GeneGini: Assessment via the Gini Coefficient of Reference "Housekeeping" Genes and Diverse Human Transporter Expression Profiles.
1University of Manchester, United Kingdom; 2KTH Royal Institue of Technology, Stockholm, Sweden
The expression levels of SLC or ABC membrane transporter transcripts typically differ 100- to 10,000-fold between different tissues. The Gini coefficient characterizes such inequalities and here is used to describe the distribution of the expression of each transporter among different human tissues and cell lines. Many transporters exhibit extremely high Gini coefficients even for common substrates, indicating considerable specialization consistent with divergent evolution. The expression profiles of SLC transporters in different cell lines behave similarly, although Gini coefficients for ABC transporters tend to be larger in cell lines than in tissues, implying selection. Transporter genes are significantly more heterogeneously expressed than the members of most non-transporter gene classes. Transcripts with the stablest expression have a low Gini index and often differ significantly from the "housekeeping" genes commonly used for normalization in transcriptomics/qPCR studies. PCBP1 has a low Gini coefficient, is reasonably expressed, and is an excellent novel reference gene. The approach, referred to as GeneGini, provides rapid and simple characterization of expression-profile distributions and general improved normalization of genome-wide expression-profiling data will be described
GenEx – The Ultimate Software for Analysis of Transcriptomic Data.
1Multid Analyses AB, Sweden; 2TATAA Biocenter, Sweden
With the emergence of RNA sequencing (RNASeq) transcriptome profiling entered a new era. High throughput high quality whole transcriptome data can today be collected routinely. The challenge is no longer acquiring data but rather analyzing and interpreting them. Analysis includes validating data quality, merging runs, normalizing the data, comparing experimental conditions, testing hypothesis and interpreting the results. GenEx is the most used software for qPCR data analysis and with the launch of GenEx 7, here at the 9th Gene Quantification Event, also RNASeq data can readily be analyzed. GenEx is developed for experimentalists, with a user-friendly intuitive interface that provides a smooth analytical workflow for statistical analyses of the data in compliance with guidelines when relevant. Very large data sets, typical of RNASeq, are easily and rapidly handled and graphical interfaces allow interactive analyses with powerful methods such as DESeq2, and Normfinder for normalization, t-test, Mann-Whitney, Wilcoxon’s test and ANOVA models for group comparisons, hierarchical clustering, self-organizing maps (SOM) and principal component analysis (PCA) for clustering, dynamic PCA with statistical filters for variable selection to find the most relevant expression markers, kinetic PCA for time studies, survival analysis to compare treatments, and artificial neural network (ANN) and support vector machines (SVM) to build predictive models. GenEx 7 is continuously updated to include new methods and strategies as they become available and to maintain compatibility with qPCR and NGS instrument software, computer operating systems, and graphical and printer routines. GenEx 7 is the only data analysis software supported by the majority of leading instrument and solution providers.
Why reporting Cq or delta-Cq is senseless.
Academic Medical Center, Amsterdam, the Netherlands, Netherlands, The
With the introduction of quantitative PCR (qPCR) it was assumed that the amplification efficiency, the fold-increase per cycle, was always close to 2. This simplification allowed the use of the so-called comparative Cq equation to calculate the fold-difference between target and reference genes in treated and control tissues. Over the years the original equation (2-ΔΔCq) seems to have lost its base and the minus sign. The remainder became so ingrained in qPCR-based papers that ‘ddCq’ currently seems to be the unit in which qPCR data are measured and have to be reported. However, the variations in annotation of the figure axes make that the presented data often cannot be interpreted.
The Cq value is defined by the general principle that the position of the amplification curve with respect to the cycle-axis, reflected in the Cq value, is a measure for the initial target quantity: the ‘later’ the curve, the higher the Cq value and the lower the starting quantity of the target-of-interest. However, this position is also dependent on the amplification efficiency. Therefore, reporting only ddCq implicitly accepts unvalidated assumptions about the amplification efficiencies involved. Reported Cq values can only be interpreted with the simplifying, and false, assumption that every PCR assay in the experiment is 100% efficient. Because of this assumption, the interpretation of Cq values always leads to an unknown bias.
The bias that is introduced by ignoring the actual PCR efficiency of target and reference genes can be prevented with the calculation of the so-called efficiency-corrected target quantities or fold-differences. This was already proposed in the early years of this millennium and is recommended in the MIQE guidelines. Indeed, such efficiency-corrected target quantities are reported by a number of qPCR data analysis methods published over a decennium ago. However, this need for efficiency-correction of qPCR results is still largely ignored by researchers, reviewers and publishers. This common shortcoming of the PCR research community may be the main reason for the limited reproducibility of reported qPCR results.