QSep-class {pRoloc} | R Documentation

## Quantify resolution of a spatial proteomics experiment

### Description

The `QSep` infrastructure provide a way to quantify the resolution of a spatial proteomics experiment, i.e. to quantify how well annotated sub-cellular clusters are separated from each other.

The `QSep` function calculates all between and within cluster average distances. These distances are then divided column-wise by the respective within cluster average distance. For example, for a dataset with only 2 spatial clusters, we would obtain

| c_1 | c_2
c_1 | d_11 | d_12
c_2 | d_21 | d_22

Normalised distance represent the ratio of between to within average distances, i.e. how much bigger the average distance between cluster c_i and c_j is compared to the average distance within cluster c_i.

| c_1 | c_2
c_1 | 1 | \frac{d_12}{d_22}
c_2 | \frac{d_21}{d_11} | 1

Note that the normalised distance matrix is not symmetric anymore and the normalised distance ratios are proportional to the tightness of the reference cluster (along the columns).

### Objects from the Class

Objects can be created by calls using the constructor `QSep` (see below).

### Slots

`x`:

  Object of class `"matrix"` containing the pairwise distance matrix, accessible with `qseq(., norm = FALSE)`.

`xnorm`:

  Object of class `"matrix"` containing the normalised pairwise distance matrix, accessible with `qsep(., norm = TRUE)` or `qsep(.)`.

`object`:

  Object of class `"character"` with the variable name of `MSnSet` object that was used to generate the `QSep` object.

`.__classVersion__`:

  Object of class `"Versions"` storing the class version of the object.

### Extends

Class `"Versioned"`, directly.

### Methods and functions

QSeq

  `signature(object = "MSnSet", fcol = "character")`: constructor for `QSep` objects. The `fcol` argument defines the name of the feature variable that annotates the sub-cellular clusters. Non-marker proteins, that are marked as `"unknown"` are automatically removed prior to distance calculation.

qsep

  `signature{object = "QSep", norm = "logical"}`: accessor for the normalised (when `norm` is `TRUE`, which is default) and raw (when `norm` is `FALSE`) pairwise distance matrices.

names

  `signature{object = "QSep"}`: method to retrieve the names of the sub-celluar clusters originally defined in `QSep`'s `fcol` argument. A replacement method `names(.) <-` is also available.

summary

  `signature(object = "QSep", ..., verbose = "logical")`: Invisible return all between cluster average distances and prints (when `verbose` is `TRUE`, default) a summary of those.

levelPlot

  `signature(object = "QSep", norm = "logical", ...)`: plots an annotated heatmap of all normalised pairwise distances. `norm` (default is `TRUE`) defines whether normalised distances should be plotted. Additional arguments `...` are passed to the `levelplot`.

plot

  `signature(object = "QSep", norm = "logical"...)`: produces a boxplot of all normalised pairwise distances. The red points represent the within average distance and black points between average distances. `norm` (default is `TRUE`) defines whether normalised distances should be plotted.

### Author(s)

Laurent Gatto <lg390@cam.ac.uk>

### Examples

    ## Test data from Christoforou et al. 2016
    library("pRolocdata")
    data(hyperLOPIT2015)

    ## Create the object and get a summary
    hlq <- QSep(hyperLOPIT2015)
    hlq
    summary(hlq)

    ## mean distance matrix
    qsep(hlq, norm = FALSE)

    ## normalised average distance matrix
    qsep(hlq)

    ## Update the organelle cluster names for better
    ## rendering on the plots
    names(hlq) <- sub("/", "\n", names(hlq))
    names(hlq) <- sub(" - ", "\n", names(hlq))
    names(hlq)

    ## Heatmap of the normalised intensities
    levelPlot(hlq)

    ## Boxplot of the normalised intensities
    par(mar = c(3, 10, 2, 1))
    plot(hlq)

    ## Boxplot of all between cluster average distances
    x <- summary(hlq, verbose = FALSE)
    boxplot(x)

[Package pRoloc version 1.20.2 Index]