LEMming: A Linear Error Model to Normalize Parallel Quantitative Real-Time PCR (qPCR) Data as an Alternative to Reference Gene Based Methods
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{"title"=>"LEMming: A linear error model to normalize parallel quantitative real-time PCR (qPCR) data as an alternative to reference gene based methods", "type"=>"journal", "authors"=>[{"first_name"=>"Ronny", "last_name"=>"Feuer", "scopus_author_id"=>"24724260000"}, {"first_name"=>"Sebastian", "last_name"=>"Vlaic", "scopus_author_id"=>"55503328700"}, {"first_name"=>"Janine", "last_name"=>"Arlt", "scopus_author_id"=>"55766247400"}, {"first_name"=>"Oliver", "last_name"=>"Sawodny", "scopus_author_id"=>"6603314950"}, {"first_name"=>"Uta", "last_name"=>"Dahmen", "scopus_author_id"=>"55789262800"}, {"first_name"=>"Ulrich M.", "last_name"=>"Zanger", "scopus_author_id"=>"7005303149"}, {"first_name"=>"Maria", "last_name"=>"Thomas", "scopus_author_id"=>"35216661600"}, {"first_name"=>"Lars", "last_name"=>"Kaderali", "scopus_author_id"=>"6506551546"}], "year"=>2015, "source"=>"PLoS ONE", "identifiers"=>{"pmid"=>"26325269", "sgr"=>"84943339567", "doi"=>"10.1371/journal.pone.0135852", "scopus"=>"2-s2.0-84943339567", "pui"=>"606226762", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "issn"=>"19326203"}, "id"=>"be96d200-29fe-3d5c-9a27-b8d40d152cd0", "abstract"=>"BACKGROUND: Gene expression analysis is an essential part of biological and medical investigations. Quantitative real-time PCR (qPCR) is characterized with excellent sensitivity, dynamic range, reproducibility and is still regarded to be the gold standard for quantifying transcripts abundance. Parallelization of qPCR such as by microfluidic Taqman Fluidigm Biomark Platform enables evaluation of multiple transcripts in samples treated under various conditions. Despite advanced technologies, correct evaluation of the measurements remains challenging. Most widely used methods for evaluating or calculating gene expression data include geNorm and ΔΔCt, respectively. They rely on one or several stable reference genes (RGs) for normalization, thus potentially causing biased results. We therefore applied multivariable regression with a tailored error model to overcome the necessity of stable RGs. RESULTS: We developed a RG independent data normalization approach based on a tailored linear error model for parallel qPCR data, called LEMming. It uses the assumption that the mean Ct values within samples of similarly treated groups are equal. Performance of LEMming was evaluated in three data sets with different stability patterns of RGs and compared to the results of geNorm normalization. Data set 1 showed that both methods gave similar results if stable RGs are available. Data set 2 included RGs which are stable according to geNorm criteria, but became differentially expressed in normalized data evaluated by a t-test. geNorm-normalized data showed an effect of a shifted mean per gene per condition whereas LEMming-normalized data did not. Comparing the decrease of standard deviation from raw data to geNorm and to LEMming, the latter was superior. In data set 3 according to geNorm calculated average expression stability and pairwise variation, stable RGs were available, but t-tests of raw data contradicted this. Normalization with RGs resulted in distorted data contradicting literature, while LEMming normalized data did not. CONCLUSIONS: If RGs are coexpressed but are not independent of the experimental conditions the stability criteria based on inter- and intragroup variation fail. The linear error model developed, LEMming, overcomes the dependency of using RGs for parallel qPCR measurements, besides resolving biases of both technical and biological nature in qPCR. However, to distinguish systematic errors per treated group from a global treatment effect an additional measurement is needed. Quantification of total cDNA content per sample helps to identify systematic errors.", "link"=>"http://www.mendeley.com/research/lemming-linear-error-model-normalize-parallel-quantitative-realtime-pcr-qpcr-data-alternative-refere", "reader_count"=>43, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>2, "Student > Doctoral Student"=>4, "Researcher"=>8, "Student > Ph. D. Student"=>13, "Student > Postgraduate"=>2, "Other"=>2, "Student > Master"=>8, "Student > Bachelor"=>1, "Professor"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>2, "Student > Doctoral Student"=>4, "Researcher"=>8, "Student > Ph. D. Student"=>13, "Student > Postgraduate"=>2, "Other"=>2, "Student > Master"=>8, "Student > Bachelor"=>1, "Professor"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>3, "Environmental Science"=>1, "Biochemistry, Genetics and Molecular Biology"=>9, "Agricultural and Biological Sciences"=>25, "Medicine and Dentistry"=>3, "Pharmacology, Toxicology and Pharmaceutical Science"=>1, "Chemistry"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>3}, "Chemistry"=>{"Chemistry"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>25}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>9}, "Unspecified"=>{"Unspecified"=>3}, "Environmental Science"=>{"Environmental Science"=>1}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>1}}, "reader_count_by_country"=>{"Austria"=>1, "United States"=>2, "Denmark"=>1, "Portugal"=>1}, "group_count"=>0}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/2249594"], "description"=>"<p>Left: Log2-fold differential expression of the gene <i>Foxo1</i> in DS2. Conditions are 0h, 24h, 48h and 72 h. Right: Variance-mean plot showing the mean over the standard deviation per condition for <i>Foxo1</i>.</p>", "links"=>[], "tags"=>["lemming", "Methods BackgroundGene expression analysis", "ΔΔ", "rg", "gene expression data", "pcr", "genorm", "microfluidic Taqman Fluidigm Biomark Platform", "error model", "qpcr", "quantifying transcripts abundance", "data normalization approach", "Linear Error Model"], "article_id"=>1532788, "categories"=>["Biological Sciences"], "users"=>["Ronny Feuer", "Sebastian Vlaic", "Janine Arlt", "Oliver Sawodny", "Uta Dahmen", "Ulrich M. Zanger", "Maria Thomas"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0135852.g002", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Example_boxplot_for_Foxo1_in_DS2_/1532788", "title"=>"Example boxplot for <i>Foxo1</i> in DS2.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-09-01 03:59:37"}
  • {"files"=>["https://ndownloader.figshare.com/files/2249635", "https://ndownloader.figshare.com/files/2249636", "https://ndownloader.figshare.com/files/2249637", "https://ndownloader.figshare.com/files/2249638", "https://ndownloader.figshare.com/files/2249639", "https://ndownloader.figshare.com/files/2249640", "https://ndownloader.figshare.com/files/2249641"], "description"=>"<div><p>Background</p><p>Gene expression analysis is an essential part of biological and medical investigations. Quantitative real-time PCR (qPCR) is characterized with excellent sensitivity, dynamic range, reproducibility and is still regarded to be the <i>gold standard</i> for quantifying transcripts abundance. Parallelization of qPCR such as by microfluidic Taqman Fluidigm Biomark Platform enables evaluation of multiple transcripts in samples treated under various conditions. Despite advanced technologies, correct evaluation of the measurements remains challenging. Most widely used methods for evaluating or calculating gene expression data include <i>geNorm</i> and ΔΔ<i>C</i><sub><i>t</i></sub>, respectively. They rely on one or several stable reference genes (RGs) for normalization, thus potentially causing biased results. We therefore applied multivariable regression with a tailored error model to overcome the necessity of stable RGs.</p><p>Results</p><p>We developed a RG independent data normalization approach based on a tailored <u>l</u>inear <u>e</u>rror <u>m</u>odel for parallel qPCR data, called LEMming. It uses the assumption that the mean <i>C</i><sub><i>t</i></sub> values within samples of similarly treated groups are equal. Performance of LEMming was evaluated in three data sets with different stability patterns of RGs and compared to the results of <i>geNorm</i> normalization. Data set 1 showed that both methods gave similar results if stable RGs are available. Data set 2 included RGs which are stable according to <i>geNorm</i> criteria, but became differentially expressed in normalized data evaluated by a t-test. <i>geNorm</i>-normalized data showed an effect of a shifted mean per gene per condition whereas LEMming-normalized data did not. Comparing the decrease of standard deviation from raw data to <i>geNorm</i> and to LEMming, the latter was superior. In data set 3 according to <i>geNorm</i> calculated average expression stability and pairwise variation, stable RGs were available, but t-tests of raw data contradicted this. Normalization with RGs resulted in distorted data contradicting literature, while LEMming normalized data did not.</p><p>Conclusions</p><p>If RGs are coexpressed but are not independent of the experimental conditions the stability criteria based on inter- and intragroup variation fail. The linear error model developed, LEMming, overcomes the dependency of using RGs for parallel qPCR measurements, besides resolving biases of both technical and biological nature in qPCR. However, to distinguish systematic errors per treated group from a global treatment effect an additional measurement is needed. Quantification of total cDNA content per sample helps to identify systematic errors.</p></div>", "links"=>[], "tags"=>["lemming", "Methods BackgroundGene expression analysis", "ΔΔ", "rg", "gene expression data", "pcr", "genorm", "microfluidic Taqman Fluidigm Biomark Platform", "error model", "qpcr", "quantifying transcripts abundance", "data normalization approach", "Linear Error Model"], "article_id"=>1532811, "categories"=>["Biological Sciences"], "users"=>["Ronny Feuer", "Sebastian Vlaic", "Janine Arlt", "Oliver Sawodny", "Uta Dahmen", "Ulrich M. Zanger", "Maria Thomas"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0135852.s001", "https://dx.doi.org/10.1371/journal.pone.0135852.s002", "https://dx.doi.org/10.1371/journal.pone.0135852.s003", "https://dx.doi.org/10.1371/journal.pone.0135852.s004", "https://dx.doi.org/10.1371/journal.pone.0135852.s005", "https://dx.doi.org/10.1371/journal.pone.0135852.s006", "https://dx.doi.org/10.1371/journal.pone.0135852.s007"], "stats"=>{"downloads"=>0, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_LEMming_A_Linear_Error_Model_to_Normalize_Parallel_Quantitative_Real_Time_PCR_qPCR_Data_as_an_Alternative_to_Reference_Gene_Based_Methods_/1532811", "title"=>"LEMming: A Linear Error Model to Normalize Parallel Quantitative Real-Time PCR (qPCR) Data as an Alternative to Reference Gene Based Methods", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-09-01 03:59:37"}
  • {"files"=>["https://ndownloader.figshare.com/files/2249617"], "description"=>"<p>Proportions of variance contribution are estimated from raw data <b>(a)</b> and from residuals of LEMming <b>(b)</b>. Blue boxplots are measurements of the control group, red boxplots are measurements of WY14,643 treated cells.</p>", "links"=>[], "tags"=>["lemming", "Methods BackgroundGene expression analysis", "ΔΔ", "rg", "gene expression data", "pcr", "genorm", "microfluidic Taqman Fluidigm Biomark Platform", "error model", "qpcr", "quantifying transcripts abundance", "data normalization approach", "Linear Error Model"], "article_id"=>1532796, "categories"=>["Biological Sciences"], "users"=>["Ronny Feuer", "Sebastian Vlaic", "Janine Arlt", "Oliver Sawodny", "Uta Dahmen", "Ulrich M. Zanger", "Maria Thomas"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0135852.g005", "stats"=>{"downloads"=>2, "page_views"=>19, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Contribution_of_biological_variance_cDNA_conversion_error_and_qPCR_error_to_the_overall_variance_for_data_set_1_/1532796", "title"=>"Contribution of biological variance, cDNA conversion error and qPCR error to the overall variance for data set 1.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-09-01 03:59:37"}
  • {"files"=>["https://ndownloader.figshare.com/files/2249612"], "description"=>"<p><b>(a)</b> Standard deviation of <i>C</i><sub><i>t</i></sub> value of each gene and well over the mean <i>C</i><sub><i>t</i></sub> value for data set 1 (DS1). Each gene is measured six times per biological replicate (3× cDNA and 2× PCR per cDNA). The regression line shows that the standard deviation increases with the <i>C</i><sub><i>t</i></sub> values (lower mRNA content). <b>(b)</b> Standard deviation of biological replicates over the mean <i>C</i><sub><i>t</i></sub> value for DS1. The mean of all six technical replicates is computed per biological replicate and gene. The standard deviation of these means is computed with 4 biological replicates for each gene. The biological variance is higher under treated conditions compared to the control conditions.</p>", "links"=>[], "tags"=>["lemming", "Methods BackgroundGene expression analysis", "ΔΔ", "rg", "gene expression data", "pcr", "genorm", "microfluidic Taqman Fluidigm Biomark Platform", "error model", "qpcr", "quantifying transcripts abundance", "data normalization approach", "Linear Error Model"], "article_id"=>1532792, "categories"=>["Biological Sciences"], "users"=>["Ronny Feuer", "Sebastian Vlaic", "Janine Arlt", "Oliver Sawodny", "Uta Dahmen", "Ulrich M. Zanger", "Maria Thomas"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0135852.g004", "stats"=>{"downloads"=>3, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Technical_and_biological_variances_over_mean_value_per_gene_/1532792", "title"=>"Technical and biological variances over mean value per gene.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-09-01 03:59:37"}
  • {"files"=>["https://ndownloader.figshare.com/files/2249611"], "description"=>"<p>Boxplots of the untreated conditions are black, boxplots of treatment conditions (dedicated in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135852#pone.0135852.s004\" target=\"_blank\">S4 File</a>) that are not significant differentially expressed compared to untreated are blue and boxplots of treatments with significant differentially expressed measurements are red. Significance was calculated by an unpaired t-test with unequal variances and Bonferroni corrected significance level <i>α</i> = 0.05/48. Outliers are marked by red circles.</p>", "links"=>[], "tags"=>["lemming", "Methods BackgroundGene expression analysis", "ΔΔ", "rg", "gene expression data", "pcr", "genorm", "microfluidic Taqman Fluidigm Biomark Platform", "error model", "qpcr", "quantifying transcripts abundance", "data normalization approach", "Linear Error Model"], "article_id"=>1532790, "categories"=>["Biological Sciences"], "users"=>["Ronny Feuer", "Sebastian Vlaic", "Janine Arlt", "Oliver Sawodny", "Uta Dahmen", "Ulrich M. Zanger", "Maria Thomas"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0135852.g003", "stats"=>{"downloads"=>0, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Raw_data_of_common_reference_genes_in_data_set_3_/1532790", "title"=>"Raw data of common reference genes in data set 3.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-09-01 03:59:37"}
  • {"files"=>["https://ndownloader.figshare.com/files/2249593"], "description"=>"<p>The x-axis shows the difference between standard deviation (sd) of raw values and sd of <i>geNorm</i> processed data per gene. Accordingly the y-axis shows the difference of sd of raw values to LEMming processed data. If points are in the positive quadrant and above the dotted line, sd of LEMming processed data is smaller compared to sd <i>geNorm</i> and raw data.</p>", "links"=>[], "tags"=>["lemming", "Methods BackgroundGene expression analysis", "ΔΔ", "rg", "gene expression data", "pcr", "genorm", "microfluidic Taqman Fluidigm Biomark Platform", "error model", "qpcr", "quantifying transcripts abundance", "data normalization approach", "Linear Error Model"], "article_id"=>1532787, "categories"=>["Biological Sciences"], "users"=>["Ronny Feuer", "Sebastian Vlaic", "Janine Arlt", "Oliver Sawodny", "Uta Dahmen", "Ulrich M. Zanger", "Maria Thomas"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0135852.g001", "stats"=>{"downloads"=>0, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_standard_deviations_for_geNorm_and_LEMming_per_gene_in_DS1_and_DS2_/1532787", "title"=>"Comparison of standard deviations for <i>geNorm</i> and LEMming per gene in DS1 and DS2.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-09-01 03:59:37"}
  • {"files"=>["https://ndownloader.figshare.com/files/2249625"], "description"=>"<p>Density plot of raw data (a) and residuals of LEM-method (b) of reference genes in DS3. Blue: kernel density estimation of raw data/residuals. Red: estimated Student-t distribution. (c) Quantile-Quantile plot with quantiles of estimated Student-t distribution versus quantiles of residuals.</p>", "links"=>[], "tags"=>["lemming", "Methods BackgroundGene expression analysis", "ΔΔ", "rg", "gene expression data", "pcr", "genorm", "microfluidic Taqman Fluidigm Biomark Platform", "error model", "qpcr", "quantifying transcripts abundance", "data normalization approach", "Linear Error Model"], "article_id"=>1532801, "categories"=>["Biological Sciences"], "users"=>["Ronny Feuer", "Sebastian Vlaic", "Janine Arlt", "Oliver Sawodny", "Uta Dahmen", "Ulrich M. Zanger", "Maria Thomas"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0135852.g007", "stats"=>{"downloads"=>2, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Distribution_of_raw_data_and_residuals_in_reference_genes_in_DS3_/1532801", "title"=>"Distribution of raw data and residuals in reference genes in DS3.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-09-01 03:59:37"}
  • {"files"=>["https://ndownloader.figshare.com/files/2249620"], "description"=>"<p>Black—raw data, Green—LEMming processed data. <b>(a)</b> Proportion of sum of squares associated to the effects time, primer pipetting, biological variance, cDNA conversion, qPCR error and sample pipetting error (SPE) resulting from a ANOVA for each gene. <b>(b)</b> Proportion of sum of squares of LEMming processed to raw data without the effect of time (treatment effect). The median is 16.9%, which means that LEMming excludes systematic effects that are responsible for 83.1% of variance of the median gene in this experiment.</p>", "links"=>[], "tags"=>["lemming", "Methods BackgroundGene expression analysis", "ΔΔ", "rg", "gene expression data", "pcr", "genorm", "microfluidic Taqman Fluidigm Biomark Platform", "error model", "qpcr", "quantifying transcripts abundance", "data normalization approach", "Linear Error Model"], "article_id"=>1532799, "categories"=>["Biological Sciences"], "users"=>["Ronny Feuer", "Sebastian Vlaic", "Janine Arlt", "Oliver Sawodny", "Uta Dahmen", "Ulrich M. Zanger", "Maria Thomas"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0135852.g006", "stats"=>{"downloads"=>2, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Proportional_contribution_of_different_effects_to_the_variance_of_a_gene_in_data_set_2_DS2_/1532799", "title"=>"Proportional contribution of different effects to the variance of a gene in data set 2 (DS2).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-09-01 03:59:37"}

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{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
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