Integration of Omics Data Using Linear Modeling

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Documentation for package ‘IntLIM’ version 2.0.2

• DESCRIPTION file.
• User guides, package vignettes and other documentation.

Help Pages

BuildDataAndLines	A helper function for the PlotPair functions (i.e. the highcharter one and the flat, base-R one).
CreateCrossValFolds	Creates multiple cross-validation folds from the data. Format is a list of IntLIMData training and testing pairs. The "training" slot contains all data except that in the given fold, and the "testing" contains all data in the fold.
DistPvalues	Visualize the distribution of unadjusted p-values from linear models
DistRSquared	Visualize the distribution of unadjusted p-values from linear models
FilterData	Filter input data by abundance values and number of missing values.
FilterDataFolds	Filter input data by abundance values (analyte data) and number of missing values.
getQuantileForInteractionCoefficient	Function that gets numeric cutoffs from percentile
getStatsAllLM	Function that runs Linear Models for all analytes
getstatsOneLM	Function that runs linear models for analyte vs. all analytes of the other type
HistogramPairs	histogram of analyte pairs depending upon independent or outcome analyte
InteractionCoefficientGraph	Graphs a scatterplot of pairs vs. the interaction coefficient for the pair
IntLimData-class	IntLimData class
IntLimResults-class	IntLimResults class
MarginalEffectsGraph	Creates a dataframe of the marginal effect of phenotype
MarginalEffectsGraphDataframe	Creates a dataframe of the marginal effect of phenotype
multi.which	A which for multidimensional arrays. Mark van der Loo 16.09.2011
OutputData	Output data into individual CSV files. All data will be zipped into one file with all data.
OutputResults	Output results into a zipped CSV file. Results include gene and metabolite pairs, along with model interaction p-values, and correlations in each group being evaluated.
PermutationCountSummary	Return the number of significant analytes and the number of permutations in which each analyte is significant. If plot = TRUE, show a box plot of number of significant analytes over permutations, overlaid with the number of significant analytes in the original data.
PermutationPairSummary	Return the number of significant analytes / pairs per permutation and the number of permutations in which each analyte is significant. If plot = TRUE, show a box plot of number of significant analytes over permutations, overlaid with the number of significant analytes in the original data.
PermuteIntLIM	Run permutations of the IntLIM code to search for random cross-omic associations in dataset
PlotDistributions	Get some stats after reading in data
PlotFoldOverlapUpSet	Makes an UpSet plot showing the filtered pairs of analytes found in each fold. This plot should only be made for cross-validation data.
PlotPair	scatter plot of pairs (based on user selection)
PlotPairFlat	scatter plot of pairs (based on user selection). This version does not use highcharter and instead plots a base R plot.
PlotPCA	PCA plots of data for QC
ProcessResults	Retrieve significant pairs, based on adjusted p-values. For each pair that is statistically significant, calculate the correlation within group1 (e.g. cancer) and the correlation within group2 (e.g. non-cancer). Users can then remove pairs with a difference in correlations between groups 1 and 2 less than a user-defined threshold.
ProcessResultsAllFolds	Retrieve significant pairs, based on adjusted p-values, interaction coefficient percentile, and r-squared values. This is a wrapper for ProcessResults.
ProcessResultsContinuous	Retrieve significant pairs (aka filter out nonsignificant pairs) based on value of analyte:type interaction coefficient from linear model
pvalCoefVolcano	'volcano' plot (difference in correlations vs p-values) of all pairs
PValueBoxPlots	Visualize the distribution of unadjusted p-values for all covariates from linear models using a bar chart.
ReadData	Read in CSV file
RemovePlusInCovars	RemovePlusInCovars
RunCrossValidation	Runs the cross-validation end-to-end using the following steps: 1. Create multiple cross-validation folds from the data. 2. Filter each fold using the filtering criteria applied to the entire dataset. 3. Run IntLIM for all folds. 4. Process the results for all folds.
RunIntLim	Run linear models and retrieve relevant statistics
RunIntLimAllFolds	Run linear models for all data folds. This is a wrapper to RunIntLim.
runIntLIMApp	run shiny app
RunLM	Function that runs linear models and returns interaction p-values.
ShowStats	Get some stats after reading in data