read.ilmn(files=NULL, ctrlfiles=NULL, path=NULL, ctrlpath=NULL, probeid="Probe", annotation=c("TargetID", "SYMBOL"), expr="AVG_Signal", other.columns="Detection", sep="\t", quote="\"", verbose=TRUE, ...)Detection is usually sufficient to identify the columns containing detection p-values.TRUE to report names of profile files being read.read.columns.EListRaw-class object with the following components:
annotation that are found in the input files.other.columns found in the input files.files and ctrlfiles are not NULL, this function will combine the data read from the two file types and save them to an EListRaw-class object.
If one of them is NULL, then only the required data are read in.Probe types are indicated in the Status column of genes, a component of the returned EListRaw-class object.
There are totally seven types of control probes including negative, biotin, labeling, cy3_hyb, housekeeping, high_stringency_hyb or low_stringency_hyb.
Regular probes have the probe type regular.
The Status column will not be created if ctrlfiles is NULL.
To read in columns other than probeid, annotation and expr, users needs to specify keywords in other.columns.
One keyword corresponds to one type of columns.
Examples of keywords are "Detection", "Avg_NBEADS", "BEAD_STDEV" etc.
read.ilmn.targets reads in Illumina expression data using the file information extracted from a target data frame which is often created by the readTargets function.
neqc performs normexp by control background correction, log transformation and quantile between-array normalization for Illumina expression data.
normexp.fit.control estimates the parameters of the normal+exponential convolution model with the help of negative control probes.
propexpr estimates the proportion of expressed probes in a microarray.
## Not run:
# x <- read.ilmn(files="sample probe profile.txt",
# ctrlfiles="control probe profile.txt")
# ## End(Not run)
# See neqc and beadCountWeights for other examples using read.ilmn
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