Hence, biological variation in sRNA composition and content between samples is preserved while technical variation is effectively minimized. Together, both spike-in sets can significantly improve the technical reproducibility of sRNA-seq.There is an increasing interest in complementing RNA-seq experiments with small-RNA (sRNA) expression data to obtain a comprehensive view of a transcriptome. Currently, two main experimental challenges concerning sRNA-seq exist: how to check the size distribution of isolated sRNAs, given the sensitive size-selection steps in the protocol; and how to normalize data between samples, given the low complexity of sRNA types. We here present two separate sets of synthetic RNA spike-ins for monitoring size-selection and for performing data normalization in sRNA-seq. The size-range quality control (SRQC) spike-in set, consisting of 11 oligoribonucleotides (10–70 nucleotides), was tested by intentionally altering the size-selection protocol and verified via several comparative experiments. We demonstrate that the SRQC set is useful to reproducibly track down biases in the size-selection in sRNA-seq. The external reference for data-normalization (ERDN) spike-in set, consisting of 19 oligoribonucleotides, was developed for sample-to-sample normalization in differential-expression analysis of sRNA-seq data. Testing and applying the ERDN set showed that it can reproducibly detect differential expression over a dynamic range of 2 Small RNA (sRNA) is commonly defined as the fraction of the transcriptome that contains all RNA molecules shorter than 200 nucleotides . This clStandard sRNA-seq protocols contain some form of size selection at one or more steps in the procedure, even when starting from total RNA. The common protocols that are being used to isolate sRNA for input in sRNA-seq enrich for RNA molecules in a limited range of roughly 15–100 nucleotides. These protocols are relatively unstable, often due to (undetected) contaminants in the samples or subtle changes in the final ethanol concentrations, which may result in sRNA isolation over a different size range or with a different efficiency per given size between samples. Such variations are undesirable and could erroneously be interpreted as the result of differential sRNA expression. As such, the performance of the size-selection steps has to be checked with proper controls. The best approach would be to design synthetic spike-in controls that cover the protocol's relevant sRNA size range and add these to all samples at the beginning of the procedure.Expression-data normalization aims at minimizing variation that is not the result of biological effects and is thus essential for detecting the biologically relevant differences in gene expression . This isTherefore, to address these sRNA-seq issues, we developed, implemented and tested two dedicated external spike-in sets for use in sRNA-seq experimentation: one set of size spike-in controls to monitor size bias in the entire sRNA-seq procedure  and another set of data-normalization spike-ins for use as an external reference in the normalization of sRNA-seq expression data . In summary: with these two spike-in sets, we are able to evaluate the quality and size-range of the RNA size-selection and perform effective data normalization in sRNA-seq.http://zfin.org). The breeding of adult fish was approved by the local animal welfare committee (DEC) of the University of Leiden, The Netherlands. All protocols adhered to the international guidelines specified by the EU Animal Protection Directive 86/609/EEC. Adult zebrafish and oocytes were flash-frozen in liquid nitrogen and stored at −80°C. Before freezing, fish were put under anesthesia using 0.02% buffered 3-aminobenzoic acid ethyl ester (Tricaine).Adult zebrafish (strain ABxTL) were handled in compliance with local animal welfare regulations and maintained according to standard protocols . In brief, powdered tissue was homogenized in TRIzol Reagent (Life Technologies) and 1-bromo-3-chloropropane (BCP) was added. After centrifugation RNA partitioned to the upper aqueous phase, which was carefully removed and subjected to column-based sRNA isolation according to the manufacturers’ instructions. RNA concentration was measured on a NanoDrop ND-2000 (Thermo Scientific) and RNA integrity was examined on a 2200 TapeStation instrument using R6K and High Sensitivity R6K ScreenTapes (Agilent Technologies).G for hairpin formation >−0.5 kcal/mol at 37°C , and an E-value >10 when aligned against the NCBI nucleotide collection (nr/nt: http://blast.ncbi.nlm.nih.gov) using BLASTN. From these candidates 19 sequences of 25 nucleotides long were selected for use as external reference for data-normalization (ERDN) spike-ins. For size-range quality control (SRQC) spike-ins different sequences were selected of 10, 16, 19, 22, 25, 28, 34, 40, 50, 60 and 70 nucleotides long. The 10-nucleotide SRQC spike-in was not checked using BLASTN because of its short size. These sequences were obtained as single-stranded oligoribonucleotides with a 5′-phosphate and polyacrylamide gel purified (Eurogentec S.A.). The oligoribonucleotides were dissolved in 100 μM in RNase-free TE (10 mM Tris–HCl pH 8 and 1 mM ethylenediaminetetraacetic acid (EDTA)) and stored in aliquots at −80°C.A list of candidate random RNA sequences of the desired length was generated using the following constraints: maximum homopolymer length of two nucleotides, GC-content between 40 and 60%, ΔsRNA libraries were prepared according to the manufacturers’ protocols using the Ion Total RNA-Seq Kit v2 (Life Technologies). Briefly, adapters were ligated to the sRNA and a reverse transcription reaction was performed. The resulting cDNA was amplified and at the same time barcoded with IonXpress RNA-Seq BC01-BC16 (Life Technologies). The yield and the size distribution of the amplified cDNA were assessed using the 2200 Tapestation with the Agilent D1K ScreenTape (Agilent Technologies). Emulsion PCR was performed using the Ion PI Template OT2 200 Kit on an Ion OneTouch 2 Instrument, after which the template-positive Ion PI Ion Sphere Particles were recovered, quantified with a Qubit 2.0 ﬂuorometer, and enriched using an Ion OneTouch ES (Life Technologies). Sequencing was carried out on the Ion Proton system using an Ion PI Chip v2 and Ion PI Sequencing 200 kit (Life Technologies) following the manufacturers’ protocols (Revision 3.0).http://www.ebi.ac.uk/ena/) under the project accession number ERP007147. For detailed info see Supplementary Data 4.All sequencing results are accessible through the European Nucleotide Archive , returning the coefficient of determination (R2) as a measure of similarity.The levels of each fold-change control in all eight dilutions were evaluated by individually plotting their relative input concentration against their normalized number of reads. These data should follow a linear relationship on a loghttp://github.com/lh3/seqtk).Down-sampled fastq files were generated by randomly sampling 2.5, 3.0, 3.5, 4.0, 4.5 and 5.0 million of reads from the original fastq files  using the Seqtk tool (In order to make RNA-seq libraries that are focused on sRNA (<200 nt), library preparation protocols contain size-selection steps at different stages in the NGS procedure. For example, the size range of Ion Torrent technology based NGS libraries is controlled by sRNA isolation from total RNA, followed by size selection after cDNA synthesis and after library amplification. Unfortunately, the performance of these size-selection steps can be sample-dependent, as well as sensitive to variation in lab handling. This may have a significant impact on the sequencing outcome. For this reason, we decided to design a set of external sRNA size-range quality controls (SRQC) to allow checking of the NGS size-selection steps. These controls are to be added to total RNA as early in the procedure as possible and enable routine monitoring of a consistent size selection of RNA isolation, library preparation and sequencing, from the beginning to the end of the procedure. As the standard sRNA isolation methods followed by standard sRNA-seq, investigate sRNA in the range of ∼10–70 nucleotides, we decided to design an SRQC spike-in set that consists of 11 synthetically-synthesized, single-stranded 5′-phosphorylated oligoribonucleotides of increasing length . The concentration of each individual spike-in was adjusted to obtain similar percentages of reads for all spike-ins in the mix (Supplementary Table S1.2). In addition, the overall concentration of the SRQC mix was chosen to be 1.9 × 1010 oligoribonucleotides/μg of total RNA, which will produce sufficient reads for accurate counting, while consuming at most 1–2% of the total number of reads in each sample.The differences in sequencing efficiency between the individual spike-ins can be considerable, so we started by levelling-out most of these variances. At the same time, we sought for an appropriate concentration for each SRQC oligonucleotide that allows reliable measurement, but avoids using-up too many sequencing reads. For this purpose, an equimolar mix of all SRQC oligoribonucleotides was added to identical zebrafish sRNA samples at 9.6 × 10It is obviously extremely important for any control to be robust. Hence, to evaluate the reproducibility of the SRQC spike-in set, we performed a test experiment. Zebrafish total RNA (including sRNA) was split in six equal aliquots to which equal an amount of SRQC mix was added. On these spiked samples, a standard sRNA-seq analysis was performed, including all size-selection steps. The results show that the technical reproducibility of the spiking and sequencing procedure is high Figure . AlthougNext to reproducibility, sensitivity is another important issue of experiment controls. To investigate this for the SRQC spike-in set, we designed an experiment in which we manipulated the size-selectivity range of the sRNA isolation protocol. In the Ion Torrent sRNA-seq workflow , it is recommended to first isolate sRNA by removing large nucleic acids (>200 nucleotides) from total RNA using a low ethanol concentration in combination with silica-based spin columns. Changing the ethanol concentration affects the sRNA isolation size range. Therefore, zebrafish total RNA was spiked with the SRQC mix, split into equal aliquots and subjected to sRNA isolation using either: standard, standard plus 6% or standard minus 6% ethanol concentrations. sRNA-seq analysis of the isolated sRNA samples showed a clear effect of ethanol on the SRQC read counts Figure . At a hiTo test our SRQC spike-in set in a real-life scenario, we evaluated the isolation of sRNA from two different biological samples. It is our experience that the standard sRNA isolation protocol performs differently when using zebrafish oocytes as compared to somatic zebrafish tissues, presumably due to impurities from the oocytes’ chorion. Hence, total RNA from adult zebrafish (whole body) and oocytes was spiked at a fixed SRQC/total RNA ratio and analysed by sRNA-seq. The size range that was shown by the SRQCs revealed a marked loss of spike-ins smaller than 28 nucleotides in the sRNA fraction from oocytes compared to the adult Figure . This meIn conclusion, these findings demonstrate that the SRQCs can be used to reliably detect differences in the size selection of sRNA-seq. We recommend including SRQCs in each sample to enable routine quality control monitoring of the sRNA-seq procedure for undesirable size-range biases that might occur anywhere in the RNA isolation and/or sequencing procedures. Size-range variations between samples will result in noisy expression data, thereby frustrating the reliable identification of e.g. differentially expressed miRNAs. Lastly, we believe that SRQCs can be useful for all NGS platforms that rely on size-selection steps, such as the gel-base size selection in Illumina's TruSeq sRNA-seq protocols.Data normalization is an important element in omics experimentation. Between samples there are differences in technical efficiency that result in variations in the generated data. Correct normalization of gene-expression data is therefore paramount for the accurate comparison of expression levels. Normalization is typically performed with the premise that the expression of the vast majority of genes does not change. This assumption does not necessarily hold true for the different types of sRNA because of the lower complexity of the sRNA pool in comparison with a full transcriptome. As an example, we investigated the quantitative diversity of the miRNA pool in adult zebrafish Figure . The 10 The use of synthetic spike-ins for normalization of sRNA-seq data was previously published . As ackn5 reads per 4 million total reads on average per technical replicate. We conclude that, from a technical perspective, the ERDN mix could be suitable for use as a reliable reference for data normalization in sRNA-seq.Given that the normalization spike-ins will function as a fixed reference in a variable biological sample, high technical reproducibility of spike-in sequencing is essential. To evaluate this, the ERDN mix was added six times independently to equal aliquots taken from a single batch of zebrafish total RNA. The samples were individually subjected to sRNA isolation and sRNA-seq. Reproducibility was high for spike-ins with >100 reads and gradually decreased for lower read counts Figure , as was 4, thus covering a large part of the dynamic range of the ERDNs (105 reads). To then evaluate the efficacy of the ERDN spike-in set in normalization, the following approach was taken: since the fold-change mixes were added to an identical background of zebrafish RNA, a scaling based on the number of miRNA reads would be the best available normalization method to compare ERDN-based normalization to. We therefore performed a data normalization using the ERDN spike-in set and evaluated how close the results would match with the absolute data normalisation based on the identical miRNA background in these samples. Subsequently, the coefficient of determination (R2) between the observed and expected fold-changes was calculated and used to check the performance of the normalization procedure. The average R2 values for all fold-change test spike-ins were 0.87 ± 0.12, 0.92 ± 0.06 and 0.91 ± 0.07 for non-normalized reads, total-number of mapped miRNA reads normalized data and ERDN-normalized data, respectively . The effect of ERDN-based normalization on these fold-changes was assessed. After sRNA isolation and sRNA-seq the number of reads mapping to the fold-change spike-ins varied between 0 and 10y Figure . We concn = 8), which represent identical technical replicates. For this purpose, the standard deviation distribution of the mapped zebrafish miRNAs was calculated for each miRNA over all eight replicates and plotted in a kernel density plot  to monitor a clear improvement of SRQC reproducibility. Next, normalization based on the ERDN spike-ins was performed, showing a robust reduction of the variance of the SRQC reads over most of the tested size range Figure . The var18 while having a ∼2-fold difference in number of reads between consecutive spike-ins. ERDN-based normalization improves the fold-change response of artificial fold-change controls and improves technical reproducibility of sRNA sequencing. We suggest the use of ERDNs for sample-to-sample normalization of sRNA-seq data, in order to achieve a more accurate quantification of differential expression between biological samples.In summary, the ERDN spike-in set yields reproducible results in the size range from 10 to 70 nucleotides and covers a dynamic range of 22 ratios close to zero for both miRNAs and piRNAs (Figure et al. suggest to treat the small RNA population not as a whole, but as a set composed by smaller subsets such as miRNA, piRNA, etcetera and then to normalize on the number of reads of each subset (et al. checked for variability through identical replicates sequenced in different laboratories, while in our case we compared two biologically very different samples. Together, these findings prove that normalization on subsets mapped reads is not suitable in experiments, where the composition of the sRNA pool is different between samples. The addition of ERDN spike-ins to total RNA samples at a fixed ratio, however, effectively enables normalization relative to the amount of starting total RNA. Hence, biological variation in sRNA composition and content between samples is preserved while at the same time technical variation is effectively minimized.In contrast to our test experiment with identical sRNA zebrafish samples, normalization on the number of mapped reads will fail to correctly preserve information on differential expression in cases where sRNA content and composition differ significantly between biological samples. To demonstrate the applicability of ERDN-based normalization in such a scenario, we applied our normalization spike-in set in a sRNA-seq experiment comparing sRNA expression profiles of male and female zebrafish. For the analysis, we have chosen to focus on miRNA and piRNA, as these are abundant and their sequences are readily available. Total RNA was isolated from the whole bodies of four adult male and four adult female zebrafish and spiked with the SRQC and ERDN spike-in mixes at a fixed total-RNA/spike-in ratio. sRNA-seq analysis showed that the size distribution of the NGS reads is remarkably different between female Figure  and males Figure . In conts Figure . Hoen eth subset . HoweverSupplementary Data are available at NAR Online.


Single-cell RNA-seq data allows insight into normal cellular function and various disease states through molecular characterization of gene expression on the single cell level. Dimensionality reduction of such high-dimensional data sets is essential for visualization and analysis, but single-cell RNA-seq data are challenging for classical dimensionality-reduction methods because of the prevalence of dropout events, which lead to zero-inflated data. Here, we develop a dimensionality-reduction method, (Z)ero (I)nflated (F)actor (A)nalysis (ZIFA), which explicitly models the dropout characteristics, and show that it improves modeling accuracy on simulated and biological data sets.The online version of this article (doi:10.1186/s13059-015-0805-z) contains supplementary material, which is available to authorized users. Single-cell RNA expression analysis (scRNA-seq) is revolutionizing whole-organism science , 2 allowDespite the success of early single-cell studies, the statistical tools that have been applied to date are largely generic, rarely taking into account the particular structural features of single-cell expression data. In particular, single-cell gene expression data contain an abundance of dropout events that lead to zero expression measurements. These dropout events may be the result of technical sampling effects (due to low transcript numbers) or real biology arising from stochastic transcriptional activity Fig. a. Previolatent space reducing the analytical problem from a simultaneous examination of tens of thousands of individual genes to a much smaller number of (weighted) collections that exploit gene co-expression patterns. In the low-dimensional latent space, it is hoped that patterns or connections between data points that are hard or impossible to identify in the high-dimensional space will be easy to visualize.Dimensionality reduction is a universal data-processing step in high-dimensional gene expression analysis. It involves projecting data points from the very high-dimensional gene expression measurement space to a low-dimensional The most frequently used technique is PCA, which identifies the directions of largest variance  and uses a linear transformation of the data into a latent space spanned by these principal components. The transformation is linear as the coordinates of the data points in the low-dimensional latent space are a weighted sum of the coordinates in the original high-dimensional space and no non-linear transformations are used. Other linear techniques include factor analysis (FA), which is similar to PCA but focuses on modeling correlations rather than covariances. Many non-linear dimensionality techniques are also available but linear methods are often used in an initial step in any dimensionality-reduction processing since non-linear techniques are typically more computationally complex and do not scale well to simultaneously handling many thousands of genes and samples.In this article, we focus on the impact of dropout events on the output of dimensionality-reduction algorithms  and propose a novel extension of the framework of probabilistic principal components analysis (PPCA)  or FA toμ and the dropout rate for that gene by p0, we have found that this dropout relationship can be approximately modeled with a parametric form p0= exp(−λμ2), where λ is a fitted parameter, based on a double exponential function. This relationship is consistent with previous investigations , where yij is the level of expression (log read count) of the jth gene in the ith sample. The data are assumed to be generated from a projection of a latent low-dimensional N×K matrix Z=  (K≪D). In all derivations below, we use use i=1,…,N to index over samples (cells), j=1,…,D to index over genes and k=1,…,K to index over latent dimensions. Each sample yi is drawn independently: Let I denotes the K×K identity matrix, A denotes a D×K factor loadings matrix, H is a D×N masking matrix, D×D diagonal matrix and μ is a D×1 mean vector. We choose the dropout probability to be a function of the latent expression level, λ is the exponential decay parameter in the zero-inflation model. Note that λ is shared across genes, which reduces the number of parameters to be estimated and captures that technical noise should have similar effects across genes.where Y, we wish to identify model parameters Θ= that maximize the likelihood p(Y|θ). We do this using the expectation-maximization (EM) algorithm. We summarize the algorithm in the box below and then describe the algebraic details:Given an observed single-cell gene expression matrix nth iteration, Θn, as the value that maximizes the expected value of the complete log likelihood p under the conditional distribution over the latent variables given the observed data and the parameters at the last iteration. Computing the value of the parameters at each iteration requires two steps: the expectation step (E-step) and the maximization step (M-step). In the E-step, we derive an expression for the complete log likelihood p and compute all necessary expectations under the distribution p. The approximate zero-inflation model that we adopt admits closed form expressions for the expectations allowing the algorithm to be applied to realistically sized data sets. In the M-step, we maximize the expected value of the complete log likelihood with respect to Θn.We denote the value of the parameters at the The EM algorithm structurally resembles the equivalent algorithm for FA that iterates between imputing the coordinates of the observed data points in the low-dimensional latent space (E-step) and optimizing model parameters (M-step). In ZIFA, the expectation step incorporates a data imputation stage to compute the expected gene expression levels for genes/cells with observed null values. Note that if the noise measurement variances attributed to each gene are identical, we obtain a zero-inflated version of the probabilistic PCA algorithm  , the diagonal elements of the covariance matrix were drawn from a uniform distribution Uσ2, where σ2 is a simulation parameter, and μj were drawn from U. We experimented with three choices of f(·): a decaying squared exponential, f(Xij)=1−λXij; and a uniform (missing at random) function for each gene j, f(Xij)=1−λj.We used the assumed generative model to produce simulated data. For the simulations, the values N=150, K=10, D=50, σ2=0.3 and λ=0.1, and we explored the effects of altering the decay parameter λ, the number of latent dimensions K, the cluster spread σ2, the number of observed dimensions D and the number of samples N.We used a base setting of zi to the j,k and defined a pairwise distance matrix F such that Fjk=||zj−zk||2. We compared this to the estimated distance matrix F and zi and As a measure of algorithm performance, we compared the true λ), as the relative distances between data points in the gene expression measurement space will be distorted by the presence of zeros and hence there will be an error when projecting back into the latent space. Performance also falls if the gene set is small since there is less scope to exploit strong co-expression signatures across genes to mitigate for the presence of zeros. These regimes are important to consider in the context of linear transformation techniques  that are often applied only to curated gene sets where the linearity constraints may be approximately applicable. The application of non-linear techniques did not cure the problems induced by dropouts.Although the data sets were generated according to a PPCA/FA model (up to the dropout stage), in the presence of cells with genes possessing zero expression, the performance of all standard dimensionality-reduction methods (even PPCA/FA) deteriorated relative to ZIFA. An example is illustrated in Fig. 33Overall, ZIFA outperformed the standard dimensionality-reduction algorithms. This would be expected for those simulations adopting the same generative model assumed by ZIFA Fig. b but perZIFA should, therefore, be considered a safe alternative in that it converges in performance to PPCA/FA in the large-data low-noise limit but is robust to dropout events that might distort the outcomes of these methods in non-ideal situations.We next sought to test these methods in an experiment based on real single-cell expression data sets , 6, 14. j, we compared the posterior predictive distribution Yj as follows: (1) we computed the proportion of values in Yj and Δj. If hn is the proportion of values in bin n for the true distribution, and For each gene Δj from ZIFA was less than Δj from PPCA and FA. To prevent overfitting, we assessed the fit on a test set: we fitted the model for each data set on a training set containing 70 % of the data points, and computed the difference between the histograms on the remaining 30 % of data points.We computed the fraction of genes for which the Note that it is not possible to do this comparison with standard PCA or other dimensionality methods, such as t-SNE, since these are not based on a probabilistic generative model framework and therefore, it is not possible to derive the posterior predictive distributions that we use for performance comparisons.Using this criterion, we found that predictive distributions from PPCA and FA showed high divergence for genes that exhibited a high dropout rate or possessed a low non-zero expression level. This meant that the predictive data distributions were a poor fit for the empirical data. The performance of ZIFA was largely unaffected in contrast Fig. c. Exampl3The statistical frameworks underlying PPCA and FA employ Gaussianity assumptions that are unable to account explicitly for zero-inflation in single-cell expression data. The dropout model used by ZIFA modulates this Gaussianity assumption allowing for zero-inflation leading to drastically improved modeling accuracy. Across the four data sets, we found that the predictive distribution derived by ZIFA was superior to those of PPCA and FA on at least 80 % of the genes examined and often over 95 % Table .Table 2We further assessed whether the low-dimensional projections by ZIFA were more consistent than those of PPCA. For the four data sets, we repeated the following procedure 100 times: we sampled 100 genes at random, ran ZIFA or PPCA, and computed the pairwise distances between points in the low-dimensional space. This yielded 100 distance matrices, one for each iterate. We computed the Spearman correlation between each pair of distance matrices  and recorded the average Spearman correlation for both ZIFA and PPCA. Figure We now address the utility of ZIFA for a common analytical problem in single-cell expression analysis: the identification of distinct cellular subtypes or states. Typically, this occurs by reducing the high-dimensional gene expression measurements to a low-dimensional representation (often with PCA). The data are then clustered in this low-dimensional space to identify groups of cells exhibiting similar expression behaviors. Similarity is usually defined in terms of the relative positions of the cells in this low-dimensional space: cells that are close together are more likely to be of the same subtype, whilst cells that are far apart are more likely to be of different types.cell type separability.We speculated that dropout events may distort the relative positions of cells in the low-dimensional subspace potentially leading to misclassification of cell types. To test this, we utilized single-cell data from two recent studies , 16 wherFigure λ used in the simulations. The simulation algorithm is detailed in Additional file The previous simulation study was limited because each of the gene subsets had very similar dropout rates that were approximately 50 % and 60 %, respectively, for the Pollen and Usoskin data sets . The results show that for low dropout rates, the performance of PCA and ZIFA converges to the baseline. However, at higher dropout rates, ZIFA proves more effective at maintaining cell type separation than PCA for both data sets. We observed from the magnitude of the absolute misclassification errors that separating the neuronal cell types in the Usoskin data is more challenging than with the cell types in the Pollen data set. Classification performance quickly declines as dropout rates increase with the Usoskin data but, even when the average dropout rate was nearly 90 %, it was still possible to achieve less than 10 % misclassification errors with the Pollen data.Figure In conclusion, the performance gain of ZIFA over PCA for cell type identification problems will heavily depend on the intrinsic separability of the cell subtypes and the dropout rate. Our analysis of the Pollen data suggests there is little to gain from ZIFA over PCA for cell types that are straightforward to separate and would be expected to lie far apart in latent space. However, the Usoskin results suggest there may be greater advantages from modeling dropouts when cellular expression behaviors are more similar and the positions of the cells in latent space are close.The density of dropout events in scRNA-seq data can render classical dimensionality-reduction algorithms unsuitable and to date it has not been possible to assess the potential ramifications of applying such methods on zero-inflated data. We have modified the PPCA/FA framework to account for dropout to produce a safe method for the dimensionality reduction of single-cell gene expression data that provides robustness against such uncertainties. In the absence of dropout events, the method is essentially equivalent to PPCA/FA, and therefore, software implementations can straightforwardly substitute our approach for existing methods  to Z = ZIFA). Therefore, users could use ZIFA as a direct substitute with the benefit that it will automatically account for dropouts whereas remedial efforts may be required with standard PCA. Note that our methodology differs from approaches, such as the many variants of robust PCA, that aim to model corrupted observations. ZIFA treats dropouts as real observations, not outliers, whose occurrence properties have been characterized using an empirically informed statistical model.impute latent expression values for zero measurements. If a gene has a very low frequency of expression and is zero across most cells, this imputation process is unlikely to yield further information and these genes are best removed before analysis to avoid redundant computations.The inclusion of a zero-inflation model gives ZIFA greater expressive power than standard PPCA/FA but increases the computational complexity. We have developed an approximate inference method for ZIFA and shown that it is possible to handle usefully larger data sets involving thousands of genes and hundreds of samples. Whilst improved approximation methods and parallelization could yield further performance gains, a particularly important factor in determining computational complexity is the selection of the gene set. Potential users should note that ZIFA attempts to One of the limitations of ZIFA is that it models strictly zero measurements rather than near-zero values. It has been possible to account for near-zero values in a univariate mixture modeling framework by placing a small-variance distribution around zero rather than a point mass , 8. AchiFinally, the ZIFA framework lies strictly in the linear transformation framework but non-linear dimensionality-reduction approaches, such as t-SNE , have pr


Connectivity map (cMap) is a recent developed dataset and algorithm for uncovering and understanding the treatment effect of small molecules on different cancer cell lines. It is widely used but there are still remaining challenges for accurate predictions.Here, we propose BRCA-MoNet, a network of drug mode of action (MoA) specific to breast cancer, which is constructed based on the cMap dataset. A drug signature selection algorithm fitting the characteristic of cMap data, a quality control scheme as well as a novel query algorithm based on BRCA-MoNet are developed for more effective prediction of drug effects.BRCA-MoNet was applied to three independent data sets obtained from the GEO database: Estrodial treated MCF7 cell line, BMS-754807 treated MCF7 cell line, and a breast cancer patient microarray dataset. In the first case, BRCA-MoNet could identify drug MoAs likely to share same and reverse treatment effect. In the second case, the result demonstrated the potential of BRCA-MoNet to reposition drugs and predict treatment effects for drugs not in cMap data. In the third case, a possible procedure of personalized drug selection is showcased.The results clearly demonstrated that the proposed BRCA-MoNet approach can provide increased prediction power to cMap and thus will be useful for identification of new therapeutic candidates.Website: The web based application is developed and can be access through the following link http://compgenomics.utsa.edu/BRCAMoNet/ The ultimate goal of personalized medicine is to design treatments that optimize the therapeutic benefits and minimize the potential risk of toxicity for individual patient. Current pharmacogenomics research is striving to guide compound development and drug selection for such purpose. This growing need for personalized treatment has pushed the development of high-through technologies such as microarray and high throughput sequencing to the research forefront, where compound selection methods based on DNA or mRNA profiling have been developed to achieve highest benefit from therapeutic intervention but at the same time lowest risk of side effects -6. In thBesides genome-wide association studies, the current gene expression based approaches are mainly based on the "signature gene set" concept, which has been perfected during the past 14 years of relentless efforts in gene expression profiles of cancer, cardiovascular disease, diabetes and other disease researches. -10. The BRCA-MoNet Application Case 1). However, even if we focused on E2 for MCF7 cell line, its ranking is still low (3091). Close look at the detailed results revealed that, the ranking E2 treated MCF7 cell line was a summary of the results from 19 individual E2 treated MCF7 cell line and their enrichment scores did not agree with each other  suggested that the drug effect is cell line dependent and the higher ranks of the drugs from other cell lines would be more of cell line effects as opposed to drug effects. As a result, considering drug samples from other cell lines introduces only noise to drug prediction. Second, the quality of the data samples in cMap is inconsistent. Some samples from the same drug treatment can behave considerably different from the rest. These samples will inevitably present erroneous predictions. Third, the query signature gene set in cMap is chosen to include the top up- and down- regulated genes. However, size of the gene set is determined quite ad hoc. As a result, one might miss the important signature genes by choosing a smaller gene set, or on the contrary, bring in unrelated genes that would only serve to degrade the prediction. As an example, we used the expression data for estradiol (E2) treated MCF7 cell line  as a queTo address the above challenges, we proposed BRCA-MoNet in this paper. BRCA-MoNet is advantageous in three aspects compared with cMap. First, it focuses only on breast cancer cell line. Although doing so ignores other cell line data in the cMap data, it nevertheless removes the cell-line dependent interference from the true drug effect. Second, a quality control procedure as well as new drug signature gene set selection algorithm are developed to remove the possible noise in cMap data and characterize drug's treatment effect in a more systematic manner. Third, we define a Mode of Action (MoA) as a group of compounds that share the similar differential gene expression signature. Since the drug expression signature is indicative of the degree of its sensitivity to a cell, a MoA drug group should possess similar therapeutic effect. The construction of MoA introduces extra prediction power. This is because drugs with similar treatment effect might be ranked low due to high noise in data if we treat prediction of each drug independently. In contrast, this high noise sample could be ranked high because the query agrees with its MoA. The MoA is also different from other existing defined compound groups such as those by their anatomical therapeutic compound (ATC) classification since MoA is defined by differential gene expression after treatment, even though some overlapping between various compound classifications might be expected. The relationship of different MoAs in terms of their therapeutic effect can be modeled and visualized by a BRCA-MoNet. BRCA-MoNet presents a global view of drug effects at a genomic level. This network augments and improves the current understanding of compound MoA defined mainly from a physiology perspective, and underscores the relationship of different compounds. From a computational perspective, the MoAs and the quantified relationship between drugs in BRCA-MoNet provide a system-level model crucial for optimal drug screening: a new compound can be easily assigned to a MoA in the BRCA-MoNet such that compound's therapeutic effectiveness can be extrapolated or inferred accordingly.In the cMap data, each drug treatment profile includes several treated samples from different cell lines. Whether the effects of the same drug treatments differ for different cell lines need to be investigated before a drug MoA network can be constructed. To this end, samples of cMap data were first grouped based on compounds and the compounds with more than 30 samples were retained. Note that since the data have already been normalized and fold changed over the control sample in the same cell line, the cell line dependent bias should be eliminated; any differences in expression levels within the samples of the same compound are manifestation of differences in chemo-effectiveness due to differences in cell line, drug concentrations, or a combination of both. Hierarchical clustering was performed to the samples in each compound group to reveal potential differences in expression patterns within the same compound. Correlating the clustering results with cell line types and concentrations Figure  revealedt-test is hampered by the lack of the biological replicates in the cMap data set. This limitation becomes even severer after the quality control. For the MCF7 cell line, among all 1251 drugs in cMap, only 32 drugs have more than 5 samples and 1055 drugs have ≤ 2 samples. With such small sample size, any statistically based differential analysis becomes infeasible. To this end, we proposed two criteria based on which the signature gene set of drug was selected: first, the signature genes should have high fold-change expression, and second, the fold change levels of the signature genes should be consistently high among the replicate samples. Based on these two criteria, new signature gene set selection algorithm tailored for small samples were developed (see METHOD for details). For MCF7 cell line, among 1251 drugs, signature gene sets of different size were identified for 1108 drugs. No gene sets were produced for the rest 118 drugs because no genes in their samples were consistently differential expressed. There are also 25 drugs which have only 1 sample in MCF7 cell line. As the result, these 118 MCF7 cell line inconsistent drugs as well as the 25 single-sample drugs were removed. Figure BRCA-MoNet Application Case 1). These results demonstrated that the signature gene sets selected by our proposed algorithm are biologically meaningful.The goal of signature gene set selection is to identify a set of genes that have significant differential expression after the drug treatment. However, the use of the conventional differential analysis methods such as Quality control is applied on the drugs of cMap MCF7 cell line drugs with more than 3 samples. The goal of quality control is to remove the samples that are not consistently expressed with the others. Our investigation of the cMap data revealed that, there was a considerable amount of outlier samples, whose expression patterns differ significantly from the rest in the same drug Figure . IncludiMethod for details). In this network, each node represented one drug; a group of nodes share the same color edges represent a BRCA-MoA obtained by HC. For each MoA, the betweenness centrality of each drug was calculated and the drug with the largest betweenness centrality was set to be the center of the MoA. Two MoAs were linked with a black edge if the distance between them was smaller than the threshold and this link indicated the secondary level relationship between two MoAs.A compound mode of Action (MoA) is defined as a group of compounds that share similar gene signature expression patterns. Drugs forming one MoA will therefore have substantially shared genes in their signature gene set, which also have similar expression patterns. To obtain MoAs, clustering is applied to group the drugs with similar signature gene expression patterns. Multiple clustering algorithms exist and the simple yet effective Hierarchical Clustering (HC) method is adopted in our work. There are two major reasons to choose HC. First, the number of clusters is not required for HC; second, it is reasonable to expect that some drugs form distinct MoAs by itself and HC can produce clusters with a single member. To perform HC, a distance matrix that measures pair-wise distances between drugs was obtained after quality control. With this distance matrix, a total of 109 MoAs were obtained at a threshold and a BRCA-MoNet Figure  was consAfter the BRCA-MoNet being constructed, its prediction power was tested. Three questions were investigated. First, can BRCA-MoNet predict correct drug MoA? Second, to what extent can BRCA-MoNet predict the drug MoA of an unknown or new drug? Third, to what extend can BRCA-MoNet recommend drugs for patients? To answer these questions, independent microarray expression datasets were downloaded from Gene Expression Omnibus (GEO) for the investigation. In order to avoid possible platform and experimental bias, the following criteria were followed when we select the data sets. First, the data must be compound treated on the MCF7 cell line and contain one or multiple control samples; this was consistent with the condition of the cMap data. Second, we only choose those datasets that were treated with drugs existed in the cMap project or of known treatment effect in breast cancer. Third, to avoid possible across platform complication, the data must be generated from the same platform as the cMap data, or GPL96 (Affymetrix Human Genome U133A Array). With the above considerations, the following three case studies were carried out.We first chose the data set GSE 4025 as our qThis query data were also applied to the original cMap prediction, where the most up- and down-regulated 200 genes were used as the query signature genes. As expected, the cMap project gave a mix results Table  in both One additional dataset treated with drug BMS-754807 was tested against our BRCA-MoNet. This dataset (GSE33366) came from breast xenograft MCF7 bearing mice treated with BMS-754807. MBS-784807 is a dual IGF-1R/InsR inhibitor that can synergize hormonal agents and has been shown to be a potential breast cancer drug -47. StudPrediction power of BRCA-MoNet on the real breast cancer patients was investigated. To this end, dataset GSE2740  was downA drug effect MoA network for breast cancer cell lines, BRCA-MoNet, was constructed by using the cMap expression data. It was developed to address the problems of the cMap algorithm and to provide robustness and more accurate predictions for treatment effectiveness prediction and drug screening. This improvement came partially as a result of careful quality control on cMap data. In contrast to cMap, BRCA-MoNet prediction is cell line specific and removes the burden for user to select an effect signature gene set. Moreover, BRCA-MoNet assesses the therapeutics influence based on MoA instead of those for individually drugs. This network model not only leads to improved prediction results but it also uncovered the underlying MoA structure of the cMap data that has not been fully discovered before.The case studies we analyzed here returned favorable results and insightful leads. For the E2 treated MCF7 cell line case, the detection power and insight of the BRCA-MoNet E2-related MoA were exploited. The BMS-754807 case showed that BRCA-MoNet is capable of assigning new anti-cancer drug to the existing anti-cancer MoA and yielding insight understanding of drug MoA detection. The UNC breast cancer patients' case demonstrated the potential of BRCA-MoNet to be used as a tool for personalized treatment recommendation based on patients' gene expression.The BRCA-MoNet approach provides added values to the connectivity map project and allowed for new and better capability in identification of possible therapeutic candidates. Future direction will likely lend itself to two paths: to expand the MoNet concept to other cancer and cell lines by incorporating multiple drug treatment dataset, and to mature BRCA-MoNet's capability of prediction for the real patients. We expect that the rapid development in cancer profiling projects including The Cancer Genome Atlas (TCGA) will greatly benefit our effort in these future directionsThe proposed scheme of generating a breast cancer specific MoA network or BRCA-MoNet from cMap data is summarized in Figure http://hRp://www.broadinsUtute.org/cmap/). Two Affymetrix arrays were utilized in this study (excluding 184 arrays from early-access version of HT-HG-U133A): HG-U133A  and HT-HG-U133A (6029 arrays), representing 1,267 compound treatments at different dosages. In addition, data includes 5 cell lines: HL60, PC3, SKMEL5 and MCF7/ssMCF7. Each treated sample is accompanied by multiple control/vehicle samples. As for the normalization, the Perfect-Match(PM) probe level intensities, obtained from one Affymetrix array type (including treated and untreated hybridization), was first performed background adjustment together by using Robust Multi-array Average (RMA) procedure. after RMA background adjustment for both array types, quantile normalization was performed to all untreated samples; treated samples were then partitioned according to the array type, vehicle cell-line, and compound; for each group  at probe-level to correct possible nonlinear abnormality. After normalization, the treated samples expression values were calculated by median polish procedure. At last, all samples  were reassembled into matrix according to Affymetrix probe set IDs.Gene expression profiles of compound treatments were downloaded from Broad Institute's Connectivity Map web site  for sample a and b, respectfully, and a and b, respectfully.where Quality control is done in two rounds of processing. In the first round, which is part of the gene selection, some drugs came by with no signature gene sets; this is a result that no genes were consistently differentially expressed in samples from this drug. The samples from those drugs were removed. Although some drugs were determined with a signature gene set, one or more of the outlier samples may not agree with the rest. To address this problem, a second round of further quality control process was also performed on the cMap samples. In order to remove these inconsistent samples, a new scheme was proposed in Figure i and j belong to a MoA, a hypothesis testing formulation is developed with the null hypothesis defined byAccording to the definition of MoA, two compounds are in the same MoA if they share the same genomic signature. This is equivalent to say that the samples from these two compounds are highly correlated. In contrast, the samples from different MoAs should have a correlation distributed according to the distribution of the population correlation. To determine if two drugs i and j, and p value of 0.01 is chosen as the significance level and the corresponding distance is determined as the threshold. Hierarchical clustering is performed on all the samples distances; then clusters are determined by cutting the linkage at the threshold and the resulted clusters were defined as the MoAs. Notice that since each MoA was generated totally based on the threshold obtained from the background distribution, some MoAs may contain large number of samples while other MoAs only contain few samples from one or two drugs; this is natural and reasonable because some compounds just do not share the treatment effectiveness with others.where Once the MoAs were identified, it was then desirable to reveal the relationship of the MoAs in terms of their therapeutic effects. Instead of investigating individual compound in an isolated fashion, MoNet will enable research to explore a set of compounds (MoAs) that share the same MoA-Signature genes , as well as their correlated MoAs.Using the MoNet and the MoA, one can 1) predict drug effectiveness of a new compound (Similar Prediction) and/or 2) screen compounds to predict the therapeutic effectiveness of different compounds if applied to an individual tumor (Reverse Prediction). For drug effectiveness prediction, the expression profile of cells/tissue treated by a new compound needs to be obtained and the goal is to identify the MoA of the compound. For the therapeutic prediction, a query gene expression profile of the tumor sample is required. The goal is to determine the degree of the adverse relationship between the MoAs and the tumor marker genes expression that reveals how likely the compound is to reverse the expression of tumor marker genes. From the perspective of algorithm development, prediction of drug effect and compound screening are essentially the same. The only difference is the distance criteria: When similar prediction is applied, the MoA is first ranked for the largest positive distance and then each drugs within the MoA are then ranked with the same criteria; when reverse prediction is applied, then the MoA is first ranked for the smallest negative distance and then each drugs within each MoA are ranked the same.Connectivity map: cMap; Mode of action: MoA; Breast Cancer Mode of Action Network: BRCA-MoNet; Hierarchical Clustering: HC; Gene Expression Omnibus: GEO; 17beta-estrodiol: E2; The Cancer Genome Atlas: TCGA.The authors declare that they have no competing interests.CM, YH, and YC conceived the idea and designed the experiments. CM and HC prepared the data and conducted the experiments. MF developed the web application. MF, YH and YC wrote the paper.Detailed cMap prediction result for E2 treatment query.Click here for fileDetailed BRCA-MoNet prediction result for similar prediction of individual E2 treatment query.Click here for fileDetailed BRCA-MoNet prediction result for reverse prediction of individual E2 treatment query.Click here for fileDetailed BRCA-MoNet prediction result for similar prediction of individual BMS784807 treatment query.Click here for fileDetailed BRCA-MoNet prediction result for breast cancer patient microarray dataset.Click here for file


Classification of ovarian cancer by morphologic features has a limited effect on serous ovarian cancer (SOC) treatment and prognosis. Here, we proposed a new system for SOC subtyping based on the molecular categories from the Cancer Genome Atlas project. We analyzed the DNA methylation, protein, microRNA, and gene expression of 1203 samples from 599 serous ovarian cancer patients. These samples were divided into nine subtypes based on RNA-seq data, and each subtype was found to be associated with the activation and/or suppression of the following four biological processes: immunoactivity, hormone metabolic, mesenchymal development and the MAPK signaling pathway. We also identified four DNA methylation, two protein expression, six microRNA sequencing and four pathway subtypes. By integrating the subtyping results across different omics platforms, we found that most RNA-seq subtypes overlapped with one or two subtypes from other omics data. Our study sheds light on the molecular mechanisms of SOC and provides a new perspective for the more accurate stratification of its subtypes. Ovarian cancer is the eighth-leading cancer type worldwide and the leading cause of gynecological cancer-related death among women21423Studies on ovarian cancer subtyping are expected to shed light on the understanding of the molecular mechanisms of this disease, aid in the development of more specific therapies, and identify novel genetic and environmental risk factors1011The classification of serous ovarian cancer by surgical-pathological staging has helped guide clinical practice; however, it has added little prognostic or predictive information for clinical decisionsThe multi-omic SOC dataset from the TCGA project contains a total of 1203 samples that were collected from tumor tissues, adjacent normal tissues or blood samples of 599 SOC patients. The TCGA cohorts also contained information on the overall survival time and clinical variables . We obtained four types of molecular data that are available for open access: (i) DNA methylation: Illumina DNA Methylation microarray, ~20,000 genes; (ii) protein expression: reverse-phase protein array, ~170 proteins; (iii) microRNA (miRNA) expression: Agilent Human miRNA-specific microarray or Illumina miRNA-seq, >500 microRNAs; and (iv) mRNA expression: Agilent 244 K microarray or Illumina mRNA-seq, ~20,000 genes. P-value < 0.05) were selected to constitute the final feature set for further classification. In addition, we developed a novel method for reducing the feature dimensions without using the patient survival data. Specifically, we converted the gene expression levels to the activity levels of 137 KEGG pathways, which were then used as input features for unsupervised clustering. This pathway level-based method classified patients into cancer subtypes without any a priori knowledge of patient survival information, which is a good alternative for molecular level-based subtyping methods.Feature selection is necessary to improve the robustness of clustering. We used a Cox regression model to select biologically important features from all of the available genomic features in the dataset14For each molecular data type, we classified the SOC samples into subtypes using the following three unsupervised clustering methods: Partitioning Around Medoids (PAM)16P-value < 0.05, Cox regression analysis). We performed the three clustering algorithms  using the 1384 significant genes and found NMF with eight subtypes achieved the highest score . The counts of genes in the two gene sets of different SOC subtypes are shown in https://ncit.nci.nih.gov/), while the clinical stage was based on the AJCC (American Joint Committee on Cancer) staging criteria. The eight quantitative variables were analyzed using one-way ANOVA  and another 10 qualitative variables were analyzed using the chi-square test for one-way tables (−5) and treatment outcome from the first course (P = 0.03) exhibited significant difference across nine SOC subtypes, while the presence of a vascular (P = 0.477) or lymphoma invasion (P = 0.528) and the tumor grade (P = 0.169) showed no significant difference among those subtypes.We also analyzed the differences of a total of 18 clinical variables among the nine SOC subtypes. These clinical variables included the tumor size, lymphovascular invasion, diagnosis days, new tumor event days, tumor grade, clinical stage, and so on. The clinical stage was obtained from clinical information on the tumor size, extent of the primary tumor and whether the tumor spread to other parts of the body. The tumor grade was used to assess the degree of differentiation that was related to the clinical behavior and used to classify cancer cells as G1, G2, G3 or G4. The tumor grade is the histology description based on NCI . To annotate the 201 CpG sites, we mapped these sites to the human genome, and the genes closest to these sites were obtained. We then applied the three unsupervised clustering algorithms to these tumor samples on the basis of the methylation levels of the 201 CpG sites. For each algorithm, we sought to find the optimal number of clusters by screening a range of clusters from 2 to 9. We found that the NMF model with four clusters achieved the highest penalized score among all models , including MAPK, MEK, YB1, EGFR (two fragments), HSP70, c-Met, p90RSK, Akt, N-Cadherin, p38, GAB2, AR, Notch1, c-Jun, and GSK3. MAPK/ ERK signaling pathway was reported to be associated with ovarian cancer ascites development and carboplatin resistance242526272830The SOC protein array dataset in TCGA contains the expression profiles of 165 proteins that were detected by a reverse-phase protein array platform in 1203 samples. After excluding the samples from the recurrent solid tumor and normal solid tissue cohorts as well as patients whose survival information was unavailable, we selected a total of 406 samples with both genomic and survival data. We conducted Cox regression analysis and found that 16 proteins were significantly related to patient survival (The SOC microRNA (miRNA) sequencing dataset contained the expression profiles of 705 miRNAs. The unavailable sequencing data and recurrent solid tumor cohort were excluded, resulting in 473 primary solid tumor cohort samples with both genomic and survival data available. We identified 26 miRNAs that were significantly related to patient survival . Using these miRNAs as input features, we performed sample clustering with the three methods, and their performances are shown in P-value < 0.05, Cox regression analysis). These pathways are involved in bladder cancer, hepatitis C, renal cell carcinoma, thyroid cancer, glioma and chronic myeloid leukemia. Our study indicated that the stress response of the MAPK signaling pathway plays an important role in the survival of patients with SOC, which was consistent with a previous observation in a protein array. Immunoregulation of antigen processing also affected the survival time of SOC patients. Unsupervised classification and survival analyses were conducted according to the activity information of 16 pathways. We found that the PAM model with four clusters achieved the highest penalized score among all other models . We obtained the activity information of 428 SOC samples that had both gene expression and patient survival data. We associated the activities of the 137 pathways with the survival time of the patients and identified 16 pathways that were significantly associated with patient survival , protein array (2 groups), miRNA-seq (6 groups), and pathway activity (4 groups) across nine SOC subtypes generated by RNA-seq data. Specifically, we overlapped the nine RNA-seq SOC subtypes with subtypes by DNA methylation, protein, microRNA expression or pathway activity respectively and found that most of these subtypes overlapped with one or two specific RNA-seq subtypes . We condP-value < 0.05, Chi-squared test), and cluster C4 in DNA methylation overlapped with cluster C2, C7 and C9 in RNA-seq datasets   and C9 (upregulation of MAPK pathway). C7 , and C9 (upregulation of MAPK pathway)  . We founpathway) .For microRNA-seq, five out of six clusters overlapped with subtypes in RNA-seq. For instance, miRNA cluster C3 overlapped with RNA-seq cluster C2 , and miRNA cluster C6 overlapped with RNA-seq cluster C9  . MiRNAs 3538For protein array and KEGG pathway, we found that two protein subtypes C1 , C2  and KEGG pathway subtype C4  significantly overlapped with RNA-seq subtype C9, which is associated with the activation of the MAPK signaling pathway. In addition, pathway cluster C1  and C2  overlapped with RNA-seq cluster C4, and pathway cluster C3  and C4  overlapped with RNA-seq cluster C5 . We compThe TCGA database provides an integrated perspective of ovarian cancer. We have described a comprehensive classification analysis of a large multicenter cohort of SOC that is correlated with the clinical outcome. Nine novel and robust SOC subtypes were identified using unsupervised clustering. We evaluated three unsupervised clustering algorithms: HC, PAM and NMF and found that, in most cases, the NMF model outperformed the other two models, indicating NMF is a more accurate and robust algorithm for cancer subtyping based on omics data.et al. also reported that serous and endometrioid ovarian cancer are characterized by the stromal response, mesenchymal, immune signature, MAPK pathway and β-catenin/LEF/TCF complexOf greatest interest were the nine molecular groups that almost exclusively comprised the following four specific biological processes: immunoactivity, hormone metabolic, mesenchymal development and the MAPK signaling pathway. This result was consistent with previously published research in which the high-grade serous ovarian adenocarcinomas gene groups were differentiated, immunoreactive, mesenchymal and proliferativePreviously research has often provided a single platform. One example is the mutational spectrum. High-grade serous ovarian adenocarcinomas have prevalent TP53 mutations; mucinous ovarian cancer tumors have frequent KRAS mutations; and clear cell ovarian cancer and endometrioid ovarian cancer tumors have a lower rate of TP53 and frequent ARID1A and PIK3CA mutationsWe are dedicated to performing basic work to develop individualized treatment and precision medicine. One important further research topic is building prognostic models that incorporate clinical variables and multiple types of molecular data. In that regard, more effective treatment strategies should be developed for patients based on these models. Meanwhile, more specific drugs that target the novel genes and pathways should be developed. In addition, further efforts, particularly clinical trials and subsequent validations, are essential to evaluating our findings from the TCGA data.https://tcga-data.nci.nih.gov/tcga/). There were 3 levels of molecular data. Level 1 included raw data, while levels 2 and 3 were processed data and normalized data, respectively. Specifically, these omics data from the following platforms were used and selected in our study  from the TCGA Data Portal . We then calculated the log-transformed fold change (logFC) of gene expression in each sample by dividing the expression level of each gene in this sample with the average expression level in all samples. We next used SPIA  in the R package, which implemented the SPIA algorithm to analyze KEGG signaling pathwayshttps://david.ncifcrf.gov/) for RNA-seq data and DNA methylation data. We performed miRNA target genes functional enrichment using the DIANA-mirPath v.3.0 web server with TarBase v.7.0 microRNA-target databaseP-value < 0.05 were treated as significantly enriched functions for each testing gene set.We performed Gene Ontology19For DNA methylation, miRNA, gene and protein expression, we conducted a Cox regression analysis  to identify molecular features that had a significant relationship with the patient survival. Using this method, we reduced the data dimensions from a variety of molecular data. DNA methylation and RNA were expressed by gene name and further analyzed by GO and KEGG. The miRNA and protein levels were analyzed by a literature review. For the selected dataset, unsupervised clustering was summarized using three methods, PAM, HC and NMF (NMF package R). We then conducted a survival analysis  that combined a selected two-dimensional matrix with the patient survival. We tried to divide the data into 2 to 9 categories, which were used to compare the quality of clustering through the clustering score. The clustering score was defined as:where X represents the P value of 2 to 9 clusters in each method and N corresponds to number of the cluster, from C2 to C9. The more categories in the penalty value, the greater its value. In the gene expression dataset, which was aimed at identifying the up-regulated and down-regulated genes in nine groups, we performed division operation to each set and the rest sets. We used the thresholds of 2-fold and 0.5-fold change to determine the up-regulated and down-regulated genes.How to cite this article: Zhang, Z. et al. Molecular Subtyping of Serous Ovarian Cancer Based on Multi-omics Data. Sci. Rep.6, 26001; doi: 10.1038/srep26001 (2016).


The widespread application of microarray experiments to cancer research is astounding including lung cancer, one of the most common fatal human tumors. Among non-small cell lung carcinoma (NSCLC), there are two major histological types of NSCLC, adenocarcinoma (AC) and squamous cell carcinoma (SCC).In this paper, we proposed to integrate a visualization method called Radial Coordinate Visualization (Radviz) with a suitable classifier, aiming at discriminating two NSCLC subtypes using patients' gene expression profiles. Our analyses on simulated data and a real microarray dataset show that combining with a classification method, Radviz may play a role in selecting relevant features and ameliorating parsimony, while the final model suffers no or least loss of accuracy. Most importantly, a graphic representation is more easily understandable and implementable for a clinician than statistical methods and/or mathematic equations.To conclude, using the NSCLC microarray data presented here as a benchmark, the comprehensive understanding of the underlying mechanism associated with NSCLC and of the mechanisms with its subtypes and respective stages will become reality in the near future. Lung cancer is one of the most common fatal human tumors, accounting for 25% of cancer death in both men and women throughout the world In clinical practice, homogeneous treatment strategies have been traditionally implemented for both subtypes, which may explain the poor treatment response achieved in NSCLC. The recent-developed molecular targeted therapies such as an anti-epidermal growth factor receptor (EGFR) antibody are effective in patients harboring mutations in corresponding genes, which are exclusively found in AC. This indicates fundamental differences in the underlying mechanisms of tumor development, growth and invasion between the two subtypes. The prognosis of NSCLC also depends on tumor stage. The low survival rate is mainly attributable to late diagnosis, when the tumor has become unresectable. On the contrary, early-stage NSCLC patients have a significantly better prognosis. Therefore, the successful classification of NSCLC patients into their corresponding subtypes and stages is of clinical significance.The widespread application of microarray experiments to cancer research is astounding. The lung cancer research is no exception. Many researchers have employed microarray technology to disclose the molecular nature of etiological differences in between these two NSCLC subtypes and/or their stages In this subtask, Tian and Suarez-Farinas proposed a regularization method called hierarchical- Threshold Gradient Decent Regularization (TGDR) Among many data visualization methods, prevalent means for exposing interesting patterns graphically, Radviz can display data with three or more attributes in a 2-dimensional projection. Radviz has been demonstrated as a complement to computational methods for inference of classification models to search for biologically interesting patterns f. Specifically, there were 3 classes, 71 samples, and 384 features. The logit functions for class 2 and 3 having class 1 as reference were given by following relationship for three synthesized datasets,In order to evaluate the empirical performance of RadViz in terms of selecting informative features and eliminating irrelevant features, we used the simulations presented in one previous study Simulation 1.where the logits for class 2 and 3 are two functions with different parameters and the number of relevant features is 5. This is exactly one simulated data used in Tian et al Simulation 2.1∼ X6 while the logit for class 3 depends on features X7∼ X12. The number of relevant features is 12, several features more than the usually considered number of features in a RadViz projection .where the logit for class 2 depends only on features XSimulation 3.where the number of relevant features is two, less than the minimum number of features in RadViz projections. All parameters used in the simulations were simulated from a uniform distribution on the interval of 0 to 2, i.e., unif (0∼2). By this means, the true relevant features are known and whether RadViz can select them correctly can be investigated.Upon the simulated data, 50000 RadViz projections were evaluated. Then we locally optimized best projections for 10000 times. The choice of the number of RadViz projections appeared to have little impact on the final results. We obtained very similar results with different numbers, i.e., 10000, 20000, 30000, 40000, 50000, 80000, and 100000. Because the study in This procedure had been run by varying the maximum number of features in a projection from 3 to 8. The obtained best projection for each run was listed in The study schema is shown in keratin 5 (KRT5) is the only gene used to discriminate SCC and AC with an astounding misclassification rate on the test set of 15.3% (23/150). TGDR analysis selected 20 genes as shown in our previous work For the winner of the SBV LC subtask keratin 5 (KRT5), RAR-related orphan receptor C (RORC), and melanoma antigen family A 4 (MAGEA4). Interesting, both KRT5 and MAGEA4 were selected by the 23-gene signature of Ben-Hamo's study. On these 3 genes, the performance of different classifiers was evaluated and their respective statistics are shown in With the aid of Radviz, the best projection obtained from the training set, which ignores the stages, consisted of 3 genes. They are Considered that the minimum number of features in a Radviz is three, we additionally evaluated on the different combinations of these 3 genes to find the optimal subset. The respective ROC curves were plotted in In the test set, there are six samples that might be wrong-labelled with extremely high misclassification rates in the SBV LC subtask, as shown by the Ben-Hamo's study. Here, we evaluated all considered methods herein and found out almost all of those methods including RadViz + SVM/TGDR/NB or RadViz alone indicated opposite labels to the given ones for these six samples, as shown in Generalized Brier Score (GBS) and SVM had the best Area Under the Precision-Recall Curve (AUPR) while naïve Bayes outperformed in terms of Belief Confusion Metric (BCM). Moreover, the performance of these 8 genes on SCC and AC classification was evaluated and the results were shown in Another Radviz was plotted by considering both subtypes  and stages  strata. The best projection involved 8 genes, including KRT5 and RORC also selected by the two-class Radviz best projection. With these 8 genes, the performances of different classifiers were evaluated. The results were shown in desmocollin 3 (DSC3) and desmoglein 3 (DSG3) had been recently justified as valuable in classification of NSCLC subtypes Chloride channel accessory 2 (CLCA2) had been recognized as a tumorigenesis gene.An alternative way of utilizing Radviz seamlessly is to consider the most frequent selected genes by all projections and then to find the potential informative features. In this method, the features were ranked based on the frequencies they had appeared in all RadViz projections. Those most frequently appearing features were considered. Among them, we selected a subset comprised of 7 over-expressed genes in SCC and 3 over-expressed genes in AC. Using these 10 genes, the performances of different classifiers were re-evaluated and the respective statistics were listed in Radviz makes two implicitly false assumptions by evenly placing features around the circle From our analysis, it is found that TGDR/multi-TGDR methods might be in favor of AUPR and GBS but less preferable to BCM. Notably, TGDR and multi-TGDR frameworks ignore the co-expression features among genes. Thus those genes that belong to the same/similar pathways or networks tend to end up the final model. To tackle with this limitation, Ma and Huang File S1, those classifiers were applied to another independent test set. The good separation between AC and SCC samples and the poor performance of multi-class classifiers were consistently observed, which provides some justification on the validity and robustness of our study here.Finally, the poor performance of multi-class classifiers may be due to the lack of molecular signature difference in different stages of NSCLC. If this is true, it means that most of the selected features are purely random noises. The fact that no team in the SBV LC subtask can separate the respective stages within each subtype shed some evidence on this conjecture. Nevertheless, our observation is that the discrimination of ACI and ACII seems to be easier compared to that of SCCI and SCCII. In File S1.Our proposal of using Radviz to do feature selection, especially using the most frequently selected features by thousands of Radviz best projects, is a unique feature. Other visualization methods such as a heat-map cannot cope with feature selection, thus Radviz has its advantageous merit. Additionally, even though visualization ensembles were originally proposed upon microarray experiments, its broad applications to other “omics” data are out of question as shown by its applications to a RNA-seq data and to a metabolomics data in On the complicated LC classification task, the ensemble of Radviz and a classifier can obtain perfect model parsimony with reasonable predictive performance. With this proposal the segmentation of early stages in two LC major subtypes is still unachievable, however, we believe more novel algorithms or novel ensembles of existing methods will be developed by quantitative researchers in the near future. Then using these benchmark data sets, the comprehensive understanding of the underlying mechanism associated with NSCLC subtypes and respective stages becomes highly possible.The lung cancer microarray experiments under consideration included all chips in the Gene Expression Omnibus' (GEO) repository series GSE10245, GSE18842, GSE2109, GSE31908, and GSE43580 (test set) all of which were hybridized on Affymetrix HGU133 Plus 2.0 chips.http://www.bu.edu/jlab/wp-assets/ComBat/Abstract.html) was used to adjust for the combined expression values for these studies because a comprehensive evaluation on several commonly-used batch-effect adjustment methods showed that COMBAT outperformed others overall The raw Affymetrix data (CEL files) of all lung cancer data sets were downloaded from the GEO repository, and expression values were obtained using the fRMA algorithm Moderated t-tests (limma package)were conducted to identify differentially expressed genes (DEGs) with cutoffs for False Discovery Rate (FDR) and fold change as 0.05 and 2, respectively. When there were multiple probe sets representing the same gene, the one with the largest fold change was chosen. Finally, expression values were further centralized and normalized to have a mean of 0 and a variance of 1 for both training data and test data, respectively. The resulting normalized expression values for 676 unique genes were fed into the downstream classification analysis.In a Radviz, the features such as genes are presented as anchor points spaced around the perimeter of a circle while samples are as points inside the circle. The position of one sample is determined by a metaphor from physics, saying each point is held in place with springs that are attached at the other end to the feature anchors. The stiffness of each spring is proportional to the value of the corresponding feature and the point ends up at the position where the spring forces are in equilibrium. Therefore, subjects that are close to a set of feature anchors have high values on these features than on the others.In order to obtain a clear and good separation among different classes using just several features, Radviz needs to search over a myriad of possible combinations. Therefore the search is tedious. To automatically solve this problem, an approach called VizRank had been proposed by Here, we used Radviz to do the redundant feature elimination, and used an extra classifier to classify samples and to compute posterior probabilities. Those classifiers were described briefly as follows.As mentioned in the Introduction section, the TGDR framework was presented and described by Ma and Huang A naïve Bayes (NB) classifier is a simple probabilistic classifier. Based upon Bayes' theorem, it makes an independence assumption, which assumes that features are independent giventhe class. The performance of NB on data sets with redundant features can be improved by removing such features usually with a forward search strategy being used with NB since it can immediately detect dependencies as many redundant features being added.Simply put, SVM Belief Confusion Metric (BCM), Area Under the Precision-Recall Curve (AUPR), Generalized Brier Score (GBS), and predictive error rate on the test set, to evaluate the performance of the combinations between Radviz and different classifiers more precisely and rigorously.According to http://www.sbvimprover.com/sites/default/files/scoring_metrics.pdf).GBS is described in details in our previous work www.r-project.org) and packages such as limma were from the Bioconductor project (www.bioconductor.org). The analysis using visualization methods including RadViz and VizRank was conducted in the Orange software, version 2.7 (www.orange.biolab.si).The statistical analysis was carried out in the R language version 3.0 Click here for additional data file.


Recent advances in RNA sequencing (RNA-Seq) technology have offered unprecedented scope and resolution for transcriptome analysis. However, precise quantification of mRNA abundance and identification of differentially expressed genes are complicated due to biological and technical variations in RNA-Seq data.We systematically study the variation in count data and dissect the sources of variation into between-sample variation and within-sample variation. A novel Bayesian framework is developed for joint estimate of gene level mRNA abundance and differential state, which models the intrinsic variability in RNA-Seq to improve the estimation. Specifically, a Poisson-Lognormal model is incorporated into the Bayesian framework to model within-sample variation; a Gamma-Gamma model is then used to model between-sample variation, which accounts for over-dispersion of read counts among multiple samples. Simulation studies, where sequencing counts are synthesized based on parameters learned from real datasets, have demonstrated the advantage of the proposed method in both quantification of mRNA abundance and identification of differentially expressed genes. Moreover, performance comparison on data from the Sequencing Quality Control (SEQC) Project with ERCC spike-in controls has shown that the proposed method outperforms existing RNA-Seq methods in differential analysis. Application on breast cancer dataset has further illustrated that the proposed Bayesian model can 'blindly' estimate sources of variation caused by sequencing biases.http://www.cbil.ece.vt.edu/software.htm.We have developed a novel Bayesian hierarchical approach to investigate within-sample and between-sample variations in RNA-Seq data. Simulation and real data applications have validated desirable performance of the proposed method. The software package is available at  Next Generation Sequencing (NGS) technology has opened a new era for transcriptome analysis, which grants the ability to investigate novel biological problems, such as alternative splicing, differential isoforms, gene fusion, etc. By piling up millions of reads along the reference genome, RNA-Seq technology can obtain signals in a much larger dynamic range with much higher accuracy compared to traditional microarray based technologies. For RNA-Seq analysis, the most popular routine is to determine the expression of genes (abundance quantification) and to identify differentially expressed genes (DEGs).Methods that quantify gene/isoform level expression mainly fall into two categories: Poisson count mode  anDifferential analysis of RNA-Seq data has been focused on modeling variance among biological replicates or samples in the same phenotype group. EdgeR  is the fDespite initial success to model uncertainties associated with sequencing counts from different aspects, there lacks a systematic effort to address variability in RNA-Seq data. We dissect the variance in sequencing counts along two dimensions: over-dispersion of read counts within the same sample  and over-dispersion of read counts among individuals from the same biological group . Within-sample variation typically leads to large variance of read counts among genomic loci , which have similar expression level in the same sample. It is typically caused by technical artifacts such as uncorrected systematic bias and gene specific random effects. On the other hand, between-sample variation is mostly due to biological differences among samples under the same condition. To increase the accuracy of abundance estimation so as to improve DEG identification, immediate attention is needed to developing a unified model that takes care of both forms of variation in RNA-Seq data. We propose a computational method, namely Bayesian Analysis of Dispersed Gene Expression ('BADGE'), to model variability in RNA-Seq data. A full Bayesian model is employed to simultaneously account for within-sample variation and between-sample variation to improve inference. The proposed method has several novel contributions compared to existing methodologies: 1) a unified Bayesian causality model is developed for joint abundance estimation and DEG identification. The improved accuracy in profiling mRNA abundance can facilitate the identification of DEGs, which may in turn refine the parameter learning in abundance quantification. 2) A Poisson-Lognormal regression model is incorporated to model within-sample variation . InsteadWe have developed a computational method, namely Bayesian Analysis of Dispersed Gene Expression (BADGE), to model extensive variability in RNA-Seq data. BADGE explicitly models both between-sample variation and within-sample variation to improve abundance quantification and DEG identification. In this paper, we only focus on the gene level analysis, while the concept can be straight-forwardly generalized for genes with multiple transcripts (isoforms).yg,i,j represent observed counts that fall into the ith (1 ≤ i ≤ Ig) exon region of gene g (1 ≤ g ≤ G) in sample j (1 ≤ j ≤ J), which follows Poisson distribution with mean γg,i,j. Ig is the number of exons in gene g. G is the total number of genes. J = J1 + J2 is the total number of samples, where J1 and J2 denote samples in condition 1 and 2, respectively. Within-sample over-dispersion indicates that γg,i,j has unknown heterogeneity across the gene rather than taking constant value. A hierarchical Bayesian model is constructed to model within-sample variation of RNA-Seq data as follows:Let βg,j is the true expression level of gene g for sample j. xg,i is the length of the ith exon weighted by the library size of sample j. Ug,i,j is the unknown within-sample variation parameter, which follows normal distribution with mean 0 and precision τ. 'Flat' prior is assigned for τ by setting its shape a = 1 and rate b = 0. Equations (1-4) are also known as the Poisson-Lognormal regression model with identity link function.where yg,i,j exhibits over-dispersion, but also βg,j has variation across multiple samples in the same biological group. To model between-sample variation carried by βg,j, we adopt the Gamma-Gamma model that is widely used in microarray gene expression analysis .JG and JX designed and implemented the algorithm. JG, XW, JX designed and performed the computational experiments. JG and JX contributed to the writing. LHC and RC provided their biological guidance on the breast cancer study. All authors read and approved the final manuscript.Supplementary materials. This additional file contains supplementary information for the main text, including parameter setting for BADGE method, computational implementation, simulation design, supplementary figures and tables, etc.Click here for file