bbum: bbum: A package to account for secondary effects signal in...
In wyppeter/bbum: BBUM correction for significance testing of p values
bbum R Documentation
bbum: A package to account for secondary effects signal in multiple testing
Description
Standard multiple testing correction methods cannot directly handle datasets
that contain a weaker background secondary signal confounding the primary
signal of interest. A bi-beta-uniform mixture (BBUM) model allows the
modeling and correction for the false discovery rate (FDR) of both null and
secondary effects.
Authors
Author: Peter Y. Wang
Advisor: David P. Bartel
References
Wang PY, Bartel DP. 2022. A statistical approach for identifying primary
substrates of ZSWIM8-mediated microRNA degradation in small-RNA sequencing
data. bioRxiv. doi:10.1101/2022.02.17.480958
Markitsis A, Lai Y. 2010. A censored beta mixture model for the estimation of
the proportion of non-differentially expressed genes. Bioinformatics
26:640-646. doi:10.1093/bioinformatics/btq001
Pounds S, Morris SW. 2003. Estimating the occurrence of false positives and
false negatives in microarray studies by approximating and partitioning the
empirical distribution of p-values. Bioinformatics 19:1236-1242.
doi:10.1093/bioinformatics/btg148
wyppeter/bbum documentation built on Oct. 3, 2023, 3:29 p.m.
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| bbum | R Documentation |
bbum: A package to account for secondary effects signal in multiple testing
Description
Standard multiple testing correction methods cannot directly handle datasets that contain a weaker background secondary signal confounding the primary signal of interest. A bi-beta-uniform mixture (BBUM) model allows the modeling and correction for the false discovery rate (FDR) of both null and secondary effects.
Authors
Author: Peter Y. Wang
Advisor: David P. Bartel
References
Wang PY, Bartel DP. 2022. A statistical approach for identifying primary substrates of ZSWIM8-mediated microRNA degradation in small-RNA sequencing data. bioRxiv. doi:10.1101/2022.02.17.480958
Markitsis A, Lai Y. 2010. A censored beta mixture model for the estimation of the proportion of non-differentially expressed genes. Bioinformatics 26:640-646. doi:10.1093/bioinformatics/btq001
Pounds S, Morris SW. 2003. Estimating the occurrence of false positives and false negatives in microarray studies by approximating and partitioning the empirical distribution of p-values. Bioinformatics 19:1236-1242. doi:10.1093/bioinformatics/btg148
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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