tepr: Perform the tepr differential nascent rna-seq analysis

In tepr: Transcription Elongation Profiling

Perform the tepr differential nascent rna-seq analysis

Description

This function wraps the different steps of tepr to identify transcripts with a significantly different nascent rna-seq signal.

Usage

tepr(expdf, alldf, expthres, nbcpu = 1, rounding = 10,
controlcondname = "ctrl", stresscondname = "HS", replaceval = NA, pval = 0.1,
significant = FALSE, 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

expdf	A data frame containing experiment data that should have columns named 'condition', 'replicate', 'strand', and 'path'.
alldf	A data frame containing all transcript-related information, including biotype, chromosome, coordinates, transcript, gene, strand, window, ID and scores retrieved from the bedgraph files.
expthres	A numeric value specifying the expression threshold. Transcripts with average expression values below this threshold will be filtered out from the returned transcript vector.
nbcpu	An integer specifying the number of CPU cores to use for parallel computation on transcripts. The number of transcripts is equal to the number of lines provided as input of 'averageandfilterexprs'. Defaults to 1.
rounding	An integer specifying the rounding factor for computing ECDF. Default is 10.
controlcondname	A string specifying the name of the control condition Defaults to "ctrl".
stresscondname	A string specifying the name of the stress condition. Defaults to "HS".
replaceval	A value to replace non-significant attenuation values Defaults to NA.
pval	A numeric value specifying the p-value threshold for significance of the KS test. Defaults to 0.1.
significant	A logical indicating whether to filter out non-significant attenuation values. Defaults to FALSE.
windsizethres	A numeric threshold for the minimum window size. Default is 50.
countnathres	A numeric threshold for the maximum number of missing data points for an experiment (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.

Details

The tepr function calls successively:

• averageandfilterexprs This function calculates the average expression levels for transcripts from alldf that was obtained with the 'preprocessing' function. It filters out transcripts based on the 'expthres' expression threshold. The function also renames the columns in the output data frame to include mean expression values. It returns a list containing the original alldf with the mean columns added and a character vector of transcripts that meet the filtering criteria.

• countna It takes the result of 'averageandfilterexprs' as input and counts the number of NA values for each transcript based on strand and condition. NA represent missing scores that were filtered out from the black list and mappability track. It returns a data frame where each row corresponds to a transcript, along with its associated gene, strand, and the count of NA values.

• genesECDF It takes the result of 'averageandfilterexprs' as input and a) filters the main expression table to retain only the expressed transcripts; b) Splits the data by each transcript; c) For each transcript, computes ECDF values for the score columns while respecting the strand orientation ("plus" or "minus"); d) Combines the ECDF results into a final data frame. It returns a list containing two elements: A data frame with ECDF results for each transcript (concatdf) and an integer indicating the number of rows in each transcript table.

• meandifference It takes the result of 'genesECDF' as input and calculates the mean values, mean Fx (ECDF) and ECDF differences (Fx) for expression data, across different experimental conditions. It returns a data frame that contains, for each condition: mean values for the "value" and "Fx" columns (e.g., mean_value_ctrl, mean_Fx_ctrl) and the differences between the Fx column and coordinate ratios (e.g., diff_Fx_ctrl).

• Splitting Split the results of 'meandifference' by transcripts and stores the list into bytranslistmean.

• allauc It uses the previously computed variable 'bytranslistmean' and it computes the Area Under Curve (AUC) and the differences of AUC between two conditions for a list of transcript data. It returns a data frame containing the AUC and dAUC results for each transcript, along with associated statistical information.

• kneeid It uses the previously computed variable 'bytranslistmean' and identifies the knee point (i.e., point of maximum change) and the maximum difference in the empirical cumulative distribution function (ECDF) for each transcript, across different experimental conditions. It returns a data frame where each row corresponds to a transcript and contains the coordinates of the knee point and the maximum ECDF difference for each condition.

• attenuation It uses the results of the previous functions and it computes the attenuation values for each window of each transcript. It returns a data frame containing the computed attenuation values along with associated transcript information.

• universegroup Using the table produced by 'attenuation', it 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. A transcript belongs to "Universe" if (window_size > windsizethres & Count_NA < countnathres & meanctrl > meanctrlthres & meanstress > meanstressthres & 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.

Value

A list of data.frame made of two elements. The first data.frame is the result of the function 'meandifference': For each condition, it provides the mean values for the "value" and "Fx" columns (e.g. mean_value_ctrl, mean_Fx_ctrl columns) as the differences between the Fx column and coordinate ratios (e.g., diff_Fx_ctrl column). The second data.frame is the result of the function 'universegroup': It contains columns concerning AUC, knee position, attenuation information, and columns defining the universe and groups (see details).

See Also

[averageandfilterexprs()], [countna()], [genesECDF()], [meandifference()], [allauc()], [kneeid()], [attenuation()], [universegroup()], [preprocessing()]

Examples

exppath <-  system.file("extdata", "exptab.csv", package="tepr")
transpath <- system.file("extdata", "cugusi_6.tsv", package="tepr")
expthres <- 0.1

## Reading files
expdf <- read.csv(exppath)
transdf <- read.delim(transpath, header = FALSE)

## Testing tepr
res <- tepr(expdf, transdf, expthres, verbose = FALSE)

tepr documentation built on June 8, 2025, 10:46 a.m.