Primary PCI: false positives versus false negatives

Primary percutaneous coronary intervention (PCI) performed within 12 hours after onset of symptoms in acute ST-elevation myocardial infarction (STEMI) is the currently recommended therapy in the guidelines of the European Society of Cardiology.1 The basis of these recommendations is an array of clinical trials comparing PCI with thrombolysis on short-term as well as long-term follow-up.2 Initially and intuitively larger STEMIs in younger patients were eligible for this therapy, but increasingly also smaller infarcts and older patients have proven to benefit from primary PCI.3 Typically, in centres providing the service of primary PCI in the Netherlands, about a third of the total number of PCIs is now for STEMI.     

Minimizing false positives in kinase virtual screens

In spite of recent improvements in docking and scoring methods, high false-positive rates remain a common issue in structure-based virtual screening. In this study, the distinctive features of false positives in kinase virtual screens were investigated. A series of retrospective virtual screens on kinase targets was performed on specifically designed test sets, each combining true ligands and experimentally confirmed inactive compounds. A systematic analysis of the docking poses generated for the top-ranking compounds highlighted key aspects differentiating true hits from false positives. The most recurring feature in the poses of false positives was the absence of certain key interactions known to be required for kinase binding. A systematic analysis of 444 crystal structures of ligand-bound kinases showed that at least two hydrogen bonds between the ligand and the backbone protein atoms in the kinase hinge region are present in 90% of the complexes, with very little variability across targets. Closer inspection showed that when the two hydrogen bonds are present, one of three preferred hinge-binding motifs is involved in 96.5% of the cases. Less than 10% of the false positives satisfied these two criteria in the minimized docking poses generated by our standard protocol. Ligand conformational artifacts were also shown to contribute to the occurrence of false positives in a number of cases. Application of this knowledge in the form of docking constraints and post-processing filters provided consistent improvements in virtual screening performance on all systems. The false-positive rates were significantly reduced and the enrichment factors increased by an average of twofold. On the basis of these results, a generalized two-step protocol for virtual screening on kinase targets is suggested.     

Accurate decisions in an uncertain world: collective cognition increases true positives while decreasing false positives

In a wide range of contexts, including predator avoidance, medical decision-making and security screening, decision accuracy is fundamentally constrained by the trade-off between true and false positives. Increased true positives are possible only at the cost of increased false positives; conversely, decreased false positives are associated with decreased true positives. We use an integrated theoretical and experimental approach to show that a group of decision-makers can overcome this basic limitation. Using a mathematical model, we show that a simple quorum decision rule enables individuals in groups to simultaneously increase true positives and decrease false positives. The results from a predator-detection experiment that we performed with humans are in line with these predictions: (i) after observing the choices of the other group members, individuals both increase true positives and decrease false positives, (ii) this effect gets stronger as group size increases, (iii) individuals use a quorum threshold set between the average true- and false-positive rates of the other group members, and (iv) individuals adjust their quorum adaptively to the performance of the group. Our results have broad implications for our understanding of the ecology and evolution of group-living animals and lend themselves for applications in the human domain such as the design of improved screening methods in medical, forensic, security and business applications.     

Approaches to detecting false positives in yeast two-hybrid systems

While many novel associations predicted by two-hybrid library screens reflect actual biological associations of two proteins in vivo, at times the functional co-relevance of two proteins scored as interacting in the two-hybrid system is unlikely. The reason for this positive score remains obscure, which leads to designating such clones as false positives. After investigating the effect of over-expressing a series of putative false positives in yeast, we determined that expression of some of these clones induces an array of biological effects in yeast, including altered growth rate and cell permeability, that bias perceived activity of LacZ reporters. Based on these observations, we identify four simple strategies that can assist in determining whether a protein is likely to have been selected in a two-hybrid screen because of indirect metabolic effects.     

"Patchy-tachy" leads to false positives for recombination

Indirect tests have detected recombination in mitochondrial DNA (mtDNA) from many animal lineages, including mammals. However, it is possible that features of the molecular evolutionary process without recombination could be incorrectly inferred by indirect tests as being due to recombination. We have identified one such example, which we call "patchy-tachy" (PT), where different partitions of sequences evolve at different rates, that leads to an excess of false positives for recombination inferred by indirect tests. To explore this phenomena, we characterized the false positive rates of six widely used indirect tests for recombination using simulations of general models for mtDNA evolution with PT but without recombination. All tests produced 30-99% false positives for recombination, although the conditions that produced the maximal level of false positives differed between the tests. To evaluate the degree to which conditions that exacerbate false positives are found in published sequence data, we turned to 20 animal mtDNA data sets in which recombination is suggested by indirect tests. Using a model where different regions of the sequences were free to evolve at different rates in different lineages, we demonstrated that PT is prevalent in many data sets in which recombination was previously inferred using indirect tests. Taken together, our results argue that PT without recombination is a viable alternative explanation for detection of widespread recombination in animal mtDNA using indirect tests.     

A note on controlling the number of false positives

Summary :   Lehmann and Romano (2005, Annals of Statistics 33, 1138–1154) discuss a Bonferroni-type procedure that bounds the probability that the number of false positives is larger than a specified number. We note that this procedure will have poor power as compared to a multivariate permutation test type procedure when the experimental design accommodates a permutation test. An example is given involving gene expression microarray data of breast cancer tumors.     

Controlling the proportion of false positives in multiple dependent tests

Genome scan mapping experiments involve multiple tests of significance. Thus, controlling the error rate in such experiments is important. Simple extension of classical concepts results in attempts to control the genomewise error rate (GWER), i.e., the probability of even a single false positive among all tests. This results in very stringent comparisonwise error rates (CWER) and, consequently, low experimental power. We here present an approach based on controlling the proportion of false positives (PFP) among all positive test results. The CWER needed to attain a desired PFP level does not depend on the correlation among the tests or on the number of tests as in other approaches. To estimate the PFP it is necessary to estimate the proportion of true null hypotheses. Here we show how this can be estimated directly from experimental results. The PFP approach is similar to the false discovery rate (FDR) and positive false discovery rate (pFDR) approaches. For a fixed CWER, we have estimated PFP, FDR, pFDR, and GWER through simulation under a variety of models to illustrate practical and philosophical similarities and differences among the methods.     

Screening for ribosomal-based false positives following prokaryotic mRNA differential display

Differential display (DD) and the closely related RNA arbitrarily primed PCR (RAP-PCR) have become the molecular tools of choice for identifying and isolating differentially expressed genes in both eukaryotic and prokaryotic systems. However, one of the current drawbacks of both techniques is the high number of false positives generated. In prokaryotic applications, the many false positive typically generated by DD are subsequently identified as rRNAs because of their greater abundance compared to mRNAs. To circumvent this problem, full-length 16S and 23S rDNA probes, derived from Pseudomonas putida G7 and Pseudomonas aeruginosa FRD1, respectively, were used as a prescreening approach to discriminate between those bands, which appear to be differentially expressed mRNAs, but in fact are rRNAs, following prokaryotic mRNA DD.     

探讨植物单染色体测序中的假阳性问题

单细胞测序技术目前虽已广泛应用于人类、动物、微生物等物种的研究,但由于细胞壁的存在,使得该技术在植物上的应用举步维艰。如果能先分离出植物单条染色体,然后再应用单细胞测序技术进行扩增和测序,则可以避免细胞壁的干扰,从而具有重要的应用价值。虽然科研人员一直尝试针对单条植物染色体进行测序,但目前尚未见有成功的报道,也未见有文章对失败的原因进行讨论。该研究以六倍体中国春小麦为材料,针对使用单细胞测序技术进行单染色体扩增过程中出现的假阳性问题进行了探讨,分析了单染色体扩增失败的主要原因,并通过高通量测序技术研究了外源污染的可能来源。结果表明:在对实验器具、耗材、环境污染严格防控的基础上,人体接触可能是造成污染的主要来源;同时,推测单染色体之所以扩增失败可能是因为染色体自身超螺旋状态造成引物难以结合和复制。该研究还针对存在的问题提出了可行性建议,为将来成功进行单染色体测序提供了有益的借鉴。     

Predicting residue contacts using pragmatic correlated mutations method: reducing the false positives

Background  

Predicting residues' contacts using primary amino acid sequence alone is an important task that can guide 3D structure modeling and can verify the quality of the predicted 3D structures. The correlated mutations (CM) method serves as the most promising approach and it has been used to predict amino acids pairs that are distant in the primary sequence but form contacts in the native 3D structure of homologous proteins.     

Estimating an oncogenetic tree when false negatives and positives are present

Human solid tumors are believed to be caused by a sequence of genetic abnormalities arising in the tumor cells. The understanding of these sequences is extremely important for improving cancer treatment. Models for the occurrence of the abnormalities include linear structure and a recently proposed tree-based structure. In this paper we extend the pure oncogenetic tree model by introducing false positive and false negative observations. We state conditions sufficient for the reconstruction of the generating tree. As an example we analyze a comparative genomic hybridization data set and show that addition of the error model significantly improves the ability of the model to describe the data.     

Uncovering false positives on a virtual screening search for cruzain inhibitors

Some unexpected promiscuous inhibitors were observed in a virtual screening protocol applied to select cruzain inhibitors from the ZINC database. Physical-chemical and pharmacophore model filters were used to reduce the database size. The selected compounds were docked into the cruzain active site. Six hit compounds were tested as inhibitors. Although the compounds were designed to be nucleophilically attacked by the catalytic cysteine of cruzain, three of them showed typical promiscuous behavior, revealing that false positives are a prevalent concern in VS programs.     

Minimizing the risk of reporting false positives in large-scale RNAi screens

Large-scale RNA interference (RNAi)-based analyses, very much as other 'omic' approaches, have inherent rates of false positives and negatives. The variability in the standards of care applied to validate results from these studies, if left unchecked, could eventually begin to undermine the credibility of RNAi as a powerful functional approach. This Commentary is an invitation to an open discussion started among various users of RNAi to set forth accepted standards that would insure the quality and accuracy of information in the large datasets coming out of genome-scale screens.     

Approaches to reduce false positives and false negatives in the analysis of microarray data: applications in type 1 diabetes research

Background

As studies of molecular biology system attempt to achieve a comprehensive understanding of a particular system, Type 1 errors may be a significant problem. However, few investigators are inclined to accept the increase in Type 2 errors (false positives) that may result when less stringent statistical cut-off values are used. To address this dilemma, we developed an analysis strategy that used a stringent statistical analysis to create a list of differentially expressed genes that served as "bait" to "fish out" other genes with similar patterns of expression.

Results

Comparing two strains of mice (NOD and C57Bl/6), we identified 93 genes with statistically significant differences in their patterns of expression. Hierarchical clustering identified an additional 39 genes with similar patterns of expression differences between the two strains. Pathway analysis was then employed: 1) identify the central genes and define biological processes that may be regulated by the genes identified, and 2) identify genes on the lists that could not be connected to each other in pathways (potential false positives). For networks created by both gene lists, the most connected (central) genes were interferon gamma (IFN-γ) and tumor necrosis factor alpha (TNF-α). These two cytokines are relevant to the biological differences between the two strains of mice. Furthermore, the network created by the list of 39 genes also suggested other biological differences between the strains.

Conclusion

Taken together, these data demonstrate how stringent statistical analysis, combined with hierarchical clustering and pathway analysis may offer deeper insight into the biological processes reflected from a set of expression array data. This approach allows us to 'recapture" false negative genes that otherwise would have been missed by the statistical analysis.

     

Positional characterisation of false positives from computational prediction of human splice sites

The performance of computational tools that can predict human splice sites are reviewed using a test set of EST-confirmed splice sites. The programs (namely HMMgene, NetGene2, HSPL, NNSPLICE, SpliceView and GeneID-3) differ from one another in the degree of discriminatory information used for prediction. The results indicate that, as expected, HMMgene and NetGene2 (which use global as well as local coding information and splice signals) followed by HSPL (which uses local coding information and splice signals) performed better than the other three programs (which use only splice signals). For the former three programs, one in every three false positive splice sites was predicted in the vicinity of true splice sites while only one in every 12 was expected to occur in such a region by chance. The persistence of this observation for programs (namely FEXH, GRAIL2, MZEF, GeneID-3, HMMgene and GENSCAN) that can predict all the potential exons (including optimal and sub-optimal) was assessed. In a high proportion (>50%) of the partially correct predicted exons, the incorrect exon ends were located in the vicinity of the real splice sites. Analysis of the distribution of proximal false positives indicated that the splice signals used by the algorithms are not strong enough to discriminate particularly those false predictions that occur within ± 25 nt around the real sites. It is therefore suggested that specialised statistics that can discriminate real splice sites from proximal false positives be incorporated in gene prediction programs.