Limits...
LEMming: A Linear Error Model to Normalize Parallel Quantitative Real-Time PCR (qPCR) Data as an Alternative to Reference Gene Based Methods.

Feuer R, Vlaic S, Arlt J, Sawodny O, Dahmen U, Zanger UM, Thomas M - PLoS ONE (2015)

Bottom Line: Comparing the decrease of standard deviation from raw data to geNorm and to LEMming, the latter was superior.Normalization with RGs resulted in distorted data contradicting literature, while LEMming normalized data did not.However, to distinguish systematic errors per treated group from a global treatment effect an additional measurement is needed.

View Article: PubMed Central - PubMed

Affiliation: Institute for System Dynamics, University of Stuttgart, Stuttgart, Germany.

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.

No MeSH data available.


Related in: MedlinePlus

Proportional contribution of different effects to the variance of a gene in data set 2 (DS2).Black—raw data, Green—LEMming processed data. (a) 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) 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.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4556681&req=5

pone.0135852.g006: Proportional contribution of different effects to the variance of a gene in data set 2 (DS2).Black—raw data, Green—LEMming processed data. (a) 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) 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.

Mentions: The same analysis was performed with DS2 (see Fig 6). With DS2 it is possible to split up the sample error ϵS into a cDNA conversion error and a sample pipetting error. Here the cDNA conversion is the dominant variance source in the raw data. Due to the multiple technical replicates in the experiment, this effect is nearly completely removed by the LEMming approach.


LEMming: A Linear Error Model to Normalize Parallel Quantitative Real-Time PCR (qPCR) Data as an Alternative to Reference Gene Based Methods.

Feuer R, Vlaic S, Arlt J, Sawodny O, Dahmen U, Zanger UM, Thomas M - PLoS ONE (2015)

Proportional contribution of different effects to the variance of a gene in data set 2 (DS2).Black—raw data, Green—LEMming processed data. (a) 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) 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.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4556681&req=5

pone.0135852.g006: Proportional contribution of different effects to the variance of a gene in data set 2 (DS2).Black—raw data, Green—LEMming processed data. (a) 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) 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.
Mentions: The same analysis was performed with DS2 (see Fig 6). With DS2 it is possible to split up the sample error ϵS into a cDNA conversion error and a sample pipetting error. Here the cDNA conversion is the dominant variance source in the raw data. Due to the multiple technical replicates in the experiment, this effect is nearly completely removed by the LEMming approach.

Bottom Line: Comparing the decrease of standard deviation from raw data to geNorm and to LEMming, the latter was superior.Normalization with RGs resulted in distorted data contradicting literature, while LEMming normalized data did not.However, to distinguish systematic errors per treated group from a global treatment effect an additional measurement is needed.

View Article: PubMed Central - PubMed

Affiliation: Institute for System Dynamics, University of Stuttgart, Stuttgart, Germany.

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.

No MeSH data available.


Related in: MedlinePlus