export.scores {tigre} | R Documentation |
Exports the results to an SQLite database which can then be browsed with a result browser. The function will export log likelihoods, z-scores, model figures and gene aliases.
export.scores(scores, datasetName='', experimentSet='', databaseFile='database.sqlite', preprocData=NULL, models=NULL, figpath=NULL, aliasTypes=c("SYMBOL", "GENENAME", "ENTREZID"), datasetSource='', datasetDescription='', datasetSaveLocation='', datasetFigureFilename='', experimentTimestamp=as.character(Sys.Date()), figureDesc='', figurePrio=0, regulator=NULL)
scores |
The scoreList to export. |
datasetName |
Name of the dataset in the database. |
experimentSet |
Name of the experiment set in the database. |
databaseFile |
Filename of the database. New database is created if the file does not exist. |
preprocData |
Preprocessed data. This is required in order to generate models and figures and to calculate z-scores. Also, inserting aliases requires preprocessed data. |
models |
Learned models. If not given, the function will generate models if preprocessed data is available. |
figpath |
Figure path. If this is defined, the function will generate figures to the given path instead of inserting them to the database. |
aliasTypes |
Types of aliases that are inserted to the database. |
datasetSource |
Additional information that is inserted to the database if defined. |
datasetDescription |
Additional information that is inserted to the database if defined. |
datasetSaveLocation |
Additional information that is inserted to the database if defined. |
datasetFigureFilename |
Additional information that is inserted to the database if defined. |
experimentTimestamp |
Timestamp that is inserted to the database. The default value is current date in ISO-8601 format. |
figureDesc |
Additional information that is inserted to the database if defined. |
figurePrio |
Additional information that is inserted to the database if defined. |
regulator |
If defined, override the regulator name from scoreList. |
Miika-Petteri Matikainen, Antti Honkela
## Not run: # Load a mmgmos preprocessed fragment of the Drosophila developmental # time series data(drosophila_gpsim_fragment) # FBgn names of target genes targets <- c('FBgn0003486', 'FBgn0033188', 'FBgn0035257') # Load gene annotations library(annotate) aliasMapping <- getAnnMap("ALIAS2PROBE", annotation(drosophila_gpsim_fragment)) # Get the probe identifier for TF 'twi' twi <- get('twi', env=aliasMapping) # Load alternative gene annotations fbgnMapping <- getAnnMap("FLYBASE2PROBE", annotation(drosophila_gpsim_fragment)) # Get the probe identifiers for target genes targetProbes <- mget(targets, env=fbgnMapping) # Rank the targets, filtering weakly expressed genes with average # expression z-score below 1.8 scores <- GPRankTargets(drosophila_gpsim_fragment, TF=twi, testTargets=targetProbes, options=list(quiet=TRUE), filterLimit=1.8) # Export data from scoreList and preprocessed data to a database export.scores(scores, datasetName='Drosophila', experimentSet='GPSIM/GPDISIM', database='database.sqlite', preprocData=drosophila_gpsim_fragment, aliasTypes=c('SYMBOL', 'GENENAME', 'FLYBASE', 'ENTREZID')) ## End(Not run)