gedProportions
implements a pre-processing
pipeline for applying deconvolution methodologies that
use a known set of cell type-specific signatures in order
to estimate cell proportions in heterogeneous samples
(e.g., Abbas et al. (2009) or Gong et al.
(2011)).
gedProportions(object, x, method = "lsfit", CLsubset = NULL, subset = NULL, map.method = "auto", ..., log = NULL, lbase = 2, normalize = c("none", "quantiles"), verbose = FALSE)
ged
.ExpressionSet-class
object or a
matrix
, whose columns contains cell-specific
expression for each feature in the target data.
If the gene identifier type from the basis matrix do not
match the one from the target matrix, these are converted
using convertIDs
. If needed, this automatic
conversion can be disabled using map.method=NA
, as
it is by default when x
is a matrix
, whose
rows are assumed to match the rows in the target matrix.x
.mapIDs
. Identifier
conversion can be disabled using map.method=NA
.ged
TRUE
,
all non-log-scaled data (signatures and/or target) are
log-transformed using with base lbase
. If
FALSE
, all log-scaled data (signatures and/or
target) are exp-transformed using with base lbase
.
If a number, then the function acts as if log=TRUE
using the value of log
as lbase
. If
NULL
, then log-transform is applied only if either
the signatures or the target data is in log scale,
otherwise non-log-scaled data is exp-transformed into
linear values, via expb(A, lbase)
. If
log=NA
no transformation is performed at all.verbose=Inf
toggles debug mode (all messages).
Note that because it appears after ...
it must be
fully named.The actual estimation is performed via the
ged
interface, using a suitable
deconvolution method.
Before calling ged
, the following
pre-processing pipeline is applied to the data and/or the
signature matrix:
GEO2R
. All steps are optional and can be disabled if needed (see argument details).
Abbas AR, Wolslegel K, Seshasayee D, Modrusan Z and Clark
HF (2009). "Deconvolution of blood microarray data
identifies cellular activation patterns in systemic lupus
erythematosus." _PloS one_, *4*(7), pp. e6098. ISSN
1932-6203,
Gong T, Hartmann N, Kohane IS, Brinkmann V, Staedtler F,
Letzkus M, Bongiovanni S and Szustakowski JD (2011).
"Optimal deconvolution of transcriptional profiling data
using quadratic programming with application to complex
clinical blood samples." _PloS one_, *6*(11), pp. e27156.
ISSN 1932-6203,