# Generate an object from omicsData and/or statRes objects to pass to trelliscope building functions Either an omicData and/or a statRes object are accepted. omicData must be transformed and normalized, unless the data is isobaric protein or NMR data. If group_designation() has been run on the omicData object to add "main_effects", the resulting plots will include groups. The main effects group_designation and e_meta columns are merged to the e_data in long format to create the trelliData.omics dataframe, and e_meta is merged to statRes in long format to create trelliData.stat dataframe. ```r as.trelliData(omicsData = NULL, statRes = NULL) ``` ## Arguments - `omicsData`: an object of the class 'pepData', 'isobaricpepData', proData', 'metabData', 'lipidData', or 'nmrData', created by `as.pepData`, `as.isobaricpepData`, `as.proData`, `as.metabData`, `as.lipidData`, or `as.nmrData`, respectively. - `statRes`: statRes an object of the class 'statRes', created by `imd_anova` ## Returns An object of class 'trelliData' containing the raw data and optionally, statRes. To be passed to trelliscope building functions. ## Examples ```r library(pmartRdata) ########################### ## MS/NMR OMICS EXAMPLES ## ########################### # Transform the data omicsData <- edata_transform(omicsData = pep_object, data_scale = "log2") # Group the data by condition omicsData <- group_designation(omicsData = omicsData, main_effects = c("Phenotype")) # Apply the IMD ANOVA filter imdanova_Filt <- imdanova_filter(omicsData = omicsData) omicsData <- applyFilt(filter_object = imdanova_Filt, omicsData = omicsData, min_nonmiss_anova = 2) # Normalize my pepData omicsData <- normalize_global(omicsData, "subset_fn" = "all", "norm_fn" = "median", "apply_norm" = TRUE, "backtransform" = TRUE) # Implement the IMD ANOVA method and compute all pairwise comparisons # (i.e. leave the `comparisons` argument NULL) statRes <- imd_anova(omicsData = omicsData, test_method = 'combined') # Generate the trelliData object trelliData2 <- as.trelliData(omicsData = omicsData) trelliData3 <- as.trelliData(statRes = statRes) trelliData4 <- as.trelliData(omicsData = omicsData, statRes = statRes) ###################### ## RNA-SEQ EXAMPLES ## ###################### # Group data by condition omicsData_seq <- group_designation(omicsData = rnaseq_object, main_effects = c("Virus")) # Filter low transcript counts omicsData_seq <- applyFilt(filter_object = total_count_filter(omicsData = omicsData_seq), omicsData = omicsData_seq, min_count = 15) # Select a normalization and statistics method (options are 'edgeR', 'DESeq2', and 'voom'). # See ?difexp_seq for more details statRes_seq <- diffexp_seq(omicsData = omicsData_seq, method = "voom") # Generate the trelliData object trelliData_seq2 <- as.trelliData(omicsData = omicsData_seq) trelliData_seq3 <- as.trelliData(statRes = statRes_seq) trelliData_seq4 <- as.trelliData(omicsData = omicsData_seq, statRes = statRes_seq) ``` ## Author(s) David Degnan, Lisa Bramer

as.trelliData function