Description Objects from the Class Slots Extends Methods and functions Author(s) References Examples
The QSep
infrastructure provide a way to quantify the
resolution of a spatial proteomics experiment, i.e. to quantify how
well annotated subcellular clusters are separated from each other.
The QSep
function calculates all between and within cluster
average distances. These distances are then divided columnwise 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).
Missing values only affect the fractions containing the NA
when
the distance is computed (see the example below) and further used when
calculating mean distances. Few missing values are expected to have
negligible effect, but data with a high proportion of missing data
will will produce skewed distances. In QSep
, we take a
conservative approach, using the data as provided by the user, and
expect that the data missingness is handled before proceeding with this
or any other analysis.
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
subcellular clusters. Nonmarker 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 subcelluar 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>
Assessing subcellular resolution in spatial proteomics experiments Laurent Gatto, Lisa M Breckels, Kathryn S Lilley bioRxiv 377630; doi: https://doi.org/10.1101/377630
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46  ## 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)
## Missing data example, for 4 proteins and 3 fractions
x < rbind(c(1.1, 1.2, 1.3), rep(1, 3), c(NA, 1, 1), c(1, 1, NA))
rownames(x) < paste0("P", 1:4)
colnames(x) < paste0("F", 1:3)
## P1 is the reference, against which we will calculate distances. P2
## has a complete profile, producing the *real* distance. P3 and P4 have
## missing values in the first and last fraction respectively.
x
## If we drop F1 in P3, which represents a small difference of 0.1, the
## distance only considers F2 and F3, and increases. If we drop F3 in
## P4, which represents a large distance of 0.3, the distance only
## considers F1 and F2, and decreases. dist(x)

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