Strictly Standardized Mean Difference Research Articles

Serum Vitamin D Level and the Risk of Urinary Tract Infection in Children: A Systematic Review and Meta-Analysis.

This systematic review and meta-analysis aimed to evaluate the association between serum vitamin D concentration and the risk of urinary tract infection (UTI) in children. Human studies reported the serum vitamin D level in children with UTI and healthy controls were collected from PubMed, Scopus, Embase, and Cochrane databases. The strictly standardized mean difference (SSMD) and 95% confidence interval (CI) were calculated to evaluate the relationship between serum vitamin D levels and risk of UTI. The results of analysis showed that serum vitamin D levels in children with UTI were significantly lower than healthy control children (SSMD: 0.891, 95% CI: 0.707–1.075, p < 0.000; SSMD: 0.797, 95% CI: 0.500–1.094, p < 0.000, respectively). It can be concluded that there is a significant negative relationship between serum vitamin D level and risk of UTI in children.

The inhibition of adipogenesis via an in vitro assay can reduce animal use by more precisely estimating the starting dose for the acute toxic class method

In the present work, we established an adipogenesis inhibition assay as an adequate and sensitive in vitro model for reducing animal use by estimating the starting dose for the acute toxic class (ATC) method. First, human adipose-derived stem cells (ADSCs) underwent adipogenic differentiation induction for 14 days. Then, by high-content imaging analysis, we determined the percentage and area of cell differentiation that we considered suitable for negative and positive internal control according to the quality control criteria strictly standardized mean difference (SSMD) and robust SSMD. Moreover, we established sodium dodecyl sulfate (SDS) as an external positive control in this assay. To measure reduction in animal use to estimate the starting dose for the ATC method, we evaluated 10 chemicals representing Globally Harmonized System of Classification and Labeling of Chemicals (GHS) toxicity categories 1–5 and unclassified toxicity and determined the dose-response curves for percentage and area of cell differentiation by using the Hill function with an R2 ≥ 0.85. The resulting IC50 values were used for LD50 prediction and for estimating the starting dose for the ATC method. Our results indicated that use of the inhibition of adipogenesis assay to estimate the starting dose for the ATC method would decrease animal use for 7 out of 10 tested substances, possibly all substances if we consider the more toxic test substances in GHS categories 1, 2, and 3. We can conclude that the present assay is a suitable alternative to reduce animal testing in the first steps of predicting highly toxic substances. Moreover, this method also presents internal and external controls as differentials, which guarantee the quality of the assay as well as the results. These features are important for suggesting a methodology for regulatory purposes.

Development of a Cell-Based High-Throughput Screening Assay to Identify Porcine Host Defense Peptide-Inducing Compounds.

Novel alternatives to antibiotics are needed for the swine industry, given increasing restrictions on subtherapeutic use of antibiotics. Augmenting the synthesis of endogenous host defense peptides (HDPs) has emerged as a promising antibiotic-alternative approach to disease control and prevention. To facilitate the identification of HDP inducers for swine use, we developed a stable luciferase reporter cell line, IPEC-J2/PBD3-luc, through permanent integration of a luciferase reporter gene driven by a 1.1 kb porcine β-defensin 3 (PBD3) gene promoter in porcine IPEC-J2 intestinal epithelial cells. Such a stable reporter cell line was employed in a high-throughput screening of 148 epigenetic compounds and 584 natural products, resulting in the identification of 41 unique hits with a minimum strictly standardized mean difference (SSMD) value of 3.0. Among them, 13 compounds were further confirmed to give at least a 5-fold increase in the luciferase activity in the stable reporter cell line, with 12 being histone deacetylase (HDAC) inhibitors. Eight compounds were subsequently observed to be comparable to sodium butyrate in inducing PBD3 mRNA expression in parental IPEC-J2 cells in the low micromolar range. Six HDAC inhibitors including suberoylanilide hydroxamine (SAHA), HC toxin, apicidin, panobinostat, SB939, and LAQ824 were additionally found to be highly effective HDP inducers in a porcine 3D4/31 macrophage cell line. Besides PBD3, other HDP genes such as PBD2 and cathelicidins (PG1–5) were concentration-dependently induced by those compounds in both IPEC-J2 and 3D4/31 cells. Furthermore, the antibacterial activities of 3D4/31 cells were augmented following 24 h exposure to HDAC inhibitors. In conclusion, a cell-based high-throughput screening assay was developed for the discovery of porcine HDP inducers, and newly identified HDP-inducing compounds may have potential to be developed as alternatives to antibiotics for applications in swine and possibly other animal species.

식스시그마 프로젝트 사례에서 혁신효과 분석을 위한 품질척도의 특성 및 적용

This research discusses the characteristics and the implementation strategies for two types of quality metrics to analyze innovation effects in six sigma projects: fixed specification type and moving specification type.  ,  are quality metrics of fixed specification type that are influenced by predetermined specification. In contrast, the quality metrics of moving specification type such as Strictly Standardized Mean Difference(SSMD), Z-Score, F-Statistic and t-Statistic are independent from predetermined specification.  sigma level obtains defective rates of Parts Per Million(PPM) and Defects Per Million Opportunities(DPMO). However, the defective rates between different industrial sectors are incomparable due to their own technological inherence. In order to explore relative method to compare defective rates between different industrial sectors, the ratio of specification and natural tolerance called,  , is used. The drawback of this  metric is that it is highly dependent on the specification. The metrics of F-Statistic and t-Statistic identify innovation effect by comparing before-and-after of accuracy and precision. These statistics are not affected by specification, but affected by type of statistical distribution models and sample size. Hence, statistical significance determined by above two statistics cannot give a same conclusion as practical significance. In conclusion, SSMD and Z-Score are the best quality metrics that are uninfluenced by fixed specification, theoretical distribution model and arbitrary sample size. Those metrics also identify the innovation effects for before-and-after of accuracy and precision. It is beneficial to use SSMD and Z-Score methods along with popular methods of  sigma level and  that are commonly employed in six sigma projects. The case studies from national six sigma contest from 2011 to 2012 are proposed and analyzed to provide the guidelines for the usage of quality metrics for quality practitioners.

Evaluation of a Screening System for Obesogenic Compounds: Screening of Endocrine Disrupting Compounds and Evaluation of the PPAR Dependency of the Effect

Recently the environmental obesogen hypothesis has been formulated, proposing a role for endocrine disrupting compounds (EDCs) in the development of obesity. To evaluate this hypothesis, a screening system for obesogenic compounds is urgently needed. In this study, we suggest a standardised protocol for obesogen screening based on the 3T3-L1 cell line, a well-characterised adipogenesis model, and direct fluorescent measurement using Nile red lipid staining technique. In a first phase, we characterised the assay using the acknowledged obesogens rosiglitazone and tributyltin. Based on the obtained dose-response curves for these model compounds, a lipid accumulation threshold value was calculated to ensure the biological relevance and reliability of statistically significant effects. This threshold based method was combined with the well described strictly standardized mean difference (SSMD) method for classification of non-, weak- or strong obesogenic compounds. In the next step, a range of EDCs, used in personal and household care products (parabens, musks, phthalates and alkylphenol compounds), were tested to further evaluate the obesogenicity screening assay for its discriminative power and sensitivity. Additionally, the peroxisome proliferator activated receptor γ (PPARγ) dependency of the positive compounds was evaluated using PPARγ activation and antagonist experiments. Our results showed the adipogenic potential of all tested parabens, several musks and phthalate compounds and bisphenol A (BPA). PPARγ activation was associated with adipogenesis for parabens, phthalates and BPA, however not required for obesogenic effects induced by Tonalide, indicating the role of other obesogenic mechanisms for this compound.

GUItars: A GUI Tool for Analysis of High-Throughput RNA Interference Screening Data

BackgroundHigh-throughput RNA interference (RNAi) screening has become a widely used approach to elucidating gene functions. However, analysis and annotation of large data sets generated from these screens has been a challenge for researchers without a programming background. Over the years, numerous data analysis methods were produced for plate quality control and hit selection and implemented by a few open-access software packages. Recently, strictly standardized mean difference (SSMD) has become a widely used method for RNAi screening analysis mainly due to its better control of false negative and false positive rates and its ability to quantify RNAi effects with a statistical basis. We have developed GUItars to enable researchers without a programming background to use SSMD as both a plate quality and a hit selection metric to analyze large data sets.ResultsThe software is accompanied by an intuitive graphical user interface for easy and rapid analysis workflow. SSMD analysis methods have been provided to the users along with traditionally-used z-score, normalized percent activity, and t-test methods for hit selection. GUItars is capable of analyzing large-scale data sets from screens with or without replicates. The software is designed to automatically generate and save numerous graphical outputs known to be among the most informative high-throughput data visualization tools capturing plate-wise and screen-wise performances. Graphical outputs are also written in HTML format for easy access, and a comprehensive summary of screening results is written into tab-delimited output files.ConclusionWith GUItars, we demonstrated robust SSMD-based analysis workflow on a 3840-gene small interfering RNA (siRNA) library and identified 200 siRNAs that increased and 150 siRNAs that decreased the assay activities with moderate to stronger effects. GUItars enables rapid analysis and illustration of data from large- or small-scale RNAi screens using SSMD and other traditional analysis methods. The software is freely available at http://sourceforge.net/projects/guitars/.

Illustration of SSMD, z Score, SSMD*, z* Score, and t Statistic for Hit Selection in RNAi High-Throughput Screens

Hit selection is the ultimate goal in many high-throughput screens. Various analytic methods are available for this purpose. Some commonly used ones are z score, z* score, strictly standardized mean difference (SSMD), SSMD*, and t statistic. It is critical to know how to use them correctly because the misusage of them can readily produce misleading results. Here, the author presents basic concepts, elaborates their commonality and difference, describes some common misusage that people should avoid, and uses simulated simple examples to illustrate how to use them correctly.

Self-Organizing Map Analysis of Toxicity-Related Cell Signaling Pathways for Metal and Metal Oxide Nanoparticles

The response of a murine macrophage cell line exposed to a library of seven metal and metal oxide nanoparticles was evaluated via High Throughput Screening (HTS) assay employing luciferase-reporters for ten independent toxicity-related signaling pathways. Similarities of toxicity response among the nanoparticles were identified via Self-Organizing Map (SOM) analysis. This analysis, applied to the HTS data, quantified the significance of the signaling pathway responses (SPRs) of the cell population exposed to nanomaterials relative to a population of untreated cells, using the Strictly Standardized Mean Difference (SSMD). Given the high dimensionality of the data and relatively small data set, the validity of the SOM clusters was established via a consensus clustering technique. Analysis of the SPR signatures revealed two cluster groups corresponding to (i) sublethal pro-inflammatory responses to Al2O3, Au, Ag, SiO2 nanoparticles possibly related to ROS generation, and (ii) lethal genotoxic responses due to exposure to ZnO and Pt nanoparticles at a concentration range of 25-100 μg/mL at 12 h exposure. In addition to identifying and visualizing clusters and quantifying similarity measures, the SOM approach can aid in developing predictive quantitative-structure relations; however, this would require significantly larger data sets generated from combinatorial libraries of engineered nanoparticles.

The Use of SSMD-Based False Discovery and False Nondiscovery Rates in Genome-Scale RNAi Screens

In genome-scale RNA interference (RNAi) screens, it is critical to control false positives and false negatives statistically. Traditional statistical methods for controlling false discovery and false nondiscovery rates are inappropriate for hit selection in RNAi screens because the major goal in RNAi screens is to control both the proportion of short interfering RNAs (siRNAs) with a small effect among selected hits and the proportion of siRNAs with a large effect among declared nonhits. An effective method based on strictly standardized mean difference (SSMD) has been proposed for statistically controlling false discovery rate (FDR) and false nondiscovery rate (FNDR) appropriate for RNAi screens. In this article, the authors explore the utility of the SSMD-based method for hit selection in RNAi screens. As demonstrated in 2 genome-scale RNAi screens, the SSMD-based method addresses the unmet need of controlling for the proportion of siRNAs with a small effect among selected hits, as well as controlling for the proportion of siRNAs with a large effect among declared nonhits. Furthermore, the SSMD-based method results in reasonably low FDR and FNDR for selecting inhibition or activation hits. This method works effectively and should have a broad utility for hit selection in RNAi screens with replicates.

Strictly Standardized Mean Difference, Standardized Mean Difference and Classical t-test for the Comparison of Two Groups

Statistical significance or p-value of t-test for testing mean difference has been widely used for the comparison of two groups. However, because of many issues that the statistical significance has, it has been intensively criticized in medical and social sciences. Consequently, effect sizes such as Cohen’s d have been proposed as an alternative to statistical significance. Recently, strictly standardized mean difference (SSMD) has been proposed for the comparison of two groups with applications in data analysis in high-throughput screening experiments. In this article, from a theoretical basis, I explore the relationships between SSMD, standardized mean difference, and p-value of classical t-test for comparing two groups. The relationships among these measures hint that SSMD may serve as an alternative to statistical significance in medical sciences and as an alternative to traditional effect sizes in social sciences.

Error Rates and Powers in Genome-Scale RNAi Screens

For hit selection in genome-scale RNAi research, we do not want to miss small interfering RNAs (siRNAs) with large effects; meanwhile, we do not want to include siRNAs with small or no effects in the list of selected hits. There is a strong need to control both the false-negative rate (FNR), in which the siRNAs with large effects are not selected as hits, and the restricted false-positive rate (RFPR), in which the siRNAs with no or small effects are selected as hits. An error control method based on strictly standardized mean difference (SSMD) has been proposed to maintain a flexible and balanced control of FNR and RFPR. In this article, the authors illustrate how to maintain a balanced control of both FNR and RFPR using the plot of error rate versus SSMD as well as how to keep high powers using the plot of power versus SSMD in RNAi high-throughput screening experiments. There are relationships among FNR, RFPR, Type I and II errors, and power. Understanding the differences and links among these concepts is essential for people to use statistical terminology correctly and effectively for data analysis in genome-scale RNAi screens. Here the authors explore these differences and links.

Novel Analytic Criteria and Effective Plate Designs for Quality Control in Genome-Scale RNAi Screens

One of the most fundamental challenges in genome-wide RNA interference (RNAi) screens is to glean biological significance from mounds of data, which relies on the development and adoption of appropriate analytic methods and designs for quality control (QC) and hit selection. Currently, a Z-factor-based QC criterion is widely used to evaluate data quality. However, this criterion cannot take into account the fact that different positive controls may have different effect sizes and leads to inconsistent QC results in experiments with 2 or more positive controls with different effect sizes. In this study, based on a recently proposed parameter, strictly standardized mean difference (SSMD), novel QC criteria are constructed for evaluating data quality in genome-wide RNAi screens. Two good features of these novel criteria are: (1) SSMD has both clear original and probability meanings for evaluating the differentiation between positive and negative controls and hence the SSMD-based QC criteria have a solid probabilistic and statistical basis, and (2) these QC criteria obtain consistent QC results for multiple positive controls with different effect sizes. In addition, I propose multiple plate designs and the guidelines for using them in genome-wide RNAi screens. Finally, I provide strategies for using the SSMD-based QC criteria and effective plate design together to improve data quality. The novel SSMD-based QC criteria, effective plate designs, and related guidelines and strategies may greatly help to obtain high quality of data in genome-wide RNAi screens.

Genome-wide screens for effective siRNAs through assessing the size of siRNA effects

BackgroundRNA interference (RNAi) has been seen as a revolution in functional genomics and system biology. Genome-wide RNAi research relies on the development of RNAi high-throughput screening (HTS) assays. One of the most fundamental challenges in RNAi HTS is to glean biological significance from mounds of data, which relies on the development of effective analytic methods for selecting effective small interfering RNAs (siRNAs).FindingsBased on a recently proposed parameter, strictly standardized mean difference (SSMD), I propose an analytic method for genome-wide screens of effective siRNAs through assessing and testing the size of siRNA effects. Central to this method is the capability of SSMD in quantifying siRNA effects. This method has relied on normal approximation, which works only in the primary screens but not in the confirmatory screens. In this paper, I explore the non-central t-distribution property of SSMD estimates and use this property to extend the SSMD-based method so that it works effectively in either primary or confirmatory screens as well as in any HTS screens with or without replicates. The SSMD-based method maintains a balanced control of false positives and false negatives.ConclusionThe central interest in genome-wide RNAi research is the selection of effective siRNAs which relies on the development of analytic methods to measure the size of siRNA effects. The new analytic method for hit selection provided in this paper offers a good analytic tool for selecting effective siRNAs, better than current analytic methods, and thus may have broad utility in genome-wide RNAi research.

A New Method with Flexible and Balanced Control of False Negatives and False Positives for Hit Selection in RNA Interference High-Throughput Screening Assays

The z-score method and its variants for testing mean difference are commonly used for hit selection in high-throughput screening (HTS) assays. Strictly standardized mean difference (SSMD) offers a way to measure and classify the short interfering RNA (siRNA) effects. In this article, based on SSMD, the authors propose a new testing method for hit selection in RNA interference (RNAi) HTS assays. This SSMD-based method allows the differentiation between siRNAs with large and small effects on the assay output and maintains flexible and balanced control of both the false-negative rate, in which the siRNAs with strong effects are not selected as hits, and the restricted false-positive rate, in which the siRNAs with weak or no effects are selected as hits. This method directly addresses the size of siRNA effects represented by the strength of difference between an siRNA and a negative reference, whereas the classic z-score method and t-test of testing no mean difference address whether the mean of an siRNA is exactly the same as the mean of a negative reference. This method can readily control the false-negative rate, whereas it is nontrivial for the classic z-score method and t-test to control the false-negative rate. Therefore, theoretically, the SSMD-based method offers better control of the sizes of siRNA effects and the associated false-positive and false-negative rates than the commonly used z-score method and t-test for hit selection in HTS assays. The SSMD-based method should generally be applicable to any assay in which the end point is a difference in signal compared to a reference sample, including those for RNAi, receptor, enzyme, and cellular function.

The Use of Strictly Standardized Mean Difference for Hit Selection in Primary RNA Interference High-Throughput Screening Experiments

RNA interference (RNAi) high-throughput screening (HTS) has been hailed as the 2nd genomics wave following the 1st genomics wave of gene expression microarrays and single-nucleotide polymorphism discovery platforms. Following an RNAi HTS, the authors are interested in identifying short interfering RNA (siRNA) hits with large inhibition/activation effects. For hit selection, the z-score method and its variants are commonly used in primary RNAi HTS experiments. Recently, strictly standardized mean difference (SSMD) has been proposed to measure the siRNA effect represented by the magnitude of difference between an siRNA and a negative reference group. The links between SSMD and d+-probability offer a clear interpretation of siRNA effects from a probability perspective. Hence, SSMD can be used as a ranking metric for hit selection. In this article, the authors investigated both the SSMD-based testing process and the use of SSMD as a ranking metric for hit selection in 2 primary siRNA HTS experiments. The analysis results showed that, as a ranking metric, SSMD was more stable and reliable than percentage inhibition and led to more robust hit selection results. Using the SSMD -based testing method, the false-negative rate can more readily be obtained. More important, the use of the SSMD-based method can result in a reduction in both the false-negative and false-positive rates. The applications presented in this article demonstrate that the SSMD method addresses scientific questions and fills scientific needs better than both percentage inhibition and the commonly used z-score method for hit selection.

A pair of new statistical parameters for quality control in RNA interference high-throughput screening assays

RNA interference (RNAi) high-throughput screening (HTS) enables massive parallel gene silencing and is increasingly being used to reveal novel connections between genes and disease-relevant phenotypes. The application of genome-scale RNAi relies on the development of high-quality RNAi HTS assays. To obtain high-quality HTS assays, there is a strong need for an easily interpretable and theoretically based quality control (QC) metric. Signal-to-noise ratio ( S/N), signal-to-background ratio ( S/B), and Z-factor have been adopted as QC metrics in HTS assays. In this paper, I proposed a pair of new parameters, strictly standardized mean difference ( SSMD) and coefficient of variability in difference ( CVD), as QC metrics in RNAi HTS assays. Compared to S/B and S/N, SSMD and CVD capture the variabilities in both compared populations. Compared to Z-factor, SSMD and CVD have a clear probability interpretation and a solid statistical basis. Accordingly, the cutoff criteria of using SSMD or CVD as a QC metric in HTS assays are fully theoretically based. In addition, I discuss the relationship between the SSMD-based criterion and the popular Z-factor-based criterion and elucidate why p-value from t-test of testing mean difference fails to serve as a QC metric.