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by Keyword: Microarrays


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Rodríguez-Pérez, R., Fernández, L., Marco, S., (2018). Overoptimism in cross-validation when using partial least squares-discriminant analysis for omics data: a systematic study Analytical and Bioanalytical Chemistry 410, (23), 5981-5992

Advances in analytical instrumentation have provided the possibility of examining thousands of genes, peptides, or metabolites in parallel. However, the cost and time-consuming data acquisition process causes a generalized lack of samples. From a data analysis perspective, omics data are characterized by high dimensionality and small sample counts. In many scenarios, the analytical aim is to differentiate between two different conditions or classes combining an analytical method plus a tailored qualitative predictive model using available examples collected in a dataset. For this purpose, partial least squares-discriminant analysis (PLS-DA) is frequently employed in omics research. Recently, there has been growing concern about the uncritical use of this method, since it is prone to overfitting and may aggravate problems of false discoveries. In many applications involving a small number of subjects or samples, predictive model performance estimation is only based on cross-validation (CV) results with a strong preference for reporting results using leave one out (LOO). The combination of PLS-DA for high dimensionality data and small sample conditions, together with a weak validation methodology is a recipe for unreliable estimations of model performance. In this work, we present a systematic study about the impact of the dataset size, the dimensionality, and the CV technique used on PLS-DA overoptimism when performance estimation is done in cross-validation. Firstly, by using synthetic data generated from a same probability distribution and with assigned random binary labels, we have obtained a dataset where the true classification rate (CR) is 50%. As expected, our results confirm that internal validation provides overoptimistic estimations of the classification accuracy (i.e., overfitting). We have characterized the CR estimator in terms of bias and variance depending on the internal CV technique used and sample to dimensionality ratio. In small sample conditions, due to the large bias and variance of the estimator, the occurrence of extremely good CRs is common. We have found that overfitting peaks when the sample size in the training subset approaches the feature vector dimensionality minus one. In these conditions, the models are neither under- or overdetermined with a unique solution. This effect is particularly intense for LOO and peaks higher in small sample conditions. Overoptimism is decreased beyond this point where the abundance of noisy produces a regularization effect leading to less complex models. In terms of overfitting, our study ranks CV methods as follows: Bootstrap produces the most accurate estimator of the CR, followed by bootstrapped Latin partitions, random subsampling, K-Fold, and finally, the very popular LOO provides the worst results. Simulation results are further confirmed in real datasets from mass spectrometry and microarrays.

Keywords: Metabolomics, Mass spectrometry, Microarrays, Chemometrics, Data analysis, Classification, Method validation


Pla-Roca, M., Altay, G., Giralt, X., Casals, A., Samitier, J., (2016). Design and development of a microarray processing station (MPS) for automated miniaturized immunoassays Biomedical Microdevices 18, (4)

Here we describe the design and evaluation of a fluidic device for the automatic processing of microarrays, called microarray processing station or MPS. The microarray processing station once installed on a commercial microarrayer allows automating the washing, and drying steps, which are often performed manually. The substrate where the assay occurs remains on place during the microarray printing, incubation and processing steps, therefore the addressing of nL volumes of the distinct immunoassay reagents such as capture and detection antibodies and samples can be performed on the same coordinate of the substrate with a perfect alignment without requiring any additional mechanical or optical re-alignment methods. This allows the performance of independent immunoassays in a single microarray spot.

Keywords: Automation, Customization, High-throughput screening, Immunoassays, Microarrays


Llorens, Franc, Hummel, Manuela, Pastor, Xavier, Ferrer, Anna, Pluvinet, Raquel, Vivancos, Ana, Castillo, Ester, Iraola, Susana, Mosquera, Ana M., Gonzalez, Eva, Lozano, Juanjo, Ingham, Matthew, Dohm, Juliane C., Noguera, Marc, Kofler, Robert, Antonio del Rio, Jose, Bayes, Monica, Himmelbauer, Heinz, Sumoy, Lauro, (2011). Multiple platform assessment of the EGF dependent transcriptome by microarray and deep tag sequencing analysis BMC Genomics 12, 326

Background: Epidermal Growth Factor (EGF) is a key regulatory growth factor activating many processes relevant to normal development and disease, affecting cell proliferation and survival. Here we use a combined approach to study the EGF dependent transcriptome of HeLa cells by using multiple long oligonucleotide based microarray platforms (from Agilent, Operon, and Illumina) in combination with digital gene expression profiling (DGE) with the Illumina Genome Analyzer. Results: By applying a procedure for cross-platform data meta-analysis based on RankProd and GlobalAncova tests, we establish a well validated gene set with transcript levels altered after EGF treatment. We use this robust gene list to build higher order networks of gene interaction by interconnecting associated networks, supporting and extending the important role of the EGF signaling pathway in cancer. In addition, we find an entirely new set of genes previously unrelated to the currently accepted EGF associated cellular functions. Conclusions: We propose that the use of global genomic cross-validation derived from high content technologies (microarrays or deep sequencing) can be used to generate more reliable datasets. This approach should help to improve the confidence of downstream in silico functional inference analyses based on high content data.

Keywords: Gene-expression measurements, Quality-control maqc, Cancer-cell-lines, Real-time pcr, Oligonucleotide microarrays, Phosphorylation dynamics, In-vivo, Networks, Signal, Technologies


Rodriguez-Segui, Santiago A., Pons Ximenez, Jose Ignacio, Sevilla, Lidia, Ruiz, Ana, Colpo, Pascal, Rossi, Francois, Martinez, Elena, Samitier, Josep, (2011). Quantification of protein immobilization on substrates for cellular microarray applications Journal of Biomedical Materials Research - Part A 98A, (2), 245-256

Cellular microarray developments and its applications are the next step after DNA and protein microarrays. The choice of the surface chemistry of the substrates used for the implementation of this technique, that must favor proper protein immobilization while avoiding cell adhesion on the nonspotted areas, presents a complex challenge. This is a key issue since usually the best nonfouling surfaces are also the ones that retain immobilized the smallest amounts of printed protein. To quantitatively assess the amount of protein immobilization, in this study several combinations of fluorescently labeled fibronectin (Fn*) and streptavidin (SA*) were microspotted, with and without glycerol addition in the printing buffer, on several substrates suitable for cellular microarrays. The substrates assayed included chemically activated surfaces as well as Poly ethylene oxide (PEO) films that are nonfouling in solution but accept adhesion of proteins in dry conditions. The results showed that the spotted Fn* was retained by all the surfaces, although the PEO surface did show smaller amounts of immobilization. The SA*, on the other hand, was only retained by the chemically activated surfaces. The inclusion of glycerol in the printing buffer significantly reduced the immobilization of both proteins. The results presented in this article provide quantitative evidence of the convenience of using a chemically activated surface to immobilize proteins relevant for cellular microarray applications, particularly when ECM proteins are cospotted with smaller factors which are more difficult to be retained by the surfaces.

Keywords: Protein immobilization, Quantification, Microarray, Substrate, Surface chemistry


Rodriguez-Segui, S. A., Pla, M., Engel, E., Planell, J. A., Martinez, E., Samitier, J., (2009). Influence of fabrication parameters in cellular microarrays for stem cell studies Journal of Materials Science: Materials in Medicine 20, (7), 1525-1533

Lately there has been an increasing interest in the development of tools that enable the high throughput analysis of combinations of surface-immobilized signaling factors and which examine their effect on stem cell biology and differentiation. These surface-immobilized factors function as artificial microenvironments that can be ordered in a microarray format. These microarrays could be useful for applications such as the study of stem cell biology to get a deeper understanding of their differentiation process. Here, the evaluation of several key process parameters affecting the cellular microarray fabrication is reported in terms of its effects on the mesenchymal stem cell culture time on these microarrays. Substrate and protein solution requirements, passivation strategies and cell culture conditions are investigated. The results described in this article serve as a basis for the future development of cellular microarrays aiming to provide a deeper understanding of the stem cell differentiation process.

Keywords: Bone-marrow, Protein microarrays, Progenitor cells, Differentiation, Surfaces, Growth, Biomaterials, Commitment, Pathways, Culture media