The past two decades of microRNA (miRNA) research has solidified the role of these small non-coding RNAs as key regulators of many biological processes and promising biomarkers for disease. The concurrent development in high-throughput profiling technology has further advanced our understanding of the impact of their dysregulation on a global scale. Currently, next-generation sequencing is the platform of choice for the discovery and quantification of miRNAs. Despite this, there is no clear consensus on how the data should be preprocessed before conducting downstream analyses. Often overlooked, data preprocessing is an essential step in data analysis: the presence of unreliable features and noise can affect the conclusions drawn from downstream analyses. Using a spike-in dilution study, we evaluated the effects of several general-purpose aligners , and normalization methods  with respect to the final miRNA count data distribution, variance, bias and accuracy of differential expression analysis. We make practical recommendations on the optimal preprocessing methods for the extraction and interpretation of miRNA count data from small RNA-sequencing experiments. Depending on the degree of sequence complementarity, this interaction can mediate either translational repression or mRNA degradation. More than 30% of human protein-coding genes are predicted to be conserved targets of miRNAs [With the decreasing cost and high multiplexing capability of next-generation sequencing (NGS), this technology is increasingly being used for the comprehensive profiling of miRNA abundance. Although several platforms and protocols exist, including the Illumina HiSeq systems, Life Technologies SOLiD™ sequencing and Roche 454 sequencing, the analysis of the resulting data follows a general scheme: (1) short reads are filtered for low-quality sequences and adapters, (2) the remaining sequences are mapped to a reference , (3) the abundance for each biological entity of interest is determined (to give an expression measure of specific targets), (4) the resulting count data are normalized and (5) downstream analyses are conducted to probe biologically relevant questions. Despite this, no standard method or clear consensus exists on how the data should be preprocessed and the consequences of a chosen method on subsequent analyses.Several software packages have been developed over the past few years for the preprocessing and mapping of miRNA-seq data . These tet al. suggested the opposite, advising against quantile normalization but advocating for the TMM method—data simulations were used to confirm these findings. While different normalization techniques will likely affect the accurate quantification of miRNA abundance, the downstream consequences of using different aligners to map the raw sequencing reads to a given reference is unknown: the former study used data aligned using SHRiMP [Following alignment, the recovered miRNA counts need to be normalized to remove variations in the data that are of non-biological origins and can affect the measured abundance levels. Even in replicate experiments, some variations will be observed, stemming from the experimental procedure—sample handling, library preparation and sequencing. An effective normalization technique should minimize technical and experimental bias without introducing noise. The differences that remain should be truly biological effects. Several normalization methods for miRNA-seq data have been proposed, including linear scaling, nonlinear scaling and quantile normalization. However, no standard method is currently used; read counts from each experiment are usually simply adjusted for differences in sequencing depth to counts-per-million (cpm), despite studies showing that this is insufficient to account for the technical differences across samples , 18. Theg SHRiMP  and Bowtg SHRiMP , whereasg SHRiMP . Data noTo assess the relative merits of different preprocessing methods in terms of variance, bias and sensitivity and specificity for the detection of differential expression, data for which true values are known are required . Here, wArabidopsis thaliana genome that are not present in the human genome were selected as spike-in sequences. RNA oligonucleotides were synthesized with phosphorylated 5′ ends , and added at 12 different concentrations  to 1 μg Universal Human Reference RNA (Agilent Technologies), with each concentration appearing once in each row and column of the design matrix. The samples were subjected to all experimental steps, including small RNA purification. The second data set, consisting of miRNA-seq data from matched cell lines and xenografts, has been previously published [Two data sets were used for this study: a spike-in experiment and a miRNA-seq profiling data set comparing cell lines and xenografts. The spike-in data set was created using a 12 × 12 cyclic Latin Square design. Twelve miRNAs from the ublished ; this dacDNA libraries for sequencing were constructed as per Illumina’s TruSeq Small RNA protocol (Illumina). In brief, 3′ and 5′ adapters were sequentially ligated to the ends of RNA < 200 nt long, fractionated from 1 μg of total RNA  and reverse transcribed to generate cDNA. The cDNA was amplified (11 cycles of PCR) using a common primer complementary to the 3′ adapter, and a 5′ primer containing 1 of 48 index sequences. Samples were size-selected on a 6% polyacrylamide gel, purified, quantified and pooled for multiplexed sequencing. The resulting pooled libraries were hybridized to oligonucleotide-coated single-read flow cells for cluster generation on-instrument and subsequent sequencing on an Illumina HiSeq 2500 instrument. Fifty sequencing cycles were performed.BWA 0.7.4: bwa aln -n 1 -o 0 -e 0 -k 1 -t 4BWA 0.7.4 (0 mismatch in seed): bwa aln -n 1 -o 0 -e 0 -l 8 -k 0 -t 4Bowtie 0.12.9: bowtie -n 1 -l 8 -a --best --strata --phred33-qualsBowtie 0.12.9 (0 mismatch in seed): bowtie -n 0 -l 8 -a --best --strata --phred33-qualsBowtie2 2.1.0: bowtie2 --local -p 8 -q --phred33 -D 20 -R 3 -N 0 -L 8 -i S,1,0.50Novoalign 3.00.05: novoalign -a TGGAATTCTCGGGTGCCAAGG -l 15 -t 30 -r AShort read sequences were output in FASTQ format with corresponding base quality scores. The raw data were initially filtered for reads containing ambiguous base calls, which did not meet the Illumina chastity filter based on quality measures. Quality control of the remaining sequences from each sequenced library was investigated using FastQC (v0.11.2)  to checkOnly reads uniquely aligned to miRNAs were retained and counted. The miRNAs were filtered for targets with a minimum of 5 counts in at least 25% of the samples. The raw and processed data has been deposited in the Gene Expression Omnibus repository, under the accession GSE67074.Several normalization methods were evaluated, including (1) cpm, (2) total count scaling, (3) upper quartile scaling (UQ), (4) TMM, (5) DESeq, (6) linear regression, (7) cyclic loess normalization and (8) quantile-based normalization. Each of these methods is described briefly. Count-per-million—the simplest form of normalization, whereby each library is adjusted for differences in sequencing depth. The counts can then be adjusted to reads per million to facilitate comparison between samples.(1) Total count scaling—After scaling each sample to its library size, they can be rescaled to a common value across all samples. The baseline reference can be chosen to be the sample with the median library size. If baselines is the size of the reference library, and is is the sum of all reads of the any given library, then the normalization factor is as follows:ix is the raw count for a specific target.(2) Upper-quartile scaling—In RNA-seq experiments, the predominance of zero and low-gene counts has led to the suggestion of a modified quantile-normalization method: the upper quartile of expressed miRNAs is used instead as a linear scaling factor [th percentile of the data will also be found at only 1 or 2 copies/library.(3)g factor . This meg factor . It is e Trimmed mean of M—Normalization by total count scaling makes intuitive sense because it gives us the proportion of counts for a specific target across all samples. If a miRNA is present in the same proportion across all samples, it will be deemed as non-differentially expressed. However, this method does not take into consideration the potentially different RNA composition across the samples. TMM, proposed by Robinson et al. for RNA-seq data normalization, calculates a linear scaling factor, id, for sample i, based on a weighted mean after trimming the data by log fold-changes (M) relative to a reference sample and by absolute intensity (A) [(4)sity (A) . TMM norsity (A) . DESeq—To perform differential expression analysis using count data, Anders and Huber proposed modeling the data with the negative binomial distribution, and incorporating data-driven prior distributions to estimate the dispersion and fold changes [(5) changes . As a da Linear regression—This normalization technique assumes that the systematic bias is linearly dependent on the count abundance. In microarray data, the log2 ratios, M, between two samples have been observed to have systematic dependence on the intensity values, A. This can be visualized by plotting the M values for each element as a function A (MA plot), wherei.jx as the abundance for a given miRNA, i, in sample j [i.j=1 and xi.j=2x values are given by [(6)sample j . A lineasample j (5)M′=β0equation ,(6)Mi′=given by (7)xi,j= Nonlinear regression—Although linear normalization is simple and robust, the linearity assumption does not always hold, especially at extreme values. Lowess analysis was initially proposed for the removal of intensity-dependent effects of the log2 ratios in two-color microarray experiments [et al. for single channel microarrays [(7)eriments . A weigheriments . The regeriments (8)M′=M−roarrays . Quantile-based normalization—Initially proposed for the normalization of microarray data, quantile normalization forces the distribution of read counts in all samples across an experiment to be equivalent [(8)uivalent . This nonormalizeBetweenArraysfunction in the limma package (v3.22.4) [calcNormFactors in the edgeR (v3.8.5) package [estimateSizeFactors in the DESeq (v1.18.0) package was used to normalize the count data using size factors [P-values were adjusted for multiple testing using the Benjamini and Hochberg approach. This analysis was performed using the limma package (v3.22.4).Data analyses and graphical representations were performed and generated in the R statistical environment (v3.1.2). Normalization methods were implemented either in R or Bioconductor (3.0) libraries. Cyclic loess and quantile normalization were performed using the v3.22.4) , whereas package . The fun factors . For dif factors . The sam factors . A paireSupplementary Figure S1). Despite this, the resulting Bowtie output had a number of miRNAs that were estimated to have higher abundance levels than the count estimates from BWA and Novoalign, which was run with the recommended parameter settings for miRNA analysis .As an illustration of the different normalization methods, the absolute distribution of the miRNA count data following alignment and normalization can be visualized using density distribution curves A. To avoAs the samples were prepared using the same background reference, most of the detected species should have similar abundance levels, except for the 12 spike-in sequences. Adjusting the data by cpm or total count scaling introduces more variability, whereas UQ and TMM decreased the variance across all miRNAs compared with the raw data A. The peTo identify any dependency on abundance levels, the mean and variance of each miRNA was computed across the samples, and the log ratio of the variance of normalized versus unnormalized data was plotted against the mean abundance levels B. From tC is the count value and a is the amount, in attomole, of oligonucleotides added. The sample with spike-in concentration of 0.0 amol was excluded from the model fit. The closer the slope (β1) is to 1, the more the data are representative of the true values. The R2 value was determined to assess the model fit.An effective normalization method should decrease the variance without increasing bias. The following linear model was fit to each spike-in dilution series to assess the bias in the preprocessed data:1 for the spike-in dilution series are closer to 1; however, increase in bias is observed in the data adjusted by cpm, total count scaling and quantile normalization compared with the raw data  and recall (true positive rate) were determined for a range of thresholds for all data sets. As the smallest difference in the amount of spike-in sequences added across the samples is 2 folds, miRNAs with a fold-change ≥2 were considered to be differentially expressed. Precision-recall (PR) curves were used to assess the performance of the different preprocessing methods in calling miRNAs differentially expressed. When the number of positive and negative examples is highly imbalanced, PR curves are better performance estimators than receiver-operating characteristic curves. An ideal PR curve will have a precision value of 1 for all values of recall. Data normalized by cpm or total count scaling performed unequivocally worse than the raw data, which were normalized by library concentration before sequencing. Data normalized by UQ or TMM clearly dominated the PR curves, with BWA-aligned reads (with 1 mismatch in the seed regions) showing a slight advantage A. ComparThe increased sensitivity and specificity of these particular methods can be assessed using M versus A plots; one sample was selected as a reference, and the mean and average for each miRNA was computed for all comparison of this sample to all other samples C. Data nSupplementary Figure S2a). However, when large differences in read depth exist, the efficiency of the different normalization techniques at stabilizing the read distribution across a data set differs . We explore this concept using the cell lines–xenografts comparison study because of the more variable count distribution and higher coverage compared with the spike-in experiment . The read depths in this data set ranged from over 2 to 16 million reads per sample, whereas the library sizes in the spike-in experiment ranged from over 1 to 11 million reads per sample. To assess the impact of read depth on data normalization, subsampling was performed to simulate data with similar read counts across all the samples (∼2.2 million reads/samples). The effect of alignment and normalization was assessed on this subsampled and full data set using similar metrics as was performed for the spike-in experiment.For data with similar library sizes, the raw and normalized data have comparable distributions, regardless of the normalization technique used . Unsurprisingly, the subsampled data showed comparable RLE distributions regardless of the normalization method used . Decrease in variance was only observed in data adjusted by cpm, UQ and TMM in both the full and subsampled data set . To assess the presence of bias introduced by data normalization, log2 ratios for each matched cell line and xenograft pair were calculated for 56 miRNAs that were assayed and detected by qPCR. The absolute bias was defined as the deviation of these ratios from those determined by qPCR . The unnormalized raw counts showed the largest amount of bias compared with the normalized data. The unnormalized subsampled data, on the other hand, had a bias distribution centered at zero. While cpm, UQ and quantile normalization successfully reduced the bias in the full data set, most normalization methods had little effect in reducing bias in the subsampled data.In accordance with the results observed in the spike-in data set, adjusting the data by UQ and TMM normalization resulted in more similar count distributions and RLE values centered at zero, without forcing the distribution to be exactly the same across the data set, as in quantile normalization (Supplementary Table S2). Normalization by cpm, UQ and TMM resulted in similar sensitivity, specificity and accuracy for differential expression analysis, whereas quantile normalization showed reduced specificity. The subsampled data did not perform well in this analysis, despite the superior results observed in the assessment of bias and variance.Finally, the sensitivity, specificity and accuracy for the detection of differential expression can be assessed relative to qPCR, the current gold standard for validating expression profiling results. Regardless of the normalization method, data aligned using novalign resulted in lower accuracy because of the lower sensitivity of detection . In accordance to the results observed by Dillies et al., we support the use of TMM (and UQ) for the normalization of miRNA count data. Garmire and Subramaniam, on the other hand, advocated for the use of quantile and Lowess normalization, while discouraging the use of TMM. Because TMM was not applied properly in their original publication, the authors reanalyzed their data with the correct implementation [Contrary to initial beliefs that miRNA-seq data will not require sophisticated normalization , we and entation , 47. DesTo ensure comparability across different miRNA-seq studies, a standardized data preprocessing pipeline should be established. To this end, we have conducted the present study to evaluate small RNA-seq data preprocessing procedures with the end-goal of retrieving miRNA abundance profiles. The combinations of different aligners and normalization methods were assessed in terms of variance, bias and accuracy for the differential expression analysis of miRNAs based on fold-change estimates. Based on these evaluation criteria, we conclude that the alignment of sequencing reads and the subsequent normalization of the miRNA count data using BWA with one mismatch across the entire read and UQ or TMM, respectively, lead to more accurate results in downstream analyses. We also note that the output from different aligners is not necessarily the same—a topic which has not been previously explored for small RNA-seq data. Finally, we make available data with spike-in controls that can be used for the future development of methods for the preprocessing and analysis of miRNA-seq data.Supplementary data are available online at http://bib.oxfordjournals.org/.In recent years, a number of tools have been made available for the preprocessing of small RNA-seq data to retrieve miRNA abundance profiles. Different methods will affect the outcome of downstream analyses. Despite this, no standard pipeline has been implemented.Previous comparative studies evaluating different normalization procedures resulted in conflicting conclusions.A spike-in dilution study was designed to evaluate the effects of different aligners and normalization methods with respect to the final miRNA count data distribution, variance, bias and accuracy of differential expression analysis.We recommend the use of BWA for alignment of small RNA-seq data to recover miRNA abundance profiles, followed by TMM or upper quartile scaling for normalization before conducting any downstream analyses.


Summary: Robust conversion between microarray platforms is needed to leverage the wide variety of microarray expression studies that have been conducted to date. Currently available conversion methods rely on manufacturer annotations, which are often incomplete, or on direct alignment of probes from different platforms, which often fail to yield acceptable genewise correlation. Here, we describe aRrayLasso, which uses the Lasso-penalized generalized linear model to model the relationships between individual probes in different probe sets. We have implemented aRrayLasso in a set of five open-source R functions that allow the user to acquire data from public sources such as Gene Expression Omnibus, train a set of Lasso models on that data and directly map one microarray platform to another. aRrayLasso significantly predicts expression levels with similar fidelity to technical replicates of the same RNA pool, demonstrating its utility in the integration of datasets from different platforms.Availability and implementation: All functions are available, along with descriptions, at https://github.com/adam-sam-brown/aRrayLasso.Contact:chirag_patel@hms.harvard.eduSupplementary information:Supplementary data are available at Bioinformatics online. Because microarrays have greater than 10 000 individual probes, we chose to use the Lasso algorithm for generalized linear regression . For both datasets, we used aRrayLasso to first train models to intraconvert between each individual platform and then predicted intraconversions between each pair of platforms for all technical replicates. To assess the accuracy of our conversions, we calculated the average Pearson’s r between the predicted values and actual experimental values for each platform and replicate. We also calculated the average inter-replicate Pearson’s r for each platform (see Supplementary Table S2).To demonstrate the utility of our methodology, we utilized three datasets: (i) GSE6313, containing C57/B6 adult mouse retina cDNA profiles (Supplementary Table S2). In addition, once built, aRrayLasso models can be used between experimental conditions: using the models built on GSE6313, we predicted expression levels in GSE4854 with no significant loss of signal . While the results presented here do not guarantee similar results for all training and testing datasets, these analyses serve as a promising proof of concept. Furthermore, our success with a relatively small dataset suggests that aRrayLasso may reach even higher levels of performance as the size of the datasets involved increases.To explore the performance of aRrayLasso, we began by comparing our method’s ability to predict expression to the biological variation between replicates on the same platform. We assessed the degree to which aRrayLasso could accurately predict platform interconversions in three datasets, representative of different experimental systems, organisms and platforms. For the five platforms tested, aRrayLasso predictions are within the technical variation of each microarray platform when compared with technical replicates from the same cDNA pool, even when subjected to multiple sequential conversions (Implementation: In this investigation, we propose a data-driven method for integrating across high-throughput genomic measurement modalities that avoids the use of annotation- or sequence alignment-based tools. We have implemented a Lasso regression-based modeling approach to model the expression level of each sequence tag in a target microarray as a linear combination of all sequence tags in a source microarray. Our implementation represents a straightforward, easy-to-use and open-source methodology for conversion between microarray platforms.Limitations: One drawback of our method is the need for extant or newly generated matched samples in the source and target platforms. In our experience, however, there are a large number of datasets available that have matched samples with replicates for a number of popular microarray platforms. A second limitation to our method is in conversion which lack overlap in gene coverage. In these cases, as with currently available methodologies, our method will fail to provide meaningful conversions. Lastly, while we have shown in one case that interexperiment conversions are feasible, we caution that systematic technical error in a single experiment may lead to the creation of a biased model. In general, however, when coupled with one of several cross-experiment dataset integration tools, aRrayLasso will enable mining of the remarkable and untapped historical pool of microarray datasets for large-scale metastudies for well-powered discovery.


Detecting and quantifying isoforms from RNA-seq data is an important but challenging task. The problem is often ill-posed, particularly at low coverage. One promising direction is to exploit several samples simultaneously.We propose a new method for solving the isoform deconvolution problem jointly across several samples. We formulate a convex optimization problem that allows to share information between samples and that we solve efficiently. We demonstrate the benefits of combining several samples on simulated and real data, and show that our approach outperforms pooling strategies and methods based on integer programming.http://cbio.ensmp.fr/flipflop.Our convex formulation to jointly detect and quantify isoforms from RNA-seq data of multiple related samples is a computationally efficient approach to leverage the hypotheses that some isoforms are likely to be present in several samples. The software and source code are available at The online version of this article (doi:10.1186/s12859-015-0695-9) contains supplementary material, which is available to authorized users. Most genes in eukaryote genomes are subject to alternative splicing , meaningisoform deconvolution problem, is often challenging because RNA-seq technologies usually only sequence short portions of mRNA molecules, called reads. A given read sequenced by RNA-seq can therefore originate from different transcripts that share a particular portion containing the read, and a deconvolution step is needed to assign the read to a particular isoform or at least estimate globally which isoforms are present and in which quantity based on all sequenced reads.Next-generation sequencing (NGS) technologies can be used to identify and quantify these isoforms, using the RNA-seq protocol –9. Howevde novo isoform reconstruction based on the read alignment includeWhen a reference genome is available, the RNA-seq reads can be aligned on it using a dedicated splice mapper –12, and Cufflinks , ScriptuOne promising direction to improve isoform deconvolution is to exploit several samples at the same time, such as biological replicates or time course experiments. If some isoforms are shared by several samples, potentially with different abundances, the identifiability issue may vanish and the statistical power of the deconvolution methods may increase due to the availability of more data for estimation. For example, the state-of-the-art methods CLIIQ  and MiTiIn this paper, we propose a new method for isoform deconvolution from multiple samples. When applied to a single sample, the method boils down to FlipFlop ; thus, wℓ1 penalty controls the number of expressed isoforms. This approach is implemented by several of the referenced methods, including IsoLasso [The deconvolution problem for a single sample can be cast as a sparse regression problem of the observed reads against expressed isoforms, and solved by penalized regression techniques like the Lasso, where the IsoLasso  and FlipIsoLasso . When seThe splicing graph for a gene in a single sample is a directed acyclic graph with a one-to-one mapping between the set of possible isoforms of the gene and the set of paths in the graph. The nodes of the graph typically correspond to exons, sub-exons , 17, 20 When working with several samples, we choose to build the graph based on the read alignments of all samples pooled together. Since the exons used to build the graph are estimated from read clusters, this step already takes advantage of information from multiple samples, and leads to a more accurate graph. We associate a list of read counts, as many as samples, with each node of the graph. In other words, we extend the notion of splicing graph to the multiple-sample framework, using a shared graph structure with specific count values on each node. Our multi-dimensional splicing graph is illustrated in Fig. G= the multi-dimensional splicing graph where V is the set of vertices and E the set of edges. We denote by G. By construction of the graph, each path v∈V for each sample t∈{1,…,T}, where T is the number of samples. We denote by p for sample t. Finally, we define for every path p in T-dimensional vector of abundances T columns.Throughout the paper, we call β through the following penalized regression problem:We propose to estimate p across the samples, and λ is a non-negative regularization parameter that controls the trade-off between loss and sparsity. The ℓ1,2-norm β). It can be thought of as a convex relaxation of the number of isoforms present in at least one sample, which is used as criterion in the combinatorial formulations of CLIIQ and MiTie.where  penalty , inducesmentclass2pt{minim penalty , while tT sample losses, thus assuming independence between samples as reads are sampled independently from each sample. The loss is derived from the Poisson negative likelihood fined in . The sumG, we need to avoid an exhaustive enumeration of all candidate isoforms T=1, i.e., the ℓ1-regularized regression Since mentclass2pt{minimT+1 one-dimensional problems—the T independent ones, plus the one corresponding to all samples pooled together—for different values of λ, and taking the union of all selected isoforms, and we then solve . Each solution, i.e., each set of selected isoforms obtained with a particular λ value, is then re-fitted against individual samples—without regularization but keeping the non-negativity contraint—so that the estimated abundances do not suffer from shrinkage [all isoforms at the end of the deconvolution.We solve  for a lahrinkage . The solhrinkage , where thrinkage , is finade novo setting, without feeding any of the methods with prior transcript annotations .We show results on simulated human RNA-seq data with both increasing coverage and increasing number of samples, with different simulation settings, and on real RNA-seq data. In all cases, reads are mapped to the reference with TopHat2 . We comphttp://alumni.cs.ucr.edu/~liw/rnaseqreadsimulator.html). We vary the number of reads from 10 thousand – 10 million per sample (corresponding approximately to sequencing depth from 1 to 1000 ×) and the number of samples from 1 – 10. All methods are run with default parameters, except that we fix region-filter to 40 and max-num-trans to 10 in MiTie as we notice that choosing these two parameter values greatly increases its performances , while in the Different setting the abundance profiles of each sample are generated independently. Hence in that case the samples share the same set of expressed transcripts but have very different expression values (the maximum correlation between two abundance vectors is 0.088).Figure Equal case, the group-lasso and merging strategies give almost identical results, which shows the good behavior of the group-lasso, as pooling samples in that case corresponds to learning the shared abundance profile. In the Equal case again, for all methods the different Fscore curves obtained with increasing number of samples converge to different plateaux. None of these levels reaches a Fscore of 100, but the group-lasso level is the highest (together with the merging strategy). In the Different case, the group-lasso shows equal or higher Fscore than the merging strategy, with a great improvement when the coverage or the number of samples increases. The group-lasso also outperforms the Cufflinks/Cuffmerge method for all numbers of samples when the coverage is larger than 80. When using more than 5 samples the group-lasso shows greater Fscore as soon as the coverage is bigger than 15 . Finally, the group-lasso outperforms MiTie for all number of samples and all coverages. Of note, the group-lasso performances are better in the Different setting than in the Equal setting, showing that our multi-sample method can efficiently deal with diversity among samples.In all cases and for all methods, the higher the coverage or the number of samples, the higher the Fscore. In the Different setting. Figure 4 coverage, while read length varies from 75 to 300 bp. Because we properly model long reads in our splicing graph the group-lasso performance greatly increases with the read length, proportionally much more than other state-of-the-art methods. When using 5 samples and long 300 bp reads, the group-lasso reaches a very high Fscore of 90 (compared to 84 for the second best Cufflinks/Cuffmerge method), showing that our method is very well adapted to RNA-Seq design with long reads and several biological replicates.We also investigate the influence of the read length on the performance of the compared methods in the Different setting using both paired or single-end reads at comparable coverage.Note finally that our method generalizes to paired-end reads. We show in Additional file 5 reads and 150 bp, and we run experiments for one up to five samples. We study the influence of hyper-parameters on the performances of the compared methods, and show that our approach leads to better results with optimized parameters as well. Hyper-parameters are first tuned on a training set of 600 transcripts from the positive strand of chromosome 11, which is subsequently left aside from the evaluation procedure after tuning. We start by jointly optimizing a set of pre-processing hyperparameters. We then keep the combination that leads to the best training Fscore, and we jointly optimize a set of prediction hyperparameters. More specifically, we optimize 7 values of 3 different pre-processing or prediction parameters (hence 73 different combinations in both cases), except that for MiTie we add 2 values of one pre-processing parameter and 3 values of a fourth prediction parameter (hence optimizing over 9×72 and 3×73 parameters). A more detailed description of the optimized parameters is given in tables D.1 and D.2 of the supplementary.The second set of simulations is performed using a different and more realistic simulator, the Flux Simulator , in orderegion-filter to 40 and max-num-trans to 10 in MiTie instead of using all default parameters as it greatly improves its performances, see Additional file Fscore is shown on Fig. D. melanogaster embryonic development. Each sample corresponds to a 2-hour period, from 0–10 h . Data is available from the modENCODE [We use five samples from time course experiments on odENCODE  website.odENCODE .Because the exact true sets of expressed transcripts is not known, we validated predictions based on public transcript annotations. We built a comprehensive reference using three different databases available on the UCSC genome browser , namely Figure Considering running time, each method was run on a 48 CPU machine at 2.2 GHz with 256 GB of RAM using 6 threads . When using only a single sample and 6 threads, Cufflinks, FlipFlop and MiTie respectively completed in ∼4.2 min, ∼9.5 min and ∼26.6 min. while when using 5 samples and 6 threads, Cufflinks/Cuffmerge, FlipFlop with group-lasso and MiTie took ∼0.45 h, ∼1 h and ∼25 h, with the same number of valid transcripts, except for the first sample where FlipFlop makes 2 good guesses against 1 for Cufflinks. This difference might be due to the fact that FlipFlop not only tries to explain the read alignement as Cufflinks does, but also the coverage discrepancies along the gene.For multi-sample predictions, FlipFlop gives much more reliable results, with 4 validated transcripts (among 4 predictions), while Cufflinks/Cuffmerge makes only 1 good guess out of 2 predictions. FlipFlop uses evidences from all samples together to find transcripts with for instance missing junction reads in one of the sample  or lowly expressed transcripts . Cufflinks/Cuffmerge explains all read junctions but does not seek to explain the multi-sample coverage, which seems important in that example.Importantly, one can note that the results of multi-sample group-lasso FlipFlop are different from the union of all single-sample FlipFlop predictions (the union coincides here to the results of FlipFlop on the merged sample—data not shown). This illustrates the fact that designing a dedicated multi-sample procedure can lead to more statistical power than merging individual results obtained on each sample independently. We display an additional example in Additional file We proposed a multi-sample extension of FlipFlop, which implements a new convex optimization formulation for RNA isoform identification and quantification jointly across several samples. Experiments on simulated and real data show that an appropriate method for joint estimation is more powerful than a naive pooling of reads across samples. We also obtained promising results compared to MiTie, which tries to solve a combinatorial formulation of the problem.Accurately estimating isoforms in multiple samples is an important preliminary step to differential expression studies at the level of isoforms , 37. IndFinally, as future multi-sample applications such as jointly analyzing large cohorts of cancer samples or many cells in single-cell RNA-seq are likely to involve hundreds or thousands of samples, more efficient implementations involving in particular distributed optimization may be needed.


Long noncoding RNAs (lncRNAs) regulate gene expression by acting with microRNAs (miRNAs). However, the roles of cancer specific lncRNA and its related competitive endogenous RNAs (ceRNA) network in hepatocellular cell carcinoma (HCC) are not fully understood. The lncRNA profiles in 372 HCC patients, including 372 tumor and 48 adjacent non-tumor liver tissues, from The Cancer Genome Atlas (TCGA) and NCBI GEO omnibus (GSE65485) were analyzed. Cancer specific lncRNAs (or HCC related lncRNAs) were identified and correlated with clinical features. Based on bioinformatics generated from miRcode, starBase, and miRTarBase, we constructed an lncRNA-miRNA-mRNA network (ceRNA network) in HCC. We found 177 cancer specific lncRNAs in HCC , 41 of them were also discriminatively expressed with gender, race, tumor grade, AJCC tumor stage, and AJCC TNM staging system. Six lncRNAs  were found to be significantly associated with overall survival . Collectively, our results showed the lncRNA expression patterns and a complex ceRNA network in HCC, and identified a complex cancer specific ceRNA network, which includes 14 lncRNAs and 17 miRNAs in HCC. They can be divided into several subtypes including long non-coding RNA (lncRNA), microRNA (miRNA), ribosomal RNA (rRNA), small nucleolar RNA (snoRNA), and transfer RNA according to HUGO Gene Nomenclature Committee (HGNC)  hypothesis, which unified the transcriptome and formed a regulatory RNA network [In 2011, Salmena  network . The mai network .Zhu et al. reported that the lncRNA expression profile of HCC by microarray analysis from three HCC patients . Howeverhttp://cancergenome.nih.gov/publications/publicationguidelines). Another GEO data set (GSE65485) was downloaded from GEO (http://www.ncbi.nlm.nih.gov/geo/) which included 50 HCC tissues and 5 adjacent non-tumor liver tissues. As the data were obtained from TCGA and GEO, further approval by an ethics committee was not required.A total of 360 patients with HCC were retrieved from the TCGA data portal. The exclusion criteria were set as follows: 1) histologic diagnosis is not HCC; 2) samples without completed data for analysis; and 3) Overall survival more than 2000 days. Overall, a total of 322 HCC patients were included in our study. Among these 322 HCC patients, the adjacent non-tumor liver tissues were retrieved from 43 subjects. This study meets the publication guidelines provided by TCGA (The RNA expression data (level 3) of the corresponding patients (tumor and/or adjacent non-tumor tissues) were downloaded from TCGA data portal . The lncRNA and mRNA expression profiles were generated from RNA sequencing raw reads by RNASeqV2 post-processing pipelines and demonstrated as RSEM (RNA-Seq by Expectation-Maximization) normalized count data. The miRNA expression profile was performed using the Illumina HiSeq 2000 miRNA sequencing platforms  and demonstrated as reads per million miRNA (RPM) mapped data. Because the mRNA, lncRNA, and miRNA expression profile data were already normalized by TCGA, no further normalizations were applied to these data. The GEO data set was also generated from the Illumina HiSeq 2000 platform and normalized as FPKM (fragments per kilo bases of exons for per million mapped reads) data. The lncRNA analyses were performed using BRB-ArrayTools (version 4.4) developed by Dr. Richard Simon and the BRB-ArrayTools Development Team .http://www.gencodegenes.org/) were discarded; (ii) lncRNA-miRNA interactions were predicted by miRcode (http://www.mircode.org/) and starBase v2.0 (http://starbase.sysu.edu.cn/); (iii) mRNAs targeted by miRNAs with experimental support were retrieved from miRTarBase (http://mirtarbase.mbc.nctu.edu.tw/). To further enhance the robust of this ceRNA network, the maximal information coefficient (MIC) algorithm and maximal information-based nonparametric exploration (MINE) statistics were used in TCGA data set to filter the pair-wised relationships [The construction of ceRNA network included three steps: (i) cancer specific lncRNA filtration: cancer specific lncRNAs with absolute fold change ≥ 3.0 (either up-regulation or down-regulation) and P < 0.05 were retained. To improve the data reliability, cancer specific lncRNAs have not been annotated by GENCODE . The univariate Cox proportional hazards regression was conducted to find out the lncRNAs correlated with overall survival . P valueWe identified 604 lncRNAs from TCGA data set and 357 lncRNAs from GSE65485. For any single lncRNA, it appeared in TCGA data set or both TCGA and GEO data sets. We found that 177 lncRNAs and 37 lncRNAs were differentially expressed between HCC tissues and adjacent non-tumor tissues in TCGA data set and GEO data set . Among the six significant lncRNAs, three lncRNA  were negatively associated with OS, while the remaining three  were positively correlated with OS .Our previous study has found 207 HCC-associated miRNAs which were differentially expressed between HCC tissues and adjacent non-tumor tissues . We selehttp://www.bushmanlab.org/links/genelists), most of their targets are cancer-associated genes such as SIRT1, VEGFA, RASA1, RAF1, PTEN, MAPK9, MAPK8, MAPK1, MYC, MYB, KRAS, JAK2, IGF1R, IDH2, FOXO3, FOXO1, E2F3, E2F1, MAPK14, CDKN2A, CDKN1A, CDK6, CD44, CCNF, CCNE1, CCND3, CCND2, RUNX2, BCL2, CCND1, APC, AKT2, AKT1, ABCA1, etc.To establish lncRNA-miRNA-mRNA network (ceRNA network), the next step was to search for mRNA targeted by miRNAs. Based on the miRNAs described in 2 > 0.15, please refer to Methods). 14 lncRNAs and 17 miRNAs were involved in the proposed ceRNA network  algorithm and maximal information-based nonparametric exploration (MINE) statistics were used to filter the pair-wised relationships based on lncRNA-miRNA-mRNA expression correlations. In general gene co-expression network analysis, Pearson’s correlation is a measure for linear regression, but it is very sensitive to outliers. MIC and MINE are able to examine and characterize all potentially interesting relationships in a complex data set .The ceRNA network we built brings to light an unknown ceRNA regulatory network in HCC. In this newly-identified ceRNA network, many oncogenes and tumor suppressors participate in HCC development and treatments. A recent study also identified that lncRNA-miRNA-mRNA interactions were active and might act as potential prognostic biomarkers in cancers .In conclusion, our study has found the cancer specific lncRNAs in HCC using hundreds of candidate lncRNAs and large scale samples, and disclosed abnormal expression pattern of cancer specific lncRNAs under different clinical features. Importantly, we have constructed a ceRNA network to propose a new approach to lncRNA research in HCC. Our findings suggest that cancer specific lncRNAs in HCC may participate in a complex ceRNA network.S1 Table(XLS)Click here for additional data file.S2 Table(XLS)Click here for additional data file.S3 Table(XLS)Click here for additional data file.


High-throughput sequencing is now regularly used for studies of the transcriptome (RNA-seq), particularly for comparisons among experimental conditions. For the time being, a limited number of biological replicates are typically considered in such experiments, leading to low detection power for differential expression. As their cost continues to decrease, it is likely that additional follow-up studies will be conducted to re-address the same biological question.p-value combination techniques previously used for microarray meta-analyses can be used for the differential analysis of RNA-seq data from multiple related studies. These techniques are compared to a negative binomial generalized linear model (GLM) including a fixed study effect on simulated data and real data on human melanoma cell lines. The GLM with fixed study effect performed well for low inter-study variation and small numbers of studies, but was outperformed by the meta-analysis methods for moderate to large inter-study variability and larger numbers of studies.We demonstrate how p-value combination techniques illustrated here are a valuable tool to perform differential meta-analyses of RNA-seq data by appropriately accounting for biological and technical variability within studies as well as additional study-specific effects. An R package metaRNASeq is available on the CRAN (http://cran.r-project.org/web/packages/metaRNASeq).The  Studies of gene expression have increasingly come to rely on the use of high-throughput sequencing (HTS) techniques to directly sequence libraries of reads  arising from the transcriptome (RNA-seq), yielding counts of the number of reads arising from each gene. Due to the cost of HTS experiments, for the time being RNA-seq experiments are typically performed on very few biological replicates, and therefore analyses to detect differential expression between two experimental conditions tend to lack detection power. However, as costs continue to decrease, it is likely that additional follow-up experiments will be conducted to re-address some biological questions, suggesting a future need for methods able to jointly analyze data from multiple studies. In particular, such methods must be able to appropriately account for the biological and technical variability among samples within a given study as well as for the additional variability due to study-specific effects. Such inter-study variability may arise due to technical differences among studies  as well as additional biological variability.p-values [et al.[et al.[et al.[p-value combination technique outperformed effect size combination methods or moderated t-tests [In recent years, several methods have been proposed to analyze microarray data arising from multiple independent but related studies; these meta-analysis techniques have the advantage of increasing the available sample size by integrating related datasets, subsequently increasing the power to detect differential expression. Such meta-analyses include, for example, methods to combine p-values , estimatp-values , and ranp-values ; Hu et as [et al. and Hongs [et al. provide l.[et al. present l.[et al. showed t t-tests  obtainedt-test [et al.[In many cases the meta-analysis techniques previously used for microarray data are not directly applicable for RNA-seq data. In particular, differential analyses of microarray data, whether for one or multiple studies, typically make use of a standard or moderated t-test ,8, as sut-test  or negatt-test ,11 in ort [et al. recentlyp-value combination methods as well as a model fitted over the full data with a fixed study effect. We first demonstrate how the inverse normal and Fisher p-value combination methods can be adapted to the differential meta-analysis of RNA-seq data. Then we compare these two methods to the results of independent per-study analyses and a negative binomial generalized linear model (GLM) with a fixed study effect as implemented in the DESeq Bioconductor package [In this paper, we consider several methods for the integrated analysis of RNA-seq data arising from multiple related studies, including two  package . All metFinally, we note that our focus is on RNA-seq data arising from two or more studies in which all experimental conditions under consideration are included in every study ; differential analyses among conditions that are not studied in the same experiment are typically limited, or even compromised, due to the confounding of condition and study effects.ygcrs be the observed count for gene g , condition c , biological replicate r , and study s . Note that the number of biological replicates Rcs may vary between conditions and among studies. We use dot notation to indicate summations in various directions, e.g., μgcs be the mean expression level for gene g in condition c and study s. For an integrated differential analysis of gene expression across all studies, two approaches can be envisaged: the combination of p-values from per-study differential analyses, and a global differential analysis. We illustrate both using the default methods and parameters of the DESeq (v1.10.1) analysis pipeline [edgeR[Let DESeq v1..1 analysne [edgeR, could ane [edgeR providess, we assume that gene counts ygcrs follow a negative binomial distribution parameterized by its mean ηgcrs = ℓcrsμgcs and dispersion ϕgs, where ℓcrs is a normalization factor to correct for differences in library size. A comparison of different methods to estimate ℓcrs may be found in Dillies et al.[For the differential analysis of gene expression within a given study es et al..p-values pgs are computed using a conditioned test analogous to Fisher’s exact test, where the p-value of a pair of observed count sums  is calculated as the sum of all probabilities less than p given the overall sum yg··s: After obtaining per-gene mean and dispersion parameter estimates in each study independently, a parametric gamma regression is used to obtain fitted dispersion estimates by pooling information from genes with similar expression strengths. Subsequently, for each gene in each study, the null hypothesis to be tested is that there is no difference in the relative proportion of read counts attributed to each condition, or in other words, that the gene is non-differentially expressed. Per-gene and per-study p = p(a)p(b), and p(a) and p(b) represent the probability of a and b counts in the first and second conditions, respectively. These probabilities are calculated using the negative binomial distributions parameterized by the corresponding estimated mean and dispersion parameters, μgcs and ϕgs.where it is assumed that DESeq package vignette. Once these vectors of raw p-values have been obtained for each study, we consider two possible approaches to combine them: the inverse normal and the Fisher combination methods. We note that both of these approaches assume that under the null hypothesis, each vector of p-values is assumed to be uniformly distributed.Additional details are described by Anders and Huber  and in tg, we define For each gene pgs corresponds to the raw p-value obtained for gene g in a differential analysis for study s, Φ the cumulative distribution function of the standard normal distribution, and ws a set of weights [ws, as described by Marot and Mayer [where  weights ,16. We pnd Mayer : s. This allows studies with large numbers of biological replicates to be attributed a larger weight than smaller studies. We note that other weights may also be defined by the user depending on the quality of the data in each study, if this information is available. where Ng in Equation (1) follows a p-values to control the false discovery rate at a desired level α.Under the null hypothesis, the test statistic Hochberg  may subsg may be defined as For the Fisher combination method , the tespgs corresponds to the raw p-value obtained for gene g in a differential analysis for study s. Under the null hypothesis, the test statistic Fg in Equation (2) follows a χ2 distribution with 2S degrees of freedom. As with the inverse normal p-value combination method, classical procedures for the correction of multiple testing [p-values to control the false discovery rate at a desired level α.where as before  testing  may be ap-value combination techniques requires two additional considerations to be taken into account when dealing with RNA-seq data.We note that the implementation of the previously described p-values for all genes arising from the per-study differential analyses are uniformly distributed under the null hypothesis. This assumption is, however, not always satisfied for RNA-seq data; in particular, a peak is often observed for p-values close to 1 due to the discretization of p-values for very low counts. To circumvent this first difficulty, as is commonly done for differential analyses in practice, we propose to filter the weakly expressed genes in each study, using the HTSFilter Bioconductor package [p-values, resulting in p-values that are roughly uniformly distributed under the null hypothesis.First, a crucial underlying assumption for the statistics defined in Equations 1) and (2) is that  package  as descr and (2) p-value combination methods described above, unlike microarray data, under- and over-expressed genes are analyzed together for RNA-seq data. As such, some care must be taken to identify genes exhibiting conflicting expression patterns . In the case of microarray data, Marot et al.[p-values for each study to avoid directional conflicts; as the inverse normal combination method was used in their work, the combined statistic thus follows a normal distribution, which is symmetric. Because under- and over-expressed genes may be found in the left and right tail, respectively, of the corresponding normal distribution, it is thus possible to use a two-tailed test to simultaneously study over and under-expressed genes. Note that Pearson [et al.[metaRNASeq.Second, for the two ot et al. suggeste Pearson  and Owen Pearson  proposedn [et al. or Owen n [et al.. We thusygcrs follow a negative binomial distribution parameterized by mean ηgcrs = ℓcrsμgcs and dispersion ϕg, where ℓcrs is the library size normalization factor. In order to estimate a possible effect due to study, a full and reduced model are fitted for each gene using negative binomial generalized linear models (GLM); the full model regresses gene expression on fixed effects for the experimental condition and study, while the reduced model regresses gene expression only on a fixed effect for the study.For a global analysis of RNA-seq data arising from multiple studies, we assume that gene counts ηgcrs) = βg + λgc + δgs + log (ℓcrs), where βg is an intercept, λgc is a fixed condition effect, δgs a fixed study effect, and λg1 = δg1 = 0, with the choice of the condition and study to be used as references being arbitrary. The reduced model is log(ηgcrs) = βg + δgs + log(ℓcrs). Per-gene dispersion parameters are estimated as before, where a parametric gamma regression is used to obtain fitted dispersion estimates by pooling information from genes with similar expression strengths across all studies.Specifically, the full model is log  to determine whether including the experimental condition significantly improves the model fit. Note that for the global differential analysis we use the HTSFilter Bioconductor package [p-values, resulting in a single vector of raw filtered per-gene p-values that may be corrected for multiple testing using classical procedures [α. Additional details may be found in the DESeq package vignette.Following parameter estimation, the two models are compared using a  package  to filteocedures  to contrp-value combination methods were applied to a pair of real RNA-seq studies performed to compare two human melanoma cell lines [The negative binomial GLM and ll lines . Each stll lines , and thell lines .The characteristics of the data from these two studies are summarized in Additional file p-values for each study  and (2), and the corresponding results were compared to those of the intersection of independent per-study analyses and the global analysis using a negative binomial GLM with a fixed study effect as previously described. We note that for the independent per-study differential analyses, a gene is declared to be differentially expressed if identified in both studies with no differential expression conflict. For the Inverse normal and Fisher methods, a total of 310  and 439  genes were respectively found to exhibit conflicting expression between the two studies, and were subsequently removed from the final list of differentially expressed genes. Unsurprisingly, these genes tended to be those with relatively large p-values in both studies ,p-values in only one study  considerably increase the detection power. In addition, a large number of genes are found in common among the p-value combination methods and the global analysis . In order to determine whether the genes uniquely identified by a particular method appear to be biologically pertinent, an Ingenuity Pathways Analysis  was performed to identify functional annotation for the genes uniquely identified by the Fisher p-value combination method with respect to the global analysis, and vice versa. We note that the sets of genes uniquely identified by the Fisher method or the global analysis  has a multiplicative effect on the mean. where θgc, ϕgs, σ}, we first performed individual per-study differential analyses by fitting a negative binomial model with the default methods and parameters of the DESeq package on the unfiltered human data presented above. The per-study false discovery rate was subsequently controlled at the α=0.05 level [θg1 and θg2 were fixed to be the values of the empirical means  for each condition across studies. For the remaining genes, we set θg1 = θg2 = θg to be the overall empirical mean  for gene g across both conditions and studies. Using the gamma-family GLM fitted to the per-gene mean and dispersion parameter estimates for each study ; for genes with θg < 10, the dispersion parameter is therefore fixed to be ϕgs = 1010, which corresponds to nearly zero overdispersion .where σ is chosen to represent a range of values for the amount of inter-study variability. The observed human data exhibit a considerable amount of inter-study variability, corresponding to a value of roughly σ = 0.5  or to the full set of data .In addition to the intersection of independent per-study analyses , the Fisher and inverse normal au et al. was applp-value combination methods with respect to the global analysis, and vice versa: the proportion of true positives among those uniquely identified by a given method  as compared to another .For each method, performance was assessed using the sensitivity, false discovery rate (FDR) and area under the receiver operating characteristic (ROC) curve (AUC). In addition, we also considered a criterion to assess the “value added” for the σ = 0 or σ = 0.15), no practical difference may be observed among the methods. On the other hand, for moderate to large inter-study variability (σ = 0.3 or σ = 0.5) differences among the methods become more apparent; this pattern is observed for any number of studies. As expected, including a study effect in the global analysis improves the performance over a naive global analysis without such an effect. We note that the two proposed meta-analysis methods  were found to perform very similarly and were able, in the case of large inter-study variability, to outperform the global analysis in terms of AUC , the global analysis with study effect and meta-analyses appear to have similar detection power. Finally, we also note that for all methods the FDR was well controlled below 5% , the proportion of truly DE genes uniquely found with the Fisher method was equal to more than 80%, whereas it was only around 40% for the global analysis with a study effect.Based on these criteria, the two proposed meta-analysis methods  seem to perform very similarly. In order to more thoroughly investigate the differences between p-value combination methods are implemented in an R package called metaRNASeq, available on the CRAN; a package vignette describing the use of metaRNASeq may be found in Additional file vignette(~metaRNASeq~) after loading the package in R.The aim of this paper was to present and compare different strategies for the differential meta-analysis of RNA-seq data arising from multiple, related studies. As expected, naive analyses such as the overlap of lists of differentially expressed genes found by individual studies or a global analysis not accounting for a study effect perform very poorly. On the other hand, the two proposed meta-analysis methods seem to have very similar performances. For low inter-study variability, the results are very close to those of a global GLM analysis including a study effect. When the inter-study variability increases, however, the gains in performance in terms of AUC, sensitivity, and proportion of true positives among uniquely identified genes for the meta-analysis techniques are significant as compared to the global analysis, particularly for the analysis of data from more than two studies. We note that both of the proposed p-value combination techniques presented here must overcome differences in experimental objectives, design, and populations of interest, as well as differences in sequencing technology, library preparation, and laboratory-specific effects.Our focus in this work is on differential analyses between two experimental conditions, but can readily be extended to multi-group comparisons. However, as previously noted, the methods presented here are intended for the analysis of data in which all experimental conditions under consideration are included in every study, thus avoiding problems due to the confounding of condition and study effects. As with all meta-analyses, the p-value combination methods rely on the fact that the same test statistics, or in the case of RNA-seq data conditioned tests, are used to obtain p-values for each study. An important challenge for the future will be to propose methods able to jointly analyze related heterogeneous data, such as microarray and RNA-seq data, or other kinds of genomic data. This is not straightforward in a meta-analysis framework and remains an open research question.The differential meta-analyses presented here concern expression studies based on RNA-seq data. However, other genomic data are generated by high-throughput sequencing techniques, including chromatin immunoprecipitation sequencing (CHIP-seq) and DNA methylation sequencing (methyl-seq), and the proposed techniques could potentially be extended to these other kinds of data. However, in order to be biologically relevant, the The authors declare that they have no competing interests.AR participated in the design of the study, performed simulations and data analyses, and helped draft the manuscript. GM designed the study, wrote the associated R package, and helped draft the manuscript. FJ conceived of the study, participated in its design, and drafted the manuscript. All authors read and approved the final manuscript.Supplementary materials. This document contains a discussion concerning the filtering of RNA-seq data, supplementary information about the characteristics of the data and the Ingenuity Pathways Analysis discussed in the real data analysis, and supplementary figures.Click here for filemetaRNASeq vignette. This document contains a vignette describing in greater detail the metaRNASeq R package. It can also be obtained by running the following commands in the R console:> library(metaRNASeq)> vignette(“metaRNASeq”)Click here for file


However, transferring the multi-gene signatures from one analytical platform to another without loss of classification accuracy is a major challenge. Here, we compared three unsupervised data discretization methods--Equal-width binning, Equal-frequency binning, and We applied an integrated machine learning framework that involves three sequential steps; feature selection, data discretization, and classification. For models trained and tested on exon-array data, the addition of data discretization step led to robust and accurate predictive models with fewer number of variables in the final models. For models trained on exon-array data and tested on RNA-seq data, the addition of data discretization step dramatically improved the classification accuracies with Equal-frequency binning showing the highest improvement with more than 90% accuracies for all the models with features chosen by Random Forest based feature selection. Overall, SVM classifier coupled with Equal-frequency binning achieved the best accuracy (> 95%). Without data discretization, however, only 73.6% accuracy was achieved at most.The classification algorithms, trained and tested on data from the same platform, yielded similar accuracies in predicting the four GBM subgroups. However, when dealing with cross-platform data, from exon-array to RNA-seq, the classifiers yielded stable models with highest classification accuracies on data transformed by Equal frequency binning. The approach presented here is generally applicable to other cancer types for classification and identification of molecular subgroups by integrating data across different gene expression platforms. Molecular understanding of tumor heterogeneity is key to personalized medicine and effective cancer treatments. Numerous studies have identified molecularly distinct cancer subtypes among individual patients of the same histopathological type by performing a high-throughput gene expression analysis of the patient tumor samples . While mloess) [A crucial step in the translation of gene signatures derived from high-throughput platforms is validation in a clinical setting, using robust and quantitative assay platforms  without loss of any classification accuracy . A majorloess) ,16, rankloess) -19, haveData discretization is a popular data pre-processing step used in supervised learning for creating the training sets. Data discretization transforms continuous values of feature variables to discrete ones ,22. It cFeature selection algorithms seek for a subset of relevant features to use in model construction in order to simplify and reduce over-fitting of the models. The wrapper, filter, and embedded methods are the three main categories that have been widely used in biomedical research to deal with a large feature space ,27. BrieUsing a subset of most important genes (variables/features) screened by the variable selection methods, numerous classification methods have been applied to tackle disease sample classification problems. For example, SVM was applied for characterizing functional roles of genes in yeast genome and cancer tissues ,29, RF fk for the discretization, we explored various bin sizes including the optimal bin number (k = 11) based on Dougherty's formula [k = 10 as it consistently achieved good accuracy. Then we applied two advanced feature selection algorithms, SVM-recursive feature elimination (SVM-RFE) [th (257 samples) for training and 1/4th (85 samples) for testing. We describe below the classification performance for each variable selection method--CV, SVM-RFE and RF_based_FS.Because gene isoforms (variables) whose expression levels do not vary much across the samples are less useful for discriminating the four GBM subtypes, We selected 2,000 isoforms with the highest variability across the samples, using CV (coefficient of variation). To search for an optimal bin number  formula , and choSVM-RFE)  and RF bk-means clustering yielded the best accuracy of 90.6% , we trained the classifiers using the data from exon-array and tested on matched RNA-seq datasets for the same TCGA samples. First, we observed that the classification framework resulted in poor classification accuracies when the classification and feature selection algorithms were trained on FC data from exon-array data and tested on corresponding FC data from RNA-seq platform Table . The besk-means with SVM . Equal-W binning improved the accuracy for RF , but not for the other three classifiers. When using the top 100 features in the final model, RF with Equal-F binning correctly predicted 68 samples out of 76, achieving ~90% accuracy  was rather poor and ranged from 27.6 to 69.7% Figure . Howeverk-means clustering discretization, RF achieved the best performance. Using the top 100 features, RF with Equal-F binning achieved 86.8% accuracy that is 31.6% higher than the best accuracy with FC (55.2%).Similarly, for SVM-RFE features selection, the prediction accuracy of the models on FC data is quite low, within the range of 35.5-61.8%. While Equal-F binning improved the accuracy of all the four classifiers, Equal-W binning improved the accuracy for SVM and RF only  for classification methods that can handle only discrete variables, (2) for improving the human interpretation, (3) for faster computation process with a reduced level of data complexity, (4) for handling non-linear relations in the data, e.g., very highly and very lowly expressed genes are more relevant to cancer subtype, and (5) to harmonize the heterogeneous data. In this study, we showed that simple unsupervised discretization indeed improved the classification accuracy by harmonizing the data that come in different scale and magnitude from different gene expression platforms. The discretization step lead to numerically comparable measures of gene expression between different platforms, and translate the classification models (consisting of multiple transcript variables) across platforms. However, the discretization methods applied in this study have some limitations. For example, Equal-W binning is prone to outliers that may skew the distribution . The k-mThe choice of classification algorithms is often important dealing with a certain dataset as each of the algorithms has its own strengths and weaknesses. We experimented on the four state-of-the-art machine learning approaches based on maximum margin, decision tree, probabilistic and clustering classification. While SVM achieved the best accuracy, the performance of RF was more consistent when tested with various numbers of features and data types. We used the linear kernel SVM because it is known to be less prone to overfitting than nonlinear kernels such as radial basis function (RBF); intuitively, the RBF kernel could perform better when the data is linearly not separable or the feature and sample spaces are well balanced. PAM and NB also performed fairly well with the features chosen by RF_based_FS. NB is known to be robust with irrelevant features, but the performance would be quickly degraded when correlated features are added.k-means clustering. With Equal-F binning, RF_based_FS identified important features more efficiently than the CV and SVM-RFE when fewer gene isoforms are considered in classification. Based on these encouraging results, we propose an integrative machine learning framework that involves feature selection, data discretization, and classification model build up by training and testing for independent platform ; obtained the isoform expression of 114,930 transcript variants  using the Multi-Mapping Bayesian Gene eXpression program [We obtained isoform-level gene expression estimates and molecular subtype information for 342 and 155 GBM samples profiled by Affymetrix exon-arrays and RNA-seq, respectively. The four molecular subgroups are neural (N), proneural (PN), mesenchymal (M) and classical (CL). Gene expression profiles for 76  samples were available from both RNA-seq and exon-array platforms. The common samples were used to assess classification performance for platform transition. We followed the data pre-processing procedure and obtained patients' GBM subtype information (class labels) from our recent study ; briefly program . The estk-means clustering--on the continuous FC data.We processed the gene expression data to estimate the FC values, and then three unsupervised discretization techniques--Equal-W binning, Equal-F binning, and 2 (T/N), where T is estimated expression values of a tumor sample and N is median expression of normal brain samples.FC is a measure of a quantitative change of gene expression, defined by FC=loget al [k = max ), where l is the number of distinct values of the attribute. Boulle [k = 10, considered as a default for most cases. While the former approach resulted the maximum bin number k = 11, we extensively evaluated by exploring various bin numbers of k = 2i for i={1, 2, ..., 10}.To determine the number of bins for discretization, Dougherty et al  suggeste. Boulle  proposedi))-min(GE(i)))/number of bins, where GE is isoform-level transcript gene expression of sample i. Then, continuous variables are assigned into the corresponding bin numbers.Equal-W binning algorithm seeks for maximum and minimum values, and then divides the range into the user-defined equal width intervals defined as Equal-W=(max(GE(i))/number of bins.Equal-F binning algorithm sorts all continuous variables in ascending order, and then divides the range into the user-defined intervals so that every interval contains the same number of sorted values defined as Equal-F=sort/2 binary classifiers are trained and the appropriate class is determined by major voting. In this study, we used the latter approach with a linear kernel method as the size of features is larger than samples.SVM is primarily a two-class classifier that constructs a hyperplane to separate the data with maximum margin ,36. For RF is an ensemble learning method that builds decision trees with binary splits . Each trx1,...,nx) of an instance X are conditionally independent given the class C. For example, for a class C that maximizes the likelihood is P(X|C)=P. The conditional independence enables the conditional probability as a product of simpler probabilities defined by P(X|C)=Π P(iX|C).NB is a simple probabilistic classification method grounded in Bayes' theorem, for calculating conditional probabilities, with an independence assumption . For a gPAM is a sample classification method that uses the nearest shrunken centroid approach for transcript-variants gene expression data . Brieflyi, tnitp, and ifn, are true positive, true negative, false positive, and false negative for class iC, respectively.We estimated the overall classification accuracy based on the number of correct predictions divided by the total number of prediction samples defined as ACC=(number of correct predictions)/. In addition, sensitivity (Sn) and specificity (Sp) for each sub-group (one GBM sub-group vs the rest of the GBM groups together) are calculated as The authors declare that they have no competing interests.SJ participated in the design of the project, performed the bioinformatics and statistical analysis, and drafted the manuscript. YB and RVD conceived the idea and participated in the design of the project. SJ, YB, and RVD revised the manuscript. All authors read and approved the final manuscript.


Cancer classification by doctors and radiologists was based on morphological and clinical features and had limited diagnostic ability in olden days. The recent arrival of DNA microarray technology has led to the concurrent monitoring of thousands of gene expressions in a single chip which stimulates the progress in cancer classification. In this paper, we have proposed a hybrid approach for microarray data classification based on nearest neighbor (KNN), naive Bayes, and support vector machine (SVM). Feature selection prior to classification plays a vital role and a feature selection technique which combines discrete wavelet transform (DWT) and moving window technique (MWT) is used. The performance of the proposed method is compared with the conventional classifiers like support vector machine, nearest neighbor, and naive Bayes. Experiments have been conducted on both real and benchmark datasets and the results indicate that the ensemble approach produces higher classification accuracy than conventional classifiers. This paper serves as an automated system for the classification of cancer and can be applied by doctors in real cases which serve as a boon to the medical community. This work further reduces the misclassification of cancers which is highly not allowed in cancer detection. Despite the skill of the doctors or radiologists, there is always a possibility of missing the detection of cancers by using image modalities for the detection of cancers such as breast, lungs, and colon. This is due to various reasons including technical issues in capturing the images, unobservable abnormalities, and misinterpretation of abnormalities. We hereby propose an automated classification system to reduce the diagnosis error using microarray data. The proposed system has the capability to distinguish between normal and abnormal cases of different cancers based on discrete wavelet transform (DWT).Microarray is the technology for measuring the expression levels of tens of thousands of genes in parallel in a single chip –3. Each Several classification techniques have been employed in the past to deal with microarray data. Reference  used a wFeature selection prior to classification is an essential task. Feature selection methods  remove iThis paper deals with the classification of particular microarray data into normal or abnormal based on discrete wavelet transform (DWT). DWT is an important multiresolution analysis tool that has been commonly applied to signal processing, image analysis, and various classification systems . A novelThe rest of the paper is organized as follows. Microarray and wavelet transforms theoretical framework is discussed in Large amount of data useful for solving many biological problems can be generated by a technique called microarray. Microarray is a technique which measures the level of activity of thousands of genes concurrently. If the gene is overexpressed then there will be too much protein which gives the conclusion that the particular gene is abnormal. Even much smaller changes can be detected by microarrays compared to karyotypes. The domain where microarray is used in the recent years is in disease classification. Gene expression data is data rich and information poor. Public microarray databases include NCBI, Genbank, Array Express, Gene Expression Omnibus, and Stanford Microarray . Microar (1)Collect samples from healthy and cancer patients and keep the samples in two separate tubes. (2)Isolate RNA followed by mRNA isolation from healthy and cancerous samples. (3)Convert to complimentary DNA (cDNA) with healthy cDNA green in color and cancerous cDNA green in color. (4)Apply cDNA from both samples to the microarray. (5)Scanning the microarray will tell the difference between infected cell and uninfected cell.The red spots represent genes that are turned up and the green spots represent genes that are turned down. More mRNA is produced in red spots and less mRNA is produced in green spots. Red spots represent cancer cells and green spots represent normal cells and the yellow spots represent genes that are expressed in both cancer and healthy patients.Listed below is the protocol for microarray technology.Discrete wavelet transform is a signal processing method by which gene expression data is processed. Wavelet transform is used in gene expression analysis because of its multiresolution approach in signal processing. In this microarray data is transformed into time-scale domain and used as classification features. Gene expression data is represented by a matrix in which rows represent genes and columns represent samples. Since each sample contains thousands of genes, the number of genes can be viewed as the length of the signals. Hence signal processing techniques can be used for microarray data analysis. Wavelets are a family of basis functions. symlet, coiflet, Daubechies, and biorthogonal and reverse biorthogonal wavelets are the wavelet families which are already in use . They vaThe main objective of the proposed classification system is to distinguish between the normal and abnormal microarray data of different types of cancer. Five real world microarray datasets used by many researchers are taken to evaluate this study. The two different stages involved in the proposed classification system are feature extraction and classification. They are discussed in the following sections.  t-test, feature selection method is applied to the selected feature at the same approximation value. After ranking the wavelet coefficients inside the window, the top ranked wavelet coefficient is selected as a dominant feature of that window. A similar process is applied for all the windows placed in given microarray data and the resultant top ranked coefficients are stored in the database for further classification. In this paper, the algorithm based on DWT used to perform effective feature selection [A novel feature extraction technique based on DWT and MWT is proposed. Feature extraction involves simplifying the amount of resources required to describe a large set of data accurately. DWT can be used for high dimensionality data analyses, such as image processing and image data analysis. The proposed method rearranges the data giving a threshold to the wavelet coefficient using DWT and then calculates the approximate value of the raw data after applying an inverse function to the transformed data. Afterelection  is shownt-test is applied to select the top ranking features (genes). The algorithm is represented in The algorithm works as follows. First the window size is defined. Then for each window apply the wavelet transform and define the level of decomposition. It divides into two subbands, namely, scaling coefficient and detailed coefficient, which are called wavelet coefficients. Rearrange the data giving a threshold to the wavelet coefficient and then calculate the approximate value of the raw data after applying an inverse function to the transformed data.  K-fold cross-validation the data is randomly divided into K partitions. Data in one partition is used to test and the remaining partitions are used to train. This means that the training data needs to be calculated K times as each partition gets tested. In order to evaluate the robustness of the proposed system threefold cross-validation is used. Every fold is used to test the accuracy by using one classifier each. The classification accuracy obtained from each of the classifiers is considered as the weight of the same while the classifiers are hybridized.Classification of microarray data into normal and abnormal is done by designing hybrid classifier based on NN, naive Bayes, and SVM. In the classification stage, the same kinds of features are extracted and compared with the references obtained in the training stage. Cross-validation is used to estimate how a machine learning algorithm will perform when faced with unfamiliar data. It is intended to reduce error associated with one of the pit falls of machine learning where a hypothesis is formed on the same data to test it. In (1)Colon dataset: colon dataset  is deriv (2)Ovarian dataset: ovarian dataset  often se (3)CNS dataset: CNS dataset  contains (4)Leukemia dataset: leukemia dataset  is anoth (5)Breast dataset: breast dataset  consistsTo demonstrate the efficiency of our method, the proposed method is evaluated on five gene expression datasets used in the literature.In this section, the experimental results and their implications are discussed. Here the performance of the proposed classification system of cancerous microarray data based on DWT and hybrid classifier is explained. To evaluate the performance of the proposed system, computer simulations and experiments with microarray data are performed. The system is implemented in MATLAB version 7.6. The training and testing are run on a modern standard PC  running under Windows XP. The metric used to analyze the performance of the proposed system is classification accuracy. In this study, 4 datasets having large number of genes and 1 dataset with minimum number of genes are considered.As the microarray dataset has different number of samples, 60% of cases are selected for training the classifier and the remaining 40% are used for classification using hybrid classifier. The robustness of the proposed system is evaluated by changing three parameters, namely, different types of wavelet, decomposition level of DWT, and also the window size of the proposed MWT. The types of wavelets used are Daubechies 7 (db7), coiflet 2 (coif2), biorthogonal 2.2 (bior2.2), Symlet 2 (sym2), and reverse biorthogonal 2.2 (rbio2.2), respectively . The Daubechies family bases are chosen because of their properties of compact support and orthonormality. The biorthogonal wavelets are chosen for their property of exact reconstruction. The window sizes used in the proposed MWT are 32, 64, 128, 256, 512, and 1024. The performance analysis starts from one-level decomposition to maximum level of decomposition of window size using the wavelet.Tables Individual classifiers show considerably very poor performance compared to our hybrid classifier for the breast dataset. The maximum accuracy in terms of individual classifier is 64.44 for KNN in level 1 with a window size of 128. Our ensemble approach produces 100% accuracy with just 24 features for level 4 using window size of 1024. It is obvious from From the figures and In this paper we have proposed a new technique for the classification of microarray data based on DWT. The multiresolutional representation of microarray data is achieved by DWT inside a window of predefined size as test pattern. The performance of the proposed system is assessed by means of extensive computational tests concerning the classification of five cancer microarray datasets: breast, colon, ovarian, CNS, and leukemia. Experimental results show that the proposed method is successful in classifying the microarray data and the successful classification rate is 100% for all five microarray datasets. It is observed from the results that DWT emerges as a potentially dominant feature extraction technique for microarray data classification.