computeSpillmat {CATALYST} | R Documentation |
Computes a spillover matrix from a priori identified single-positive populations.
computeSpillmat(x, ...) ## S4 method for signature 'dbFrame' computeSpillmat(x, method = "default", interactions = "default", trim = 0.5, th = 1e-05)
x |
a |
... |
optional arguments. |
method |
|
interactions |
|
trim |
numeric. Specifies the trim value used for estimation of spill values.
Note that |
th |
single non-negative numeric. Specifies the threshold value below which spill estimates will be set to 0. |
The default
method estimates the spillover as the median ratio
between the unstained spillover receiving and the stained spillover
emitting channel in the corresponding single stained populations.
method = "classic"
will compute the slope of a line through
the medians (or trimmed means) of stained and unstained populations.
The medians (or trimmed means) computed from events that are i) negative
in the respective channels; and, ii) not assigned to interacting channels;
and, iii) not unassigned are subtracted as to account for background.
interactions="default"
considers only expected interactions, that is,
M+/-1 (detection sensitivity), M+16 (oxide formation) and channels measuring
metals that are potentially contaminated by isotopic impurites
(see reference below and isotope_list
).
interaction="all"
will estimate spill for all n x n - n
interactions, where n denotes the number of single-color controls
(= nrow(bc_key(re))
).
Returns a square compensation matrix with dimensions and dimension names matching those of the input flowFrame. Spillover is assumed to be linear, and, on the basis of their additive nature, spillover values are computed independently for each interacting pair of channels.
Helena Lucia Crowell crowellh@student.ethz.ch
Coursey, J.S., Schwab, D.J., Tsai, J.J., Dragoset, R.A. (2015). Atomic weights and isotopic compositions, (available at http://physics.nist.gov/Comp).
# get single-stained control samples data(ss_exp) # specify mass channels stained for bc_ms <- c(139, 141:156, 158:176) # debarcode single-positive populations re <- assignPrelim(x = ss_exp, y = bc_ms) re <- estCutoffs(x = re) re <- applyCutoffs(x = re) head(computeSpillmat(x = re))