This function runs the FastCMH algorithm on a particular data set.
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The folder in which the data is saved. If the any of
The filename for the data file. Default is
The filename for the phenotype label file. Default is
The filename for the covariate label file. Default is
The value of the FWER; must be a number between 0 and 1.
The maximum length of significant intervals which is
considered. Must be a non-negative integer. For example,
A flag which will turn printing to screen on/off.
A flag which controls whether or not all the intervals
(less than minimum attainable pvalue) will be returned. Default is
A flag which controls whether or not Gilbert's Tarone FDR
procedure (while accounting for positive regression dependence) is
performed. Default is
A flag which controls whether or not Gilbert's
Tarone FDR procedure uses the dependent formulation by Benjamini and
Yekutieli (2001), which further adjusts alpha by dividing by the harmonic
mean. This flag is only used if
A flag which controls whether or not the results are
saved to file. By default,
A string which gives the filename to which the output
is saved (needs to have
A string which gives the path to which the output will
be saved (needs to have
This function runs the FastCMH algorithm on a particular data set in order to discover intervals that are statistically significantly associated with a particular label, while accounting for categorical covariates.
The user must either supply the folder, which contains files named
"cov.txt", or the
non-default filenames must be specified individually. See the descriptions of arguments
cov to see the format of
the input files, or make a small sample data file using the
By default, filtered results are provided. The user also has the option
of using an FDR procedure rather than the standard FWER-preserving
runfastcmh will return a list if
setting), otherwise it will save the list in an .RData file. The fields
of the list are:
a dataframe listing the significant intervals, after
the start and end points of the interval (inclusive), and the
p-value for that interval.
a dataframe listing all the significant intervals
before filtering. The filtering compares the overlapping intervals and
returns the interval with the smallest p-value in each cluster of
overlapping intervals. Dataframe has has structure as
(if doFDR==TRUE) significant intervals using Gilbert's
FDR-Tarone procedure, after filtering. Dataframe has same structure as
(if doFDR==TRUE) a dataframe listing all the significant intervals before filtering. See description of
(if saveAllPvals==TRUE) a dataframe listing all
the testable intervals, many of which will not be significant. Dataframe
has same structure as
Together with histFreq gives a histogram of maximum attainable CMH statistics.
Histogram of maximum attainable CMH statistics (only reliable in the testable range).
a character string summarising the results. Use
cat(...$summary) to print the results with the correct
a list containing (i)
details, a character
string summarising the runtime values for the experiment - use
cat(...$timing$details) for correct indentation, etc.
exec, the total execution time. (iii)
init, the time
to initialise the objects. (iv)
fileIO, the time to read the input
compSigThresh, the time to compute the significance
compSigInt, the time to compute the significant
Felipe Llinares Lopez, Dean Bodenham
Gilbert, P. B. (2005) A modified false discovery rate multipl-comparisons procedure for discrete data, applied to human immunodeficiency virus genetics. Journal of the Royal Statistical Society: Series C (Applied Statistics), 54(1), 143-158.
Benjamini, Y., Yekutieli, D. (2001). The control of the false discovery rate in multiple testing under dependency. Annals of Statistics, 29(4), 1165-1188.
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#Example with default naming convention used for data, label and cov files # Note: using "/data/" as the argument for folder # accesses the data/ directory in the fastcmh package folder mylist <- runfastcmh("/data/") #Example where the progress will be shown mylist <- runfastcmh(folder="/data/", showProcessing=TRUE) #Example where many parameters are specified mylist <- runfastcmh(folder="/data/", data="data2.txt", alpha=0.01, Lmax=7) #Example where Gilbert's Tarone-FDR procedure is used mylist <- runfastcmh("/data/", doFDR=TRUE) #Example where FDR procedure takes some dependence structures into account mylist <- runfastcmh("/data/", doFDR=TRUE, useDependenceFDR=TRUE)
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