# dense.plot: Density plots of feature associations in observed and... In swamp: Visualization, Analysis and Adjustment of High-Dimensional Data in Respect to Sample Annotations

## Description

The function plots the distribution of feature associations for a specified sample annotation for both observed and reshuffled data.

## Usage

 ```1 2``` ```dense.plot(feature.assoc, lty = 1:2, col = 1:2, lwd = c(2, 2), ylab = "", main = "", cex.main = 1, cex.lab = 1, cex.axis = 1) ```

## Arguments

 `feature.assoc` A list of feature associations, typically created by the function feature.assoc(). (If not created by feature.assoc() the list has to contain the elements observed, permuted and method.) `lty` a numeric vector containing the line types for the observed and permuted density lines. default=1:2. `col` the colors for the observed and permuted density lines. default=1:2. `lwd` the line widths. default=c(2,2). `ylab` optional labeling of y-axis. `main` optional titel. `cex.main` optional titel font size. `cex.lab` optional axis label font size. `cex.axis` optional axis font size.

## Details

The function plots the distribution of associations of features with a sample annotation calculated by feature.assoc(). The function uses plot.density() for the observed data and adds the permuted data using lines(density()). The x-axis is dependent on the method used to measure association, e.g. if the method was "correlation", then xlim is c(-1,1) and xlab="Corrlation".

Martin Lauss

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25``` ```## 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 # plot distribution of associations in observed and permuted data dense.plot(res4a) dense.plot(res4b) dense.plot(res4c) ```

swamp documentation built on May 2, 2019, 2:14 p.m.