feature.assoc {swamp}

Associations of the features to a sample annotation in observed and reshuffled data.

Description

This function calculates the associations of each feature of the data matrix to a specified sample annotation. Either Pearson correlation, t-test statistic, Area Under Curve or R squared is used as measure of association. In parallel, the features in permuted data are tested for comparison.

Usage

feature.assoc(g, y, method = "correlation", g1 = NULL, exact = 1)

Arguments

g

the input data in form of a matrix with features as rows and samples as columns. Missing values are allowed.

y

a factor or numeric vector which contains the sample information. Typically a variable of the data.frame o used in the remaining functions of this package. y can be a factor with 2 or more levels or a numeric vector. y cannot be a character vecor. y has to be of the same length as ncol (g). Missing values are allowed and those cases are removed from the calculations.

method

if y is a factor with two levels, this method is used for calculation of the association. The method can be one of "correlation", "t.test", or "AUC". If y is a factor with >2 levels lm() is used automatically, if y is numeric cor() is used automatically to determine the associations.

g1

As there are different ways to generate a randomized dataset, a pre-calculated permutation set can be specified here. Else the permutation data is generated within the function by reshuffling the values for each feature. g1 has to be a matrix with the same dimensions as g.

exact

if method="AUC", exact determines how wilcox.test() treats ties.

Details

For each feature the association to the sample annotation is calculated. If the sample annotaion is a factor with 2 levels, it can be chosen whether Pearson correlation, t.test statistic or Area Under Curve (AUC) is used as measure of association. The uncorrected p-values for the strength of associations are calculated by cor.test(), t.test() and wilcox.test() respectively. The distribution of these associations can be seen using dense.plot() function. For instance this can reveal a group of positively associated features. The order of the levels in levels(y) is decisive, e.g. for correlation the factors are transformed by as.numeric(), whereby the first level becomes 1 and the second level becomes 2. Hence, a positive association means higher values in samples with level 2 and a negative assocation means higher values in level 1. This should also be true for t.test and AUC, but please re-check. If the annotation is a factor with more than 2 levels, lm() is automatically used with R squared as the measure of association and the p-value as obtained from the F statistic. If the annotation is a numeric vector, correlation is used (with cor.test() for p-value). NAs are allowed in both the data matrix and the annotation vector and is treated by case-wise deletion for the calculations. To see the relelvance of the associations, the calculations are repeated with permuted data, which can be either pre-entered as g1 or otherwise is calculated within the function by reshuffling the values for each feature.

Value

a list with components

observed

a numeric vector containing the association of features to sample annotation in the observed data.

permuted

a numeric vector containing the association of features to sample annotation in the permuted data.

observed.p

a numeric vector containing the p-values for association of features to sample annotation in the observed data.

permuted.p

a numeric vector containing the p-values for association of features to sample annotation in the permuted data.

method

the method used as measure of association, which can be one of "correlation", "t.test", "AUC" or "linear.model".

class.of.y

a character that states the class of y.

permuted.data

the matrix of the permuted data.

Author(s)

Martin Lauss

Examples

## data as a matrix
set.seed(100)
g<-matrix(nrow=1000,ncol=50,rnorm(1000*50),dimnames=list(paste("Feature",1:1000),
   paste("Sample",1:50)))
g[1:100,26:50]<-g[1:100,26:50]+1 # the first 100 features show
# higher values in the samples 26:50
## patient annotations as a data.frame, annotations should be numbers and factor
# but not characters.
## rownames have to be the same as colnames of the data matrix 
set.seed(200)
o<-data.frame(Factor1=factor(c(rep("A",25),rep("B",25))),
              Factor2=factor(rep(c("A","B"),25)),
              Numeric1=rnorm(50),row.names=colnames(g))

# calculate the associations to Factor 1
res4a<-feature.assoc(g,o$Factor1,method="correlation")
res4b<-feature.assoc(g,o$Factor1,method="t.test",g1=res4a$permuted.data) 
    # uses t.test instead, reuses the permuted data generated in res4a
res4c<-feature.assoc(g,o$Factor1,method="AUC",g1=res4a$permuted.data) 
    # uses AUC instead, reuses the permuted data generated in res4a
str(res4a)