Description Details Fields Base method, for both modes regionGWAS Methods higherOrderEpistasis Methods References Examples
Constructor for CASMAP class object.
Constructor for CASMAP class object, which needs the mode
parameter to be set by the user. Please see the examples.
mode
Either 'regionGWAS'
or 'higherOrderEpistasis'
.
alpha
A numeric value setting the Family-wise Error Rate (FWER).
Must be strictly between 0
and 1
. Default
value is 0.05
.
max_comb_size
A numeric specifying the maximum length of
combinations. For example, if set to 4
,
then only combinations of size between 1
and 4
(inclusive) will be considered.
To consider combinations of arbitrary (maximal)
length, use value 0
, which is the default
value.
readFiles
Read the data, label and possibly covariates
files. Parameters are genotype_file
,
for the data, phenotype_file
for the
labels and (optional) covariates_file
for the covariates. The option
plink_file_root
is not supported
in the current version, but will be supported
in future versions.
setMode
Can set/change the mode, but note that any
data files will need to read in again using
the readFiles
command.
setTargetFWER
Can set/change the Family-wise
Error Rate (FWER). Takes a numeric
parameter alpha
, strictly between
0
and 1
.
execute
Once the data files have been read, can execute the algorithm. Please note that, depending on the size of the data files, this could take a long time.
getSummary
Returns a data frame with a summary of the
results from the execution, but not any
significant regions/itemsets. See
getSignificantRegions
,
getSignificantInteractions
, and
getSignificantClusterRepresentatives
.
writeSummary
Directly write the information
from getSummary
to file.
regionGWAS
MethodsgetSignificantRegions
Returns a data frame with the
the significant regions. Only valid when
mode='regionGWAS'
.
getSignificantClusterRepresentatives
Returns a data
frame with the
the representatives of the significant
clusters. This will be a subset of the regions
returned from getSignificantRegions
.
Only valid when mode='regionGWAS'
.
writeSignificantRegions
Writes the data from
getSignificantRegions
to file, which
must be specified in the parameter
path
.
Only valid when mode='regionGWAS'
.
writeSignificantClusterRepresentatives
Writes the data
from
getSignificantClusterRepresentatives
to
file, which must be specified in the parameter
path
.
Only valid when mode='regionGWAS'
.
higherOrderEpistasis
MethodsgetSignificantInteractions
Returns the frame
from getSignificantInteractions
to
file, which must be specified in the parameter
path
. Only valid
when mode='higherOrderEpistasis'
.
writeSignificantInteractions
Writes a data frame with
the significant interactions. Only valid
when mode='higherOrderEpistasis'
.
A. Terada, M. Okada-Hatakeyama, K. Tsuda and J. Sese Statistical significance of combinatorial regulations, Proceedings of the National Academy of Sciences (2013) 110 (32): 12996-13001
F. Llinares-Lopez, D. G. Grimm, D. Bodenham, U. Gieraths, M. Sugiyama, B. Rowan and K. Borgwardt, Genome-wide detection of intervals of genetic heterogeneity associated with complex traits, ISMB 2015, Bioinformatics (2015) 31 (12): i240-i249
L. Papaxanthos, F. Llinares-Lopez, D. Bodenham, K .Borgwardt, Finding significant combinations of features in the presence of categorical covariates, Advances in Neural Information Processing Systems 29 (NIPS 2016), 2271-2279.
F. Llinares-Lopez, L. Papaxanthos, D. Bodenham, D. Roqueiro and K .Borgwardt, Genome-wide genetic heterogeneity discovery with categorical covariates. Bioinformatics 2017, 33 (12): 1820-1828.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 | ## An example using the "regionGWAS" mode
fastcmh <- CASMAP(mode="regionGWAS") # initialise object
datafile <- getExampleDataFilename() # file name of example data
labelsfile <- getExampleLabelsFilename() # file name of example labels
covfile <- getExampleCovariatesFilename() # file name of example covariates
# read the data, labels and covariate files
fastcmh$readFiles(genotype_file=getExampleDataFilename(),
phenotype_file=getExampleLabelsFilename(),
covariate_file=getExampleCovariatesFilename() )
# execute the algorithm (this may take some time)
fastcmh$execute()
#get the summary results
summary_results <- fastcmh$getSummary()
#get the significant regions
sig_regions <- fastcmh$getSignificantRegions()
#get the clustered representatives for the significant regions
sig_cluster_rep <- fastcmh$getSignificantClusterRepresentatives()
## Another example of regionGWAS
fais <- CASMAP(mode="regionGWAS") # initialise object
# read the data and labels, but no covariates
fastcmh$readFiles(genotype_file=getExampleDataFilename(),
phenotype_file=getExampleLabelsFilename())
## Another example, doing higher order epistasis search
facs <- CASMAP(mode="higherOrderEpistasis") # initialise object
|
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