# Define Universe and Group of Genes Based on Expression Data This function categorizes genes into a "Universe" and assigns them into groups such as "Attenuated" or "Outgroup" based on transcription data and thresholds. The universe is defined by thresholds for window size, missing data count, mean transcription levels, and p-values. Genes are further classified into groups based on conditions related to AUC and p-value thresholds. ```r universegroup(completedf, expdf, controlname = "ctrl", stressname = "HS", windsizethres = 50, countnathres = 20, meanctrlthres = 0.5, meanstressthres = 0.5, pvaltheorythres = 0.1, aucctrlthreshigher = -10, aucctrlthreslower = 15, aucstressthres = 15, attenuatedpvalksthres = 2, outgrouppvalksthres = 0.2, showtime = FALSE, verbose = TRUE) ``` ## Arguments - `completedf`: A data frame obtained with the function attenuation. - `expdf`: A data frame containing experiment data that should have columns named 'condition', 'replicate', 'strand', and 'path'. - `controlname`: A string representing the control condition name. Default is `"ctrl"`. - `stressname`: A string representing the stress condition name. Default is `"HS"`. - `windsizethres`: A numeric threshold for the minimum window size. Default is 50. - `countnathres`: A numeric threshold for the maximum number of missing data points (NA values). Default is 20. - `meanctrlthres`: A numeric threshold for the minimum mean transcription value in the control condition. Default is 0.5. - `meanstressthres`: A numeric threshold for the minimum mean transcription value in the stress condition. Default is 0.5. - `pvaltheorythres`: A numeric threshold for the minimum p-value used to define the universe of genes. Default is 0.1. - `aucctrlthreshigher`: A numeric threshold for the lower bound of the control AUC value in the outgroup classification. Default is -10. - `aucctrlthreslower`: A numeric threshold for the upper bound of the control AUC value in the outgroup classification. Default is 15. - `aucstressthres`: A numeric threshold for the minimum stress AUC value used to classify attenuated genes. Default is 15. - `attenuatedpvalksthres`: A numeric threshold for the negative log10 of the p-value (from KS test) for defining attenuated genes. Default is 2. - `outgrouppvalksthres`: A numeric threshold for the maximum KS p-value used to define the outgroup. Default is 0.2. - `showtime`: A logical value indicating if the duration of the function processing should be indicated before ending. Defaults to `FALSE`. - `verbose`: A logical flag indicating whether to print progress messages. Defaults to `TRUE`. ## Returns A modified data frame with two additional columns: `Universe`, indicating whether each gene is part of the universe, and `Group`, classifying the genes into groups such as "Attenuated", "Outgroup", or `NA`. ## Details A transcript belongs to "Universe" if: window_size > windsizethres & Count_NA < countnathres & meanctrl > meanctrlthres & meanstress > meanstressthres & pvaltheory > pvaltheorythres If only one condition is provided, a transcript belongs to "Universe" if: window_size > windsizethres & Count_NA < countnathres & meanctrl > meanctrlthres & pvaltheory > pvaltheorythres A transcript belongs to the groups: - Attenuated : if Universe == TRUE & aucstress > aucstressthres & -log10(pvalks) > attenuatedpvalksthres - Outgroup : if Universe == TRUE & pvalks > outgrouppvalksthres & aucctrl > aucctrlthreshigher & aucctrl < aucctrlthreslower If only one condition is provided: - Attenuated : if Universe == TRUE & aucctrl > aucctrlthreslower - Outgroup : if Universe == TRUE & aucctrl > aucctrlthreshigher & aucctrl < aucctrlthreslower This function is useful for classifying genes in transcriptomics data based on their transcriptional response to different experimental conditions. ## Examples ```r exppath <- system.file("extdata", "exptab.csv", package="tepr") transpath <- system.file("extdata", "cugusi_6.tsv", package="tepr") expthres <- 0.1 ## Calculating necessary results expdf <- read.csv(exppath) transdf <- read.delim(transpath, header = FALSE) avfilt <- averageandfilterexprs(expdf, transdf, expthres, showtime = FALSE, verbose = FALSE) rescountna <- countna(avfilt, expdf, nbcpu = 1, verbose = FALSE) ecdf <- genesECDF(avfilt, verbose = FALSE) resecdf <- ecdf[[1]] nbwindows <- ecdf[[2]] resmeandiff <- meandifference(resecdf, expdf, nbwindows, verbose = FALSE) bytranslistmean <- split(resmeandiff, factor(resmeandiff$transcript)) resknee <- kneeid(bytranslistmean, expdf, verbose = FALSE) resauc <- allauc(bytranslistmean, expdf, nbwindows, verbose = FALSE) resatt <- attenuation(resauc, resknee, rescountna, bytranslistmean, expdf, resmeandiff, verbose = FALSE) ## Testing universegroup resug <- universegroup(resatt, expdf, verbose = FALSE) ``` ## See Also [attenuation]

universegroup function