mostSimilarTwo: A function which indentifies two columns of a matrix, or...
In clippda: A package for the clinical proteomic profiling data analysis

Description Usage Arguments Details Value Author(s) References Examples

A common practice in the analysis of repeated mass spectrometry data is to average the replicate expression values, a method which is only valid if there is some coherence in the peak information across replicates. The function mostSimilarTwo identifies the two columns of a matrix (or a dataframe) with the highest pairwise positive correlations. The most highly correlated replicates contain the most similar compounds. This function may also be used to reduce the number of spectra being analysed to two.

1	mostSimilarTwo(Mat)

Mat

A dataframe, with the columns being the variables of interest, for example the spectra.

The main application of this function is in the pre-processing of mass spectrometry data. In a mass spectrometry experiment, it often happens that there is mislabelling of samples, which results in some replicates being assigned to the wrong sample class. This function sifts through this data to identify the two spectra with the most coherent signal information between them. Thus, its function has the potential to help in reducing the number of false-positive discoveries. Its other application is in the reduction of the number of replicates to two, which are then analysed using tools for duplicate peak (or gene) expression data.

It returns a vector with two elements, being the column indices for the two most correlated variables.

Stephen Nyangoma

Ward DG, Nyangoma S, Joy H, Hamilton E, Wei W, Tselepis C, Steven N, Wakelam MJ, Johnson PJ, Ismail T, Martin A: Proteomic profiling of urine for the detection of colon cancer. Proteome Sci. 2008, 16(6):19

n <- 10

Mat <- data.frame(x1=rnorm(n, mean = 0, sd = 1),x2=rnorm(n, mean = 0, sd = 3),x3=rnorm(n, mean = 1, sd = 1),x4=
rnorm(n,mean=2,sd=2))

mostSimilarTwo(Mat)