missing-data {MSnbase} | R Documentation

## Documenting missing data visualisation

### Description

There is a need for adequate handling of missing value impuation in quantitative proteomics. Before developing a framework to handle missing data imputation optimally, we propose a set of visualisation tools. This document serves as an internal notebook for current progress and ideas that will eventually materialise in exported functionality in the `MSnbase` package.

### Details

The explore the structure of missing values, we propose to

1. Explore missing values in the frame of the experimental design. The `imageNA2` function offers such a simple visualisation. It is currently limited to 2-group designs/comparisons. In case of time course experiments or sub-cellular fractionation along a density gradient, we propose to split the time/gradient into 2 groups (early/late, top/bottom) as a first approximation.

2. Explore the proportion of missing values in each group.

3. Explore the total and group-wise feature intensity distributions.

The existing `plotNA` function illustrates the completeness/missingness of the data.

### Author(s)

Laurent Gatto <lg390@cam.ac.uk>, Samuel Wieczorek and Thomas Burger

### See Also

`plotNA`, `imageNA2`.

### Examples

    ## Other suggestions
    library("pRolocdata")
    library("pRoloc")
    data(dunkley2006)
    set.seed(1)
    nax <- makeNaData(dunkley2006, pNA = 0.10)
    pcol <- factor(ifelse(dunkley2006$fraction <= 5, "A", "B"))
    sel1 <- pcol == "A"

    ## missing values in each sample
    barplot(colSums(is.na(nax)), col = pcol)

    ## table of missing values in proteins
    par(mfrow = c(3, 1))
    barplot(table(rowSums(is.na(nax))), main = "All")
    barplot(table(rowSums(is.na(nax)[sel1,])), main = "Group A")
    barplot(table(rowSums(is.na(nax)[!sel1,])), main = "Group B")

    fData(nax)$nNA1 <- rowSums(is.na(nax)[, sel1])
    fData(nax)$nNA2 <- rowSums(is.na(nax)[, !sel1])
    fData(nax)$nNA <- rowSums(is.na(nax))
    o <- MSnbase:::imageNA2(nax, pcol)

    plot((fData(nax)$nNA1 - fData(nax)$nNA2)[o], type = "l")
    grid()

    plot(sort(fData(nax)$nNA1 - fData(nax)$nNA2), type = "l")
    grid()

    o2 <- order(fData(nax)$nNA1 - fData(nax)$nNA2)
    MSnbase:::imageNA2(nax, pcol, Rowv=o2)

    layout(matrix(c(rep(1, 10), rep(2, 5)), nc = 3))
    MSnbase:::imageNA2(nax, pcol, Rowv=o2)
    plot((fData(nax)$nNA1 - fData(nax)$nNA)[o2], type = "l", col = "red",
         ylim = c(-9, 9), ylab = "")
    lines((fData(nax)$nNA - fData(nax)$nNA2)[o2], col = "steelblue")
    lines((fData(nax)$nNA1 - fData(nax)$nNA2)[o2], type = "l",
         lwd = 2)

[Package MSnbase version 2.20.1 Index]