plotCV2: Plot mean-CV^2 trend

In msImpute: Imputation of label-free mass spectrometry peptides

Description

For each peptide, the squares of coefficient of variations are computed and plotted against average log-intensity. Additionally, a loess trend is fitted to the plotted values. Outlier observations (possibly originated from incorrect match between runs), are detected and highlighted. Users can use this plot as a diagnostic plot to determine if filtering by average intensity is required.

Usage

plotCV2(y, trend = TRUE, outlier = FALSE, sigma = 1, eps = 0.001, main = NULL)

Arguments

y	numeric matrix of log-intensity
trend	logical. Should a loess trend be fitted to CV^2 and mean values. Default to TRUE.
outlier	logical. Should outlier points be highlighted? Defaults to FALSE.
sigma	numeric. Kernel width in RBF kernel. Default to 1.
eps	numeric. This is threshold used to call a data point as an outlier. Default to 0.001
main	character string. Title of the plot. Default to NULL

Details

Outliers are determined by computing the RBF kernels, which reflect the chance that an observed point belong to the dataset (i.e. is close enough in distance to other data points). Users can determine the cut-off for intensity-based filtering with respect to the mean log-intensity of the outlier points.

Value

A plot is created on the current graphics device.

Examples

data(pxd010943)
y <- pxd010943
y <- log2(y)
ppCV2 <- plotCV2(y)

msImpute documentation built on Nov. 8, 2020, 5:26 p.m.