scholarly journals An Effective Method for Controlling False Discovery and False Nondiscovery Rates in Genome-Scale RNAi Screens

2010 ◽  
Vol 15 (9) ◽  
pp. 1116-1122 ◽  
Author(s):  
Xiaohua Douglas Zhang
Keyword(s):  
False Negative ◽  
False Negative Rate ◽  
False Positives ◽  
P Value ◽  
Statistical Control ◽  
Small Interfering Rnas ◽  
False Negatives ◽  
Activation Effect ◽  
False Discovery ◽  
Genome Scale

In most genome-scale RNA interference (RNAi) screens, the ultimate goal is to select siRNAs with a large inhibition or activation effect. The selection of hits typically requires statistical control of 2 errors: false positives and false negatives. Traditional methods of controlling false positives and false negatives do not take into account the important feature in RNAi screens: many small-interfering RNAs (siRNAs) may have very small but real nonzero average effects on the measured response and thus cannot allow us to effectively control false positives and false negatives. To address for deficiencies in the application of traditional approaches in RNAi screening, the author proposes a new method for controlling false positives and false negatives in RNAi high-throughput screens. The false negatives are statistically controlled through a false-negative rate (FNR) or false nondiscovery rate (FNDR). FNR is the proportion of false negatives among all siRNAs examined, whereas FNDR is the proportion of false negatives among declared nonhits. The author also proposes new concepts, q*-value and p*-value, to control FNR and FNDR, respectively. The proposed method should have broad utility for hit selection in which one needs to control both false discovery and false nondiscovery rates in genome-scale RNAi screens in a robust manner.

Correct Classification, False Positives, and False Negatives in Predicting Completion of the Ph.d. from GRE Scores

1996 ◽  
Vol 79 (3) ◽  
pp. 939-945 ◽  
Author(s):  
Cooper B. Holmes ◽  
Megan J. Beishline
Keyword(s):  
Success Rate ◽  
False Negative ◽  
False Negative Rate ◽  
Doctoral Program ◽  
False Positives ◽  
Correct Classification ◽  
False Negatives ◽  
Program Success ◽  
High False Negative Rate ◽  
Psychology Doctoral

Combined Verbal and Quantitative GRE scores were obtained from the records of 24 former students of a master's degree program (from a total of 128 students) who had successfully completed a doctorate in psychology or who had withdrawn from a psychology doctoral program. Success rate by classification with the GRE was calculated using both a cut-off of 1000 and a cut-off of 1100. The results indicated a high false negative rate, that is, students whose GRE scores would not predict success but who obtained a Ph.D.

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

2010 ◽  
Vol 15 (9) ◽  
pp. 1123-1131 ◽  
Author(s):  
Xiaohua Douglas Zhang ◽  
Raul Lacson ◽  
Ruojing Yang ◽  
Shane D. Marine ◽  
Alex McCampbell ◽  
...  
Keyword(s):  
Rna Interference ◽  
False Discovery Rate ◽  
Statistical Methods ◽  
Unmet Need ◽  
False Positives ◽  
Major Goal ◽  
False Negatives ◽  
False Discovery ◽  
Standardized Mean Difference ◽  
Genome Scale

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.

scholarly journals Error Rates and Powers in Genome-Scale RNAi Screens

2009 ◽  
Vol 14 (3) ◽  
pp. 230-238 ◽  
Author(s):  
Xiaohua Douglas Zhang ◽  
Shane D. Marine ◽  
Marc Ferrer
Keyword(s):  
High Throughput Screening ◽  
Control Method ◽  
False Positive Rate ◽  
False Negative ◽  
False Negative Rate ◽  
Error Rates ◽  
Type I ◽  
Small Interfering Rnas ◽  
Screening Experiments ◽  
Genome Scale

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. (Journal of Biomolecular Screening 2009:230-238)

Clinically Meaningful Change

Methodology ◽  
2019 ◽  
Vol 15 (3) ◽  
pp. 97-105
Author(s):  
Rodrigo Ferrer ◽  
Antonio Pardo
Keyword(s):  
Effect Size ◽  
False Negative ◽  
False Negative Rate ◽  
Point Of View ◽  
Skewed Distribution ◽  
Effect Sizes ◽  
False Negatives ◽  
Large Size ◽  
Before And After ◽  
Post Test

Abstract. In a recent paper, Ferrer and Pardo (2014) tested several distribution-based methods designed to assess when test scores obtained before and after an intervention reflect a statistically reliable change. However, we still do not know how these methods perform from the point of view of false negatives. For this purpose, we have simulated change scenarios (different effect sizes in a pre-post-test design) with distributions of different shapes and with different sample sizes. For each simulated scenario, we generated 1,000 samples. In each sample, we recorded the false-negative rate of the five distribution-based methods with the best performance from the point of view of the false positives. Our results have revealed unacceptable rates of false negatives even with effects of very large size, starting from 31.8% in an optimistic scenario (effect size of 2.0 and a normal distribution) to 99.9% in the worst scenario (effect size of 0.2 and a highly skewed distribution). Therefore, our results suggest that the widely used distribution-based methods must be applied with caution in a clinical context, because they need huge effect sizes to detect a true change. However, we made some considerations regarding the effect size and the cut-off points commonly used which allow us to be more precise in our estimates.

Evaluation of Positive T- and B-Cell Gene Rearrangement Studies Among Patients Without a Definitive Diagnosis by Other Assays

2019 ◽  
Vol 152 (Supplement_1) ◽  
pp. S35-S36
Author(s):  
Hadrian Mendoza ◽  
Christopher Tormey ◽  
Alexa Siddon
Keyword(s):  
T Cell ◽  
False Positive ◽  
Gene Rearrangement ◽  
Hematologic Malignancy ◽  
False Negative ◽  
False Positives ◽  
False Negatives ◽  
True Negative ◽  
Flow Cytometric ◽  
Pathology Reports

Abstract In the evaluation of bone marrow (BM) and peripheral blood (PB) for hematologic malignancy, positive immunoglobulin heavy chain (IG) or T-cell receptor (TCR) gene rearrangement results may be detected despite unrevealing results from morphologic, flow cytometric, immunohistochemical (IHC), and/or cytogenetic studies. The significance of positive rearrangement studies in the context of otherwise normal ancillary findings is unknown, and as such, we hypothesized that gene rearrangement studies may be predictive of an emerging B- or T-cell clone in the absence of other abnormal laboratory tests. Data from all patients who underwent IG or TCR gene rearrangement testing at the authors’ affiliated VA hospital between January 1, 2013, and July 6, 2018, were extracted from the electronic medical record. Date of testing; specimen source; and morphologic, flow cytometric, IHC, and cytogenetic characterization of the tissue source were recorded from pathology reports. Gene rearrangement results were categorized as true positive, false positive, false negative, or true negative. Lastly, patient records were reviewed for subsequent diagnosis of hematologic malignancy in patients with positive gene rearrangement results with negative ancillary testing. A total of 136 patients, who had 203 gene rearrangement studies (50 PB and 153 BM), were analyzed. In TCR studies, there were 2 false positives and 1 false negative in 47 PB assays, as well as 7 false positives and 1 false negative in 54 BM assays. Regarding IG studies, 3 false positives and 12 false negatives in 99 BM studies were identified. Sensitivity and specificity, respectively, were calculated for PB TCR studies (94% and 93%), BM IG studies (71% and 95%), and BM TCR studies (92% and 83%). Analysis of PB IG gene rearrangement studies was not performed due to the small number of tests (3; all true negative). None of the 12 patients with false-positive IG/TCR gene rearrangement studies later developed a lymphoproliferative disorder, although 2 patients were later diagnosed with acute myeloid leukemia. Of the 14 false negatives, 10 (71%) were related to a diagnosis of plasma cell neoplasms. Results from the present study suggest that positive IG/TCR gene rearrangement studies are not predictive of lymphoproliferative disorders in the context of otherwise negative BM or PB findings. As such, when faced with equivocal pathology reports, clinicians can be practically advised that isolated positive IG/TCR gene rearrangement results may not indicate the need for closer surveillance.

scholarly journals Application of Immunosignatures for Diagnosis of Valley Fever

2014 ◽  
Vol 21 (8) ◽  
pp. 1169-1177 ◽  
Author(s):  
Krupa Arun Navalkar ◽  
Stephen Albert Johnston ◽  
Neal Woodbury ◽  
John N. Galgiani ◽  
D. Mitchell Magee ◽  
...  
Keyword(s):  
Bacterial Infections ◽  
Random Sequence ◽  
False Negative ◽  
False Negative Rate ◽  
Peptide Array ◽  
Igg Antibodies ◽  
False Negatives ◽  
Peptide Microarray ◽  
Valley Fever ◽  
Antibody Levels

ABSTRACTValley fever (VF) is difficult to diagnose, partly because the symptoms of VF are confounded with those of other community-acquired pneumonias. Confirmatory diagnostics detect IgM and IgG antibodies against coccidioidal antigens via immunodiffusion (ID). The false-negative rate can be as high as 50% to 70%, with 5% of symptomatic patients never showing detectable antibody levels. In this study, we tested whether the immunosignature diagnostic can resolve VF false negatives. An immunosignature is the pattern of antibody binding to random-sequence peptides on a peptide microarray. A 10,000-peptide microarray was first used to determine whether valley fever patients can be distinguished from 3 other cohorts with similar infections. After determining the VF-specific peptides, a small 96-peptide diagnostic array was created and tested. The performances of the 10,000-peptide array and the 96-peptide diagnostic array were compared to that of the ID diagnostic standard. The 10,000-peptide microarray classified the VF samples from the other 3 infections with 98% accuracy. It also classified VF false-negative patients with 100% sensitivity in a blinded test set versus 28% sensitivity for ID. The immunosignature microarray has potential for simultaneously distinguishing valley fever patients from those with other fungal or bacterial infections. The same 10,000-peptide array can diagnose VF false-negative patients with 100% sensitivity. The smaller 96-peptide diagnostic array was less specific for diagnosing false negatives. We conclude that the performance of the immunosignature diagnostic exceeds that of the existing standard, and the immunosignature can distinguish related infections and might be used in lieu of existing diagnostics.

scholarly journals Evaluation of the anterior cervical angle of the uterus to predict spontaneous preterm birth

2017 ◽  
Vol 6 (6) ◽  
pp. 2323 ◽  
Author(s):  
Bijay Sur ◽  
Sujata Misra ◽  
Sanghamitra Dash
Keyword(s):  
Preterm Birth ◽  
Predictive Value ◽  
False Negative ◽  
False Negative Rate ◽  
Cervical Length ◽  
Small Sample ◽  
Transvaginal Sonography ◽  
P Value ◽  
Spontaneous Preterm Birth ◽  
Preterm Group

Background: This prospective observational study was conducted to evaluate the anterior cervical angle (ACA) of the uterus by transvaginal sonography (TVS) and to determine the feasibility to predict spontaneous preterm birth (PTB). The duration of the study was from December 2014-December 2016.The participants included 100 pregnant women with singleton pregnancy who were asymptomatic. They were enrolled after excluding all known risk factors of preterm birth.Methods: The ACA and cervical length were measured in all cases by transvaginal sonography either in the 1st trimester or 2nd trimester. All cases were followed and well documented with respect to the gestational age at delivery.Results: There was a significant risk of preterm labour in women with cervical length <2.5cm in the 2nd trimester with Odds Ratio 3.625, P value=0.001, sensitivity 75% and specificity 79.31%. The positive predictive value was 33.33% and negative predictive value 95.83%. The false positive rate was 20.65% and false negative rate 25%. The difference of mean cervical angle in women who delivered preterm and that of those who delivered at term, in the 1st    trimester (preterm group 114.2°Vs term group 93.0°, P<0.001) and in the 2nd trimester (preterm group 127.66° Vs term group 103.65°, P <0.001) was significant. An ACA of 114.2° in the 1st trimester was associated with a risk of spontaneous preterm birth (P value 0.0065, sensitivity 90% and specificity 80%). An ACA of 127.66° in 2nd trimester was associated with a risk of spontaneous preterm birth (P value 0.0004, sensitivity 80%and specificity 88.23%).Conclusions: Despite the limitations of a small sample size, the results suggest that the anterior cervical angle has potential as a new predictor of spontaneous preterm birth especially when measured in the 1st trimester.

Measurement differences between three versus five photocells during collection of ground reaction forces in dogs

2007 ◽  
Vol 02 (02) ◽  
pp. 98-101 ◽  
Author(s):  
J. P. Punke ◽  
A. L. Speas ◽  
L. R. Reynolds ◽  
C. M. Andrews ◽  
S. C. Budsberg
Keyword(s):  
Ground Reaction Force ◽  
Reaction Force ◽  
False Negative ◽  
False Negative Rate ◽  
Second Phase ◽  
False Negatives ◽  
Two Systems ◽  
Reaction Forces ◽  
Force Data ◽  
Gathering Data

SummaryThe differences between velocities and accelerations obtained from three and five photocells were examined when obtaining ground reaction force (GRF) data in dogs. Ground reaction force data was collected 259 times from 16 different dogs in two experimental phases. The first phase compared velocities and accelerations reported by the two systems based on trials accepted by the three photocell system. The second phase accepted trials based on data from five photocells. Three photocell data were calculated mathematically in the second phase in order to compare the values of both systems. The velocity and acceleration values obtained from each system were significantly different (at the hundredth of a meter per second). Differences in measured values did not result in acceptance of data by the three photocell system that would not have been acceptable with the five photocell system (false positives), but did result in rejection of acceptable data by the three photocell system (11% false negative rate). Given the small differences between the two systems, GRF data collected should not be significantly different, though the three photocell system is less efficient in gathering data due to the number of trials rejected as false negatives.

scholarly journals Novel low-delta value troponin algorithms have good precision for rule-out of NSTE-ACS

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
H Tjora ◽  
O.T Steiro ◽  
J Langorgen ◽  
T Omland ◽  
P Collinson ◽  
...  
Keyword(s):  
False Negative ◽  
False Negative Rate ◽  
High Sensitivity ◽  
Public Institution ◽  
University Hospital ◽  
P Value ◽  
Funding Source ◽  
Good Precision ◽  
Serum Samples ◽  
Rule Out

Abstract Background Rapid rule-out algorithms for non-ST-elevation infarction (NSTEMI) may be beneficial for logistics in the emergency room. Current algorithms are designed to rule-out NSTEMI, but do not differentiate between unstable angina (UAP) in need of revascularization and non-cardiac chest pain patients (NCCP) who could be discharged. Recent improvements in analytical precision of high sensitivity troponin (cTn) assays allow for trialing algorithms with very small delta values. Purpose Could the use of lower delta values produce rule-out algorithms for NSTE-ACS with a false negative rate of ≤5%, and a sufficient high rule-out rate of patients with NCCP. Method 927 patients with suspected NSTE-ACS were consecutively included. Serum samples were collected at 0, 3 and 8–12 hours. The final diagnosis was adjudicated by two independent cardiologists based on all clinical data including routine cTnT. The 0- and 3-hour samples were additionally measured for cTnIand cTnI from Singulex Clarity System (cTnI(sgx)). The diagnostic performance to rule-out NSTE-ACS was compared between one low-delta value algorithm from each assay (cTnT, cTnI and cTnT). Results The prevalence of NSTEMI was 13.4%, UAP 11.4% and NCCP 60%. Median age was 63 years, 60% males. Fig. 1 shows baseline and 3-hour delta cTn values for the UAP and NCCP patients for the three different assays. The baseline cTn value differed significantly between UAP and NCCP for all assays, p value &lt;0.001. The novel low-delta cTnT algorithm (Table 1) ruled out 8 NSTE-ACS patients (3.5%), the cTnI algorithm and cTnI (sgx) algorithm ruled out 11 (4.8%) and 12 (5.2%) patients with NSTE-ACS, respectively. Moreover, the cTnT algorithm allocated 35.3% of the NCCP patients to discharge. Respective numbers for the cTnI(sgx) and cTnI algorithm were 30.6% and 33.5%. Comparing the ROC curves, the cTnT algorithm had significantly higher AUC compared to the cTnI(sgx) algorithm (p value =0.005, DeLong test). Conclusion The low-delta value algorithms correctly ruled in ≥95% of the NSTE-ACS patients whilst &gt;30% of NCCP patients were ruled out. The cTnT algorithm had the best performance with a significant higher AUC compared to the cTnI(sgx) algorithm. Figure 1 Funding Acknowledgement Type of funding source: Public Institution(s). Main funding source(s): Western Norway Regional Health Authority, Haukeland and Stavanger University hospital

scholarly journals Biases and Blind-Spots in Genome-Wide CRISPR Knockout Screens

2020 ◽  
Author(s):  
Merve Dede ◽  
Eiru Kim ◽  
Traver Hart
Keyword(s):  
False Negative ◽  
False Negative Rate ◽  
Blind Spot ◽  
Integrated Analysis ◽  
False Negatives ◽  
Genome Wide ◽  
Cancer Subtype ◽  
Crispr Screen ◽  
Single Knockout ◽  
Blind Spots

AbstractIt is widely accepted that pooled library CRISPR knockout screens offer greater sensitivity and specificity than prior technologies in detecting genes whose disruption leads to fitness defects, a critical step in identifying candidate cancer targets. However, the assumption that CRISPR screens are saturating has been largely untested. Through integrated analysis of screen data in cancer cell lines generated by the Cancer Dependency Map, we show that a typical CRISPR screen has a ∼20% false negative rate, beyond library-specific false negatives previously described. Replicability falls sharply as gene expression decreases, while cancer subtype-specific genes within a tissue show distinct profiles compared to false negatives. Cumulative analyses across tissues suggest only a small number of lineage-specific essential genes and that these genes are highly enriched for transcription factors that define pathways of tissue differentiation. In addition, we show that half of all constitutively-expressed genes are never hits in any CRISPR screen, and that these never-essentials are highly enriched for paralogs. Together these observations strongly suggest that functional buffering masks single knockout phenotypes for a substantial number of genes, describing a major blind spot in CRISPR-based mammalian functional genomics approaches.

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