normalizeCoverage | R Documentation |
The function normalizes coverage values between samples using a scaling factor derived from differences between mean or median of coverage distributions
normalizeCoverage(obj,method="median",chunk.size,save.db,...)
## S4 method for signature 'methylRawList'
normalizeCoverage(
obj,
method = "median",
chunk.size = 1e+06,
save.db = FALSE,
...
)
## S4 method for signature 'methylRawListDB'
normalizeCoverage(
obj,
method = "median",
chunk.size = 1e+06,
save.db = TRUE,
...
)
obj |
|
method |
a string "mean" or "median" which denotes median or mean should be used to calculate scaling factor. (Default:median) |
chunk.size |
Number of rows to be taken as a chunk for processing
the |
save.db |
A Logical to decide whether the resulting object should be saved as flat file database or not, default: explained in Details sections |
... |
optional Arguments used when save.db is TRUE
|
a methylRawList
or methylRawList
object
depending on class of input object
The parameter chunk.size
is only used when working with
methylRawListDB
objects,
as they are read in chunk by chunk to enable processing large-sized
objects which are stored as flat file database.
Per default the chunk.size is set to 1M rows, which should work for
most systems. If you encounter memory problems or
have a high amount of memory available feel free to adjust the
chunk.size
.
The parameter save.db
is per default TRUE for methylDB objects as
methylRawListDB
,
while being per default FALSE for methylRawList
. If you wish to
save the result of an
in-memory-calculation as flat file database or if the size of the
database allows the calculation in-memory,
then you might want to change the value of this parameter.
Altuna Akalin
data(methylKit)
# normalize by the median coverage
newObj = normalizeCoverage(methylRawList.obj,method="median")
# normalize by mean coverage and save to database in folder methylDB
newDBObj = normalizeCoverage(methylRawList.obj,method="mean",
save.db=TRUE,dbdir="methylDB")
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