A quantum leap in the reproducibility, precision, and sensitivity of gene expression profile analysis even when sample size is extremely small

2015 ◽  
Vol 13 (04) ◽  
pp. 1550018 ◽  
Author(s):  
Kevin Lim ◽  
Zhenhua Li ◽  
Kwok Pui Choi ◽  
Limsoon Wong
Keyword(s):  
Gene Expression ◽  
Sample Size ◽  
Small Sample Size ◽  
Statistical Tests ◽  
Transcript Level ◽  
Small Sample ◽  
Sample Sizes ◽  
Quantum Leap ◽  
Two Samples ◽  
Small Sample Sizes

Transcript-level quantification is often measured across two groups of patients to aid the discovery of biomarkers and detection of biological mechanisms involving these biomarkers. Statistical tests lack power and false discovery rate is high when sample size is small. Yet, many experiments have very few samples (≤ 5). This creates the impetus for a method to discover biomarkers and mechanisms under very small sample sizes. We present a powerful method, ESSNet, that is able to identify subnetworks consistently across independent datasets of the same disease phenotypes even under very small sample sizes. The key idea of ESSNet is to fragment large pathways into smaller subnetworks and compute a statistic that discriminates the subnetworks in two phenotypes. We do not greedily select genes to be included based on differential expression but rely on gene-expression-level ranking within a phenotype, which is shown to be stable even under extremely small sample sizes. We test our subnetworks on null distributions obtained by array rotation; this preserves the gene–gene correlation structure and is suitable for datasets with small sample size allowing us to consistently predict relevant subnetworks even when sample size is small. For most other methods, this consistency drops to less than 10% when we test them on datasets with only two samples from each phenotype, whereas ESSNet is able to achieve an average consistency of 58% (72% when we consider genes within the subnetworks) and continues to be superior when sample size is large. We further show that the subnetworks identified by ESSNet are highly correlated to many references in the biological literature. ESSNet and supplementary material are available at: http://compbio.ddns.comp.nus.edu.sg:8080/essnet .

scholarly journals What can we Learn from Studies Based on Small Sample Sizes? Comment on Regan, Lakhanpal, and Anguiano (2012)

2013 ◽  
Vol 113 (1) ◽  
pp. 221-224 ◽  
Author(s):  
David R. Johnson ◽  
Lauren K. Bachan
Keyword(s):  
Sample Size ◽  
Recent Article ◽  
Small Sample ◽  
Sample Sizes ◽  
Relationship Outcomes ◽  
Small Sample Sizes

In a recent article, Regan, Lakhanpal, and Anguiano (2012) highlighted the lack of evidence for different relationship outcomes between arranged and love-based marriages. Yet the sample size ( n = 58) used in the study is insufficient for making such inferences. This reply discusses and demonstrates how small sample sizes reduce the utility of this research.

scholarly journals Sample Size Determination and Optimal Design of Simple Pretest-Posttest Experimental Designs: Introduction, Software, and Illustrations

2021 ◽  
Author(s):  
Metin Bulus
Keyword(s):  
Optimal Design ◽  
Sample Size ◽  
Small Sample ◽  
Experimental Designs ◽  
Sample Size Determination ◽  
Size Determination ◽  
Sample Sizes ◽  
Control Groups ◽  
Small Sample Sizes ◽  
And Control

A recent systematic review of experimental studies conducted in Turkey between 2010 and 2020 reported that small sample sizes had been a significant drawback (Bulus and Koyuncu, 2021). A small chunk of the studies were small-scale true experiments (subjects randomized into the treatment and control groups). The remaining studies consisted of quasi-experiments (subjects in treatment and control groups were matched on pretest or other covariates) and weak experiments (neither randomized nor matched but had the control group). They had an average sample size below 70 for different domains and outcomes. These small sample sizes imply a strong (and perhaps erroneous) assumption about the minimum relevant effect size (MRES) of intervention before an experiment is conducted; that is, a standardized intervention effect of Cohen’s d < 0.50 is not relevant to education policy or practice. Thus, an introduction to sample size determination for pretest-posttest simple experimental designs is warranted. This study describes nuts and bolts of sample size determination, derives expressions for optimal design under differential cost per treatment and control units, provide convenient tables to guide sample size decisions for MRES values between 0.20 ≤ Cohen’s d ≤ 0.50, and describe the relevant software along with illustrations.

scholarly journals Comparison of statistical methods for analysis of small sample sizes for detecting the differences in efficacy between treatments for knee osteoarthritis

2020 ◽  
Author(s):  
Chia-Lung Shih ◽  
Te-Yu Hung
Keyword(s):  
Knee Osteoarthritis ◽  
Sample Size ◽  
Statistical Methods ◽  
Statistical Power ◽  
Permutation Test ◽  
Small Sample Size ◽  
Small Sample ◽  
T Test ◽  
Sample Sizes ◽  
Knee Oa

Abstract Background A small sample size (n < 30 for each treatment group) is usually enrolled to investigate the differences in efficacy between treatments for knee osteoarthritis (OA). The objective of this study was to use simulation for comparing the power of four statistical methods for analysis of small sample size for detecting the differences in efficacy between two treatments for knee OA. Methods A total of 10,000 replicates of 5 sample sizes (n=10, 15, 20, 25, and 30 for each group) were generated based on the previous reported measures of treatment efficacy. Four statistical methods were used to compare the differences in efficacy between treatments, including the two-sample t-test (t-test), the Mann-Whitney U-test (M-W test), the Kolmogorov-Smirnov test (K-S test), and the permutation test (perm-test). Results The bias of simulated parameter means showed a decreased trend with sample size but the CV% of simulated parameter means varied with sample sizes for all parameters. For the largest sample size (n=30), the CV% could achieve a small level (<20%) for almost all parameters but the bias could not. Among the non-parametric tests for analysis of small sample size, the perm-test had the highest statistical power, and its false positive rate was not affected by sample size. However, the power of the perm-test could not achieve a high value (80%) even using the largest sample size (n=30). Conclusion The perm-test is suggested for analysis of small sample size to compare the differences in efficacy between two treatments for knee OA.

scholarly journals A Cascade Flexible Neural Forest Model for Cancer Subtypes Classification on Gene Expression Data

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Lianxin Zhong ◽  
Qingfang Meng ◽  
Yuehui Chen
Keyword(s):  
Gene Expression ◽  
Sample Size ◽  
Gene Expression Data ◽  
Small Sample Size ◽  
Small Sample ◽  
Expression Data ◽  
Cancer Subtypes ◽  
Subtype Classification ◽  
Cancer Subtype

The correct classification of cancer subtypes is of great significance for the in-depth study of cancer pathogenesis and the realization of accurate treatment for cancer patients. In recent years, the classification of cancer subtypes using deep neural networks and gene expression data has become a hot topic. However, most classifiers may face the challenges of overfitting and low classification accuracy when dealing with small sample size and high-dimensional biological data. In this paper, the Cascade Flexible Neural Forest (CFNForest) Model was proposed to accomplish cancer subtype classification. CFNForest extended the traditional flexible neural tree structure to FNT Group Forest exploiting a bagging ensemble strategy and could automatically generate the model’s structure and parameters. In order to deepen the FNT Group Forest without introducing new hyperparameters, the multilayer cascade framework was exploited to design the FNT Group Forest model, which transformed features between levels and improved the performance of the model. The proposed CFNForest model also improved the operational efficiency and the robustness of the model by sample selection mechanism between layers and setting different weights for the output of each layer. To accomplish cancer subtype classification, FNT Group Forest with different feature sets was used to enrich the structural diversity of the model, which make it more suitable for processing small sample size datasets. The experiments on RNA-seq gene expression data showed that CFNForest effectively improves the accuracy of cancer subtype classification. The classification results have good robustness.

scholarly journals Digitizing Dunhuang Cultural Heritage: A User Evaluation of Mogao Cave Panorama Digital Library

2017 ◽  
Vol 2 (3) ◽  
pp. 49-67 ◽  
Author(s):  
Xiao Hu ◽  
Eric M. Y. Ho ◽  
Chen Qiao
Keyword(s):  
Cultural Heritage ◽  
Sample Size ◽  
Digital Library ◽  
Information Seeking ◽  
Small Sample Size ◽  
Statistical Tests ◽  
Small Sample ◽  
Evaluation Framework ◽  
User Evaluation ◽  
Usability Tests

Abstract Purpose This study is a user evaluation on the usability of the Mogao Cave Panorama Digital Library (DL), aiming to measure its effectiveness from the users’ perspective and to propose suggestions for improvement. Design/methodology/approach Usability tests were conducted based on a framework of evaluation criteria and a set of information seeking tasks designed for the Dunhuang cultural heritage, and interviews were conducted for soliciting in-depth opinions from participants. Findings The results of the usability tests indicate that the DL was more efficient in supporting simple information seeking tasks than those of higher-complexity levels. Statistical tests reveal that there were correlations among dimensions of usability criteria and user effectiveness measures. Moreover, interview discourses exposed specific usability issues of the DL. Research limitations This research is based on a relatively small sample size, resulting in a limited representativeness of user diversity. A larger sample size is needed for a systematic cross group comparison. Practical implications This study evaluated the usability of the Mogao Cave Panorama DL and proposed suggestions for its improvement for better experience. The results also provide a reference to other cultural heritage DLs with panorama functions. Originality/value This study is one of the first evaluating cultural heritage DLs from the perspective of user experience. It provides methodological references for relevant studies: the evaluation framework, the designed information seeking tasks, and the interview questions can be adopted or adapted in evaluating other visually centric DLs of cultural heritage.

scholarly journals Comparative evaluation of goodness of fit tests for normal distribution using simulation and empirical data

2020 ◽  
Vol 57 (2) ◽  
pp. 237-251
Author(s):  
Achilleas Anastasiou ◽  
Alex Karagrigoriou ◽  
Anastasios Katsileros
Keyword(s):  
Sample Size ◽  
Normal Distribution ◽  
Empirical Data ◽  
Goodness Of Fit ◽  
Small Sample Size ◽  
Small Sample ◽  
Type I ◽  
Sample Sizes ◽  
Omnibus Test ◽  
Wide Range

SummaryThe normal distribution is considered to be one of the most important distributions, with numerous applications in various fields, including the field of agricultural sciences. The purpose of this study is to evaluate the most popular normality tests, comparing the performance in terms of the size (type I error) and the power against a large spectrum of distributions with simulations for various sample sizes and significance levels, as well as through empirical data from agricultural experiments. The simulation results show that the power of all normality tests is low for small sample size, but as the sample size increases, the power increases as well. Also, the results show that the Shapiro–Wilk test is powerful over a wide range of alternative distributions and sample sizes and especially in asymmetric distributions. Moreover the D’Agostino–Pearson Omnibus test is powerful for small sample sizes against symmetric alternative distributions, while the same is true for the Kurtosis test for moderate and large sample sizes.

The Relationship Between Sample Sizes and Effect Sizes in Systematic Reviews in Education

2009 ◽  
Vol 31 (4) ◽  
pp. 500-506 ◽  
Author(s):  
Robert Slavin ◽  
Dewi Smith
Keyword(s):  
Sample Size ◽  
Effect Size ◽  
Secondary Mathematics ◽  
Significant Negative Correlation ◽  
Small Sample ◽  
Effect Sizes ◽  
Sample Sizes ◽  
Large Samples ◽  
Small Sample Sizes ◽  
The Relationship

Research in fields other than education has found that studies with small sample sizes tend to have larger effect sizes than those with large samples. This article examines the relationship between sample size and effect size in education. It analyzes data from 185 studies of elementary and secondary mathematics programs that met the standards of the Best Evidence Encyclopedia. As predicted, there was a significant negative correlation between sample size and effect size. The differences in effect sizes between small and large experiments were much greater than those between randomized and matched experiments. Explanations for the effects of sample size on effect size are discussed.

scholarly journals A Commentary on Diversity Measures UniFrac in Very Small Sample Size

2019 ◽  
Vol 15 ◽  
pp. 117693431984351
Author(s):  
Judith Agueda Roldán Ahumada ◽  
Martha Lorena Avendaño Garrido
Keyword(s):  
Sample Size ◽  
Microbial Communities ◽  
Small Sample Size ◽  
Genetic Material ◽  
Small Sample ◽  
Empirical Distributions ◽  
Diversity Measures ◽  
Two Samples ◽  
Metric Definition

In phylogenetic, the diversity measures as UniFrac, weighted UniFrac, and normalized weighted UniFrac are used to estimate the closeness between two samples of genetic material sequences. These measures are widely used in microbiology to compare microbial communities. Furthermore, when the sample size is large enough, very good results have been obtained experimentally. However, some authors do not suggest using them when the sample size is very small. Recently, it has been mentioned that the weighted UniFrac measure can be seen as the Kantorovich-Rubinstein metric between the corresponding empirical distributions of samples of genetic material. Also, it is well known that the Kantorovich-Rubinstein metric complies the metric definition. However, one of the main reasons to establish it is that the sample size is large enough. The goal of this article is to prove that the diversity measures UniFrac are not metrics when the sample size is very small, which justifies why it must not be used in that case, but yes the Kantorovich-Rubinstein metric.

261 Association of T-cell–inflamed gene expression profile and PD-L1 status with efficacy of pembrolizumab in patients with esophageal cancer from KEYNOTE-180

2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A285-A285
Author(s):  
Manish Shah ◽  
Takashi Kojima ◽  
Daniel Hochhauser ◽  
Peter Enzinger ◽  
Judith Raimbourg ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cell ◽  
Sample Size ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Small Sample Size ◽  
Gastroesophageal Junction ◽  
Small Sample ◽  
Link Type ◽  
Number Of Patients

BackgroundKey biomarkers under investigation for the ability to predict response to monotherapy PD-1 inhibitors such as pembrolizumab include PD-L1, TMB, MSI, and T-cell–inflamed gene expression profile (GEP). The KEYNOTE-180 trial (NCT02559687) was a single-arm phase 2 study of pembrolizumab as third-line or greater therapy in advanced/metastatic esophageal/gastroesophageal junction adenocarcinoma or squamous cell carcinoma (SCC). ORR was 9.9% and median DOR was NR at the primary analysis. We investigated the relationship in KEYNOTE-180 between response to pembrolizumab and T-cell–inflamed GEP or PD-L1 expression by histology.MethodsPatients received pembrolizumab 200 mg Q3W for ≤2 years until disease progression, toxicity, or withdrawal. The end points for this analysis were ORR, DOR, and PFS per RECIST v1.1 by central review and OS in the SCC and adenocarcinoma populations by GEP (non-low [≥–1.540] or low [<–1.540]; cutoff prespecified) and PD-L1 (CPS ≥10 or <10). Tumor GEP was determined using the NanoString nCounter Analysis System. PD-L1 expression was characterized using PD-L1 IHC 22C3 pharmDx. Data cutoff date was July 30, 2018.ResultsOf 121 total patients, 118 had an evaluable GEP score and 121 had an evaluable PD-L1 CPS. Fifty-one patients (42.1%) had GEPnon-lowtumors, 58 (48.0%) had CPS ≥10 tumors, and 31 (25.6%) had GEPnon-low/CPS ≥10 tumors; 63 patients (52.1%) had SCC and 58 (47.9%) had adenocarcinoma. ORR was 15.4% with GEPnon-low and 13.5% with GEPlow among patients with SCC and 12% and 0% among patients with adenocarcinoma, respectively (table 1). ORR was 20% with CPS ≥10 and 7.1% with CPS <10 among patients with SCC and 4.3% and 5.7%, respectively, among patients with adenocarcinoma (table 2). Median OS was slightly higher among patients with SCC in the GEPnon-low subgroup and the CPS ≥10 subgroup versus GEPlow and CPS <10 subgroups, respectively (tables 1, 2); this trend was reversed among patients with adenocarcinoma (tables 1, 2). Median PFS ranged from 1.9 to 2.1 across histology/biomarker subgroups. Median DOR was NR regardless of GEP or CPS status (tables 1, 2).Abstract 261 Table 1Response by GEP status and histologyaAnalysis by biomarker status was not possible because of the small sample size.Abstract 261 Table 2Response by PD-L1 status and histologyaAnalysis by biomarker status was not possible because of the small sample size.ConclusionsIn KEYNOTE-180, data in a small number of patients suggested that measures of inflammation, like PD-L1 and GEP, may enrich for responses to pembrolizumab. In SCC, some trends toward enrichment were observed for both biomarkers, although the trend was stronger for PD-L1 CPS ≥10. In adenocarcinoma, a trend was observed for GEP but not for PD-L1; the small number of responders is limiting, and further studies are needed to understand molecular correlates in adenocarcinoma.AcknowledgementsMedical writing and/or editorial assistance was provided by Tim Peoples, MA, ELS, and Holly C. Cappelli, PhD, CMPP, of the ApotheCom pembrolizumab team (Yardley, PA, USA). This assistance was funded by Merck Sharp & Dohme Corp, a subsidiary of Merck & Co., Inc., Kenilworth, NJ, USA.Trial RegistrationClinicalTrials. gov, NCT02559687Ethics ApprovalThe study and the protocol were approved by the institutional review board or ethics committee at each site.ConsentAll patients provided written informed consent to participate in the clinical trial.

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