# Integration of Omics Data Using Linear Modeling

## Documentation for package ‘IntLIM’ version 0.1.0

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

## Help Pages

CorrHeatmap | Plot correlation heatmap
CreateIntLimObject | Generic function to create constructor of MultiDataSet gene object
DistPvalues | Visualize the distribution of unadjusted p-values from linear models
FilterData | Filter input data by abundance values (gene and metabolite data) and number of missing values (metabolite data only).
getCommon | Function that returns a list of all data for samples in common between metabolite and gene datasets
getGeneList | Obtain lists of source IDs for inputting into RaMP or other pathway analysis program Requires sourceIDs and IDtypes Otherwise will output names given The IDtype should be as below +--+ | IDtype | +--+ | enzymeNomenclature | | ensembl | | entrez | | hmdb | | kegg | | uniprot | +--+
getMetabList | Obtain lists of source IDs for inputting into RaMP or other pathway analysis program Requires sourceIDs and IDtypes Otherwise will output names given The IDtype should be as below +-+ | IDtype | +-+ | CAS | | chebi | | chemspider | | hmdb | | kegg | | LIPIDMAPS | | pubchem | +-+
getstatsOneLM | Function that runs linear models for one gene vs all metabolites
HistogramGMPairs | histogram of gene-metabolite pairs depending upon metabolite or gene
IntLimResults-class | IntLimResults class
MetaboliteSet-class | MetaboliteSet class extenstion for MultiDataSet object
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.
PlotDistributions | Get some stats after reading in data
PlotGMPair | scatter plot of gene-metabolite pairs (based on user selection)
PlotPCA | PCA plots of data for QC
ProcessResults | Retrieve significant gene-metabolite pairs, based on adjusted p-values. For each gene-metabolite 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.
pvalCorrVolcano | 'volcano' plot (difference in correlations vs p-values) of all gene-metabolite pairs
ReadData | Read in CSV file
RunIntLim | Run linear models and retrieve relevant statistics
runIntLIMApp | run shiny app
RunLM | Function that runs linear models and returns interaction p-values.
ShowStats | Get some stats after reading in data