we present a pioneering report on transcriptomics- and epigenetics-based subtyping of prostate cancer. As AR is the key-driving transcription factor in prostate cancer, tightly controlled by epigenetic regulation, integrating these datastreams has the potency to reveal distinct biological features for a tumor type classically-known as difficult to classify.AR transcriptional regulation requires a permissive epigenetic environment, but the interdependent relationships thereof in clinical samples remained thus far elusive. We show here that unsupervised patient classification based on genome-wide profiles for AR, H3K27ac, and H3K27me3 (but not H3K4me3) groups patients in strong accordance to RNA-seq-based tumor profiling.17 as well as between primary prostate cancer and castration resistant prostate cancer (CRPC)18, which successfully stratified patients on outcome. As no differences are observed in AR binding profiles between cases and controls in this study, we conclude that AR reprogramming is associated with tumor onset and disease progression, but no distinct AR profiles are found in primary disease that bear prognostic potential. In order to identify differences in the location of transcription factor binding sites and epigenetic marks between samples groups, genomic regions-of-interest need to be defined. In selecting high-confidence peaks that were reliably identified in multiple samples, we selected for each datatype its own cut-off in overlap between samples , future studies should be directed in finding the optimal selection criteria while appreciating inter-tumor heterogeneity.Previously, we and others have revealed distinct AR chromatin binding profiles between healthy tissue and primary prostate cancerTMRPSS2-ERG gene fusion is found in ~50% of all primary prostate cancer samples, and provided the most-potent patient stratification based on transcriptomic, AR cistromic, and epigenetic classification. This is in accordance with a recently reported smaller study using H3K27ac ChIP-seq data from 19 samples13. We confirm these findings in a far-larger dataset, and illustrate that AR and H3K27me3 show comparable profiles, indicating that TMPRSS2-ERG fusion may have far larger consequences beyond enhancer usage, dictating AR cistromic profiles along with genomic selectivity of polycomb-mediated gene suppression.The TMPRSS2-ERG status still was a major discriminating feature in the integrative clustering analyses, we identified a third thus far unknown prostate cancer subtype that contained both high and low ERG expression samples. This subtype is hallmarked by benign features, with low AR chromatin binding and limited activity, although the receptor was readily expressed. Furthermore, this subtype is copy number-neutral with low mutation burden. Based on PAM50 classification, this subtype appeared selectively depleted from luminal B tumors, which were previously reported to be associated with poor outcome14.Integrative clustering, combining RNA-seq data with ChIP-seq for AR and the three histone modifications  revealed three distinct prostate cancer subtypes. While In clinical practice, PSA level is used as a biomarker for AR action. However, even though the tumors of cluster 3 had levels of both AR and PSA that were comparable to the other subtypes, AR activity score and AR chromatin binding were low in these samples. These data suggest not only that conventional biomarkers would support an incorrect conclusion in this setting, but also indicate that the tumors in cluster 3 may be potentially dictated by other tumor-promoting cascades than AR, such as FGF, WNT, and NGF pathways. Small molecule inhibitors targeting FGF have been developed, future clinical trials on FGF inhibitors may benefit from pre-selective patients from this cluster.Interestingly, luminal B tumors—depleted in cluster 3—have been previously reported to have a better response to ADT as compared to non-luminal B tumors. Consequently, ADT may be least-effective in this specific patient subpopulation. In contrast, tumors in cluster 2 showed high AR activity score, indicating that these patients may derive benefit from ADT. However, RNA-seq data from a large cohort of patients receiving adjuvant ADT is required to test this hypothesis.With this, we reveal that by integrating transcriptomic, epigenetic, and AR cistromic datastreams, previously unknown prostate cancer subtypes can be found with distinct biological and clinical features. Furthermore, these data will provide an invaluable resource for the community, presenting the largest prostate cancer dataset to date containing matched genomic, transcriptomic, and epigenetic datastreams. Future analyses may further expand our understanding of the interrelationships between these factors and how these could be affected by genomic and epigenetic queues.12.Primary prostate cancer specimens from radical prostatectomy resections were collected at the Portuguese Oncology Institute, Porto, Portugal. From this cohort, 49 samples (cases) were selected from patients who developed a relapse within ~5 years after diagnosis and matched on age, Gleason score, PSA level, and T-stage with 50 samples (controls) from patients with non-relapsed disease within ~10 years after diagnosis. All samples included were histological diagnosed as clinically localized acinar adenocarcinoma of the prostate. Two samples had no follow-up data available. Fresh frozen material was trimmed to maximize tumor percentage (>70%) and cut in 30-micron sections for ChIP-seq or 10-micron sections for RNA-seq. This study was approved by the institutional review board . All procedures performed in this study were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent was obtained from the participants included in the study. A subset of the cohort was previously characterized for ETS transcription factor rearrangements35. In brief, samples were crosslinked in solution A with 2 mM DSG (CovaChem) for 25 min at room temperature. After 25 min, 1% formaldehyde was added for 20 min and subsequently quenched with glycine. Samples were lysed as described36 and sonicated for at least 10 cycles of 30 s on, 30 s off using a Diagenode Bioruptor Pico. For each ChIP, 5 µg of antibody was conjugated with 50 µl Protein A magnetic beads. Antibodies used were AR , H3K27ac , H3K4me3 , and H3K27me3 . Immunoprecipitated DNA was processed for library preparation . Libraries were sequenced using an Illumina Hiseq2500 genome analyzer , and aligned to hg19 using BWA (v0.5.10). Reads with a mapping quality >20 were selected. Peak calling over input control (mixed inputs) was performed using DFilter (v1.5)37 and MACS38 for AR and H3K27ac ChIP-seq samples. MACS 1.4 was run with p-value cutoff of 10e−7 and DFilter with bs = 50, ks = 30, refine, nonzero. For H3K4me3, MACS2 and DFilter were used with broad-peak settings: (1) –broad and –broad-cutoff = 0.2 for MACS2 and (2) bs = 100 and ks = 60 for DFilter. The peaks called by both peak callers were used for analysis. H3K27me3 ChIP-seq peaks were called by genome segmentation using ChromHMM (v1.12) choosing the state with high H3K27me3 signal39. Normalized strand coefficient (NSC) and relative strand correlation (RSC) were calculated using phantompeaktools 33. BEDTools (v2.25) was used to calculated read counts in peaks44. The raw counts were normalized for library size followed by TMM normalization using EdgeR45.Genome browser snapshots were generated using Easeq (v1.03)46. The ComBat normalization method within the R package SVA (3.24.4) was used to correct the batch effects observed by day of RNA isolation47. Genes with >1 count per million in at least 10 samples were included.RNA was extracted using the AllPrep DNA/RNA universal kit (Qiagen) according to the manufacturer’s instruction with Qiagen’s QIAcube robot. RNA quantity and quality were assessed with the 2100 Bioanalyzer using a Nano chip  and samples with a RIN > 8 were considered for library preparation. Strand-specific libraries were generated with the TruSeq Stranded mRNA sample preparation kit  and sequenced on a HiSeq2500. Sequencing data was aligned to hg38 using TopHat (v2.1.0 using bowtie 1.1.0) and number of reads per gene were measured with HTSeq count (v0.5.3). EdgeR (v3.18.1) -Limma (3.34) workflow was used for gene expression analysis48. Coverage in ERG exones were measured using BEDtools (v2.25), followed by taking ratio between average expression of five exones close to 5′ end  and the exone at 3′ end . A ratio of 0.3 was chosen to determine low or high 5′–3′ transcript ratio.For detecting ERG fusion transcripts, we used STAR-fusion (v0.5.4) with suggested parameters except for chimSegmentMin = 5, chimJunctionOverhangMin = 5, alignSJDBoverhangMin = 5, alignMatesGapMax = 200000, and alignIntronMax = 20000014. The expression of one of the PAM50 genes (MIA) was not included in the Porto analysis. To calculate the CD49f signature score, normalized log2 expression value for each gene (91 genes) was multiplied by their weights and summed up49. AR activity score was calculated by the composite expression of 20 genes24. In addition, expression of 70 neuroendocrine signature genes were obtained from castration resistant neuroendocrine and prostate adenocarcinoma samples, previously published26. The expression of 14 of the 70 neuroendocrine signature genes were not included in the analysis for the Porto Cohort  and four genes for the TCGA cohort  because the genes are expressed only in <~10% of the samples. Expression fold changes between neuroendocrine tumors and adenocarcinoma samples were calculated. Concordance in expression differences (fold change sign) were measured using Pearson correlation.Tumor samples with mRNA expression data available were classified into luminal A, luminal B, or basal-like subtype using the PAM50 classifier. PAM50 clustering was performed as previously described23. The samples were clustered using the top 3000 most variable genes or the top 1000 most variable regions across the samples as determined by median absolute deviation. Consensus clustering was run by hierarchical clustering algorithm for 100 iterations with a resampling rate of 80%. For integrative clustering, we applied MIV-NMF50 using the top 3000 most varying regions and genes as determined by median absolute deviation from the five data types. For each data type, we constructed a 3000 by 97 matrix in which rows are features and columns are samples. The order of samples is maintained across the data types, and 0 values were used for missing samples. The MIV-NMF takes the five matrices at the same time, factorizes them to obtain five sample factor matrices and finds a consensus sample factor matrix, which in turn also affects the individual sample factor matrix. Given a consensus sample factor matrix, the factor with the highest weight is identified for each sample, followed by grouping the samples with the same factor identified. Following regularization parameters are used for MIV-NMF: alpha = 0.001 and beta = 0. MIV-NMF was applied 20 times with random initialization and a consensus matrix capturing the stability of the clustering was constructed to identify the optimal number of clusters. Final clusters were defined by hierarchical clustering on the consensus matrix that captures stable associations across the 20 experiments. To further assess the stability of the three chosen clusters, the same clustering was repeated for 100 times with 80% subsampling, followed by constructing the same consensus matrix with the stability measured. To assess the contribution of each data type, Pearson correlation was measured for each individual sample factor matrix and consensus sample factor matrix. The statistical relation between consensus clusters and variables was tested using linear regression and Pearson’s Chi-square test for continuous  and categorical parameters ), respectively.Cluster analysis was performed using ConsensusClusterPlus (v1.40.0)27. The code is available on GitHub: https://github.com/PeeperLab/CopywriteRCustomBed. CopywriteR was run with 400 kb bins and a custom peak file using MACS238. Furthermore, peaks were extended with 1 kb on either side to minimize the number of on-target reads. Downstream analysis of median normalized log2 values was performed using Circular Binary Segmentation (CBS) and the R package CGHcall51. To calculate copy number alteration burden per sample, number of bins with copy number gains or losses were determined and divided by the total number of bins.The CopywriteR R package (v2.6.1) was used to extract DNA copy number information from off-target reads in AR ChIP-seq datahttps://xenabrowser.net/. For RNA-seq, genes with >1 count per million in at least 43 samples were included. TCGAbiolinks52 was used to retrieve copy number data, followed by converting the data into genome-wide copy number profiles with 400 kb bin resolution. Tumor cellularity information and initial PSA values for 333 primary tumors were reported previously8.Gene expression, protein expression, and somatic mutation data from the TCGA prostate cancer cohort were downloaded from https://github.com/raphael-group/hotnet2). For initial heat, FDR score was obtained from differential gene expression analysis with limma. For reference protein–protein interaction (PPI) network, we used the three PPI networks of the original study30. After identifying the enriched PPI components in each of the three PPI networks, the components were merged with those sharing the common nodes.Differentially expressed genes between the clusters were identified by comparing one cluster with the other two using limma. Given gene expression data with cluster label, gene set enrichment analysis was performed with ggsea R package (v1.0) (10.5281/zenodo.438018) using ggsea_s2n  and ggsea_weighted_ks (weighted KS statistic) as score function and enrichment score function, respectively. For each cluster, expression of genes was compared between one cluster versus the other two. Curated gene set collection was obtained from MsigDB v6.1. For each cluster in TCGA, top gene sets with significant enrichment with FDR < 0.2 are included for radar plot visualization. Differential regulatory networks were identified using HotNet2 , in Leica Bond III platform. Antigen retrieval was performed for 10 min, with Epitope Retrieval Solution 1 for chromogranin and Epitope Retrieval Solution 2 for synaptophysin. Primary monoclonal antibodies for chromogranin  and synaptophysin  were used. Hematoxylin was used for nuclear counterstaining. Appropriate positive controls were used for each antibody and negative controls consisted on the omission of primary antibodies. Because all cases analyzed contained non-neoplastic prostatic epithelium, this served as  control as appropriate.Chromogranin and synaptophysin immunoexpression were assessed using a routine optical microscope by an experienced pathologist blinded to molecular data. Each marker was categorized as <1% positive neoplastic cells, 1–10% positive neoplastic cells and thereafter at 10% increments. Any cytoplasmic staining regardless of intensity was considered positive.Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Reporting Summary


The NanoString nCounter System has been widely used in basic science and translational science research for the past decade. The System consists of two instruments: the PrepStation and the Digital Analyzer, and both have been continuously improved with evolving technologies. A great amount of research data have been generated at multiple research laboratories with the employment of different generations of the System. With the need of integrating multiple datasets, researchers are interested to know whether signals are comparable between different generations of the System. Toward this end, we designed a profiling study to compare performance of two generations of the NanoString nCounter System using a common set of biological samples. Using graphical tools and statistical models, we found that two different generations of NanoString nCounter System produced equivalent signals and signal deviations are in the range of random background noises for the medium-high expression levels. The NanoString nCounter System is a technology based on digital hybridization assays that capture and sensitively count nucleic acids and proteins derived from different biological sources, such as tissues, cells and blood lysates, FFPE samples, body fluids, or low numbers of cells –4. The SThe Genomics Shared Resources at The Ohio State University acquired the first generation of nCounter Max System on 2009 (from now on indicated as “first generation”) and upgraded the platform on 2017 with the nCounter Flex System (from now on indicated as “second generation”). The new Digital analyzer of the nCounter Flex System, with a new light source, provides a more stable illumination power, eliminates high voltage, reduces calibration errors, removes the need of immersion oil, avoids focusing error, and improves the instrument maintenance. To evaluate similarities and differences between the Max and Flex Systems, we conducted a comparative analysis by processing a common set of biological samples with the two generations of NanoString platform. In particular, we used the Human microRNA V3 assay, which detects 800 biologically-relevant human mature microRNAs (miRNAs). Because of the mature microRNAs’ small size (19 to 22 nucleotides), the assay is designed to ligate a DNA tag (miRtag) to each mature microRNA using a ligase enzyme. The tagged microRNA molecules are combined with the Code-set that consists of a multiplexed library of two single-stranded sequence specific DNA molecules, the Capture and Reporter probes. The Capture probe is a complementary target sequence DNA molecule conjugated with Biotin that allows the tagged microRNA-Capture probe-Reporter probe complex to bind on the cartridge surface. The Reporter probe is a complementary target sequence DNA molecule that contains a color-coded barcode that produces the detection signal.Huge amount of NanoString data have been generated in the past decade by using different generations of NanoString System. Our designed profiling study for comparing performance of two generations of the NanoString System using a common set of biological samples will bridge the gap between the pressing need of integrating multiple datasets across different generations of the NanoString System and lack of comparative studies on these system differences. In this profiling study, cultured human renal primary tubular epithelial cells  treated with different Gamma radiation doses and untreated cells (used as a control) were processed for NanoString miRNA profiling by using both the first generation System and the second generation System and results were compared. For data analysis, a variety of statistical methods, such as hierarchical clustering, Pearson’s correlation coefficient (PCC), analysis of variance (ANOVA) models, and locally estimated scatterplot smoothing (LOESS), were employed for signal comparison.In summary, the main purpose of this paper is to compare two generations of NanoString System in order to provide technical insights for researchers who want to integrate multiple datasets across different generations of NanoString System. To accomplish this goal, we develop a systematic and innovative approach for such comparisons including experimental design, data visualization, statistical modeling, and data interpretation. Moving forward, the proposed approach provides a comprehensive framework that will help for implementing other similar comparative studies, e.g. across diverse platforms, multiple assays, and series of research applications.Human renal primary tubular epithelial cells (HRPTEC) were purchased from Lonza (CC-2553) and cultured in renal epithelial cell growth medium  supplemented with single Quots supplements  that includes 0.5% FBS and the growth factors. As cells reached passage number 5 , they were irradiated with GammaCell-40 Irradiator, Cs137 source at a dose rate of 0.94 Gy/min (Best Theratronics) in fractions . Cells were then harvested for RNA isolation at two time points (Day 7 and Day 14) after irradiation to screen for microRNA responders indicative of activated signaling pathways post-fractionated radiation. Untreated cells at same passage number were used for comparison. ® Ultra centrifugal filter columns C82301, Merck) was added after RNA elution step in order to dilute/exclude any contaminants that might interfere in NanoString profiling. 200 ng of RNA was used for NanoString miRNA profiling (Human V3 miRNA Panel).Total RNA  was isolated from all samples by miRVana microRNA isolation Kit . The RNA isolation protocol was followed as per the manufacturer’s instructions. A clean-up step . Mature microRNAs were bound to sequence specific miRtags using a Ligase Enzyme provided with the Kit and unligated miRtags were removed using a clean-up enzyme. Samples were diluted with 40 μl of water and denatured for 5 minutes at 85°C. 5 μl of product were combined with 20 μl of Reporter Probes in hybridization buffer and 5 μl of Capture probes and incubated at 65°C overnight (18 hours). Probe excess was removed using a two-step magnetic beads based purification method in PrepStation and target/probe complexes were bound on the cartridge for data collection. The Digital Analyzer collected the data by taking images of immobilized fluorescent reporters in the sample cartridge with a CCD camera. Each cartridge was scanned using 600 fields of view (FOV) with the first generation System scanner and 320 FOV with the second generation System scanner.Experiment layout are shown in the diagram in Positive controls (spiked by the NanoString Company in the Code-set) were used for correcting assay efficiency. Negative controls were used to filter out microRNAs with expression at noise level. Median normalization was performed to normalize across samples using all housekeeping genes. Heat-maps were used for data visualization. Pearson’s correlations and scatter plots were used for signal comparison between cartridges.ANOVA methods have been routinely used to analyze differences in outcomes in the analysis of comparative experiments and clinical trials –6. UnderY is a vector of expression levels, μ represents the overall mean, P represents the effect of the PrepStation, S represents the effect of the scanner, B represents the effect of biological samples, and ϵ represents random errors following a normal distribution N.ANOVA model for a microRNA at the first time scan isY is a vector of expression levels, μ represents the overall mean, PS represents the effect of the sum of the PrepStation and the scanner, C represents the effect of cartridge, B represents the effect of biological samples, and ϵ represents random errors following a normal distribution N.ANOVA model for a microRNA at the second time scan isY is a vector of expression levels, μ represents the overall mean, PS represents the effect of the sum of the PrepStation and the scanner, T represents the effect of scan time, B represents the effect of biological samples, and ϵ represents random errors following a normal distribution N.ANOVA model for a microRNA across both time scans isA total of four cartridges were used for assaying microRNA expression of the 6 biological samples, in duplicate with 12 samples total and applied to each cartridge. Two PrepStation instruments (from first and second generation System) were used for sample preparation, and two scanners (associated to the first and second generation Systems) were used for miRNA expression detection . In ordeHeat-map of miRNA expression of 12 samples in four cartridges scanned twice by two scanners is displayed in PCC of expression levels between each pair of cartridges were calculated separately for each of 12 samples. The range of PCC between cartridge A and B is 0.9926–0.9970; the range of PCC between cartridge A and C is 0.9920–0.9963; the range of PCC between cartridge A and D is 0.9937–0.9970; the range of PCC between cartridge B and C is 0.9938–0.9975; the range of PCC between cartridge B and D is 0.9945–0.9965; the range of PCC between cartridge C and D is 0.9932–0.9968. All PCC are above 0.99, which demonstrates the extreme consistency between technical replicates.ANOVA model (1) was applied and sources of variation were obtained for each microRNA molecule. PCC of expression levels between each pair of cartridges were calculated separately for each of 12 samples. The range of PCC between cartridge A and B is 0.9926–0.9970; the range of PCC between cartridge A and C is 0.9920–0.9963; the range of PCC between cartridge A and D is 0.9937–0.9970; the range of PCC between cartridge B and C is 0.9938–0.9975; the range of PCC between cartridge B and D is 0.9945–0.9965; the range of PCC between cartridge C and D is 0.9932–0.9968. All PCC are above 0.99, which demonstrates the extreme consistency between technical replicates.ANOVA model (2) was applied and sources of variation were obtained for each microRNA molecule. Similarly, PCC of microRNA expression levels between each pair of cartridges were calculated separately for each of 12 samples. The range of PCC between cartridge A and B at the first scan is 0.9926–0.9970; the range of PCC between cartridge A and B at the second scan is 0.9923–0.9965; the range of PCC between the first and the second scans for cartridge A is 0.9988–0.9994; the range of PCC between the first and the second scans for cartridge B is 0.9979–0.9990. All PCC are above 0.99, which demonstrates the extreme consistency between technical replicates.ANOVA model (3) is applied to both scans of cartridge A and B for each microRNA molecule. The fitted LOESS smoothing curves of RMS over the range of expression levels were plotted in Heat-map and hierarchical clustering demonstrates that there are no systemic biases between old and new PrepStation, between old and new scanners, or even between different combinations of two PrepStation and two scanners. In addition, we performed pair comparisons between cartridges using ANOVA methods to further evaluate differences due to each specific effect of the PrepStation, the scanner, cartridge, or scan time . All PCCs between each pair of cartridges for each sample are above 0.99, which further support the evidence shown in the heat-map.ANOVA models allow us to decompose sources of variation and mean square provides unbiased variance estimates for each source. Since ANOVA models are fitted at a single miRNA level, the obtained RMSs are then smoothed over the range of expression levels of all microRNAs. From the first time scan, it shows that the variability due to the PrepStation itself is as similar as the random error for within-cartridge duplicates for most microRNAs, so we can conclude there is no difference between old PrepStation and new PrepStation except at the really low expression levels. Further from the second time scan, it shows that the variability due to the sum of the PrepStation and the scanner is as similar as variability due to difference cartridges except for the low expression microRNAs. Because different cartridges were used for the combination of old PrepStation and old scanner and the combination of new PrepStation and new scanner, the variability of the sum of the PrepStation and the scanner includes the variability of cartridges. Therefore we can conclude that there is no difference between the combination of first generation System PrepStation and its scanner and the combination of the more recent second generation System PrepStation and its scanner for the medium-high expressed microRNAs. However, there could be minor technical differences at the low expression levels, but these technical differences are very small compared to variability between the 6 biological samples. To investigate whether these technical differences at the low expression levels has any impact on comparative analyses between the biological samples, we performed a differential expression analysis between day 7 and day 14 with 5 biological samples for each cartridge. We observe that the proportions of consistent microRNAs in the significance call  are equivalent for any two cartridges across two systems  and within each system  at either low or medium-high expression levels. Therefore we conclude that the variability difference at the low expression levels as showed in Figs et al. compared both technical and biologic replicates and concluded that biological variability is far greater than technical variability [The comparative results between two generations of NanoString System represent the technical reproducibility and comparability, and they are not dependent on cell line or types of sample used for the following reasons. First, the proposed comparative approach is to compare technical differences at each cell line sample; Second, the biological differences in the 6 cell line samples are only used as a reference. Even though biology variability is different for various cell lines or types of sample, it does not impact the conclusion; Third, many published studies on evaluating NanoString System reproducibility show that technical replicates have great consistency by using various types of cell lines and clinical samples –11. FurtThe comparative approach presented in this study is in particular proposed for comparing two generations of NanoString System. But it can be adapted to other platform comparison , and also can be easily extended to accommodate more complex designs. We have applied this comparative approach to a FDA Sequencing Quality Control Project (SEQC). In one of its subseries, RNA-Seq experiments were performed by multiple platforms (Illumina HiSeq and Life Technologies SOLiD) at multiple laboratory sites with multiple libraries and FlowCells using four common reference RNA samples. We used the expression data deposited in GEO (accession number GSE47774) to evaluate differences between platforms, between laboratory sites, between libraries, and between FlowCells. Since this application on SEQC data is not the focus of this study, we summarize our findings here that there is a large difference between sequencing platforms, minor difference between laboratory sites, minimal differences between libraries, and no difference between FlowCells.S1 FileMaterials and Methods’ section. Log2 normalized data is presented, where miRNAs are in rows and samples are in columns with labels for sample number, cartridge name, and scan time.Nanostring data from all 4 cartridges scanned twice were filtered and normalized as described in ‘(TXT)Click here for additional data file.