QSep-class {pRoloc} | R Documentation |
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 can be created by calls using the constructor
QSep
(see below).
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.
Class "Versioned"
, directly.
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.
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.
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.
signature(object = "QSep", ..., verbose =
"logical")
: Invisible return all between cluster average
distances and prints (when verbose
is TRUE
,
default) a summary of those.
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
.
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.
Laurent Gatto <lg390@cam.ac.uk>
## 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)