yoloHandleMake takes the a minimum of three parameters specified below
and creates either a
seHandle or an
object which can be subset and
manipulated without reading any data off the disk. At a minimum,
the rowData, colData, and backend file are needed. By default, we
assume that the file format will be an sqlite file and the format
will be sparse, but these can be differentially specified to an
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yoloHandleMake(rowData, colData, lookupFileName, lookupTableName = "data", lookupFileType = "sqlite", lookupFileFormat = "sparse") ## S4 method for signature 'ANY,ANY,character' yoloHandleMake(rowData, colData, lookupFileName, lookupTableName = "data", lookupFileType = "sparse", lookupFileFormat = "sqlite")
A data frame of per-sample annotations
String pointing to the file backend
= "data" Name of data table in file backend
= "sparse" can also be "normal". The type of matrix contained in the file/table.
= "sqlite" can also be "HDF5". The format of the file providing the data backend of this constructed object.
A series of QC measures will be called when running this function.
First, if the rowData input is a
GRanges object, then the output
will be an
rseHandle. If the rowData input is a
object, then the resulting object will be an
The length of the rowData input after coercion must be equal to the maximum
index of the row in the particular file/table.
Next, the colData length must be equal to the maximum index of the column in the linked file/table.
These maximums will be pulled on constuction, which ensures that the table name, by default is 'data' exists in the sql file.
The sparse format demands that there are three columns in the file/table being referenced; two of which contain 'row' and 'column' as labels that indicate the index of the row and column elements.
The normal format demands that there are n columns in the file/table being referenced where n is the numerb of samples and the columns are named
Returns either a
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library(GenomicRanges) nm <- c("chr", "start", "stop") rt <- read.table(system.file("extdata", "dat1_row.bed", package="yolo")) rowData <- GRanges(setNames(rt, nm)) colData <- read.table(system.file("extdata", "dat1_col.txt", package="yolo")) sqlf <- system.file("extdata", "dat1.sqlite", package="yolo") d1 <- yoloHandleMake(rowData, colData, lookupFileName=sqlf)
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