COTANObjectCreation | R Documentation |
COTAN
shortcutsThese functions create a COTAN
object and/or also run all
the necessary steps until the genes' COEX
matrix is calculated.
COTAN(raw = "ANY")
## S4 method for signature 'COTAN'
proceedToCoex(
objCOTAN,
calcCoex = TRUE,
optimizeForSpeed = TRUE,
deviceStr = "cuda",
cores = 1L,
saveObj = TRUE,
outDir = "."
)
automaticCOTANObjectCreation(
raw,
GEO,
sequencingMethod,
sampleCondition,
calcCoex = TRUE,
optimizeForSpeed = TRUE,
deviceStr = "cuda",
cores = 1L,
saveObj = TRUE,
outDir = "."
)
raw |
a matrix or dataframe with the raw counts |
objCOTAN |
a newly created |
calcCoex |
a Boolean to determine whether to calculate the genes' |
optimizeForSpeed |
Boolean; when |
deviceStr |
On the |
cores |
number of cores to use. Default is 1. |
saveObj |
Boolean flag; when |
outDir |
an existing directory for the analysis output. |
GEO |
a code reporting the GEO identification or other specific dataset code |
sequencingMethod |
a string reporting the method used for the sequencing |
sampleCondition |
a string reporting the specific sample condition or time point. |
Constructor of the class COTAN
proceedToCoex()
takes a newly created COTAN
object (or the
result of a call to dropGenesCells()
) and runs calculateCoex()
automaticCOTANObjectCreation()
takes a raw dataset, creates and
initializes a COTAN
object and runs proceedToCoex()
a COTAN
object
proceedToCoex()
returns the updated COTAN
object with genes'
COEX
calculated. If asked to, it will also store the object, along all
relevant clean-plots, in the output directory.
automaticCOTANObjectCreation()
returns the new COTAN
object with
genes' COEX
calculated. When asked, it will also store the object, along
all relevant clean-plots, in the output directory.
data("test.dataset")
obj <- COTAN(raw = test.dataset)
## In case one needs to run more steps to clean the datatset the following
## might apply
##
## objCOTAN <- initializeMetaDataset(objCOTAN,
## GEO = "test",
## sequencingMethod = "artificial",
## sampleCondition = "test dataset")
## # in case the genes' `COEX` is not needed it can be skipped
## # (e.g. for [cellsUniformClustering()])
## objCOTAN <- proceedToCoex(objCOTAN, calcCoex = FALSE,
## cores = 6L, optimizeForSpeed = TRUE,
## deviceStr = "cuda", saveObj = FALSE)
## Otherwise it is possible to run all at once.
objCOTAN <- automaticCOTANObjectCreation(
raw = test.dataset,
GEO = "code",
sequencingMethod = "10X",
sampleCondition = "mouse_dataset",
calcCoex = TRUE,
saveObj = FALSE,
outDir = tempdir(),
cores = 6L)
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