Pilot Dataset Research Articles

Characterization of pinna motion patterns in hipposiderid bats

Hipposiderid bats have a set of highly differentiated pinna muscles that support different pinna motion patterns during biosonar behaviors. It has been shown that these bats' pinna motions fall into two separate categories: Rigid motions that change only pinna orientation and non-rigid motions that also change pinna shape. It remains unclear if further subdivisions of these two categories (e.g., different orientations for rigid motions and different deformation patterns for non-rigid motions) can be made. In order to investigate this question, experiments have been conducted where the positions of a dense set of landmark points (50–80 per pinna) were reconstructed based on footage from an array of four high-speed video cameras. The resulting point trajectory data (positions as a function of time) was clustered to facilitate the detection of patterns. Clustering revealed a structure of coherent surface patches for rigid as well as non-rigid motion. For the rigid motion pilot data set, these patches followe...

A dataset of 200 structured product labels annotated for adverse drug reactions

Adverse drug reactions (ADRs), unintended and sometimes dangerous effects that a drug may have, are one of the leading causes of morbidity and mortality during medical care. To date, there is no structured machine-readable authoritative source of known ADRs. The United States Food and Drug Administration (FDA) partnered with the National Library of Medicine to create a pilot dataset containing standardised information about known adverse reactions for 200 FDA-approved drugs. The Structured Product Labels (SPLs), the documents FDA uses to exchange information about drugs and other products, were manually annotated for adverse reactions at the mention level to facilitate development and evaluation of text mining tools for extraction of ADRs from all SPLs. The ADRs were then normalised to the Unified Medical Language System (UMLS) and to the Medical Dictionary for Regulatory Activities (MedDRA). We present the curation process and the structure of the publicly available database SPL-ADR-200db containing 5,098 distinct ADRs. The database is available at https://bionlp.nlm.nih.gov/tac2017adversereactions/; the code for preparing and validating the data is available at https://github.com/lhncbc/fda-ars.

Understanding Pseudomonas status among adults with cystic fibrosis: a real-world comparison of the Leeds criteria against clinicians\u2019 decision

Pseudomonas aeruginosa status influences cystic fibrosis (CF) clinical management but no ‘gold standard’ definition exists. The Leeds criteria are commonly used but may lack sensitivity for chronic P. aeruginosa. We compared clinicians’ decision with the Leeds criteria in three adult CF centres. Two independent prospective datasets (Sheffield dataset, n = 185 adults; ACtiF pilot dataset, n = 62 adults from two different centres) were analysed. Clinicians involved in deciding P. aeruginosa status were blinded to the study objectives. Clinicians considered more adults with CF to have chronic P. aeruginosa infection compared to the Leeds criteria. This was more so for the Sheffield dataset (106/185, 57.3% with clinicians’ decision vs. 80/185, 43.2% with the Leeds criteria; kappa coefficient between these two methods 0.72) compared to the ACtiF pilot dataset (34/62, 54.8% with clinicians’ decision vs. 30/62, 48.4% with the Leeds criteria; kappa coefficient between these two methods 0.82). However, clinicians across different centres were relatively consistent once age and severity of lung disease, as indicated by the type of respiratory samples provided, were taken into account. Agreement in P. aeruginosa status was similar for both datasets among adults who predominantly provided sputum samples (kappa coefficient 0.78) or adults > 25 years old (kappa coefficient 0.82). Across three different centres, clinicians did not always agree with the Leeds criteria and tended to consider the Leeds criteria to lack sensitivity. Where disagreement occurred, clinicians tended to diagnose chronic P. aeruginosa infection because other relevant information was considered. These results suggest that a better definition for chronic P. aeruginosa might be developed by using consensus methods to move beyond a definition wholly dependent on standard microbiological results.

Neck Vessel Cross-Sectional Area Measured with MRI: Scan-Rescan Reproducibility for Longitudinal Evaluations.

The cross-sectional area (CSA) of common carotid arteries-internal carotid arteries (CCA-ICAs), vertebral arteries (VAs), and internal jugular veins (IJVs) is influenced by aging. However, the neck vessel CSA can be affected by other factors as well, including subject positioning, hydration, and respiration, especially in longitudinal studies. This study aimed to assess scan-rescan reproducibility of CCA-ICAs, VAs, and IJVs CSA measurements in order to evaluate their feasibility for longitudinal CSA assessments, and to apply the segmentation method on a longitudinal pilot dataset. Two set of 2-dimensional neck magnetic resonance angiography (MRA) images were acquired on a 3-T scanner from two separate datasets: 9 healthy individuals (HIs) were scanned 5 days apart (scan-rescan dataset) and 12 HIs were acquired 5 years apart (baseline-follow-up dataset). CCA-ICAs, VAs, and IJVs were segmented along the whole vessel length between C3 and C7 intervertebral spaces. Repeated measure analysis of covariance, adjusted for cervical level and sample, and Wilcoxon signed-rank sum test were used to assess the scan-rescan and baseline-follow-up CSA differences. Intraclass correlation coefficient (ICC) was also computed to evaluate scan-rescan reliability. No significant CSA differences were found for the scan-rescan and baseline-follow-up CSA comparisons, using the whole vessel length or single cervical level measurements. ICC analysis showed good degree of scan-rescan reproducibility (considering whole vessel measures: ICC > .9, P-value < .001 for CCA-ICAs, ICC > .6, P-value < .001 for VAs, and ICC > .7, P-value < .001 for IJVs). Scan-rescan reproducibility of CCA-ICAs, VAs, and IJVs CSA measurements is high, making longitudinal studies feasible.

Abstract 219: Impact of Combination Therapy on Cardiovascular Outcomes in Patients with Symptomatic Subclavian Artery Stenosis

Background: Subclavian artery stenosis (SAS) is narrowing of the subclavian artery most commonly caused by atherosclerosis. It serves as a marker for cerebrovascular and myocardial ischemic events. Methods: A retrospective cohort study was conducted to determine the association of treatment via combination therapy (antiplatelet drug plus either by-pass surgery or percutaneous transluminal angioplasty (PTA) with or without stent implantation) versus antiplatelet drug therapy alone on cardiovascular events and all-cause mortality in Marshfield Clinic patients diagnosed with symptomatic SAS from January 1, 1995 to December 31, 2009. Results: Of the total 2153 cases, 100 patients were identified as eligible to be included in the study. Of these 100 patients that meet inclusion criteria, 30 underwent combination therapy while 70 were managed only with pharmacological drug treatment. Adverse cardiovascular events occurred in 5/30 (17%) of combination therapy patients compared to 28/70 (40%) of antiplatelet drug therapy only patients (p = 0.0355). Accordingly, all-cause mortality was higher (47%) in the antiplatelet drug therapy only group than the combination therapy group (13%) [hazard ratio = 3.45, p = 0.0218]. Conclusions: Preliminary findings in this pilot data set suggest that combination therapy (medications plus either surgical or interventional repair) of subclavian artery stenosis is associated with less cardiovascular adverse events and higher survival rates, benefits that extend beyond the target vessel. Prospective randomized studies with larger number of patients are needed to validate these findings.

Prediction of lithium response in first-episode mania using the LITHium Intelligent Agent (LITHIA): Pilot data and proof-of-concept.

Individualized treatment for bipolar disorder based on neuroimaging treatment targets remains elusive. To address this shortcoming, we developed a linguistic machine learning system based on a cascading genetic fuzzy tree (GFT) design called the LITHium Intelligent Agent (LITHIA). Using multiple objectively defined functional magnetic resonance imaging (fMRI) and proton magnetic resonance spectroscopy (1 H-MRS) inputs, we tested whether LITHIA could accurately predict the lithium response in participants with first-episode bipolar mania. We identified 20 subjects with first-episode bipolar mania who received an adequate trial of lithium over 8weeks and both fMRI and 1 H-MRS scans at baseline pre-treatment. We trained LITHIA using 18 1 H-MRS and 90 fMRI inputs over four training runs to classify treatment response and predict symptom reductions. Each training run contained a randomly selected 80% of the total sample and was followed by a 20% validation run. Over a different randomly selected distribution of the sample, we then compared LITHIA to eight common classification methods. LITHIA demonstrated nearly perfect classification accuracy and was able to predict post-treatment symptom reductions at 8weeks with at least 88% accuracy in training and 80% accuracy in validation. Moreover, LITHIA exceeded the predictive capacity of the eight comparator methods and showed little tendency towards overfitting. The results provided proof-of-concept that a novel GFT is capable of providing control to a multidimensional bioinformatics problem-namely, prediction of the lithium response-in a pilot data set. Future work on this, and similar machine learning systems, could help assign psychiatric treatments more efficiently, thereby optimizing outcomes and limiting unnecessary treatment.

Treatment-Related Cardiovascular Outcomes in Patients with Symptomatic Subclavian Artery Stenosis.

BackgroundSubclavian artery stenosis (SAS) is narrowing of the subclavian artery most commonly caused by atherosclerosis. It serves as a marker for cerebrovascular and myocardial ischemic events.MethodsA retrospective cohort study was conducted to determine the association of treatment via combination therapy (antiplatelet drug plus either by-pass surgery or percutaneous transluminal angioplasty (PTA) with or without stent implantation) versus antiplatelet drug therapy alone on cardiovascular events and all-cause mortality in Marshfield Clinic patients diagnosed with symptomatic SAS from January 1, 1995 to December 31, 2009.ResultsOf the total 2153 cases, 100 patients were identified as eligible to be included in the study. Of these 100 patients that met inclusion criteria, 30 underwent combination therapy while 70 were managed only with drug treatment. A median length of follow-up was 8.45 years. Adverse cardiovascular events occurred in 5/30 (17%) of combination therapy patients compared to 28/70 (40%) of antiplatelet drug therapy only patients (p = 0.0355). Accordingly, all-cause mortality was higher (47%) in the antiplatelet drug therapy only group than the combination therapy group (13%) [hazard ratio = 3.45, p = 0.0218].ConclusionsPreliminary findings in this pilot data set suggest that combination therapy (medications plus either surgical or interventional repair) of subclavian artery stenosis is associated with less cardiovascular adverse events and higher survival rates. However, prospective randomized studies with larger number of patients are needed to validate these findings.

Bladder Cancer Segmentation in CT for Treatment Response Assessment: Application of Deep-Learning Convolution Neural Network—A Pilot Study

Assessing the response of bladder cancer to neoadjuvant chemotherapy is crucial for reducing morbidity and increasing quality of life of patients. Changes in tumor volume during treatment is generally used to predict treatment outcome. We are developing a method for bladder cancer segmentation in CT using a pilot data set of 62 cases. 65 000 regions of interests were extracted from pre-treatment CT images to train a deep-learning convolution neural network (DL-CNN) for tumor boundary detection using leave-one-case-out cross-validation. The results were compared to our previous AI-CALS method. For all lesions in the data set, the longest diameter and its perpendicular were measured by two radiologists, and 3D manual segmentation was obtained from one radiologist. The World Health Organization (WHO) criteria and the Response Evaluation Criteria In Solid Tumors (RECIST) were calculated, and the prediction accuracy of complete response to chemotherapy was estimated by the area under the receiver operating characteristic curve (AUC). The AUCs were 0.73 ± 0.06, 0.70 ± 0.07, and 0.70 ± 0.06, respectively, for the volume change calculated using DL-CNN segmentation, the AI-CALS and the manual contours. The differences did not achieve statistical significance. The AUCs using the WHO criteria were 0.63 ± 0.07 and 0.61 ± 0.06, while the AUCs using RECIST were 0.65 ± 007 and 0.63 ± 0.06 for the two radiologists, respectively. Our results indicate that DL-CNN can produce accurate bladder cancer segmentation for calculation of tumor size change in response to treatment. The volume change performed better than the estimations from the WHO criteria and RECIST for the prediction of complete response.

Farm economic sustainability in the European Union: A pilot study

The measurement of farm economic sustainability has received intermittent academic interest in recent times, while the conceptual discussions are often quite limited. Moreover, this concept receives more attention at periods of diffi culty for the sector. The measurement of farm viability is an important precondition to enrich these discussions. Therefore, it is necessary to develop more comprehensive and detailed measurement techniques to provide more clarity on viability and vulnerability levels in the sector. This paper refocuses attention on this issue, using a pilot dataset collected at farm level across a range of EU Member States which facilitates the assessment of an additional category of viability, namely that of economically sustainable farms, i.e. farms that are economically vulnerable but which are deemed sustainable by the presence of off-farm income. Differences in viability and economic sustainability across the eight surveyed Member States are shown. The analysis is sensitive to the factors included in the measurement of viability as well as to the threshold income used to defi ne viability. Although this is a pilot study, it enhances our understanding of the factors affecting cross-country evaluation of viability and sustainability, and the policy instruments that could improve viability levels.The measurement of farm economic sustainability has received intermittent academic interest in recent times, while the conceptual discussions are often quite limited. Moreover, this concept receives more attention at periods of diffi culty for the sector. The measurement of farm viability is an important precondition to enrich these discussions. Therefore, it is necessary to develop more comprehensive and detailed measurement techniques to provide more clarity on viability and vulnerability levels in the sector. This paper refocuses attention on this issue, using a pilot dataset collected at farm level across a range of EU Member States which facilitates the assessment of an additional category of viability, namely that of economically sustainable farms, i.e. farms that are economically vulnerable but which are deemed sustainable by the presence of off-farm income. Differences in viability and economic sustainability across the eight surveyed Member States are shown. The analysis is sensitive to the factors included in the measurement of viability as well as to the threshold income used to defi ne viability. Although this is a pilot study, it enhances our understanding of the factors affecting cross-country evaluation of viability and sustainability, and the policy instruments that could improve viability levels.

Relation between Established Glioma Risk Variants and DNA Methylation in the Tumor.

Genome-wide association studies and candidate gene studies have identified several genetic variants that increase glioma risk. The majority of these variants are non-coding and the mechanisms behind the increased risk in carriers are not known. In this study, we hypothesize that some of the established glioma risk variants induce aberrant DNA methylation in the developing tumor, either locally (gene-specific) or globally (genome-wide). In a pilot data set including 77 glioma patients, we used Illumina beadchip technology to analyze genetic variants in blood and DNA methylation in matched tumor samples. To validate our findings, we used data from the Cancer Genome Atlas, including 401 glioblastoma patients. Consensus clustering identified the glioma CpG island methylator phenotype (gCIMP) and two additional subgroups with distinct patterns of global DNA methylation. In the pilot dataset, gCIMP was associated with two genetic variants in CDKN2B-AS1, rs1412829 and rs4977756 (9p21.3, p = 8.1 x 10−7 and 4.8 x 10−5, respectively). The association was in the same direction in the TCGA dataset, although statistically significant only when combining individuals with AG and GG genotypes. We also investigated the relation between glioma risk variants and DNA methylation in the promoter region of genes located within 30 kb of each variant. One association in the pilot dataset, between the TERT risk variant rs2736100 and lower methylation of cg23827991 (in TERT; p = 0.001), was confirmed in the TCGA dataset (p = 0.001). In conclusion, we found an association between rs1412829 and rs4977756 (9p21.3, CDKN2B-AS1) and global DNA methylation pattern in glioma, for which a trend was seen also in the TCGA glioblastoma dataset. We also found an association between rs2736100 (in TERT) and levels of methylation at cg23827991 (localized in the same gene, 3.3 kbp downstream of the risk variant), which was validated in the TCGA dataset. Except for this one association, we did not find strong evidence for gene-specific DNA methylation mediated by glioma risk variants.

Bayesian fusion algorithm for improved oscillometric blood pressure estimation

A variety of oscillometric algorithms have been recently proposed in the literature for estimation of blood pressure (BP). However, these algorithms possess specific strengths and weaknesses that should be taken into account before selecting the most appropriate one. In this paper, we propose a fusion method to exploit the advantages of the oscillometric algorithms and circumvent their limitations. The proposed fusion method is based on the computation of the weighted arithmetic mean of the oscillometric algorithms estimates, and the weights are obtained using a Bayesian approach by minimizing the mean square error. The proposed approach is used to fuse four different oscillometric blood pressure estimation algorithms. The performance of the proposed method is evaluated on a pilot dataset of 150 oscillometric recordings from 10 subjects. It is found that the mean error and standard deviation of error are reduced relative to the individual estimation algorithms by up to 7 mmHg and 3 mmHg in estimation of systolic pressure, respectively, and by up to 2 mmHg and 3 mmHg in estimation of diastolic pressure, respectively.

Do graduate entry nursing student’s experience ‘Imposter Phenomenon’?: An issue for debate

The recruitment of Graduates into the nursing profession is seen as advantageous in the academic literature. Conversely educated nurses are often portrayed in the media as “too posh to wash”. We would argue these conflicting discourses have a negative effect on graduate entry nurse education. Graduate nursing students may be particularly susceptible to “Imposter Phenomenon” a concept that describes an “internal experience of intellectual phoniness” exhibited by individuals who appear successful to others, but internally feel incompetent. We would like to encourage debate through the presentation of a small set of pilot data that established that 70% of the participants had frequent to intense experiences of Imposter Phenomenon. Students experienced feelings of failure despite consistent high achievement. Our findings and the prevalent negative rhetoric surrounding highly educated student nurses raise concerns regarding the impact of the anti-intellectualism on the Graduate entry student’s perception of self. Others may argue that this could simply be a ’natural’ or expected level of anxiety in a time of transition that has no lasting impact. We debate this issue in relation to the existing literature to encourage critical dialogue.

Event Recognition for Contactless Activity Monitoring Using Phase-Modulated Continuous Wave Radar.

The use of remote sensing technologies such as radar is gaining popularity as a technique for contactless detection of physiological signals and analysis of human motion. This paper presents a methodology for classifying different events in a collection of phase modulated continuous wave radar returns. The primary application of interest is to monitor inmates where the presence of human vital signs amidst different, interferences needs to be identified. A comprehensive set of features is derived through time and frequency domain analyses of the radar returns. The Bhattacharyya distance is used to preselect the features with highest class separability as the possible candidate features for use in the classification process. The uncorrelated linear discriminant analysis is performed to decorrelate, denoise, and reduce the dimension of the candidate feature set. Linear and quadratic Bayesian classifiers are designed to distinguish breathing, different human motions, and nonhuman motions. The performance of these classifiers is evaluated on a pilot dataset of radar returns that contained different events including breathing, stopped breathing, simple human motions, and movement of fan and water. Our proposed pattern classification system achieved accuracies of up to 93% in stationary subject detection, 90% in stop-breathing detection, and 86% in interference detection. Our proposed radar pattern recognition system was able to accurately distinguish the predefined events amidst interferences. Besides inmate monitoring and suicide attempt detection, this paper can be extended to other radar applications such as home-based monitoring of elderly people, apnea detection, and home occupancy detection.

Q-space trajectory imaging for clinical schizophrenia studies

Event Abstract Back to Event Q-space trajectory imaging for clinical schizophrenia studies Ofer Pasternak1* 1 Harvard Medical School, Department of Radiology, Bringham and Women's Hospital, United States As q-space trajectory imaging (QTI), and other advanced diffusion encoding schemes, become available, an important question is the feasibility and utility of these schemes for clinical studies. Schizophrenia is one of the main clinical fields in which diffusion imaging provided new ways to identify and understand the disorder. For example, paradigms involving demyelination, neuroinflammation and disconnectivity became substantial following evidence from diffusion imaging studies. In my talk I will review some of the key findings of diffusion imaging in schizophrenia, and will outline the need for more advanced imaging modalities, such as QTI. QTI provides sensitivity to new features, such as eccentricity, however, and perhaps more important for schizophrenia studies, QTI can disentangle several contributions that affect current diffusion measures. These more specific measures provide a stronger link between the identification of group-wise imaging abnormalities, and the understanding of the underlying pathologies, bringing us closer to understanding the etiology and pathophysiology of the disorder. We have implemented QTI on clinical scanners and collected a small pilot dataset from subjects diagnosed with schizophrenia, along with controls. I will present findings from this pilot study, where QTI measures are able to explain changes that are identified in conventional diffusion MRI. I will also present our current sequences that are being used to collect QTI on larger clinical populations. The sequences require less than 15 minutes, similar to advanced DTI measures, demonstrating that QTI sequences can be acquired in clinical feasible times. Keywords: Diffusion, MRI, dMRI, multidimensional, diffusion encoding Conference: New dimensions in diffusion encoding, Fjälkinge, Sweden, 11 Jan - 14 Jan, 2016. Presentation Type: Oral presentation Topic: New Dimensions in Diffusion Encoding Citation: Pasternak O (2016). Q-space trajectory imaging for clinical schizophrenia studies. Front. Phys. Conference Abstract: New dimensions in diffusion encoding. doi: 10.3389/conf.FPHY.2016.01.00002 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 07 Jul 2016; Published Online: 07 Jul 2016. * Correspondence: Prof. Ofer Pasternak, Harvard Medical School, Department of Radiology, Bringham and Women's Hospital, Boston, Massachusetts, MA 02215, United States, ofer@bwh.harvard.edu Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Ofer Pasternak Google Ofer Pasternak Google Scholar Ofer Pasternak PubMed Ofer Pasternak Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Fast and efficient QTL mapper for thousands of molecular phenotypes.

Motivation: In order to discover quantitative trait loci, multi-dimensional genomic datasets combining DNA-seq and ChiP-/RNA-seq require methods that rapidly correlate tens of thousands of molecular phenotypes with millions of genetic variants while appropriately controlling for multiple testing.Results: We have developed FastQTL, a method that implements a popular cis-QTL mapping strategy in a user- and cluster-friendly tool. FastQTL also proposes an efficient permutation procedure to control for multiple testing. The outcome of permutations is modeled using beta distributions trained from a few permutations and from which adjusted P-values can be estimated at any level of significance with little computational cost. The Geuvadis & GTEx pilot datasets can be now easily analyzed an order of magnitude faster than previous approaches.Availability and implementation: Source code, binaries and comprehensive documentation of FastQTL are freely available to download at http://fastqtl.sourceforge.net/Contact: emmanouil.dermitzakis@unige.ch or olivier.delaneau@unige.chSupplementary information: Supplementary data are available at Bioinformatics online.

Quantitative assessment of target delineation variability for thymic cancers: Agreement evaluation of a prospective segmentation challenge.

We sought to quantitatively determine the inter-observer variability of expert radiotherapy target-volume delineation for thymic cancers, as part of a larger effort to develop an expert-consensus contouring atlas. A pilot dataset was created consisting of a standardized case presentation with pre- and post-operative DICOM CT image sets from a single patient with Masaoka-Koga Stage III thymoma. Expert thoracic radiation oncologists delineated tumor targets on the pre- and post-operative scans as they would for a definitive and adjuvant case, respectively. Respondents completed a survey including recommended dose prescription and target volume margins for definitive and post-operative scenarios. Inter-observer variability was analyzed quantitatively with Warfield's simultaneous truth, performance level estimation (STAPLE) algorithm and Dice similarity coefficient (DSC). Seven users completed contouring for definitive and adjuvant cases; of these, 5 completed online surveys. Segmentation performance was assessed, with high mean±SD STAPLE-estimated segmentation sensitivity for definitive case GTV and CTV at 0.77 and 0.80, respectively, and post-operative CTV sensitivity of 0.55; all volumes had specificity of ≥0.99. Inter-observer agreement was markedly higher for the definitive target volumes, with mean±SD DSC of 0.88±0.03 and 0.89±0.04 for GTV and CTV respectively, compared to post-op CTV DSC of 0.69±0.06 (Kruskal-Wallis p<0.01. Expert agreement for definitive case volumes was exceptionally high, though significantly lower agreement was noted post-operatively. Technique and dose prescription between experts was substantively consistent, and these preliminary results will be utilized to create an expert-consensus contouring atlas to aid the non-expert radiation oncologist in the planning of these challenging, rare tumors.

Sample size determination for training cancer classifiers from microarray and RNA-seq data

The objective of many high-dimensional microarray and RNA-seq studies is to develop a classifier of cancer patients based on characteristics of their disease. The germinal center B-cell (GCB) classifier study in lymphoma and the National Cancer Institute's Director's Challenge lung (DC-lung) study are two examples. In recent years, such classifiers are often developed using regularized regression, such as the lasso. A critical question is whether a better classifier can be developed from a larger training set size and, if so, how large the training set should be. This paper examines these two questions using an existing sample size method and a novel sample size method developed here specifically for lasso logistic regression. Both methods are based on pilot data. We reexamine the lymphoma and lung cancer data sets to evaluate the sample sizes, and use resampling to assess the estimation methods. We also study application to an RNA-seq data set. We find that it is feasible to estimate sample size for regularized logistic regression if an adequate pilot data set exists. The GCB and the DC-lung data sets appear adequate, under specific assumptions. Existing human RNA-seq data sets are by and large inadequate, and cannot be used as pilot data. Pilot RNA-seq data can be simulated, and the methods in this paper can be used for sample size estimation. A MATLAB program is made available.

Accurate and Fast Multiple-Testing Correction in eQTL Studies

In studies of expression quantitative trait loci (eQTLs), it is of increasing interest to identify eGenes, the genes whose expression levels are associated with variation at a particular genetic variant. Detecting eGenes is important for follow-up analyses and prioritization because genes are the main entities in biological processes. To detect eGenes, one typically focuses on the genetic variant with the minimum p value among all variants in cis with a gene and corrects for multiple testing to obtain a gene-level p value. For performing multiple-testing correction, a permutation test is widely used. Because of growing sample sizes of eQTL studies, however, the permutation test has become a computational bottleneck in eQTL studies. In this paper, we propose an efficient approach for correcting for multiple testing and assess eGene p values by utilizing a multivariate normal distribution. Our approach properly takes into account the linkage-disequilibrium structure among variants, and its time complexity is independent of sample size. By applying our small-sample correction techniques, our method achieves high accuracy in both small and large studies. We have shown that our method consistently produces extremely accurate p values (accuracy > 98%) for three human eQTL datasets with different sample sizes and SNP densities: the Genotype-Tissue Expression pilot dataset, the multi-region brain dataset, and the HapMap 3 dataset.

Practical approach to determine sample size for building logistic prediction models using high-throughput data

An empirical method of sample size determination for building prediction models was proposed recently. Permutation method which is used in this procedure is a commonly used method to address the problem of overfitting during cross-validation while evaluating the performance of prediction models constructed from microarray data. But major drawback of such methods which include bootstrapping and full permutations is prohibitively high cost of computation required for calculating the sample size.In this paper, we propose that a single representative null distribution can be used instead of a full permutation by using both simulated and real data sets. During simulation, we have used a dataset with zero effect size and confirmed that the empirical type I error approaches to 0.05. Hence this method can be confidently applied to reduce overfitting problem during cross-validation. We have observed that pilot data set generated by random sampling from real data could be successfully used for sample size determination. We present our results using an experiment that was repeated for 300 times while producing results comparable to that of full permutation method. Since we eliminate full permutation, sample size estimation time is not a function of pilot data size. In our experiment we have observed that this process takes around 30min.With the increasing number of clinical studies, developing efficient sample size determination methods for building prediction models is critical. But empirical methods using bootstrap and permutation usually involve high computing costs. In this study, we propose a method that can reduce required computing time drastically by using representative null distribution of permutations. We use data from pilot experiments to apply this method for designing clinical studies efficiently for high throughput data.

Generalizability and decision studies to inform observational and experimental research in classroom settings.

Attaining reliable estimates of observational measures can be challenging in school and classroom settings, as behavior can be influenced by multiple contextual factors. Generalizability (G) studies can enable researchers to estimate the reliability of observational data, and decision (D) studies can inform how many observation sessions are necessary to achieve a criterion level of reliability. We conducted G and D studies using observational data from a randomized control trial focusing on social and academic participation of students with severe disabilities in inclusive secondary classrooms. Results highlight the importance of anchoring observational decisions to reliability estimates from existing or pilot data sets. We outline steps for conducting G and D studies and address options when reliability estimates are lower than desired.