Make predictions from a bandle analysis
bandlePredict(objectCond1, objectCond2, params, fcol = "markers")A list of instances of class MSnbase::MSnSets
where each is an experimental replicate for the first condition, usually a control
A list of instance of class MSnbase::MSnSets
where each is an experimental replicate for the second condition, usually a treatment
An instance of class bandleParams, as generated by
bandle().
A feature column indicating the markers. Defaults to "markers"
bandlePredict returns an instance of class
MSnbase::MSnSet containing the localisation predictions as
a new bandle.allocation feature variable. The allocation
probability is encoded as bandle.probability
(corresponding to the mean of the distribution
probability). In addition the upper and lower quantiles of
the allocation probability distribution are available as
bandle.probability.lowerquantile and
bandle.probability.upperquantile feature variables. The
Shannon entropy is available in the bandle.mean.shannon
feature variable, measuring the uncertainty in the allocations
(a high value representing high uncertainty; the highest value
is the natural logarithm of the number of classes). An additional variable
indicating the differential localization probability is also added as
bandle.differential.localisation
library(pRolocdata)
data("tan2009r1")
set.seed(1)
tansim <- sim_dynamic(object = tan2009r1,
numRep = 6L,
numDyn = 100L)
gpParams <- lapply(tansim$lopitrep, function(x)
fitGPmaternPC(x, hyppar = matrix(c(0.5, 1, 100), nrow = 1)))
d1 <- tansim$lopitrep
control1 <- d1[1:3]
treatment1 <- d1[4:6]
mcmc1 <- bandle(objectCond1 = control1, objectCond2 = treatment1, gpParams = gpParams,
fcol = "markers", numIter = 5L, burnin = 1L, thin = 2L,
numChains = 1, BPPARAM = SerialParam(RNGseed = 1))
#> You haven't provided a seed, you may wish to provide a seed
#>
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mcmc1 <- bandleProcess(mcmc1)
out <- bandlePredict(objectCond1 = control1, objectCond2 = treatment1, params = mcmc1)