The role and robustness of the Gini coefficient as an unbiased tool for the selection of Gini genes for normalising expression profiling data

Abstract

We recently introduced the Gini coefficient (GC) for assessing the expression variation of a particular gene in a dataset, as a means of selecting improved reference genes over the cohort (‘housekeeping genes’) typically used for normalisation in expression profiling studies. Those genes (transcripts) that we determined to be useable as reference genes differed greatly from previous suggestions based on hypothesis-driven approaches. A limitation of this initial study is that a single (albeit large) dataset was employed for both tissues and cell lines.We here extend this analysis to encompass seven other large datasets. Although their absolute values differ a little, the Gini values and median expression levels of the various genes are well correlated with each other between the various cell line datasets, implying that our original choice of the more ubiquitously expressed low-Gini-coefficient genes was indeed sound. In tissues, the Gini values and median expression levels of genes showed a greater variation, with the GC of genes changing with the number and types of tissues in the data sets. In all data sets, regardless of whether this was derived from tissues or cell lines, we also show that the GC is a robust measure of gene expression stability. Using the GC as a measure of expression stability we illustrate its utility to find tissue- and cell line-optimised housekeeping genes without any prior bias, that again include only a small number of previously reported housekeeping genes. We also independently confirmed this experimentally using RT-qPCR with 40 candidate GC genes in a panel of 10 cell lines. These were termed the Gini Genes.In many cases, the variation in the expression levels of classical reference genes is really quite huge (e.g. 44 fold for GAPDH in one data set), suggesting that the cure (of using them as normalising genes) may in some cases be worse than the disease (of not doing so). We recommend the present data-driven approach for the selection of reference genes by using the easy-to-calculate and robust GC.

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“…In the literature, we found only one study that explicitly provided a list of housekeeping genes [22]. Recently a novel tool, GeneGini, has been shown to be an effective way of identifying housekeeping genes [45,46]. Housekeeping genes and reactions identified using this tool (S19 Fig) reproduced the results presented in this study (S20 and S21 Figs).…”

Heterozygous transcriptional signatures unmask variable premature termination codon (PTC) burden alongside pathway-specific adaptations in blood outgrowth endothelial cells from patients with nonsense DNA variants causing hereditary hemorrhagic telangiectasia

“…alignments across sample pairs, despite very low known biological variability, 36,37 confirmed biological replicates, and standardized culture conditions (Figure 2C).…”

“…To facilitate quantitative identification of subtle readouts, novel housekeeping genes were sought from genes with low Gini Coefficients (GC) within multiple cell lines. 36, 37 These included RBM45 (catalogue number 4351372, assay number HS00912280_M1), and SF3B2 (catalogue number 4331182, assay number HS00199190_M1). These were evaluated using bulk quantitative reverse transcriptase PCR (qRT-PCR) as described 33 using pre-designed TaqMan qRT-PCR assays (Applied Biosystems, Thermo Fisher, Waltham, MA, Supplemental Table 1).…”

“…Mean alignments across the 16 samples corresponded better with endothelial expressed genes from our earlier data in primary human dermal microvascular endothelial cells (HDMEC Figure 1Ai) than pulmonary artery endothelial cells (HPAEC, Figure 1Aii). Two genes with low Gini coefficients of variation 36,37 were defined as improved housekeeper genes compared to those previously used for reference (Figure 1B). BOEC RBM45 alignments were similar to the lowest quartile of endothelial transcripts (Figure 1Ci), including many HHT target genes examined in wider studies (data not shown).…”

“…36,37 -normalised alignments with donor age, sex, experimental variability, and HHT disease status were examined by…”