diffPatternTest  R Documentation 
The normalized gene data are pooled into a large matrix, where parameter estimations and tests are performed. Within each gene, multiplicity correction are then performed for codon/binlevel pvalues. The minimum of adjusted codon/binlevel pvalue is defined to be the genelevel pvalue.
diffPatternTest(data, classlabel, method = c('gtxr', 'qvalue'))
data 
A list of named matrices input from the 
classlabel 
For matrix input: a DataFrame or data.frame with at least a column

method 
For a 2component character vector input: the first argument is the
multiplicity correction method for codon/binlevel pvalue adjustment.
The second argument is the multiplicity correction method for
genelevel pvalue adjustment. Methods include: "qvalue" for qvalue
from 
Using binned data, this function first estimates normalizing constant by exclusing outlier bins which may represent the true differential pattern. An outlier bin is defined as that whose log2fold change value is more than 1.5 interquartile ranges below the first quartile or above the third quartile. For a given gene, the normalizing constant is defined based on the total read counts from each replicate.
It then performs differential pattern testing on Psite counts bin by bin for each gene. Briefly, counts are modeled by a negative binomial distribution to call bins with statistically significant differences across conditions, bin level pvalues are adjusted for multiple hypothesis testing for a given gene, and then the smallest pvalue for a gene is adjusted to control for multiple hypothesis testing across all genes.
Additionally, the Tvalue is a supplementary statistic that quantifies the magnitude of difference between conditions, with larger numbers indicating a greater difference. The $T$value is defined to be 1cosine of the angle between the first right singular vectors of the footprint matrices of the two conditions under comparison. It ranges from 01, with larger values representing larger differences between conditions, and practically speaking, can be used to identify genes with larger magnitude of pattern difference beyond statistical significance. This might be helpful to investigators to prioritize certain genes for investigation among many that may pass the significance test for differential pattern.
bin 
A List object of codon/binlevel results. Each element
of list is of a gene, containing codon/bin results columns: 
gene 
A DataFrame object of genelevel results. It contains columns:

method 
The same as input 
small 
Names of genes without sufficient reads, not reported in

data 
Subset of input 
classlabel 
The same as input 
p.adjust
data(data.binned) classlabel < data.frame(condition = c("mutant", "mutant", "wildtype", "wildtype"), comparison = c(2, 2, 1, 1)) rownames(classlabel) < c("mutant1", "mutant2", "wildtype1", "wildtype2") result.pst < diffPatternTest(data = data.binned, classlabel = classlabel, method = c('gtxr', 'qvalue'))
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