@article {1087,
	title = {Assessing technical performance in differential gene expression experiments with external spike-in RNA control ratio mixtures.},
	journal = {Nature communications},
	volume = {5},
	year = {2014},
	month = {2014},
	pages = {5125},
	abstract = {There is a critical need for standard approaches to assess, report and compare the technical performance of genome-scale differential gene expression experiments. Here we assess technical performance with a proposed standard {\textquoteright}dashboard{\textquoteright} of metrics derived from analysis of external spike-in RNA control ratio mixtures. These control ratio mixtures with defined abundance ratios enable assessment of diagnostic performance of differentially expressed transcript lists, limit of detection of ratio (LODR) estimates and expression ratio variability and measurement bias. The performance metrics suite is applicable to analysis of a typical experiment, and here we also apply these metrics to evaluate technical performance among laboratories. An interlaboratory study using identical samples shared among 12 laboratories with three different measurement processes demonstrates generally consistent diagnostic power across 11 laboratories. Ratio measurement variability and bias are also comparable among laboratories for the same measurement process. We observe different biases for measurement processes using different mRNA-enrichment protocols.},
	keywords = {RNA-seq},
	issn = {2041-1723},
	doi = {10.1038/ncomms6125},
	url = {http://www.nature.com/ncomms/2014/140925/ncomms6125/full/ncomms6125.html},
	author = {Munro, Sarah A and Lund, Steven P and Pine, P Scott and Binder, Hans and Clevert, Djork-Arn{\'e} and Ana Conesa and Dopazo, Joaquin and Fasold, Mario and Hochreiter, Sepp and Hong, Huixiao and Jafari, Nadereh and Kreil, David P and Labaj, Pawe{\l} P and Li, Sheng and Liao, Yang and Lin, Simon M and Meehan, Joseph and Mason, Christopher E and Santoyo-L{\'o}pez, Javier and Setterquist, Robert A and Shi, Leming and Shi, Wei and Smyth, Gordon K and Stralis-Pavese, Nancy and Su, Zhenqiang and Tong, Weida and Wang, Charles and Wang, Jian and Xu, Joshua and Ye, Zhan and Yang, Yong and Yu, Ying and Salit, Marc}
}
@article {878,
	title = {ARSyN: a method for the identification and removal of systematic noise in multifactorial time course microarray experiments.},
	journal = {Biostatistics (Oxford, England)},
	year = {2011},
	month = {2011 Nov 14},
	abstract = {Transcriptomic profiling experiments that aim to the identification of responsive genes in specific biological conditions are commonly set up under defined experimental designs that try to assess the effects of factors and their interactions on gene expression. Data from these controlled experiments, however, may also contain sources of unwanted noise that can distort the signal under study, affect the residuals of applied statistical models, and hamper data analysis. Commonly, normalization methods are applied to transcriptomics data to remove technical artifacts, but these are normally based on general assumptions of transcript distribution and greatly ignore both the characteristics of the experiment under consideration and the coordinative nature of gene expression. In this paper, we propose a novel methodology, ARSyN, for the preprocessing of microarray data that takes into account these 2 last aspects. By combining analysis of variance (ANOVA) modeling of gene expression values and multivariate analysis of estimated effects, the method identifies the nonstructured part of the signal associated to the experimental factors (the noise within the signal) and the structured variation of the ANOVA errors (the signal of the noise). By removing these noise fractions from the original data, we create a filtered data set that is rich in the information of interest and includes only the random noise required for inferential analysis. In this work, we focus on multifactorial time course microarray (MTCM) experiments with 2 factors: one quantitative such as time or dosage and the other qualitative, as tissue, strain, or treatment. However, the method can be used in other situations such as experiments with only one factor or more complex designs with more than 2 factors. The filtered data obtained after applying ARSyN can be further analyzed with the appropriate statistical technique to obtain the biological information required. To evaluate the performance of the filtering strategy, we have applied different statistical approaches for MTCM analysis to several real and simulated data sets, studying also the efficiency of these techniques. By comparing the results obtained with the original and ARSyN filtered data and also with other filtering techniques, we can conclude that the proposed method increases the statistical power to detect biological signals, especially in cases where there are high levels of structural noise. Software for ARSyN is freely available at http://www.ua.es/personal/mj.nueda.},
	issn = {1468-4357},
	author = {Nueda, Maria J and Alberto Ferrer and Ana Conesa}
}
