rowLogRegLRT: Row-wise logistic regression

In htSeqTools: Quality Control, Visualization and Processing for High-Throughput Sequencing data

Description

Row-wise logistic regressions are applied to a matrix with counts. For each row, an overall test comparing the column counts across columns is performed. Optionally, chi-square permutation tests are used when the expected counts are below 5 for some column.

Usage

 rowLogRegLRT(counts, exact = TRUE, p.adjust.method = "none") 

Arguments

counts	Matrix with counts
exact	If set to TRUE, an exact test is used whenever some expected cell counts are 5 or less
p.adjust.method	p-value adjustment method, passed on to p.adjust

Details

For each column, the proportion of counts in each row (with respect to the overall counts in that column) is computed. Then a statistical comparison of these proportions across groups is performed via a likelihood-ratio test (if exact==TRUE a permutation based chi-square test is used whenever the expected counts in some column is below 5).

Notice that data from column j can be viewed as a multinomial distribution with probabilities pj, where pj is a vector of length nrow(x). rowLogRegLRT tests the null hypothesis p1[i]=...pc[i] for i=1...nrow(x), where c is ncol(x). This actually ignores the multinomial sampling model and focuses on its binomial margins, which is a reasonable approximation when the number nrow(x) is large and substantially improves computation speed.

Examples

#The first two rows present different counts across columns
#The last two columns do not
x <- matrix(c(70,10,10,10,35,35,10,10),ncol=2)
x
rowLogRegLRT(x)

htSeqTools documentation built on May 6, 2019, 3:39 a.m.