This function is used to peform multiple beta t-test method on real count data. The result lists "geneid" or "isoformid", gene name, the other information, t-value, p-value, rho, and w.
mbetattest(X, nci, na, nb, alpha=0.05, norm="no",
side="both", level="sgRNA",padjust_methods,C=1.222)count data of RNA sequence reads with na replicates in condition A and nb replicates in condition B.
nonnegative int value: number of columns for data information, such as geneID, isoformID, gene name etc.
nonnegative int value: number of replicate libraries in condition A.
int numeric value: number of replicate libraries in condition B.
float numeric value, a probabilistic threshold. The value must be in [0,1]. User can set alpha=0.05 or 0.01 or the other values. Defalt value is 0.05
logistic value:"yes" or "no". If norm="yes", the count data will be normalized and mbetattest will work on the normalized data, if norm="no", then mbetattest will work on the unnormalized data.
string for specifying tail(s) of t-distribution. If side="up", then p-value is given with t-test in the left tail. If side="down", p-value is given with t-test in right tail. If side ="both", p-value is given with t-test in both sides.
string for specifying which level mbetattest work on. In the current version, level has 6 options: "isoform", "sgRNA", "RNA", "splicing.gene","polyA.gene", and "CRISPR.gene".
string for specifying a method for a multiple procedure. padjust_methods can choose one of "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", TX, and "none" where "fdr" = "BH", "TX" is Tan and Xu's method (2015) with C=1.222 for adjusting p-value.
float numeric value for specifying a multiple procedure. C=0 tells mbetattest to perform single tests, C=1.222 tells mbetattest to perform BH correction of pvalues, C>1000 tells mbetattest to perform Bonferroni correction of pvalues.
return a data and result list: data columns, t-values, rho.
see MBttest2-manual.
Baggerly KA, Deng L, Morris JS, Aldaz CM (2003) Differential expression in SAGE: accounting for normal between-library variation. Bioinformatics 19: 1477-1483.
Benjamini, Y., and Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series, B, 575, 289-300. doi:10.1111/j.2517-6161.1995.tb02031.x, https://www.jstor.org/stable/2346101.
Benjamini, Y., and Yekutieli, D. (2001). The control of the false discovery rate in multiple testing under dependency. Annals of Statistics, 29, 1165 -1188. doi:10.1214/aos/1013699998.
Tan YD, Xu H. A general method for accurate estimation of false discovery rates in identification of differentially expressed genes. Bioinformatics. 2014 Jul 15;30(14):2018-25. doi:10.1093/bioinformatics/btu124. Epub 2014 Mar 14. PMID: 24632499.
# NOT RUN {
data(jkttcell)
res<-mbetattest(X=jkttcell[1:70, ], nci=7, na=3,
nb=3, alpha=0.05, norm="yes", side="both",
level="isoform",padjust_methods="fdr",C=0)
# }
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