Low False-positive Error Rates Research Articles

High-order asymptotic approximations for improved inference under exceptionally low false positive error rates

High-order asymptotic approximations for improved inference under exceptionally low false positive error rates

Accurate Sequencing and Haplotyping from 10 Cells Using Long Fragment Read (LFR) Technology.

In this chapter, we describe how Long Fragment Read (LFR) technology can be applied to samples consisting of very few cells (5-20) to enable complete genome sequencing and haplotyping with a very low false positive error rate. LFR is a method for processing DNA or cells prior to sequencing on any second-generation DNA sequencing platform (e.g., MGI's DNBSEQ, Illumina sequencers, etc.). First, the LFR process incorporates a low-bias whole genome amplification step allowing accurate sequencing from very low DNA inputs (as low as 32 picograms, the mass contained within 5 diploid human cells). In addition, LFR enables the haplotyping of nearly all genomic variations with N50 contig lengths up to ~1Mb. Furthermore, if data from this method are analyzed with parental genotype data, it is possible to generate phased variants in uninterrupted contigs spanning entire chromosomes. Importantly, the barcoding process utilized in this method allows for the detection and correction of most amplification, sequencing, and mapping errors, yielding false positive error rates as low as 10-9. Finally, the cost of this method is modest and enables extremely high-quality whole genome sequence and haplotype data from as few as 5 cells. We know of few other methods that can achieve this.

Effects of the hierarchy in hierarchical, multi-label classification

The consequences of the choice of a hierarchy in hierarchical multi-label classification (HMLC) have previously not been considered in any detail. Three hierarchy-related factors in HMLC are examined here: hierarchy structure, class location in the hierarchy, and class distribution in feature space. Four general model groups are found to exist in HMLC modeling: "non-informative”, "semi-informative”, "comparable”, and "hierarchical”. Studies of synthetic and real data show that the choice of hierarchy used in the modeling is important in setting the relative error rates of false positives and false negatives. The choice of hierarchy depends upon the relative consequences of false positive and false negative errors produced by the resulting model. A low false negative error rate results from use of a "comparable” HMLC model with a hierarchy designed to maximize intergroup separation. A low false positive error rate results from use of a "hierarchical” HMLC model using any hierarchy. Modest differences in accuracy and F1 measure occur between the best-performing HMLC models built on several external and internal hierarchies for a complex, multiclass dataset. HMLC methods using "comparable” and "hierarchical” HMLC models and phylogenetic hierarchies examined slightly outperform a conventional classification using the same classifier on the Dalbergia data. BriefStudies of synthetic and real data show that the structure of a multi-label hierarchy in hierarchical, multi-label classification (HMLC) is important in setting the relative error rate of false positives and false negatives. A low false negative error rate results from use of a "comparable” HMLC model with a hierarchy designed to maximize inter-class separation. A low false positive error rate results from use of a "hierarchical” HMLC model with any hierarchy.

Improving the Continuum of Care by Bridging the Gap between Prehospital and Hospital Discharge Data through Stepwise Deterministic Linkage

Objective: To describe the process, benefits, and challenges of linking Arizona’s prehospital registry to hospital discharge data. Methods: Data were queried from the Arizona Prehospital Information and Emergency Medical Services Registry System (AZ-PIERS) and the Arizona Hospital Discharge Database (HDD) for the calendar year 2015. To maximize the number of matched records, the databases were deterministically linked in 17 steps using different combinations/variations of patient personal identifiers. Random samples of at least 1% of matched pairs from each of 16 linkage steps (excluding Step 1) were manually reviewed to assess the rate of false positive matches. Results: A total of 626,413 records were reported to AZ-PIERS in 2015. Of those, 503,715 qualified for linkage. These records were matched against 3,125,689 discharge records reported to the HDD in 2015. The first step, which involved exact matching on first name, last name, date of birth, gender, and date of incident/date of admission, yielded a linkage of 64.6% (n = 325,156). The 16 successive steps yielded a further linkage of 26.6% (n = 134,006) for a total linkage of 91.2% (n = 459,162). The manual review indicated an overall false positive match rate for the 16 reviewed steps of 6.96% (n = 99). The 2 steps with the highest false positive match rates were Step 16 (43.02%, n = 77) and Step 17 (31.43%, n = 11). Conclusion: It is feasible to link prehospital and hospital data using stepwise deterministic linkage; this method returns a high linkage rate with a low false positive error rate. Data linkage is vital to identifying and bridging gaps in the continuum of care and is a useful tool in statewide and agency-specific research and quality improvement.

Abstract LB-310: Single cell genome sequencing reveals clonal stability and diversity in breast cancer

Abstract Human breast cancers often display intratumor genomic heterogeneity. This clonal diversity confounds the clinical diagnosis and basic research of cancer, because single samples may not represent that tumor as a whole. Sequencing breast tumor cohorts en masse has identified many prevalent mutations, but has limited ability for resolving subclonal diversity. Here, we developed a whole-genome and exome single-cell sequencing approach (Nuc-Seq) using G2/M cells. To validate our method, we applied Nuc-Seq to sequence the whole genomes of two single cells from a genetically monoclonal breast cancer cell line (SK-BR-3) at high coverage depth (61X ± 5 sem, n=2) and breadth (83.70% ± 3.40 sem, n=2) to detect somatic mutations. Our analysis suggests that Nuc-Seq generates low allelic dropout rates (9.73% ± 2.19%) and low false positive error rates for point mutations (FPR = 1.24e-6). We then applied this method to sequence single normal and tumor cells from an estrogen-receptor positive breast cancer and a triple-negative ductal carcinoma at base-pair resolution. In parallel, we performed single cell copy number profiling. In both tumors, we observed a large number of rare variants that were not detected by sequencing the bulk tumor en masse. In contrast, we find that single cell copy number profiles are highly similar. Our data suggest that aneuploid rearrangements occurred early in tumor evolution and remained highly stable as the tumor mass expanded. In contrast we find that point mutations evolved gradually, generating extensive clonal diversity. Many of the diverse mutations were shown to occur at low frequencies (0.03 -10%) in the tumor mass by targeted duplex sequencing. Mathematical modeling suggests that the triple-negative tumor cells have an increased mutation rate (13.3X), while the ER+ tumor cells do not. These findings have important implications for the diagnosis, therapeutic treatment and evolution of chemoresistance in breast cancer. Citation Format: Yong Wang, Nicholas Navin, Jill Waters, Marco Leung, Anna Unruh, Xiuqing Shi, Whijae Roh, Ken Chen, Paul Scheet, Selina Vattathil, Han Liang, Asha Multani, Hong Zhang, Funda Meric-Bernstam, Franziska Michor, Rui Zhao. Single cell genome sequencing reveals clonal stability and diversity in breast cancer. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr LB-310. doi:10.1158/1538-7445.AM2014-LB-310

Sensitivity and Specificity of MMPI-2 Validity Scales and Indicators to Malingered Neurocognitive Dysfunction in Traumatic Brain Injury

The present study used a known-groups design to determine the classification accuracy of 10 MMPI-2 validity scales and indicators in the detection of cognitive malingering in traumatic brain injury. Participants were 259 traumatic brain injury and 133 general clinical patients seen for neuropsychological evaluation. The TBI patients were subdivided into groups based on a comprehensive examination of effort following Slick, Sherman, and Iverson's (1999) criteria. More extreme scores demonstrated excellent specificity; often impressive sensitivity was seen even while maintaining a low false positive error rate. Specificity was good even in stroke, memory disorder, and psychiatric patients without incentive. The results of this study are presented in frequency tables that can be easily referenced in clinical practice.

GENERALIZED SITE OCCUPANCY MODELS ALLOWING FOR FALSE POSITIVE AND FALSE NEGATIVE ERRORS

Site occupancy models have been developed that allow for imperfect species detection or "false negative" observations. Such models have become widely adopted in surveys of many taxa. The most fundamental assumption underlying these models is that "false positive" errors are not possible. That is, one cannot detect a species where it does not occur. However, such errors are possible in many sampling situations for a number of reasons, and even low false positive error rates can induce extreme bias in estimates of site occupancy when they are not accounted for. In this paper, we develop a model for site occupancy that allows for both false negative and false positive error rates. This model can be represented as a two-component finite mixture model and can be easily fitted using freely available software. We provide an analysis of avian survey data using the proposed model and present results of a brief simulation study evaluating the performance of the maximum-likelihood estimator and the naive estimator in the presence of false positive errors.

An assessment of accuracy, error, and conflict with support values from genome-scale phylogenetic data.

Despite the importance of molecular phylogenetics, few of its assumptions have been tested with real data. It is commonly assumed that nonparametric bootstrap values are an underestimate of the actual support, Bayesian posterior probabilities are an overestimate of the actual support, and among-gene phylogenetic conflict is low. We directly tested these assumptions by using a well-supported yeast reference tree. We found that bootstrap values were not significantly different from accuracy. Bayesian support values were, however, significant overestimates of accuracy but still had low false-positive error rates (0% to 2.8%) at the highest values (>99%). Although we found evidence for a branch-length bias contributing to conflict, there was little evidence for widespread, strongly supported among-gene conflict from bootstraps. The results demonstrate that caution is warranted concerning conclusions of conflict based on the assumption of underestimation for support values in real data.