On the use of the coefficient of variation to quantify and compare trait variation

Pélabon, Christophe; Hilde, Christoffer Høyvik; Einum, Sigurd; Gamelon, Marlène

doi:10.1002/evl3.171

Cited by 114 publications

(83 citation statements)

References 65 publications

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“…Theoretically, RGs should be stably expressed in all samples, and their expression levels should be unaffected by the external environment, e.g. during tumorigenesis [31,49]. The selection and validation of RGs have to be corroborated by using a large number of samples [50,51].…”

Section: Discussionmentioning

confidence: 99%

“…After obtaining the gene fragments per kilobase of exon model per million mapped reads (FPKM), transcripts which exhibited low levels (FPKM = 0 appearing over 61 times in 1217 transcriptome pro les, 1217*5%=60.85) were removed. According to the FPKM value of every gene in all transcriptome pro les [30], the coe cient of variation (CV) [31], dispersion measure (DPM) [32,33], and FC-5% were calculated to screen for novel RGs (Fig. 1B).…”

Section: Selection Of Candidate Reference Genesmentioning

confidence: 99%

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Selecting optimal reference genes for breast cancer research

Song¹,

Huang²,

Wu³

et al. 2020

Preprint

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Background Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) is the most sensitive technique for evaluating gene expression levels. Choosing appropriate reference genes (RGs) is critical for normalizing and evaluating changes in the expression of target genes. However, uniform and reliable RGs for breast cancer research have not been identified, limiting the value of target gene expression studies. Here, we provide a novel approach for mining RGs by using the RNA-seq dataset to identify reliable and accurate RGs that can be applied to different types of breast cancer tissues and cell lines. Methods First, we compiled the transcriptome profiling data from the TCGA database involving 1217 samples to identify novel RGs and then ten genes (SF1, TARDBP, THRAP3, QRICH1, TRA2B, SRSF3, YY1, DNAJC8, RNF10, and RHOA) with relatively stable expression levels were chosen as novel candidate RGs. Additionally, six conventional RGs (ACTB, TUBA1A, RPL13A, B2M, GAPDH, and GUSB) were also selected. To determine and validate the optimal RGs we performed qRT-PCR experiments on 87 samples from 5 types of surgically excised breast tumor specimens including HR+HER2-, HR+HER2+, HR-HER2-, HR-HER2+, breast cancer after neoadjuvant chemotherapy (NAC) and their matched para-carcinoma tissues, furthermore, we also included a benign breast tumor sample. Six biological replicates were included for each tissue. Moreover, we assessed 7 breast cancer cell lines (MCF-10A, MCF-7, T-47D, MDA-MB-231, MDA-MB-468, as well as MDA-MB-231 with either CNR2 knockdown or overexpression; 3 biological replicates for each line). Five statistical algorithms (geNorm, NormFinder, ΔCt method, BestKeeper, and ComprFinder) were used to assess the stability of expression of each RG across all breast cancer tissues and cell lines. Results Our results show that RG combinations SF1+TRA2B+THRAP3 and THRAP3+RHOA+QRICH1 showed stable expression in breast cancer tissues and cell lines, respectively, and that these two combinations displayed good interchangeability. Therefore, we propose that the above two combinations are optimal triplet RGs for breast cancer research. Conclusions In summary, we identified novel and reliable RG combinations for breast cancer research based on a public RNA-seq dataset which lays a solid foundation for accurate normalization of qRT-PCR results across different breast cancer tissues and cells.

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Section: Discussionmentioning

confidence: 99%

Section: Selection Of Candidate Reference Genesmentioning

confidence: 99%

Selecting optimal reference genes for breast cancer research

Song¹,

Huang²,

Wu³

et al. 2020

Preprint

View full text Add to dashboard Cite

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“…This image can then be warped to standard (MNI) space, and combined with other images for statistical analysis DAVIS against a target value, using a version of Student's t test (Doornbos & Dijkstra, 1983). For the present work a CoV value of 0.2 has been somewhat arbitrarily taken as a reference point; this value accords with other values in the biological literature (e.g., Pélabon, Hilde, Einum, & Gamelon, 2020). It is worth noting that when CoV is 0.2, 95% of observations lie within 39.2% of the mean, which would seem to be rather a large margin of error for many applications (Van Belle & Martin, 1993).…”

Section: Distance Analysismentioning

confidence: 98%

Variance in cortical depth across the brain surface: Implications for transcranial stimulation of the brain

Davis

2020

Eur J of Neuroscience

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Non-invasive technologies that read or that stimulate the brain, such as EEG, TMS, or tDCS, frequently make assumptions about the anatomical structure of the brain. This is necessary as, for most participants, the structure of the brain is not visible. Two common examples of this sort of assumption would be the use of a standard brain model in computing the source of dipole components in EEG analysis (e.g., Marin, Guerin, Baillet, Garnero, & Meunier, 1998), or the projection of a tDCS-derived computed electric field onto a standard brain model (e.g., Bikson, Rahman, & Datta, 2012). However, to what extent can we rely on a standard brain image in understanding effects in an individual person? Are there particular regions of the brain where a standardized analysis is inadvisable? The use of transcranial magnetic stimulation (TMS) is a good example of a technology that depends strongly on understanding the structure of the brain. In TMS, a brief electric current through a coil of wire induces a rapidly varying

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“…Different values of CV between the traits for the nutrient concentration and also for the fraction of DRI and NY were observed in the present study (Tables 3, 4, and 5). The CV, a mean-standardized measure of variation, is often used to compare the variability of quantitative traits, and higher CVs are ascribed to a greater relative variability (Ogunniyan and Olakojo, 2014;Pélabon et al, 2020). For nutrient concentrations, the highest CV was obtained for Zn followed by Fe ( Table 3).…”

Section: Nutrient Concentrationmentioning

confidence: 99%

Biodiversity in Tomatoes: Is It Reflected in Nutrient Density and Nutritional Yields Under Organic Outdoor Production?

et al. 2020

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In many regions of the world, human nutrition is still characterized by an insufficient intake of essential nutrients like minerals such as iron (Fe) and zinc (Zn). In view of decreasing resources and a growing world population, the efficiency and the sustainability of cultivation systems should be considered not only in terms of crop yield and profit margin but also in terms of the yield of essential nutrients. Tomatoes are the most consumed vegetable in the world. Organic outdoor tomato cultivation is generally characterized by a higher diversity of varieties and lower fertilization input compared to conventional production. A 2-year field experiment with a set of 20 cultivars was performed to evaluate their variation regarding fruit mineral concentrations [potassium (K), calcium (Ca), magnesium (Mg), phosphorous (P), Fe, and Zn], their contribution to the dietary reference intake (DRI), and the nutritional yields (adults ha–1 year–1). Results show that mineral concentrations differed significantly by cultivar and by year. However, even though significant genotype-by-year effects appear, several cultivars exhibit high genotype stability across years for the single traits studied. Taking this together with medium-to-high heritability, genetics strongly controls most studied traits. Among the cultivars, the contribution of 100 g fresh fruits varied from 4.5 to 7.7% for K, 0.8 to 1.8% for Ca, 2.3 to 4.4% for Mg, 3 to 6.6% for P, 3.1 to 6.9% for Fe, and 1.9 to 4.2% for Zn to meet daily requirements. Based on average fruit yields per hectare, the cultivars varied with regard to the nutritional yields for all the studied minerals, but most strongly for Fe (44–120 adults ha–1 year–1) and Zn (22–84 adults ha–1 year–1). In terms of contribution to the DRI and nutritional yield for Fe, the cocktail cultivar “Bartelly F1” produced the highest results, while for Zn the salad cultivar “Bocati F1” showed the highest values. Our results show that the targeted use of tomato biodiversity in organic outdoor production can be suitable to achieve high fruit yields as well as to produce high nutritional yields per unit area, thus contributing to more effective land use and improved food security. These findings also provide valuable insights for tomato breeders to improve the tomato fruit quality while maintaining yield.

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On the use of the coefficient of variation to quantify and compare trait variation

Cited by 114 publications

References 65 publications

Selecting optimal reference genes for breast cancer research

Selecting optimal reference genes for breast cancer research

Variance in cortical depth across the brain surface: Implications for transcranial stimulation of the brain

Biodiversity in Tomatoes: Is It Reflected in Nutrient Density and Nutritional Yields Under Organic Outdoor Production?

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