• Documents
  • Authors
  • Tables
  • Log in
  • Sign up
  • MetaCart
  • DMCA
  • Donate

CiteSeerX logo

Advanced Search Include Citations
Advanced Search Include Citations | Disambiguate

Normalization for cDNA microarray data: a robust composite method addressBIBLIOGRAPHY 179 ing single and multiple slide systematic variation,” Nucl (2002)

by Y H Yang, S Dudoit, P Luu, D M Lin, V Peng, J Ngai, T P Speed
Venue:Acids Res
Add To MetaCart

Tools

Sorted by:
Results 1 - 10 of 718
Next 10 →

Limma: linear models for microarray data

by Gordon K. Smyth, Matthew Ritchie, Natalie Thorne, James Wettenhall, Wei Shi - Bioinformatics and Computational Biology Solutions using R and Bioconductor , 2005
"... This free open-source software implements academic research by the authors and co-workers. If you use it, please support the project by citing the appropriate journal articles listed in Section 2.1.Contents ..."
Abstract - Cited by 774 (13 self) - Add to MetaCart
This free open-source software implements academic research by the authors and co-workers. If you use it, please support the project by citing the appropriate journal articles listed in Section 2.1.Contents
(Show Context)

Citation Context

...-pool spots, and to use the up-weighting method discussed below [23]. A whole-library-pool means that one makes a pool of a library of probes, and prints spots from the pool at various concentrations =-=[52]-=-. The library should be sufficiently large than one can be confident that the average of all the probes is not differentially expressed. The larger the library the better. Good results have been obtai...

Summaries of Affymetrix GeneChip probe level data

by Rafael A. Irizarry, Benjamin M. Bolstad, Francois Collin, Leslie M. Cope, Bridget Hobbs, Terence P. Speed - Nucleic Acids Res , 2003
"... High density oligonucleotide array technology is widely used in many areas of biomedical research for quantitative and highly parallel measurements of gene expression. Affymetrix GeneChip arrays are the most popular. In this technology each gene is typically represented by a set of 11±20 pairs of pr ..."
Abstract - Cited by 471 (21 self) - Add to MetaCart
High density oligonucleotide array technology is widely used in many areas of biomedical research for quantitative and highly parallel measurements of gene expression. Affymetrix GeneChip arrays are the most popular. In this technology each gene is typically represented by a set of 11±20 pairs of probes. In order to obtain expression measures it is necessary to summarize the probe level data. Using two extensive spike-in studies and a dilution study, we developed a set of tools for assessing the effectiveness of expression measures. We found that the performance of the current version of the default expression measure provided by Affymetrix Microarray Suite can be signi®cantly improved by the use of probe level summaries derived from empirically motivated statistical models. In particular, improvements in the ability to detect differentially expressed genes are demonstrated.
(Show Context)

Citation Context

...ons Xg and Yg from two arrays being compared for all genes, g = 1,, G. Log scale scatter plots of Yg versus Xg are commonly seen in the literature. MvA plots are 45° rotations of these scatter plots =-=(18)-=-. We found MvA plots useful because log fold change (the quantity of most interest) is represented on the y-axis and average absolute log expression (another quantity of interest) on the x-axis. We se...

Normalization of cDNA microarray data

by Gordon K. Smyth, Terry Speed - Methods , 2003
"... Normalization means to adjust microarray data for effects which arise from variation in the technology rather than from biological differences between the RNA samples or between the printed probes. This article describes normalization methods based on the fact that dye balance typically varies with ..."
Abstract - Cited by 242 (8 self) - Add to MetaCart
Normalization means to adjust microarray data for effects which arise from variation in the technology rather than from biological differences between the RNA samples or between the printed probes. This article describes normalization methods based on the fact that dye balance typically varies with spot intensity and with spatial position on the array. Print-tip loess normalization provides a well-tested general purpose normalization method which has given good results on a wide range of arrays. The method may be refined by using quality weights for individual spots. The method is best combined with diagnostic plots of the data which display the spatial and intensity trends. When diagnostic plots show that biases still remain in the data after normalization, further normalization steps such as plate-order normalization or scalenormalization between the arrays may be undertaken. Composite normalization may be used when control spots are available which are known to be not differentially expressed. Variations on loess normalization include global loess normalization and 2D normalization. Detailed commands are given to implement the normalization techniques using freely available software. 1
(Show Context)

Citation Context

...cases it can be beneficial to use a compromise between the subarray loess curves and the global titration series curve. Figure 1 shows the loess curve through a series MSP titration spots. Yang et al =-=[6]-=- propose the normalization where is the loess curve through the MSP spots and is the proportion of spots on the array with A-values less than . The idea of this proposal is that normalization will be ...

Quantitative quality control in microarray image processing and data acquisition

by Xujing Wang, Martin J. Hessner, Yan Wu, Nirupma Pati, Soumitra Ghosh - Nucleic Acids Res , 2001
"... experiments and the application in data filtering, normalization and false positive rate prediction ..."
Abstract - Cited by 118 (10 self) - Add to MetaCart
experiments and the application in data filtering, normalization and false positive rate prediction

Capturing heterogeneity in gene expression studies by ‘surrogate variable analysis’. PLoS Genetics 3:e161

by Jeffrey T. Leek, John D. Storey , 2007
"... It has unambiguously been shown that genetic, environmental, demographic, and technical factors may have substantial effects on gene expression levels. In addition to the measured variable(s) of interest, there will tend to be sources of signal due to factors that are unknown, unmeasured, or too com ..."
Abstract - Cited by 115 (19 self) - Add to MetaCart
It has unambiguously been shown that genetic, environmental, demographic, and technical factors may have substantial effects on gene expression levels. In addition to the measured variable(s) of interest, there will tend to be sources of signal due to factors that are unknown, unmeasured, or too complicated to capture through simple models. We show that failing to incorporate these sources of heterogeneity into an analysis can have widespread and detrimental effects on the study. Not only can this reduce power or induce unwanted dependence across genes, but it can also introduce sources of spurious signal to many genes. This phenomenon is true even for well-designed, randomized studies. We introduce ‘‘surrogate variable analysis’ ’ (SVA) to overcome the problems caused by heterogeneity in expression studies. SVA can be applied in conjunction with standard analysis techniques to accurately capture the relationship between expression and any modeled variables of interest. We apply SVA to disease class, time course, and genetics of gene expression studies. We show that SVA increases the biological accuracy and reproducibility of analyses in genome-wide expression studies.
(Show Context)

Citation Context

...,6], demographic [7,8], or genetic [9– 11] factors. It is well known that sources of variation due to experimental design or large-scale systematic sources of signal may be present in expression data =-=[3,4,12,13]-=-, someEditor: Greg Gibson, North Carolina State University, United States of America Received April 9, 2007; Accepted August 1, 2007; Published September 28, 2007 A previous version of this article ap...

CLICK and EXPANDER: a system for clustering and visualizing gene expression data

by Roded Sharan, Adi Maron-Katz, Ron Shamir - Bioinformatics , 2003
"... Motivation: Microarrays have become a central tool in biological research. Their applications range from functional annotation to tissue classification and genetic network inference. A key step in the analysis of gene expression data is the identification of groups of genes that manifest similar exp ..."
Abstract - Cited by 99 (6 self) - Add to MetaCart
Motivation: Microarrays have become a central tool in biological research. Their applications range from functional annotation to tissue classification and genetic network inference. A key step in the analysis of gene expression data is the identification of groups of genes that manifest similar expression patterns. This translates to the algorithmic problem of clustering genes based on their expression patterns. Results: We present a novel clustering algorithm, called CLICK, and its applications to gene expression analysis. The algorithm utilizes graph-theoretic and statistical techniques to identify tight groups (kernels) of highly similar elements, which are likely to belong to the same true cluster. Several heuristic procedures are then used to expand the kernels into the full clusters. We report on the application of CLICK to a variety of gene expression data sets. In all those applications it outperformed extant algorithms according to several common figures of merit. We also point out that CLICK can be successfully used for the identification of common regulatory motifs in the upstream regions of co-regulated genes. Furthermore, we demonstrate how CLICK can be used to accurately classify tissue samples into disease types, based on their expression profiles. Finally, we present a new java-based graphical tool, called EXPANDER, for gene expression analysis and visualization, which incorporates CLICK and several other popular clustering algorithms.
(Show Context)

Citation Context

...ngerprint to have mean zero and variance one, a fixed norm or a fixed maximum entry. Statistically based methods for data normalization have also been developed recently (see, e.g. Kerr et al., 2000; =-=Yang et al., 2002-=-). 2.1 Assessment of solutions A key question in the design and analysis of clustering techniques is how to evaluate solutions. We present here figures of merit for measuring the quality of a clusteri...

lincRNAs act in the circuitry controlling pluripotency and differentiation. Nature 2011

by Mitchell Guttman, Julie Donaghey, Bryce W. Carey, Manuel Garber, Jennifer K, Glen Munson, Geneva Young, Anne Bergstrom Lucas, Robert Ach, Xiaoping Yang, Ido Amit, Er Meissner, Aviv Regev, John L, David E. Root, Eric S. L
"... and differentiation ..."
Abstract - Cited by 97 (1 self) - Add to MetaCart
and differentiation

Statistical Issues in cDNA Microarray Data Analysis

by Gordon K. Smyth, Yee Hwa Yang, Terry Speed , 2003
"... This article summarizes some of the issues involved and provides a brief review of the analysis tools which are available to researchers to deal with them. Any microarray experiment involves a number of distinct stages. Firstly there is the design of the experiment. The researchers must decide which ..."
Abstract - Cited by 83 (6 self) - Add to MetaCart
This article summarizes some of the issues involved and provides a brief review of the analysis tools which are available to researchers to deal with them. Any microarray experiment involves a number of distinct stages. Firstly there is the design of the experiment. The researchers must decide which genes are to be printed on the arrays, which sources of RNA are to be hybridized to the arrays and on how many arrays the hybridizations will be replicated. Secondly, after hybridization, there follows a number of data-cleaning steps or `low-level analysis' of the microarray data. The microarray images must be processed to acquire red and green foreground and background intensities for each spot. The acquired red/green ratios must be normalized to adjust for dye-bias and for any systematic variation other than that due to the differences between the RNA samples being studied. Thirdly, the normalized ratios are analyzed by various graphical and numerical means to select differentially expressed genes or to find groups of genes whose expression profiles can reliably classify the different RNA sources into meaningful groups. The sections of this article correspond roughly to the various analysis steps. The following notation will be used throughout the article. The foreground red and green intensities will be written Pp and 9p for each spot. The background intensities will be Pf and 9f . The background-corrected intensities will be P and 9 where usually P Pp Pf 0 # and 9 9p 9f 0 # . The log-differential expression ratio will be vyq # E P 9 0 for each spot. Finally, the log-intensity of the spot will be vyq 3 P9 0 , a measure of the overall brightness of the spot. (The letter E is a mnemonic for minus as vyq vyq E P 9 0 # while 3 is a mnemonic for add as #vyq vyq #...
(Show Context)

Citation Context

...erms of estimating spatial and intensity dependent trends in the data. In some cases it may be beneficial to use a compromise between the sub-array loess curves and the global titration series curve (=-=Yang et al, 2002-=-b). An alternative method is to select an invariant set of genes as described for oligonucleotide arrays by Schadt et al (1999) and Tseng et al (2001). A set of genes is said to be invariant if their ...

Optimal Sample Size for Multiple Testing: the Case of Gene Expression Microarrays

by Peter Müller, Giovanni Parmigiani, Christian Robert, Judith Rousseau - Journal of the American Statistical Association , 2004
"... We consider the choice of an optimal sample size for multiple comparison problems. The motivating application is the choice of the number of microarray experiments to be carried out when learning about dierential gene expression. However, the approach is valid in any application that involves multip ..."
Abstract - Cited by 75 (5 self) - Add to MetaCart
We consider the choice of an optimal sample size for multiple comparison problems. The motivating application is the choice of the number of microarray experiments to be carried out when learning about dierential gene expression. However, the approach is valid in any application that involves multiple comparison in a large number of hypothesis tests.

Evaluation of gene expression measurements from commercial microarray platforms

by Paul K. Tan, Thomas J. Downey, Edward L. Spitznagel, Pin Xu, Dadin Fu, Dimiter S. Dimitrov, Richard A. Lempicki, Bruce M. Raaka, Margaret C. Cam - Nucleic Acids Res , 2003
"... Multiple commercial microarrays for measuring genome-wide gene expression levels are currently available, including oligonucleotide and cDNA, single- and two-channel formats. This study reports on the results of gene expression measurements generated from identical RNA preparations that were obtaine ..."
Abstract - Cited by 72 (0 self) - Add to MetaCart
Multiple commercial microarrays for measuring genome-wide gene expression levels are currently available, including oligonucleotide and cDNA, single- and two-channel formats. This study reports on the results of gene expression measurements generated from identical RNA preparations that were obtained using three commercially available microarray platforms. RNA was collected from PANC-1 cells grown in serum-rich medium and at 24 h following the removal of serum. Three biological replicates were prepared for each condition, and three experimental replicates were produced for the ®rst biological replicate. RNA was labeled and hybridized to microarrays from three major suppliers according to manufacturers ' protocols, and gene expression measurements were obtained using each platform's standard software. For each platform, gene targets from a subset of 2009 common genes were compared. Correlations in gene expression levels and comparisons for signi®cant gene expression changes in this subset were calculated, and showed considerable divergence across the different platforms, suggesting the need for establishing industrial manufacturing standards, and further independent and thorough validation of the technology.
(Show Context)

Citation Context

...cDNA arrays, the default settings of the Agilent G2566AA Feature Extraction Software (v.A.5.1.1) were used, which selects the LOWESS (locally weighted linear regression curve ®t) normalization method =-=(6)-=-. For the Amersham Codelink Array, the BioDiscovery ImaGene (v.5) software was used, and for Affymetrix GeneChips, the Microarray Suite software (MAS 5.0) was used, both of which utilize global (linea...

Powered by: Apache Solr
  • About CiteSeerX
  • Submit and Index Documents
  • Privacy Policy
  • Help
  • Data
  • Source
  • Contact Us

Developed at and hosted by The College of Information Sciences and Technology

© 2007-2019 The Pennsylvania State University