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Statistical implications of pooling RNA samples for microarray experiments
Xuejun Peng, Constance L Wood, Eric M Blalock, Kuey Chen, Philip W Landfield, Arnold J Stromberg
BMC Bioinformatics , 2003, DOI: 10.1186/1471-2105-4-26
Abstract: Modeling the resulting gene expression from sample pooling as a mixture of individual responses, we derived expressions for the experimental error and provided both upper and lower bounds for its value in terms of the variability among individuals and the number of RNA samples pooled. Using "virtual" pooling of data from real experiments and computer simulations, we investigated the statistical properties of RNA sample pooling. Our study reveals that pooling biological samples appropriately is statistically valid and efficient for microarray experiments. Furthermore, optimal pooling design(s) can be found to meet statistical requirements while minimizing total cost.Appropriate RNA pooling can provide equivalent power and improve efficiency and cost-effectiveness for microarray experiments with a modest increase in total number of subjects. Pooling schemes in terms of replicates of subjects and arrays can be compared before experiments are conducted.Researchers are increasingly realizing the importance of true biological replicates for assessing statistical confidence in microarray experiments [1-6], but replication is often hindered by financial or technical constraints. One problem is that large, prefabricated microarray chips can be relatively expensive, driving up the total cost of an experiment. (In fact, the cost of a subject is often lower.) Another obstacle to using replicates is that the biological tissues from which RNA is extracted can often be of such small quantity by nature that it is technically difficult to get enough RNA sample from one subject for hybridization to one array [3]. Either or both of these problems have motivated biologists to pool RNA samples together before hybridization. Many research papers using this method have been published [3-5], but the statistical properties of pooling have not been explicitly addressed. There are two different approaches of sample pooling. One is dubbed "complete pooling", where all samples from one treatmen
Bayesian models for pooling microarray studies with multiple sources of replications
Erin M Conlon, Joon J Song, Jun S Liu
BMC Bioinformatics , 2006, DOI: 10.1186/1471-2105-7-247
Abstract: We introduce a Bayesian hierarchical model to pool cDNA microarray data across multiple independent studies to identify highly expressed genes. Each study has multiple sources of variation, i.e. replicate slides within repeated identical experiments. Our model produces the gene-specific posterior probability of differential expression, which provides a direct method for ranking genes, and provides Bayesian estimates of false discovery rates (FDR). In simulations combining two and five independent studies, with fixed FDR levels, we observed large increases in the number of discovered genes in pooled versus individual analyses. When the number of output genes is fixed (e.g., top 100), the pooled model found appreciably more truly differentially expressed genes than the individual studies. We were also able to identify more differentially expressed genes from pooling two independent studies in Bacillus subtilis than from each individual data set. Finally, we observed that in our simulation studies our Bayesian FDR estimates tracked the true FDRs very well.Our method provides a cohesive framework for combining multiple but not identical microarray studies with several sources of replication, with data produced from the same platform. We assume that each study contains only two conditions: an experimental and a control sample. We demonstrated our model's suitability for a small number of studies that have been either pre-scaled or have no outliers.cDNA microarrays monitor gene expression for thousands of genes simultaneously. Two experimental conditions are compared by examining the ratio of expression between two samples, e.g. treatment versus control, wildtype versus mutant, or disease versus healthy. The primary goal of these experiments is to identify genes that are differentially expressed between the two conditions. The up-regulated and down-regulated genes shed light on biological mechanisms of the cell, such as functional pathways, response to treatments, and gen
Effect of pooling samples on the efficiency of comparative studies using microarrays  [PDF]
Shu-Dong Zhang,Timothy W. Gant
Quantitative Biology , 2005, DOI: 10.1093/bioinformatics/bti717
Abstract: Many biomedical experiments are carried out by pooling individual biological samples. However, pooling samples can potentially hide biological variance and give false confidence concerning the data significance. In the context of microarray experiments for detecting differentially expressed genes, recent publications have addressed the problem of the efficiency of sample-pooling, and some approximate formulas were provided for the power and sample size calculations. It is desirable to have exact formulas for these calculations and have the approximate results checked against the exact ones. We show that the difference between the approximate and exact results can be large. In this study, we have characterized quantitatively the effect of pooling samples on the efficiency of microarray experiments for the detection of differential gene expression between two classes. We present exact formulas for calculating the power of microarray experimental designs involving sample pooling and technical replications. The formulas can be used to determine the total numbers of arrays and biological subjects required in an experiment to achieve the desired power at a given significance level. The conditions under which pooled design becomes preferable to non-pooled design can then be derived given the unit cost associated with a microarray and that with a biological subject. This paper thus serves to provide guidance on sample pooling and cost effectiveness. The formulation in this paper is outlined in the context of performing microarray comparative studies, but its applicability is not limited to microarray experiments. It is also applicable to a wide range of biomedical comparative studies where sample pooling may be involved.
Identification of microRNA-mRNA modules using microarray data
Vivek Jayaswal, Mark Lutherborrow, David DF Ma, Yee H Yang
BMC Genomics , 2011, DOI: 10.1186/1471-2164-12-138
Abstract: We propose a two-step method for the identification of many-to-many relationships between miRNAs and mRNAs. In the first step, we obtain miRNA and mRNA clusters using a combination of miRNA-target mRNA prediction algorithms and microarray expression data. In the second step, we determine the associations between miRNA clusters and mRNA clusters based on changes in miRNA and mRNA expression profiles. We consider the miRNA-mRNA clusters with statistically significant associations to be potentially regulatory and, therefore, of biological interest.Our method reduces the interactions between several hundred miRNAs and several thousand mRNAs to a few miRNA-mRNA groups, thereby facilitating a more meaningful biological analysis and a more targeted experimental validation.MicroRNAs are short (20-22 nt) non-protein coding RNA sequences that are involved in the post-transcriptional regulation of genes either by mRNA cleavage and degradation or by repressing the translation of mRNA into proteins. Though they are a relatively recent discovery, they are of immense biological interest given their regulatory role in numerous cellular processes, e.g. some miRNAs can act as oncogenes or tumor-suppressors in humans [1-3].The identification of regulatory miRNAs and their target mRNAs is a major combinatorial challenge because a single miRNA regulates multiple mRNAs and several miRNAs co-regulate a single mRNA. The many-to-many relationship between miRNAs and mRNAs results in a powerful ability for miRNAs to control cellular protein output and function. Therefore, methods which are able to discover these miRNA-based regulations may provide a means for identifying the key cellular pathways that contribute to a biological event, such as the initiation of cancer. One of the widely used methods for the identification of regulatory miRNAs is based on mRNA microarray expression data. Firstly, the putative miRNA-mRNA (miRmR) pairs are identified using a prediction algorithm such as TargetSca
Signal Recovery from Pooling Representations  [PDF]
Joan Bruna,Arthur Szlam,Yann LeCun
Statistics , 2013,
Abstract: In this work we compute lower Lipschitz bounds of $\ell_p$ pooling operators for $p=1, 2, \infty$ as well as $\ell_p$ pooling operators preceded by half-rectification layers. These give sufficient conditions for the design of invertible neural network layers. Numerical experiments on MNIST and image patches confirm that pooling layers can be inverted with phase recovery algorithms. Moreover, the regularity of the inverse pooling, controlled by the lower Lipschitz constant, is empirically verified with a nearest neighbor regression.
TRIzol treatment of secretory phase endometrium allows combined proteomic and mRNA microarray analysis of the same sample in women with and without endometriosis
Amelie Fassbender, Peter Simsa, Cleophas M Kyama, Etienne Waelkens, Attila Mihalyi, Christel Meuleman, Olivier Gevaert, Raf Van de Plas, Bart de Moor, Thomas M D'Hooghe
Reproductive Biology and Endocrinology , 2010, DOI: 10.1186/1477-7827-8-123
Abstract: Proteomic analysis was performed using SELDI-TOF-MS of TRIzol-extracted EM obtained during secretory phase from patients without endometriosis (n = 6), patients with minimal-mild (n = 5) and with moderate-severe endometriosis (n = 5), classified according to the system of the American Society of Reproductive Medicine. Proteomic data were compared to mRNA microarray data obtained from the same EM samples.In our SELDI-TOF MS study 32 peaks were differentially expressed in endometrium of all women with endometriosis (stages I-IV) compared with all controls during the secretory phase. Comparison of proteomic results with those from microarray revealed no corresponding genes/proteins.TRIzol treatment of secretory phase EM allows combined proteomic and mRNA microarray analysis of the same sample, but comparison between proteomic and microarray data was not evident, probably due to post-translational modifications.Endometriosis is a gynaecological disorder, defined as the presence of endometrial-like tissue outside the uterus and is associated with chronic intrapelvic inflammation. Its symptoms can impact on general well-being [1] and include severe dysmenorrhoea; deep dyspareunia; chronic pelvic pain; cyclical or premenstrual symptoms (e.g. bowel or bladder associated) with or without abnormal bleeding; infertility and chronic fatigue.Well established biological differences between eutopic endometrium from women with and without endometriosis represent an interesting scientific basis to develop a semi-invasive diagnostic test for endometriosis based on these differences. Recent evidence suggests that significant biological differences between eutopic endometrium from women with and without endometriosis [2] may offer the basis for a semi-invasive diagnostic test based on the analysis of an endometrial biopsy. Numerous proteomic [3-10] and mRNA microarray [11-14] studies have demonstrated important biological differences between eutopic endometrium from women with and with
Removal of AU Bias from Microarray mRNA Expression Data Enhances Computational Identification of Active MicroRNAs  [PDF]
Ran Elkon,Reuven Agami
PLOS Computational Biology , 2008, DOI: 10.1371/journal.pcbi.1000189
Abstract: Elucidation of regulatory roles played by microRNAs (miRs) in various biological networks is one of the greatest challenges of present molecular and computational biology. The integrated analysis of gene expression data and 3′-UTR sequences holds great promise for being an effective means to systematically delineate active miRs in different biological processes. Applying such an integrated analysis, we uncovered a striking relationship between 3′-UTR AU content and gene response in numerous microarray datasets. We show that this relationship is secondary to a general bias that links gene response and probe AU content and reflects the fact that in the majority of current arrays probes are selected from target transcript 3′-UTRs. Therefore, removal of this bias, which is in order in any analysis of microarray datasets, is of crucial importance when integrating expression data and 3′-UTR sequences to identify regulatory elements embedded in this region. We developed visualization and normalization schemes for the detection and removal of such AU biases and demonstrate that their application to microarray data significantly enhances the computational identification of active miRs. Our results substantiate that, after removal of AU biases, mRNA expression profiles contain ample information which allows in silico detection of miRs that are active in physiological conditions.
Learnable Pooling Regions for Image Classification  [PDF]
Mateusz Malinowski,Mario Fritz
Computer Science , 2013,
Abstract: Biologically inspired, from the early HMAX model to Spatial Pyramid Matching, pooling has played an important role in visual recognition pipelines. Spatial pooling, by grouping of local codes, equips these methods with a certain degree of robustness to translation and deformation yet preserving important spatial information. Despite the predominance of this approach in current recognition systems, we have seen little progress to fully adapt the pooling strategy to the task at hand. This paper proposes a model for learning task dependent pooling scheme -- including previously proposed hand-crafted pooling schemes as a particular instantiation. In our work, we investigate the role of different regularization terms showing that the smooth regularization term is crucial to achieve strong performance using the presented architecture. Finally, we propose an efficient and parallel method to train the model. Our experiments show improved performance over hand-crafted pooling schemes on the CIFAR-10 and CIFAR-100 datasets -- in particular improving the state-of-the-art to 56.29% on the latter.
Gaucher Disease: Transcriptome Analyses Using Microarray or mRNA Sequencing in a Gba1 Mutant Mouse Model Treated with Velaglucerase alfa or Imiglucerase  [PDF]
Nupur Dasgupta, You-Hai Xu, Sunghee Oh, Ying Sun, Li Jia, Mehdi Keddache, Gregory A Grabowski
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0074912
Abstract: Gaucher disease type 1, an inherited lysosomal storage disorder, is caused by mutations in GBA1 leading to defective glucocerebrosidase (GCase) function and consequent excess accumulation of glucosylceramide/glucosylsphingosine in visceral organs. Enzyme replacement therapy (ERT) with the biosimilars, imiglucerase (imig) or velaglucerase alfa (vela) improves/reverses the visceral disease. Comparative transcriptomic effects (microarray and mRNA-Seq) of no ERT and ERT (imig or vela) were done with liver, lung, and spleen from mice having Gba1 mutant alleles, termed D409V/null. Disease-related molecular effects, dynamic ranges, and sensitivities were compared between mRNA-Seq and microarrays and their respective analytic tools, i.e. Mixed Model ANOVA (microarray), and DESeq and edgeR (mRNA-Seq). While similar gene expression patterns were observed with both platforms, mRNA-Seq identified more differentially expressed genes (DEGs) (~3-fold) than the microarrays. Among the three analytic tools, DESeq identified the maximum number of DEGs for all tissues and treatments. DESeq and edgeR comparisons revealed differences in DEGs identified. In 9V/null liver, spleen and lung, post-therapy transcriptomes approximated WT, were partially reverted, and had little change, respectively, and were concordant with the corresponding histological and biochemical findings. DEG overlaps were only 8–20% between mRNA-Seq and microarray, but the biological pathways were similar. Cell growth and proliferation, cell cycle, heme metabolism, and mitochondrial dysfunction were most altered with the Gaucher disease process. Imig and vela differentially affected specific disease pathways. Differential molecular responses were observed in direct transcriptome comparisons from imig- and vela-treated tissues. These results provide cross-validation for the mRNA-Seq and microarray platforms, and show differences between the molecular effects of two highly structurally similar ERT biopharmaceuticals.
Differentiable Pooling for Hierarchical Feature Learning  [PDF]
Matthew D. Zeiler,Rob Fergus
Computer Science , 2012,
Abstract: We introduce a parametric form of pooling, based on a Gaussian, which can be optimized alongside the features in a single global objective function. By contrast, existing pooling schemes are based on heuristics (e.g. local maximum) and have no clear link to the cost function of the model. Furthermore, the variables of the Gaussian explicitly store location information, distinct from the appearance captured by the features, thus providing a what/where decomposition of the input signal. Although the differentiable pooling scheme can be incorporated in a wide range of hierarchical models, we demonstrate it in the context of a Deconvolutional Network model (Zeiler et al. ICCV 2011). We also explore a number of secondary issues within this model and present detailed experiments on MNIST digits.
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