A Revised Design for Microarray Experiments to Account for Experimental Noise and Uncertainty of Probe Response
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{"title"=>"A revised design for microarray experiments to account for experimental noise and uncertainty of probe response", "type"=>"journal", "authors"=>[{"first_name"=>"Alex E.", "last_name"=>"Pozhitkov", "scopus_author_id"=>"10042453300"}, {"first_name"=>"Peter A.", "last_name"=>"Noble", "scopus_author_id"=>"26648823500"}, {"first_name"=>"Jarosław", "last_name"=>"Bryk", "scopus_author_id"=>"24480624200"}, {"first_name"=>"Diethard", "last_name"=>"Tautz", "scopus_author_id"=>"7005597480"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"isbn"=>"1932-6203 (online)", "sgr"=>"84897498566", "pui"=>"372764659", "doi"=>"10.1371/journal.pone.0091295", "pmid"=>"24618910", "issn"=>"19326203", "scopus"=>"2-s2.0-84897498566"}, "id"=>"1e81d441-db19-3af7-8228-3da8ffdd0fbb", "abstract"=>"Background: Abstract Although microarrays are analysis tools in biomedical research, they are known to yield noisy output that usually requires experimental confirmation. To tackle this problem, many studies have developed rules for optimizing probe design and devised complex statistical tools to analyze the output. However, less emphasis has been placed on systematically identifying the noise component as part of the experimental procedure. One source of noise is the variance in probe binding, which can be assessed by replicating array probes. The second source is poor probe performance, which can be assessed by calibrating the array based on a dilution series of target molecules. Using model experiments for copy number variation and gene expression measurements, we investigate here a revised design for microarray experiments that addresses both of these sources of variance. Results: Two custom arrays were used to evaluate the revised design: one based on 25 mer probes from an Affymetrix design and the other based on 60 mer probes from an Agilent design. To assess experimental variance in probe binding, all probes were replicated ten times. To assess probe performance, the probes were calibrated using a dilution series of target molecules and the signal response was fitted to an adsorption model. We found that significant variance of the signal could be controlled by averaging across probes and removing probes that are nonresponsive or poorly responsive in the calibration experiment. Taking this into account, one can obtain a more reliable signal with the added option of obtaining Conclusion: absolute rather than relative measurements. The assessment of technical variance within the experiments, combined with the calibration of probes allows to remove poorly responding probes and yields more reliable signals for the remaining ones. Once an array is properly calibrated, absolute quantification of signals becomes straight forward, alleviating the need for normalization and reference hybridizations. Citation:", "link"=>"http://www.mendeley.com/research/revised-design-microarray-experiments-account-experimental-noise-uncertainty-probe-response", "reader_count"=>22, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>1, "Librarian"=>1, "Student > Doctoral Student"=>2, "Researcher"=>2, "Student > Ph. D. Student"=>5, "Student > Master"=>2, "Other"=>1, "Student > Bachelor"=>5, "Professor"=>2}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>1, "Librarian"=>1, "Student > Doctoral Student"=>2, "Researcher"=>2, "Student > Ph. D. Student"=>5, "Student > Master"=>2, "Other"=>1, "Student > Bachelor"=>5, "Professor"=>2}, "reader_count_by_subject_area"=>{"Unspecified"=>1, "Biochemistry, Genetics and Molecular Biology"=>4, "Nursing and Health Professions"=>1, "Mathematics"=>1, "Agricultural and Biological Sciences"=>13, "Computer Science"=>2}, "reader_count_by_subdiscipline"=>{"Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>13}, "Computer Science"=>{"Computer Science"=>2}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>4}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>1}}, "group_count"=>0}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1414871"], "description"=>"<p>(A) Sample $ (<i>n</i> = 4,775) and (B) sample * (<i>n</i> = 4,767) from <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0091295#pone-0091295-g002\" target=\"_blank\">Figure 2</a>. Note that the calibration was conducted without sample *. Ratios were calculated from calibration curves (R<sup>2</sup>>0.98) and a value is included into the histogram only if its relative error was under 20%.</p>", "links"=>[], "tags"=>["biotechnology", "genomics", "Comparative genomics", "Model organisms", "Animal models", "mouse", "Molecular cell biology", "gene expression", "Nucleic acids", "Analytical chemistry", "Physical chemistry", "software engineering", "Software tools", "probe", "labeling", "protocols"], "article_id"=>957786, "categories"=>["Biological Sciences", "Chemistry"], "users"=>["Alex E. Pozhitkov", "Peter A. Noble", "Jarosław Bryk", "Diethard Tautz"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0091295.g008", "stats"=>{"downloads"=>5, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_probe_labeling_protocols_on_noise_reduction_/957786", "title"=>"Comparison of probe labeling protocols on noise reduction.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-03-11 02:50:20"}
  • {"files"=>["https://ndownloader.figshare.com/files/1414865"], "description"=>"<p>Panels A to F, Freundlich model, Panels G to I, Langmuir model. Panels A to C, 25: Distribution of R2 across all probes. Panels B and E: Distribution of <i>a</i> for selected probes. Panel C and F: Distribution of the exponent <i>b</i> for selected probes (Equation 2). Panel H: Distribution of <i>y<sub>max</sub></i> for selected probes. Panel I: Distribution of <i>K</i> for selected probes (Equation 1).</p>", "links"=>[], "tags"=>["biotechnology", "genomics", "Comparative genomics", "Model organisms", "Animal models", "mouse", "Molecular cell biology", "gene expression", "Nucleic acids", "Analytical chemistry", "Physical chemistry", "software engineering", "Software tools", "fitting", "isotherm"], "article_id"=>957780, "categories"=>["Biological Sciences", "Chemistry"], "users"=>["Alex E. Pozhitkov", "Peter A. Noble", "Jarosław Bryk", "Diethard Tautz"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0091295.g004", "stats"=>{"downloads"=>6, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Distribution_of_curve_fitting_parameters_for_the_isotherm_models_/957780", "title"=>"Distribution of curve fitting parameters for the isotherm models.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-03-11 02:50:20"}
  • {"files"=>["https://ndownloader.figshare.com/files/1414861"], "description"=>"<p>(A) Typical Agilent array isotherms obtained using a dilution series of genomic mouse DNA, BL6 strain for a single probe and its replicates. Raw data (gray) and predicted isotherm based on the average signal intensity (black). (B) Mean and standard deviation of the coefficient of variation (CV) across all probes at each concentration for the 25 mer array, (C) same for the 60 mer array.</p>", "links"=>[], "tags"=>["biotechnology", "genomics", "Comparative genomics", "Model organisms", "Animal models", "mouse", "Molecular cell biology", "gene expression", "Nucleic acids", "Analytical chemistry", "Physical chemistry", "software engineering", "Software tools", "variances", "replicated"], "article_id"=>957776, "categories"=>["Biological Sciences", "Chemistry"], "users"=>["Alex E. Pozhitkov", "Peter A. Noble", "Jarosław Bryk", "Diethard Tautz"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0091295.g003", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Signal_variances_between_replicated_probes_/957776", "title"=>"Signal variances between replicated probes.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-03-11 02:50:20"}
  • {"files"=>["https://ndownloader.figshare.com/files/1414859"], "description"=>"<p>The dilution series was created by pooling the labeled samples, serially diluting the pool, and hybridizing each diluted sample to an independent array. The arrays within the white box were used to calibrate the probes. One array marked with an asterisk (*) was used as a ‘reference’. The independent ‘test’ array ($) is also shown. Numerical values indicate the target concentration in folds of the recommended concentration.</p>", "links"=>[], "tags"=>["biotechnology", "genomics", "Comparative genomics", "Model organisms", "Animal models", "mouse", "Molecular cell biology", "gene expression", "Nucleic acids", "Analytical chemistry", "Physical chemistry", "software engineering", "Software tools", "dose-response", "probe", "8-plex", "agilent"], "article_id"=>957774, "categories"=>["Biological Sciences", "Chemistry"], "users"=>["Alex E. Pozhitkov", "Peter A. Noble", "Jarosław Bryk", "Diethard Tautz"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0091295.g002", "stats"=>{"downloads"=>0, "page_views"=>25, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Outline_of_the_experimental_design_for_recording_a_dose_response_curve_for_each_probe_on_an_8_plex_Agilent_microarray_/957774", "title"=>"Outline of the experimental design for recording a dose-response curve for each probe on an 8-plex Agilent microarray.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-03-11 02:50:20"}
  • {"files"=>["https://ndownloader.figshare.com/files/1414873", "https://ndownloader.figshare.com/files/1414874"], "description"=>"<div><p>Background</p><p>Although microarrays are analysis tools in biomedical research, they are known to yield noisy output that usually requires experimental confirmation. To tackle this problem, many studies have developed rules for optimizing probe design and devised complex statistical tools to analyze the output. However, less emphasis has been placed on systematically identifying the noise component as part of the experimental procedure. One source of noise is the variance in probe binding, which can be assessed by replicating array probes. The second source is poor probe performance, which can be assessed by calibrating the array based on a dilution series of target molecules. Using model experiments for copy number variation and gene expression measurements, we investigate here a revised design for microarray experiments that addresses both of these sources of variance.</p><p>Results</p><p>Two custom arrays were used to evaluate the revised design: one based on 25 mer probes from an Affymetrix design and the other based on 60 mer probes from an Agilent design. To assess experimental variance in probe binding, all probes were replicated ten times. To assess probe performance, the probes were calibrated using a dilution series of target molecules and the signal response was fitted to an adsorption model. We found that significant variance of the signal could be controlled by averaging across probes and removing probes that are nonresponsive or poorly responsive in the calibration experiment. Taking this into account, one can obtain a more reliable signal with the added option of obtaining absolute rather than relative measurements.</p><p>Conclusion</p><p>The assessment of technical variance within the experiments, combined with the calibration of probes allows to remove poorly responding probes and yields more reliable signals for the remaining ones. Once an array is properly calibrated, absolute quantification of signals becomes straight forward, alleviating the need for normalization and reference hybridizations.</p></div>", "links"=>[], "tags"=>["biotechnology", "genomics", "Comparative genomics", "Model organisms", "Animal models", "mouse", "Molecular cell biology", "gene expression", "Nucleic acids", "Analytical chemistry", "Physical chemistry", "software engineering", "Software tools", "revised", "microarray", "experiments", "probe"], "article_id"=>957788, "categories"=>["Biological Sciences", "Chemistry"], "users"=>["Alex E. Pozhitkov", "Peter A. Noble", "Jarosław Bryk", "Diethard Tautz"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0091295.s001", "https://dx.doi.org/10.1371/journal.pone.0091295.s002"], "stats"=>{"downloads"=>7, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_Revised_Design_for_Microarray_Experiments_to_Account_for_Experimental_Noise_and_Uncertainty_of_Probe_Response_/957788", "title"=>"A Revised Design for Microarray Experiments to Account for Experimental Noise and Uncertainty of Probe Response", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2014-03-11 02:50:20"}
  • {"files"=>["https://ndownloader.figshare.com/files/1414870"], "description"=>"<p>CV averages are displayed, circles, 25</p>", "links"=>[], "tags"=>["biotechnology", "genomics", "Comparative genomics", "Model organisms", "Animal models", "mouse", "Molecular cell biology", "gene expression", "Nucleic acids", "Analytical chemistry", "Physical chemistry", "software engineering", "Software tools", "estimating", "dependence", "probe"], "article_id"=>957785, "categories"=>["Biological Sciences", "Chemistry"], "users"=>["Alex E. Pozhitkov", "Peter A. Noble", "Jarosław Bryk", "Diethard Tautz"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0091295.g007", "stats"=>{"downloads"=>0, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Coefficient_of_variation_CV_decrease_for_estimating_the_true_concentration_in_dependence_of_probe_replication_/957785", "title"=>"Coefficient of variation (CV) decrease for estimating the true concentration in dependence of probe replication.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-03-11 02:50:20"}
  • {"files"=>["https://ndownloader.figshare.com/files/1414855"], "description"=>"<p>Workflows are from top to bottom and equivalent stages are set next to each other. New steps are in blue type face. Both workflows represent only general schemes and further variations are possible. For example, we discuss also an additional step for the target labeling procedure in the text (denoted by an asterisk in step 3).</p>", "links"=>[], "tags"=>["biotechnology", "genomics", "Comparative genomics", "Model organisms", "Animal models", "mouse", "Molecular cell biology", "gene expression", "Nucleic acids", "Analytical chemistry", "Physical chemistry", "software engineering", "Software tools", "revised"], "article_id"=>957770, "categories"=>["Biological Sciences", "Chemistry"], "users"=>["Alex E. Pozhitkov", "Peter A. Noble", "Jarosław Bryk", "Diethard Tautz"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0091295.g001", "stats"=>{"downloads"=>2, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_the_classical_and_the_revised_experimental_design_/957770", "title"=>"Comparison of the classical and the revised experimental design.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-03-11 02:50:20"}
  • {"files"=>["https://ndownloader.figshare.com/files/1414869"], "description"=>"<p>Four different wild type mice were analyzed, each represented as a track. Top: output from the ratio analysis implemented in the Agilent software (ratio with respect to DNA from an C57Bl/6 inbred mouse strain). The input was the concentrations derived from the ten averaged probes, but without calibration and without removal of non-responding probes. Colored dots represent values larger (blue) or smaller (red) then log<sub>2</sub> = 0.5. Bottom: concentration calculations based on the full revised method, non-responding probes removed (>20% error in any of the experiments on the array). The values were normalized with respect to average intensities on the array and are displayed as custom track in the UCSC genome browser.</p>", "links"=>[], "tags"=>["biotechnology", "genomics", "Comparative genomics", "Model organisms", "Animal models", "mouse", "Molecular cell biology", "gene expression", "Nucleic acids", "Analytical chemistry", "Physical chemistry", "software engineering", "Software tools", "procedures"], "article_id"=>957784, "categories"=>["Biological Sciences", "Chemistry"], "users"=>["Alex E. Pozhitkov", "Peter A. Noble", "Jarosław Bryk", "Diethard Tautz"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0091295.g006", "stats"=>{"downloads"=>3, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_analysis_procedures_for_copy_number_variation_of_a_gene_region_in_mice_/957784", "title"=>"Comparison of analysis procedures for copy number variation of a gene region in mice.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-03-11 02:50:20"}
  • {"files"=>["https://ndownloader.figshare.com/files/1414867"], "description"=>"<p>Since the sample is compared against itself, the log<sub>2</sub> ratio should be 0, i.e. all values above or below 0 are experimental noise. (A) Classical reference procedure - ratio of signal intensities between all individual probes (<i>n</i> = 5,912). (B) Averaging across probes - ratio of signal intensities from 10 averaged probes (<i>n</i> = 5,912). (C) Full revised procedure - concentration values from calibrated isotherms of all responsive probes (R<sup>2</sup>>0.98), a value is included only if its relative error is under 20% (<i>n</i> = 4,406).</p>", "links"=>[], "tags"=>["biotechnology", "genomics", "Comparative genomics", "Model organisms", "Animal models", "mouse", "Molecular cell biology", "gene expression", "Nucleic acids", "Analytical chemistry", "Physical chemistry", "software engineering", "Software tools", "dna"], "article_id"=>957782, "categories"=>["Biological Sciences", "Chemistry"], "users"=>["Alex E. Pozhitkov", "Peter A. Noble", "Jarosław Bryk", "Diethard Tautz"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0091295.g005", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Overall_assessment_of_noise_reduction_using_the_same_DNA_sample_/957782", "title"=>"Overall assessment of noise reduction using the same DNA sample.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-03-11 02:50:20"}

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Relative Metric

{"start_date"=>"2014-01-01T00:00:00Z", "end_date"=>"2014-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Earth sciences/Geography", "average_usage"=>[310]}, {"subject_area"=>"/Engineering and technology/Signal processing", "average_usage"=>[276]}]}
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