View source: R/Chromatograms.R
Chromatograms | R Documentation |
The Chromatograms
class encapsules chromatographic data and related
metadata. The chromatographic data is represented by a backend extending
the virtual ChromBackend class which provides the raw data to the
Chromatograms
object. Different backends and their properties are
decribed in the ChromBackend class documentation.
Chromatograms(backend = ChromBackendMemory(), processingQueue = list(), ...)
## S4 method for signature 'Chromatograms,ChromBackend'
setBackend(
object,
backend,
f = processingChunkFactor(object),
BPPARAM = SerialParam(),
...
)
## S4 method for signature 'Chromatograms'
x$name
## S4 replacement method for signature 'Chromatograms'
x$name <- value
backend |
ChromBackend object providing the raw data for the
|
processingQueue |
list a list of processing steps (i.e. functions) to
be applied to the chromatographic data. The processing steps are
applied in the order they are listed in the |
... |
Additional arguments. |
object |
A Chromatograms object. |
f |
|
BPPARAM |
Parallel setup configuration. See |
x |
A Chromatograms object. |
name |
A |
value |
The value to replace the variable with. |
Chromatograms
objects can be created using the Chromatograms()
construction function.
Chromatograms
objectThe Chromatograms
object is a container for chromatographic data, which
includes peaks data (retention time and related intensity values, also
referred to as peaks data variables in the context of Chromatograms
) and
metadata of individual chromatogram (so called chromatograms variables).
While a core set of chromatograms variables (the
coreChromatogramsVariables()
) and peaks data variables (the
corePeaksVariables()
) are guaranteed to be provided by a Chromatograms
,
it is possible to add arbitrary variables to a Chromatograms
object.
The Chromatograms
object is designed to contain chromatographic data of a
(large) set of chromatograms. The data is organized linearly and can be
thought of a list of chromatograms, i.e. each element in the Chromatograms
is one chromatogram.
The chromatograms variables information in the Chromatograms
object can
be accessed using the chromData()
function. Specific chromatograms
variables can be accessed by either precising the "columns"
parameter in
chromData()
or using $
. chromData
can be accessed, replaced but
also filtered/subsetted. Refer to the chromData documentation for more
details.
The peaks data variables information in the Chromatograms
object can be
accessed using the peaksData()
function. Specific peaks variables can be
accessed by either precising the "columns"
parameter in peaksData()
or
using $
. peaksData
can be accessed, replaced but also filtered/subsetted.
Refer to the peaksData documentation for more details.
Chromatograms
objectsFunctions that process the chromatograms data in some ways can be applied to
the object either directly or by using the processingQueue
mechanism. The
processingQueue
is a list of processing steps that are stored within the
object and only applied when needed. This was created so that the data can be
processed in a single step and is very useful for larger datasets. This is
even more true as this processing queue will call function that can be applied
on the data in a chunk-wise manner. This allows for parallel processing of
the data and reduces the memory demand. To read more about the
processingQueue
, and how to parallelize your processes, see the
processingQueue documentation.
This needs to be discussed, if we want for example to be able to set a
a backend to ChromBackendMzR
we need to implement backendInitialize()
better. = Support peaksData and chromData as arguments AND have a way to
write .mzml files (which we do not have for chromatographic data).
chromData for a general description of the chromatographic metadata available in the object, as well as how to access, replace and subset them. peaksData for a general description of the chromatographic peaks data available in the object, as well as how to access, replace and subset them. processingQueue for more information on the queuing of processings and parallelization for larger dataset.
## Create a Chromatograms object
chroms <- Chromatograms(backend = ChromBackendMemory())
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