Difference: MicroarrayBioLab (1 vs. 12)

Revision 12
27 May 2020 - Main.UnknownUser
Revision 11
02 Oct 2014 - Main.PerryMoerland
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META TOPICPARENT name="SupportBioLab"
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Medical Bioinformatics

 
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We have large experience in experimental design and analysis of microarray experiments from various platforms. Typically, the different steps in our analysis approach are
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  • experimental design: choice of platform, sample size, randomization of samples across multiple conditions
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  • experimental design: choice of platform, sample size, randomization of samples
 
  • quality control (QC): variety of QC plots and quantitative measures to aid in detecting problematic microarrays
  • preprocessing: normalization with a variety of state-of-the-art methods
  • unsupervised analysis: for example, using clustering and principal component analysis
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  • differential expression: detection of differentially expressed probes for a variety of experimental designs (multiple groups, time series, inclusion of covariates, regression), emperical Bayes, correction for multiple testing, permutation tests
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  • differential expression: detection of differentially expressed probes for a variety of experimental designs (multiple groups, time series, inclusion of covariates, regression), empirical Bayes, correction for multiple testing, permutation tests
 
  • classification: discovery of a predictive signature using a variety of statistical and machine learning models (discriminant analysis, decision trees, logistic regression, nearest centroid, support vector machines, neural networks, etc) in a double cross-validation scheme
    • complete separation of training data used for estimating the parameters of a model and test data for estimating the accuracy of the model
    • multiple estimates of classification accuracy to be able to assess its variance
    • cross-validation on the training data to determine optimal values for hyperparameters of a model and to select features
  • probe annotation: including re-annotation of Affymetrix and Illumina probe sequences
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  • geneset analysis: interpretation of a microarray experiments using Gene Ontology, biological pathways, sets of promoters, and other types of genesets
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  • geneset analysis: interpretation of a microarray experiments using Gene Ontology, biological pathways, sets of promoters, and other types of genesets from the MSigDB collection
 
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Most of our analyses are performed using R and tools available from Bioconductor.
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Most of our microarray analyses are performed using R and tools available from Bioconductor.
 

Platforms included in our standard analysis pipelines are
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We also have considerable experience with various types of custom-made arrays, SNP arrays (Illumina Infinium BeadChips), and analysis of cross-species (heterologous) experiments.
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We set up a yearly four-week course with a mix of lectures and well-structured computer exercises to learn the tricks of the trade in analyzing genome-wide expression data.
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We set up a yearly one-week course with a mix of lectures and well-structured computer exercises to learn the tricks of the trade in analyzing genome-wide expression data.
 

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Selected publications
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Nora Bijl, Milka Sokolovic, Carlos Vrins, Mirjam Langeveld, Perry D. Moerland, Nike Claessen, Roelof Ottenhoff, Cindy van Roomen, Peter Dubbelhuis, Rolf Boot, Jan Aten, Bert Groen, Johannes M.F.G. Aerts, Marco van Eijk (2009). Modulation of Glycosphingolipid Metabolism Significantly Improves Hepatic Insulin Sensitivity and Reverses Hepatic Steatosis in Mice. Hepatology; 50(5):1431-41. PubMed
Miinalainen IJ, Schmitz W, Huotari A, Autio KJ, Soininen R, Ver Loren van Themaat E, Baes M, Herzig KH, Conzelmann E, Hiltunen JK (2009). Mitochondrial 2,4-dienoyl-CoA reductase deficiency in mice results in severe hypoglycemia with stress intolerance and unimpaired ketogenesis. PLoS Genetics, 5(7):e1000453. PubMed

Stephan H. Schirmer, Joost O. Fledderus, Pieter T.G. Bot, Perry D. Moerland, Jan Baan Jr. , Jose P. Henriques, Renee J. van der Schaaf, Marije M. Vis, Anton J.G. Horrevoets, Jan J. Piek, Niels van Royen (2008). Interferon-beta Signaling Is Enhanced in Patients with Insufficient Coronary Collateral Artery Development and Inhibits Arteriogenesis in Mice. Circulation Research;102(10):1286-94. PubMed
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S Bergonzi, MC Albani, E Ver Loren van Themaat, K Nordström, R Wang, K Schneeberger, PD Moerland, G Coupland (2013). Mechanisms of age-dependent response to winter temperature in perennial flowering of Arabis alpina. Science, 340: 1094-97. PubMed
C Helbig, R Gentek, RA Backer, Y de Souza, IA Derks, E Eldering, K Wagner, D Jankovic, T Gridley, PD Moerland, RA Flavell, D Amsen (2012). Notch controls the magnitude of CD4+ T cell responses by promoting cellular longevity. PNAS, 109(23):9041-46. PubMed
KML Hertoghs, PD Moerland, A van Stijn, EBM Remmerswaal, SL Yong, PJEJ van de Berg, SM van Ham, IJM ten Berge and RAW van Lier (2010). Molecular profiling of cytomegalovirus-induced human CD8+ T cell differentiation. Journal of Clinical Investigation, 120(11):4077-90. PubMed
Revision 10
21 Mar 2011 - Main.AngelaLuijf
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META TOPICPARENT name="SupportBioLab"
Medical Bioinformatics

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Back to menuSort

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Microarray analysis People
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Microarray analysis People
 
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  We set up a yearly four-week course with a mix of lectures and well-structured computer exercises to learn the tricks of the trade in analyzing genome-wide expression data.

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Selected publications
Nora Bijl, Milka Sokolovic, Carlos Vrins, Mirjam Langeveld, Perry D. Moerland, Nike Claessen, Roelof Ottenhoff, Cindy van Roomen, Peter Dubbelhuis, Rolf Boot, Jan Aten, Bert Groen, Johannes M.F.G. Aerts, Marco van Eijk (2009). Modulation of Glycosphingolipid Metabolism Significantly Improves Hepatic Insulin Sensitivity and Reverses Hepatic Steatosis in Mice. Hepatology; 50(5):1431-41. PubMed
Miinalainen IJ, Schmitz W, Huotari A, Autio KJ, Soininen R, Ver Loren van Themaat E, Baes M, Herzig KH, Conzelmann E, Hiltunen JK (2009). Mitochondrial 2,4-dienoyl-CoA reductase deficiency in mice results in severe hypoglycemia with stress intolerance and unimpaired ketogenesis. PLoS Genetics, 5(7):e1000453. PubMed
Revision 9
31 Jan 2010 - Main.PerryMoerland
Line: 1 to 1
 
META TOPICPARENT name="SupportBioLab"
Medical Bioinformatics

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We also have considerable experience with various types of custom-made arrays, SNP arrays (Illumina Infinium BeadChips), and analysis of cross-species (heterologous) experiments.
Changed:
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We set-up a yearly four-week course with a mix of lectures and well-structured computer exercises to learn the tricks of the trade in analyzing genome-wide expression data.
>
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We set up a yearly four-week course with a mix of lectures and well-structured computer exercises to learn the tricks of the trade in analyzing genome-wide expression data.
 

Revision 8
24 Jan 2010 - Main.PerryMoerland
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META TOPICPARENT name="SupportBioLab"
Medical Bioinformatics

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We have large experience in experimental design and analysis of microarray data from various platforms. Typically, the different steps in our analysis approach are
>
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We have large experience in experimental design and analysis of microarray experiments from various platforms. Typically, the different steps in our analysis approach are
 

  • experimental design: choice of platform, sample size, randomization of samples across multiple conditions
  • quality control (QC): variety of QC plots and quantitative measures to aid in detecting problematic microarrays
Changed:
<
<
  • preprocessing: normalization with a variety of state-of-the-art methods available via R/Bioconductor
>
>
  • preprocessing: normalization with a variety of state-of-the-art methods
  • unsupervised analysis: for example, using clustering and principal component analysis
 
  • differential expression: detection of differentially expressed probes for a variety of experimental designs (multiple groups, time series, inclusion of covariates, regression), emperical Bayes, correction for multiple testing, permutation tests
Changed:
<
<
  • classification: using a variety of statistical and machine learning models (discriminant analysis, decision trees, logistic regression, nearest centroid, support vector machines, neural networks, ...) in a double cross-validation scheme
>
>
  • classification: discovery of a predictive signature using a variety of statistical and machine learning models (discriminant analysis, decision trees, logistic regression, nearest centroid, support vector machines, neural networks, etc) in a double cross-validation scheme
 
    • complete separation of training data used for estimating the parameters of a model and test data for estimating the accuracy of the model
    • multiple estimates of classification accuracy to be able to assess its variance
    • cross-validation on the training data to determine optimal values for hyperparameters of a model and to select features
Changed:
<
<
  • probe annotation
>
>
  • probe annotation: including re-annotation of Affymetrix and Illumina probe sequences
  • geneset analysis: interpretation of a microarray experiments using Gene Ontology, biological pathways, sets of promoters, and other types of genesets
 
Changed:
<
<
Platforms included in our standard analysis pipelines
>
>
Most of our analyses are performed using R and tools available from Bioconductor.
 
Changed:
<
<
  • Affymetrix: mRNA
  • Agilent: mRNA
  • Illumina: mRNA, miRNA
>
>
Platforms included in our standard analysis pipelines are
 

Changed:
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We also have considerable experience with various types of custom-made arrays, SNP arrays, and analysis of cross-species (heterologous) experiments.
>
>
  • Affymetrix: all types of Affymetrix 3' expression (IVT) arrays
  • Agilent: whole genome microarrays - 1x44k and 4x44k, one-color and two-color
  • Illumina: all types of mRNA BeadArrays (WG-6, Ref8, HT-12) and miRNA BeadArrays

We also have considerable experience with various types of custom-made arrays, SNP arrays (Illumina Infinium BeadChips), and analysis of cross-species (heterologous) experiments.

We set-up a yearly four-week course with a mix of lectures and well-structured computer exercises to learn the tricks of the trade in analyzing genome-wide expression data.
 

Revision 7
23 Jan 2010 - Main.PerryMoerland
Line: 1 to 1
 
META TOPICPARENT name="SupportBioLab"
Medical Bioinformatics

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People
Deleted:
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under construction
 

We have large experience in experimental design and analysis of microarray data from various platforms. Typically, the different steps in our analysis approach are

  • experimental design: choice of platform, sample size, randomization of samples across multiple conditions
  • quality control (QC): variety of QC plots and quantitative measures to aid in detecting problematic microarrays
Changed:
<
<
  • differential expression: detection of differentially expressed probes for a variety of experimental designs (multiple groups, time series, inclusion of covariates, regression), correction for multiple testing, emperical Bayes, permutation tests
  • classification
>
>
  • preprocessing: normalization with a variety of state-of-the-art methods available via R/Bioconductor
  • differential expression: detection of differentially expressed probes for a variety of experimental designs (multiple groups, time series, inclusion of covariates, regression), emperical Bayes, correction for multiple testing, permutation tests
  • classification: using a variety of statistical and machine learning models (discriminant analysis, decision trees, logistic regression, nearest centroid, support vector machines, neural networks, ...) in a double cross-validation scheme
    • complete separation of training data used for estimating the parameters of a model and test data for estimating the accuracy of the model
    • multiple estimates of classification accuracy to be able to assess its variance
    • cross-validation on the training data to determine optimal values for hyperparameters of a model and to select features
  • probe annotation
 

Platforms included in our standard analysis pipelines
Changed:
<
<
  • Affymetrix:
  • Agilent:
>
>
  • Affymetrix: mRNA
  • Agilent: mRNA
 
  • Illumina: mRNA, miRNA

Changed:
<
<
We also have considerable experience with various types of custom-made arrays, SNP arrays, cross-species, ...

Selected publications

Nora Bijl, Milka Sokolovic, Carlos Vrins, Mirjam Langeveld, Perry D. Moerland, Nike Claessen, Roelof Ottenhoff, Cindy van Roomen, Peter Dubbelhuis, Rolf Boot, Jan Aten, Bert Groen, Johannes M.F.G. Aerts, Marco van Eijk (2009). Modulation of Glycosphingolipid Metabolism Significantly Improves Hepatic Insulin Sensitivity and Reverses Hepatic Steatosis in Mice. Hepatology; 50(5):1431-41. PubMed

Miinalainen IJ, Schmitz W, Huotari A, Autio KJ, Soininen R, Ver Loren van Themaat E, Baes M, Herzig KH, Conzelmann E, Hiltunen JK (2009). Mitochondrial 2,4-dienoyl-CoA reductase deficiency in mice results in severe hypoglycemia with stress intolerance and unimpaired ketogenesis. PLoS Genetics, 5(7):e1000453. PubMed

Stephan H. Schirmer, Joost O. Fledderus, Pieter T.G. Bot, Perry D. Moerland, Jan Baan Jr. , Jose P. Henriques, Renee J. van der Schaaf, Marije M. Vis, Anton J.G. Horrevoets, Jan J. Piek, Niels van Royen (2008). Interferon-beta Signaling Is Enhanced in Patients with Insufficient Coronary Collateral Artery Development and Inhibits Arteriogenesis in Mice. Circulation Research;102(10):1286-94. PubMed
>
>
We also have considerable experience with various types of custom-made arrays, SNP arrays, and analysis of cross-species (heterologous) experiments.
 

Line: 44 to 41
 .
Added:
>
>

Selected publications
Nora Bijl, Milka Sokolovic, Carlos Vrins, Mirjam Langeveld, Perry D. Moerland, Nike Claessen, Roelof Ottenhoff, Cindy van Roomen, Peter Dubbelhuis, Rolf Boot, Jan Aten, Bert Groen, Johannes M.F.G. Aerts, Marco van Eijk (2009). Modulation of Glycosphingolipid Metabolism Significantly Improves Hepatic Insulin Sensitivity and Reverses Hepatic Steatosis in Mice. Hepatology; 50(5):1431-41. PubMed
Miinalainen IJ, Schmitz W, Huotari A, Autio KJ, Soininen R, Ver Loren van Themaat E, Baes M, Herzig KH, Conzelmann E, Hiltunen JK (2009). Mitochondrial 2,4-dienoyl-CoA reductase deficiency in mice results in severe hypoglycemia with stress intolerance and unimpaired ketogenesis. PLoS Genetics, 5(7):e1000453. PubMed

Stephan H. Schirmer, Joost O. Fledderus, Pieter T.G. Bot, Perry D. Moerland, Jan Baan Jr. , Jose P. Henriques, Renee J. van der Schaaf, Marije M. Vis, Anton J.G. Horrevoets, Jan J. Piek, Niels van Royen (2008). Interferon-beta Signaling Is Enhanced in Patients with Insufficient Coronary Collateral Artery Development and Inhibits Arteriogenesis in Mice. Circulation Research;102(10):1286-94. PubMed
Revision 6
22 Jan 2010 - Main.PerryMoerland
Line: 1 to 1
 
META TOPICPARENT name="SupportBioLab"
Medical Bioinformatics

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Microarray analysis People
Added:
>
>

under construction
 
Changed:
<
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Under construction
>
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We have large experience in experimental design and analysis of microarray data from various platforms. Typically, the different steps in our analysis approach are

  • experimental design: choice of platform, sample size, randomization of samples across multiple conditions
  • quality control (QC): variety of QC plots and quantitative measures to aid in detecting problematic microarrays
  • differential expression: detection of differentially expressed probes for a variety of experimental designs (multiple groups, time series, inclusion of covariates, regression), correction for multiple testing, emperical Bayes, permutation tests
  • classification

Platforms included in our standard analysis pipelines

  • Affymetrix:
  • Agilent:
  • Illumina: mRNA, miRNA

We also have considerable experience with various types of custom-made arrays, SNP arrays, cross-species, ...

Selected publications

Nora Bijl, Milka Sokolovic, Carlos Vrins, Mirjam Langeveld, Perry D. Moerland, Nike Claessen, Roelof Ottenhoff, Cindy van Roomen, Peter Dubbelhuis, Rolf Boot, Jan Aten, Bert Groen, Johannes M.F.G. Aerts, Marco van Eijk (2009). Modulation of Glycosphingolipid Metabolism Significantly Improves Hepatic Insulin Sensitivity and Reverses Hepatic Steatosis in Mice. Hepatology; 50(5):1431-41. PubMed

Miinalainen IJ, Schmitz W, Huotari A, Autio KJ, Soininen R, Ver Loren van Themaat E, Baes M, Herzig KH, Conzelmann E, Hiltunen JK (2009). Mitochondrial 2,4-dienoyl-CoA reductase deficiency in mice results in severe hypoglycemia with stress intolerance and unimpaired ketogenesis. PLoS Genetics, 5(7):e1000453. PubMed

Stephan H. Schirmer, Joost O. Fledderus, Pieter T.G. Bot, Perry D. Moerland, Jan Baan Jr. , Jose P. Henriques, Renee J. van der Schaaf, Marije M. Vis, Anton J.G. Horrevoets, Jan J. Piek, Niels van Royen (2008). Interferon-beta Signaling Is Enhanced in Patients with Insufficient Coronary Collateral Artery Development and Inhibits Arteriogenesis in Mice. Circulation Research;102(10):1286-94. PubMed
 
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Revision 5
22 Dec 2009 - Main.AntoineVanKampen
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META TOPICPARENT name="SupportBioLab"
Medical Bioinformatics

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Back to menuSort

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Microarray analysis
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Microarray analysis People
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Revision 4
21 Dec 2009 - Main.AntoineVanKampen
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META TOPICPARENT name="SupportBioLab"
Medical Bioinformatics

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Microarray analysis
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Microarray analysis
 
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Revision 3
19 Dec 2009 - Main.AntoineVanKampen
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META TOPICPARENT name="SupportBioLab"
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Medical Bioinformatics

Microarray analysis
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Medical Bioinformatics

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Microarray analysis
 

Under construction \ No newline at end of file
Revision 2
18 Dec 2009 - Main.AngelaLuijf
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META TOPICPARENT name="SequencingBioLab"
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META TOPICPARENT name="SupportBioLab"
 
Medical Bioinformatics

Microarray analysis

Under construction
 
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