1
|
Ribeiro AH, Soler JMP, Hirata R. Variance-Preserving Estimation of Intensity Values Obtained From Omics Experiments. Front Genet 2019; 10:855. [PMID: 31616468 PMCID: PMC6764481 DOI: 10.3389/fgene.2019.00855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2019] [Accepted: 08/16/2019] [Indexed: 11/29/2022] Open
Abstract
Faced with the lack of reliability and reproducibility in omics studies, more careful and robust methods are needed to overcome the existing challenges in the multi-omics analysis. In conventional omics data analysis, signal intensity values (denoted by M and values) are estimated neglecting pixel-level uncertainties, which may reflect noise and systematic artifacts. For example, intensity values from two-color microarray data are estimated by taking the mean or median of the pixel intensities within the spot and then subjected to a within-slide normalization by LOWESS. Thus, focusing on estimation and normalization of gene expression profiles, we propose a spot quantification method that takes into account pixel-level variability. Also, to preserve relevant variation that may be removed in LOWESS normalization with poorly chosen parameters, we propose a parameter selection method that is parsimonious and considers intrinsic characteristics of microarray data, such as heteroskedasticity. The usefulness of the proposed methods is illustrated by an application to real intestinal metaplasia data. Compared with the conventional approaches, the analysis is more robust and conservative, identifying fewer but more reliable differentially expressed genes. Also, the variability preservation allowed the identification of new differentially expressed genes. Using the proposed approach, we have identified differentially expressed genes involved in pathways in cancer and confirmed some molecular markers already reported in the literature.
Collapse
Affiliation(s)
- Adèle H. Ribeiro
- Department of Computer Science, Institute of Mathematics and Statistics, University of São Paulo, São Paulo, Brazil
- *Correspondence: Adèle H. Ribeiro,
| | - Julia Maria Pavan Soler
- Department of Statistics, Institute of Mathematics and Statistics, University of São Paulo, São Paulo, Brazil
| | - Roberto Hirata
- Department of Computer Science, Institute of Mathematics and Statistics, University of São Paulo, São Paulo, Brazil
| |
Collapse
|
2
|
Mollah MMH, Jamal R, Mokhtar NM, Harun R, Mollah MNH. A Hybrid One-Way ANOVA Approach for the Robust and Efficient Estimation of Differential Gene Expression with Multiple Patterns. PLoS One 2015; 10:e0138810. [PMID: 26413858 PMCID: PMC4587675 DOI: 10.1371/journal.pone.0138810] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2015] [Accepted: 09/03/2015] [Indexed: 11/22/2022] Open
Abstract
Background Identifying genes that are differentially expressed (DE) between two or more conditions with multiple patterns of expression is one of the primary objectives of gene expression data analysis. Several statistical approaches, including one-way analysis of variance (ANOVA), are used to identify DE genes. However, most of these methods provide misleading results for two or more conditions with multiple patterns of expression in the presence of outlying genes. In this paper, an attempt is made to develop a hybrid one-way ANOVA approach that unifies the robustness and efficiency of estimation using the minimum β-divergence method to overcome some problems that arise in the existing robust methods for both small- and large-sample cases with multiple patterns of expression. Results The proposed method relies on a β-weight function, which produces values between 0 and 1. The β-weight function with β = 0.2 is used as a measure of outlier detection. It assigns smaller weights (≥ 0) to outlying expressions and larger weights (≤ 1) to typical expressions. The distribution of the β-weights is used to calculate the cut-off point, which is compared to the observed β-weight of an expression to determine whether that gene expression is an outlier. This weight function plays a key role in unifying the robustness and efficiency of estimation in one-way ANOVA. Conclusion Analyses of simulated gene expression profiles revealed that all eight methods (ANOVA, SAM, LIMMA, EBarrays, eLNN, KW, robust BetaEB and proposed) perform almost identically for m = 2 conditions in the absence of outliers. However, the robust BetaEB method and the proposed method exhibited considerably better performance than the other six methods in the presence of outliers. In this case, the BetaEB method exhibited slightly better performance than the proposed method for the small-sample cases, but the the proposed method exhibited much better performance than the BetaEB method for both the small- and large-sample cases in the presence of more than 50% outlying genes. The proposed method also exhibited better performance than the other methods for m > 2 conditions with multiple patterns of expression, where the BetaEB was not extended for this condition. Therefore, the proposed approach would be more suitable and reliable on average for the identification of DE genes between two or more conditions with multiple patterns of expression.
Collapse
Affiliation(s)
- Mohammad Manir Hossain Mollah
- Institut Perubatan Molekul UKM (UMBI), University Kebangsaan Malaysia (UKM), Jalan Ya’acob Latiff, Bandar Tun Razak, Cheras 56000 Kuala Lumpur, Malaysia
- * E-mail:
| | - Rahman Jamal
- Institut Perubatan Molekul UKM (UMBI), University Kebangsaan Malaysia (UKM), Jalan Ya’acob Latiff, Bandar Tun Razak, Cheras 56000 Kuala Lumpur, Malaysia
| | - Norfilza Mohd Mokhtar
- Institut Perubatan Molekul UKM (UMBI), University Kebangsaan Malaysia (UKM), Jalan Ya’acob Latiff, Bandar Tun Razak, Cheras 56000 Kuala Lumpur, Malaysia
- Department of Physiology, Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
| | - Roslan Harun
- Institut Perubatan Molekul UKM (UMBI), University Kebangsaan Malaysia (UKM), Jalan Ya’acob Latiff, Bandar Tun Razak, Cheras 56000 Kuala Lumpur, Malaysia
| | - Md. Nurul Haque Mollah
- Laboratory of Bioinformatics, Department of Statistics, University of Rajshahi, Rajshahi-6205, Bangladesh
| |
Collapse
|
3
|
Combined Targeted DNA Sequencing in Non-Small Cell Lung Cancer (NSCLC) Using UNCseq and NGScopy, and RNA Sequencing Using UNCqeR for the Detection of Genetic Aberrations in NSCLC. PLoS One 2015; 10:e0129280. [PMID: 26076459 PMCID: PMC4468211 DOI: 10.1371/journal.pone.0129280] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2015] [Accepted: 05/06/2015] [Indexed: 01/21/2023] Open
Abstract
The recent FDA approval of the MiSeqDx platform provides a unique opportunity to develop targeted next generation sequencing (NGS) panels for human disease, including cancer. We have developed a scalable, targeted panel-based assay termed UNCseq, which involves a NGS panel of over 200 cancer-associated genes and a standardized downstream bioinformatics pipeline for detection of single nucleotide variations (SNV) as well as small insertions and deletions (indel). In addition, we developed a novel algorithm, NGScopy, designed for samples with sparse sequencing coverage to detect large-scale copy number variations (CNV), similar to human SNP Array 6.0 as well as small-scale intragenic CNV. Overall, we applied this assay to 100 snap-frozen lung cancer specimens lacking same-patient germline DNA (07–0120 tissue cohort) and validated our results against Sanger sequencing, SNP Array, and our recently published integrated DNA-seq/RNA-seq assay, UNCqeR, where RNA-seq of same-patient tumor specimens confirmed SNV detected by DNA-seq, if RNA-seq coverage depth was adequate. In addition, we applied the UNCseq assay on an independent lung cancer tumor tissue collection with available same-patient germline DNA (11–1115 tissue cohort) and confirmed mutations using assays performed in a CLIA-certified laboratory. We conclude that UNCseq can identify SNV, indel, and CNV in tumor specimens lacking germline DNA in a cost-efficient fashion.
Collapse
|
4
|
Liebner DA, Huang K, Parvin JD. MMAD: microarray microdissection with analysis of differences is a computational tool for deconvoluting cell type-specific contributions from tissue samples. ACTA ACUST UNITED AC 2013; 30:682-9. [PMID: 24085566 DOI: 10.1093/bioinformatics/btt566] [Citation(s) in RCA: 55] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
BACKGROUND One of the significant obstacles in the development of clinically relevant microarray-derived biomarkers and classifiers is tissue heterogeneity. Physical cell separation techniques, such as cell sorting and laser-capture microdissection, can enrich samples for cell types of interest, but are costly, labor intensive and can limit investigation of important interactions between different cell types. RESULTS We developed a new computational approach, called microarray microdissection with analysis of differences (MMAD), which performs microdissection in silico. Notably, MMAD (i) allows for simultaneous estimation of cell fractions and gene expression profiles of contributing cell types, (ii) adjusts for microarray normalization bias, (iii) uses the corrected Akaike information criterion during model optimization to minimize overfitting and (iv) provides mechanisms for comparing gene expression and cell fractions between samples in different classes. Computational microdissection of simulated and experimental tissue mixture datasets showed tight correlations between predicted and measured gene expression of pure tissues as well as tight correlations between reported and estimated cell fraction for each of the individual cell types. In simulation studies, MMAD showed superior ability to detect differentially expressed genes in mixed tissue samples when compared with standard metrics, including both significance analysis of microarrays and cell type-specific significance analysis of microarrays. CONCLUSIONS We have developed a new computational tool called MMAD, which is capable of performing robust tissue microdissection in silico, and which can improve the detection of differentially expressed genes. MMAD software as implemented in MATLAB is publically available for download at http://sourceforge.net/projects/mmad/.
Collapse
Affiliation(s)
- David A Liebner
- Division of Medical Oncology, Department of Internal Medicine, Department of Biomedical Informatics and Comprehensive Cancer Center, Biomedical Informatics Shared Resource, The Ohio State University, Columbus OH 43210, USA
| | | | | |
Collapse
|
5
|
Lehmann R, Machné R, Georg J, Benary M, Axmann I, Steuer R. How cyanobacteria pose new problems to old methods: challenges in microarray time series analysis. BMC Bioinformatics 2013; 14:133. [PMID: 23601192 PMCID: PMC3679775 DOI: 10.1186/1471-2105-14-133] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2012] [Accepted: 03/18/2013] [Indexed: 11/24/2022] Open
Abstract
Background The transcriptomes of several cyanobacterial strains have been shown to exhibit diurnal oscillation patterns, reflecting the diurnal phototrophic lifestyle of the organisms. The analysis of such genome-wide transcriptional oscillations is often facilitated by the use of clustering algorithms in conjunction with a number of pre-processing steps. Biological interpretation is usually focussed on the time and phase of expression of the resulting groups of genes. However, the use of microarray technology in such studies requires the normalization of pre-processing data, with unclear impact on the qualitative and quantitative features of the derived information on the number of oscillating transcripts and their respective phases. Results A microarray based evaluation of diurnal expression in the cyanobacterium Synechocystis sp. PCC 6803 is presented. As expected, the temporal expression patterns reveal strong oscillations in transcript abundance. We compare the Fourier transformation-based expression phase before and after the application of quantile normalization, median polishing, cyclical LOESS, and least oscillating set (LOS) normalization. Whereas LOS normalization mostly preserves the phases of the raw data, the remaining methods introduce systematic biases. In particular, quantile-normalization is found to introduce a phase-shift of 180°, effectively changing night-expressed genes into day-expressed ones. Comparison of a large number of clustering results of differently normalized data shows that the normalization method determines the result. Subsequent steps, such as the choice of data transformation, similarity measure, and clustering algorithm, only play minor roles. We find that the standardization and the DTF transformation are favorable for the clustering of time series in contrast to the 12 m transformation. We use the cluster-wise functional enrichment of a clustering derived by LOS normalization, clustering using flowClust, and DFT transformation to derive the diurnal biological program of Synechocystis sp.. Conclusion Application of quantile normalization, median polishing, and also cyclic LOESS normalization of the presented cyanobacterial dataset lead to increased numbers of oscillating genes and the systematic shift of the expression phase. The LOS normalization minimizes the observed detrimental effects. As previous analyses employed a variety of different normalization methods, a direct comparison of results must be treated with caution.
Collapse
Affiliation(s)
- Robert Lehmann
- Institute for Theoretical Biology, Humboldt University Berlin, Invalidenstraße 43, D-10115 Berlin, Germany.
| | | | | | | | | | | |
Collapse
|
6
|
Elucidating the Role of microRNAs in Cancer Through Data Mining Techniques. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2013; 774:291-315. [DOI: 10.1007/978-94-007-5590-1_15] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
|
7
|
Mollah MMH, Mollah MNH, Kishino H. β-empirical Bayes inference and model diagnosis of microarray data. BMC Bioinformatics 2012; 13:135. [PMID: 22713095 PMCID: PMC3464654 DOI: 10.1186/1471-2105-13-135] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2011] [Accepted: 04/23/2012] [Indexed: 12/04/2022] Open
Abstract
Background Microarray data enables the high-throughput survey of mRNA expression profiles at the genomic level; however, the data presents a challenging statistical problem because of the large number of transcripts with small sample sizes that are obtained. To reduce the dimensionality, various Bayesian or empirical Bayes hierarchical models have been developed. However, because of the complexity of the microarray data, no model can explain the data fully. It is generally difficult to scrutinize the irregular patterns of expression that are not expected by the usual statistical gene by gene models. Results As an extension of empirical Bayes (EB) procedures, we have developed the β-empirical Bayes (β-EB) approach based on a β-likelihood measure which can be regarded as an ’evidence-based’ weighted (quasi-) likelihood inference. The weight of a transcript t is described as a power function of its likelihood, fβ(yt|θ). Genes with low likelihoods have unexpected expression patterns and low weights. By assigning low weights to outliers, the inference becomes robust. The value of β, which controls the balance between the robustness and efficiency, is selected by maximizing the predictive β0-likelihood by cross-validation. The proposed β-EB approach identified six significant (p<10−5) contaminated transcripts as differentially expressed (DE) in normal/tumor tissues from the head and neck of cancer patients. These six genes were all confirmed to be related to cancer; they were not identified as DE genes by the classical EB approach. When applied to the eQTL analysis of Arabidopsis thaliana, the proposed β-EB approach identified some potential master regulators that were missed by the EB approach. Conclusions The simulation data and real gene expression data showed that the proposed β-EB method was robust against outliers. The distribution of the weights was used to scrutinize the irregular patterns of expression and diagnose the model statistically. When β-weights outside the range of the predicted distribution were observed, a detailed inspection of the data was carried out. The β-weights described here can be applied to other likelihood-based statistical models for diagnosis, and may serve as a useful tool for transcriptome and proteome studies.
Collapse
Affiliation(s)
- Mohammad Manir Hossain Mollah
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan.
| | | | | |
Collapse
|
8
|
Kamleh MA, Ebbels TMD, Spagou K, Masson P, Want EJ. Optimizing the Use of Quality Control Samples for Signal Drift Correction in Large-Scale Urine Metabolic Profiling Studies. Anal Chem 2012; 84:2670-7. [DOI: 10.1021/ac202733q] [Citation(s) in RCA: 111] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Affiliation(s)
- Muhammad Anas Kamleh
- Biomolecular Medicine, Department
of Surgery and Cancer, Faculty of Medicine, Imperial College London, London SW7 2AZ, U.K
- Faculty of Pharmacy, Damascus University, Mazzeh Campus, Syria
| | - Timothy M. D. Ebbels
- Biomolecular Medicine, Department
of Surgery and Cancer, Faculty of Medicine, Imperial College London, London SW7 2AZ, U.K
| | - Konstantina Spagou
- Biomolecular Medicine, Department
of Surgery and Cancer, Faculty of Medicine, Imperial College London, London SW7 2AZ, U.K
- Laboratory of Forensic Medicine
and Toxicology, Faculty of Medicine, Aristotle University of Thessaloniki, Thessaloniki 54124 Greece
| | - Perrine Masson
- Biomolecular Medicine, Department
of Surgery and Cancer, Faculty of Medicine, Imperial College London, London SW7 2AZ, U.K
| | - Elizabeth J. Want
- Biomolecular Medicine, Department
of Surgery and Cancer, Faculty of Medicine, Imperial College London, London SW7 2AZ, U.K
| |
Collapse
|
9
|
De Bin R, Risso D. A novel approach to the clustering of microarray data via nonparametric density estimation. BMC Bioinformatics 2011; 12:49. [PMID: 21303507 PMCID: PMC3042915 DOI: 10.1186/1471-2105-12-49] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2010] [Accepted: 02/08/2011] [Indexed: 11/21/2022] Open
Abstract
Background Cluster analysis is a crucial tool in several biological and medical studies dealing with microarray data. Such studies pose challenging statistical problems due to dimensionality issues, since the number of variables can be much higher than the number of observations. Results Here, we present a general framework to deal with the clustering of microarray data, based on a three-step procedure: (i) gene filtering; (ii) dimensionality reduction; (iii) clustering of observations in the reduced space. Via a nonparametric model-based clustering approach we obtain promising results both in simulated and real data. Conclusions The proposed algorithm is a simple and effective tool for the clustering of microarray data, in an unsupervised setting.
Collapse
Affiliation(s)
- Riccardo De Bin
- Department of Statistical Sciences, University of Padova, Padova, Italy
| | | |
Collapse
|
10
|
Dozmorov MG, Guthridge JM, Hurst RE, Dozmorov IM. A comprehensive and universal method for assessing the performance of differential gene expression analyses. PLoS One 2010; 5. [PMID: 20844739 PMCID: PMC2936572 DOI: 10.1371/journal.pone.0012657] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2010] [Accepted: 08/04/2010] [Indexed: 11/18/2022] Open
Abstract
The number of methods for pre-processing and analysis of gene expression data continues to increase, often making it difficult to select the most appropriate approach. We present a simple procedure for comparative estimation of a variety of methods for microarray data pre-processing and analysis. Our approach is based on the use of real microarray data in which controlled fold changes are introduced into 20% of the data to provide a metric for comparison with the unmodified data. The data modifications can be easily applied to raw data measured with any technological platform and retains all the complex structures and statistical characteristics of the real-world data. The power of the method is illustrated by its application to the quantitative comparison of different methods of normalization and analysis of microarray data. Our results demonstrate that the method of controlled modifications of real experimental data provides a simple tool for assessing the performance of data preprocessing and analysis methods.
Collapse
Affiliation(s)
- Mikhail G. Dozmorov
- Department of Arthritis and Immunology, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, United States of America
| | - Joel M. Guthridge
- Department of Arthritis and Immunology, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, United States of America
| | - Robert E. Hurst
- Department of Urology, Oklahoma University Health Sciences Center, Oklahoma City, Oklahoma, United States of America
- Department of Biochemistry and Molecular Biology, Oklahoma University Health Sciences Center, Oklahoma City, Oklahoma, United States of America
| | - Igor M. Dozmorov
- Department of Arthritis and Immunology, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, United States of America
- * E-mail:
| |
Collapse
|
11
|
Normalization strategies for microRNA profiling experiments: a ‘normal’ way to a hidden layer of complexity? Biotechnol Lett 2010; 32:1777-88. [DOI: 10.1007/s10529-010-0380-z] [Citation(s) in RCA: 146] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2010] [Accepted: 07/28/2010] [Indexed: 12/31/2022]
|
12
|
Dozmorov I, Lefkovits I. Internal standard-based analysis of microarray data. Part 1: analysis of differential gene expressions. Nucleic Acids Res 2009; 37:6323-39. [PMID: 19720734 PMCID: PMC2770671 DOI: 10.1093/nar/gkp706] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Genome-scale microarray experiments for comparative analysis of gene expressions produce massive amounts of information. Traditional statistical approaches fail to achieve the required accuracy in sensitivity and specificity of the analysis. Since the problem can be resolved neither by increasing the number of replicates nor by manipulating thresholds, one needs a novel approach to the analysis. This article describes methods to improve the power of microarray analyses by defining internal standards to characterize features of the biological system being studied and the technological processes underlying the microarray experiments. Applying these methods, internal standards are identified and then the obtained parameters are used to define (i) genes that are distinct in their expression from background; (ii) genes that are differentially expressed; and finally (iii) genes that have similar dynamical behavior.
Collapse
Affiliation(s)
- Igor Dozmorov
- Oklahoma Medical Research Foundation, Oklahoma City, OK 73104, USA.
| | | |
Collapse
|
13
|
Risso D, Massa MS, Chiogna M, Romualdi C. A modified LOESS normalization applied to microRNA arrays: a comparative evaluation. ACTA ACUST UNITED AC 2009; 25:2685-91. [PMID: 19628505 DOI: 10.1093/bioinformatics/btp443] [Citation(s) in RCA: 37] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
MOTIVATION Microarray normalization is a fundamental step in removing systematic bias and noise variability caused by technical and experimental artefacts. Several approaches, suitable for large-scale genome arrays, have been proposed and shown to be effective in the reduction of systematic errors. Most of these methodologies are based on specific assumptions that are reasonable for whole-genome arrays, but possibly unsuitable for small microRNA (miRNA) platforms. In this work, we propose a novel normalization (loessM), and we investigate, through simulated and real datasets, the influence that normalizations for two-colour miRNA arrays have on the identification of differentially expressed genes. RESULTS We show that normalizations usually applied to large-scale arrays, in several cases, modify the actual structure of miRNA data, leading to large portions of false positives and false negatives. Nevertheless, loessM is able to outperform other techniques in most experimental scenarios. Moreover, when usual assumptions on differential expression distribution are missed, channel effect has a strikingly negative influence on small arrays, bias that cannot be removed by normalizations but rather by an appropriate experimental design. We find that the combination of loessM with eCADS, an experimental design based on biological replicates dye-swap recently proposed for channel-effect reduction, gives better results in most of the experimental conditions in terms of specificity/sensitivity both on simulated and real data. AVAILABILITY LoessM R function is freely available at http://gefu.cribi.unipd.it/papers/miRNA-simulation/
Collapse
Affiliation(s)
- Davide Risso
- Department of Statistical Sciences, University of Padova, via C. Battisti 241 and Department of Biology, University of Padova, via U. Bassi 58/B, 35121 Padova, Italy
| | | | | | | |
Collapse
|