| Title: | Detection of Statistically Significant Combinations of SNPs in Association Mapping |
|---|---|
| Description: | A significant pattern mining-based toolbox for region-based genome-wide association studies and higher-order epistasis analyses, implementing the methods described in Llinares-López et al. (2017) <doi:10.1093/bioinformatics/btx071>. |
| Authors: | Felipe Llinares-López [aut, cph], Laetitia Papaxanthos [aut, cph], Damian Roqueiro [aut, cph], Matthew Baker [ctr], Mikołaj Rybiński [ctr], Uwe Schmitt [ctr], Dean Bodenham [aut, cre, cph], Karsten Borgwardt [aut, fnd, cph] |
| Maintainer: | Dean Bodenham <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 0.6.1 |
| Built: | 2024-11-27 06:31:41 UTC |
| Source: | CRAN |
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.
modeEither 'regionGWAS' or 'higherOrderEpistasis'.
alphaA numeric value setting the Family-wise Error Rate (FWER).
Must be strictly between 0 and 1. Default
value is 0.05.
max_comb_sizeA 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.
readFilesRead 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.
setModeCan set/change the mode, but note that any
data files will need to read in again using
the readFiles command.
setTargetFWERCan set/change the Family-wise
Error Rate (FWER). Takes a numeric
parameter alpha, strictly between
0 and 1.
executeOnce 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.
getSummaryReturns a data frame with a summary of the
results from the execution, but not any
significant regions/itemsets. See
getSignificantRegions,
getSignificantInteractions, and
getSignificantClusterRepresentatives.
writeSummaryDirectly write the information
from getSummary to file.
regionGWAS MethodsgetSignificantRegionsReturns a data frame with the
the significant regions. Only valid when
mode='regionGWAS'.
getSignificantClusterRepresentativesReturns 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'.
writeSignificantRegionsWrites the data from
getSignificantRegions to file, which
must be specified in the parameter
path.
Only valid when mode='regionGWAS'.
writeSignificantClusterRepresentativesWrites the data
from
getSignificantClusterRepresentatives to
file, which must be specified in the parameter
path.
Only valid when mode='regionGWAS'.
higherOrderEpistasis MethodsgetSignificantInteractionsReturns the frame
from getSignificantInteractions to
file, which must be specified in the parameter
path. Only valid
when mode='higherOrderEpistasis'.
writeSignificantInteractionsWrites 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.
## 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## 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
Path to CASMAP_example_covariates_1.txt in inst/extdata.
The covariates categories for the data set
CASMAP_example_data_1.txt, the path to which is given by
getExampleDataFilename.
getExampleCovariatesFilename()getExampleCovariatesFilename()
A single column vector of 100 labels, each of which
is 0 or 1 (same format as labels file).
Path to the file containing the labels, for reading in to
CASMAP object using the readFiles function.
getExampleDataFilename,
getExampleLabelsFilename
covfile <- getExampleCovariatesFilename()covfile <- getExampleCovariatesFilename()
Path to CASMAP_example_data_1.txt in inst/extdata.
A dataset containing binary samples for the regionGWAS method.
There are accompanying labels and covariates dataset.
getExampleDataFilename()getExampleDataFilename()
A matrix of 0s and 1s, with 1000 rows (features)
and 100 columns
(samples). In other words, each column is a sample, and each sample
has 1000 binary features.
Path to the file containing the data, for reading in to
CASMAP object using the readFiles function.
Note that the significant region is [99, 102].
getExampleLabelsFilename,
getExampleCovariatesFilename
datafile <- getExampleDataFilename()datafile <- getExampleDataFilename()
Path to CASMAP_example_labels_1.txt in inst/extdata.
A dataset containing the binary labels for the data in the file
CASMAP_example_data_1.txt, the path to which is given by
getExampleDataFilename.
getExampleLabelsFilename()getExampleLabelsFilename()
A single column of 100 labels, each of which is either 0
or 1.
Path to the file containing the labels, for reading in to
CASMAP object using the readFiles function.
getExampleDataFilename,
getExampleCovariatesFilename
labelsfile <- getExampleLabelsFilename()labelsfile <- getExampleLabelsFilename()