scholarly journals Confidence maps: statistical inference of cryo-EM maps

2020 ◽  
Vol 76 (4) ◽  
pp. 332-339
Author(s):  
Maximilian Beckers ◽  
Colin M. Palmer ◽  
Carsten Sachse

Confidence maps provide complementary information for interpreting cryo-EM densities as they indicate statistical significance with respect to background noise. They can be thresholded by specifying the expected false-discovery rate (FDR), and the displayed volume shows the parts of the map that have the corresponding level of significance. Here, the basic statistical concepts of confidence maps are reviewed and practical guidance is provided for their interpretation and usage inside the CCP-EM suite. Limitations of the approach are discussed and extensions towards other error criteria such as the family-wise error rate are presented. The observed map features can be rendered at a common isosurface threshold, which is particularly beneficial for the interpretation of weak and noisy densities. In the current article, a practical guide is provided to the recommended usage of confidence maps.

2008 ◽  
Vol 27 (21) ◽  
pp. 4145-4160 ◽  
Author(s):  
Sonja Zehetmayer ◽  
Peter Bauer ◽  
Martin Posch

Author(s):  
Jeong-Seok Choi

Multiple testings are instances that contain simultaneous tests for more than one hypothesis. When multiple testings are conducted at the same time, it is more likely that the null hypothesis is rejected, even if the null hypothesis is correct. If individual hypothesis decisions are based on unadjusted <i>p</i>-values, it is usually more likely that some of the true null hypotheses will be rejected. In order to solve the multiple testing problems, various studies have attempted to increase the power by taking into account the family-wise error rate or false discovery rate and statistics required for testing hypotheses. This article discuss methods that account for the multiplicity issue and introduces various statistical techniques.


2016 ◽  
Vol 111 ◽  
pp. 32-40 ◽  
Author(s):  
Jens Stange ◽  
Thorsten Dickhaus ◽  
Arcadi Navarro ◽  
Daniel Schunk

2012 ◽  
Vol 32 (2) ◽  
pp. 181-195 ◽  
Author(s):  
Haihong Li ◽  
Abdul J. Sankoh ◽  
Ralph B. D'Agostino

2019 ◽  
Vol 16 (2) ◽  
pp. 73-82
Author(s):  
I. A. ADELEKE ◽  
A. O. ADEYEMI ◽  
E. E.E. AKARAWAK

Multiple testing is associated with simultaneous testing of many hypotheses, and frequently calls for adjusting level of significance in some way that the probability of observing at least one significant result due to chance remains below the desired significance levels. This study developed a Binomial Model Approximations (BMA) method as an alternative to addressing the multiplicity problem associated with testing more than one hypothesis at a time. The proposed method has demonstrated capacity for controlling Type I Error Rate as sample size increases when compared with the existing Bonferroni and False Discovery Rate (FDR).      


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