Calculate Area Under Curve (AUC) and Differences of AUC for Transcript Data

Description

This function computes the Area Under Curve (AUC) and the differences of AUC between two conditions for a list of transcript data. It supports parallel computation for efficiency. If only one condition is given, the differences are not computed.

Usage

allauc(bytranslistmean, expdf, nbwindows, nbcpu = 1,
 controlcondname = "ctrl", stresscondname = "HS", showtime = FALSE,
 verbose = TRUE)

Arguments

Details

The function first checks if exactly two conditions are present in 'expdf'. If so, it computes the differences in AUC between the two conditions using a Kolmogorov-Smirnov test. It then calculates the AUC for all conditions against a reference line (y=x). Results are merged by transcript and include adjusted p-values.

Value

A data frame containing the AUC and dAUC results for each transcript, along with associated statistical information.

See Also

[genesECDF]

Examples

exppath <- system.file("extdata", "exptab.csv", package="tepr")
transpath <- system.file("extdata", "cugusi_6.tsv", package="tepr")
expthres <- 0.1
## Reading tables
expdf <- read.csv(exppath)
transdf <- read.delim(transpath, header = FALSE)
## Computing intermediate steps
avfilt <- averageandfilterexprs(expdf, transdf, expthres,
 showtime = FALSE, verbose = FALSE)
ecdf <- genesECDF(avfilt, expdf, verbose = FALSE)
resecdf <- ecdf[[1]]
nbwindows <- ecdf[[2]]
meandiff <- meandifference(resecdf, expdf, nbwindows,
 verbose = FALSE)
bytranslistmean <- split(meandiff, factor(meandiff$transcript))
## Testing allauc
res <- allauc(bytranslistmean, expdf, nbwindows, verbose = FALSE)

Calculate Attenuation from AUC and Other Transcript Features

Description

This function computes the attenuation values for each window of each transcript based on the data frames obtained with the functions 'allauc', 'kneeid', and 'countna'.

Usage

attenuation(allaucdf, kneedf, matnatrans, bytranslistmean, expdf, dfmeandiff,
nbcpu = 1, significant = FALSE, replaceval = NA, pval = 0.1,
showtime = FALSE, verbose = TRUE)

Arguments

Details

The function merges several data frames to create a comprehensive dataset for each transcript. It computes mean values for the "up" and "down" segments of the transcript. The direction is determined by comparing the coordinates to the knee values. up = coord < knee and down = coord > knee. The up and down indexes are then retrieved and the attenuation scores are computed as: att <- 100 - downmean / upmean * 100

Value

A data frame containing the computed attenuation values along with associated transcript information.

See Also

[allauc()], [kneeid()], [countna()], [meandifference()]

Examples

exppath <- system.file("extdata", "exptab.csv", package="tepr")
transpath <- system.file("extdata", "cugusi_6.tsv", package="tepr")
expthres <- 0.1
## Reading tables
expdf <- read.csv(exppath)
transdf <- read.delim(transpath, header = FALSE)
## Computing intermediate steps
avfilt <- averageandfilterexprs(expdf, transdf, expthres,
 showtime = FALSE, verbose = FALSE)
rescountna <- countna(avfilt, expdf, nbcpu = 1, verbose = FALSE)
ecdf <- genesECDF(avfilt, expdf, 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)
## Testing attenuation
resatt <- attenuation(resauc, resknee, rescountna, bytranslistmean, expdf,
 resmeandiff, verbose = FALSE)

Calculate Average Expression and Filter Transcript Data

Description

This function calculates the average expression levels for transcripts from a provided expression data frame and filters out transcripts based on a specified expression threshold. The function also renames the columns in the output data frame to include mean expression values.

Usage

averageandfilterexprs(expdf, alldf, expthres, showtime = FALSE,
verbose = TRUE)

Arguments

Details

If no transcript is selected as expressed, the function throws an error.

Value

A list containing:

Examples

exppath <- system.file("extdata", "exptab.csv", package="tepr")
transpath <- system.file("extdata", "cugusi_6.tsv", package="tepr")
expthres <- 0.1
## Reading tables
expdf <- read.csv(exppath)
transdf <- read.delim(transpath, header = FALSE)
## Testing averageandfilterexprs
resavfilt <- averageandfilterexprs(expdf, transdf, expthres,
 showtime = FALSE, verbose = FALSE)

Blacklist High Mappability Regions in Genomic Data

Description

This function processes genomic data to remove scores that fall within blacklisted regions or have low mappability, and computes weighted means for overlapping windows. The process ensures the integrity of genomic scores by focusing on high mappability regions and excluding blacklisted intervals.

Usage

blacklisthighmap(maptrackpath, blacklistpath, exptabpath,
 nbcputrans, allwindowsbed, windsize, genomename, saveobjectpath = NA,
 tmpfold = file.path(tempdir(), "tmptepr"), reload = FALSE, showtime = FALSE,
 showmemory = FALSE, chromtab = NA, forcechrom = FALSE, verbose = TRUE)

Arguments

Details

The 'blacklisthighmap' function iterates through chromosomes, processes genomic scores by removing those overlapping with blacklisted regions, and ensures that scores within windows are computed using a weighted mean when overlaps occur. The function uses parallel processing for efficiency and supports saving (saveobjectpath) and reloading (reload) intermediate results to optimize workflow.

The main steps include: - Reading and processing bedGraph values. - Removing scores overlapping with blacklisted or low mappability regions. - Computing weighted means for overlapping scores in genomic windows. - Saving the processed results to specified path (tmpfold).

If chromtab is left to NA, the chromosome information is automatically retrieved from the UCSC server using 'genomename'. Otherwise, the Seqinfo object can be retrieved with: chromtab <- rtracklayer::SeqinfoForUCSCGenome(genomename)

Value

This function does not return a value directly. It saves intermediate results to 'tmpfold'. These intermediates files are then combined by the function 'createtablescores'.

See Also

[createtablescores][makewindows]

Examples

exptabpath <- system.file("extdata", "exptab-preprocessing.csv", package="tepr")
gencodepath <- system.file("extdata", "gencode-chr13.gtf", package = "tepr")
maptrackpath <- system.file("extdata", "k50.umap.chr13.hg38.0.8.bed",
 package = "tepr")
blacklistpath <- system.file("extdata", "hg38-blacklist-chr13.v2.bed",
 package = "tepr")
windsize <- 200
genomename <- "hg38"
chromtabtest <- rtracklayer::SeqinfoForUCSCGenome(genomename)
allchromvec <- GenomeInfoDb::seqnames(chromtabtest)
chromtabtest <- chromtabtest[allchromvec[which(allchromvec == "chr13")], ]
## Copying bedgraphs to the current directory
expdfpre <- read.csv(exptabpath)
bgpathvec <- sapply(expdfpre$path, function(x) system.file("extdata", x,
 package = "tepr"))
expdfpre$path <- bgpathvec
write.csv(expdfpre, file = "exptab-preprocessing.csv", row.names = FALSE,
 quote = FALSE)
exptabpath <- "exptab-preprocessing.csv"
## Necessary result to call blacklisthighmap
allannobed <- retrieveanno(exptabpath, gencodepath, verbose = FALSE)
allwindowsbed <- makewindows(allannobed, windsize, verbose = FALSE)
## Test blacklisthighmap
blacklisthighmap(maptrackpath, blacklistpath, exptabpath,
 nbcputrans = 1, allwindowsbed, windsize, genomename,
 chromtab = chromtabtest, verbose = FALSE)

Check Validity of Experiment Table

Description

The 'checkexptab' function verifies the structure and content of an experiment table to ensure it meets specific formatting requirements. It checks for the presence of required columns, and validates that the 'direction' and 'strand' columns contain only allowable values.

Usage

checkexptab(exptab)

Arguments

Details

The function performs the following checks: - The column names of 'exptab' must match exactly: '"condition"', '"replicate"', '"direction"', and '"strand"'. - The 'direction' column must contain only '"forward"' and '"reverse"'. - The 'strand' column must contain only '"plus"' and '"minus"'.

Value

If the experiment table is valid, the function returns 'NULL'. If the table is invalid, the function throws an error specifying the issue.

Examples

# Create a valid experiment table
exptab <- data.frame(
 condition = c("cond1", "cond2"),
 replicate = c(1, 1),
 direction = c("forward", "reverse"),
 strand = c("plus", "minus"),
 path = c("toto/", "toto/"))
checkexptab(exptab) # Should pass without errors
# Invalid experiment table (wrong column names)
invalid_exptab <- data.frame(
 cond = c("cond1", "cond2"),
 rep = c(1, 1),
 dir = c("forward", "reverse"),
 str = c("+", "-"),
 paths = c("toto/", "toto/"))
try(checkexptab(invalid_exptab))

Count NA values per transcript and condition

Description

This function takes a list of expression data frames, a condition information data frame, 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. The function operates in parallel on transcripts to speed up the process using multiple CPU cores.

Usage

countna(allexprsdfs, expdf, nbcpu = 1, showtime = FALSE, verbose = TRUE)

Arguments

Value

A data frame where each row corresponds to a transcript, along with its associated gene, strand, and the count of NA values.

See Also

[averageandfilterexprs]

Examples

exppath <- system.file("extdata", "exptab.csv", package="tepr")
transpath <- system.file("extdata", "cugusi_6.tsv", package="tepr")
expthres <- 0.1
## Calculating averageandfilterexprs to call countNA
expdf <- read.csv(exppath)
transdf <- read.delim(transpath, header = FALSE)
avfilttest <- averageandfilterexprs(expdf, transdf, expthres,
 showtime = FALSE, verbose = FALSE)
## Testing countna
res <- countna(avfilttest, expdf, nbcpu = 1, verbose = FALSE)

Create a Unified Table of Scores

Description

This function processes and combines table scores of each bedgraph and each chromosome stored in the temporary folder into a unified table.

Usage

createtablescores(tmpfold, exptabpath, showmemory = FALSE, showtime = FALSE,
 savefinaltable = TRUE, finaltabpath = tempdir(), finaltabname = "anno.tsv",
 verbose)

Arguments

Details

This function first merges files belonging to the same experiment and direction. These files are combined into a single table providing two columns per experiment. The first gives the name of the experiment and the second the scores. The resulting table also includes annotations for each transcript.

Value

A data frame containing the unified table of scores.

See Also

[blacklisthighmap]

Examples

exptabpath <- system.file("extdata", "exptab-preprocessing.csv", package="tepr")
gencodepath <- system.file("extdata", "gencode-chr13.gtf", package = "tepr")
maptrackpath <- system.file("extdata", "k50.umap.chr13.hg38.0.8.bed",
package = "tepr")
blacklistpath <- system.file("extdata", "hg38-blacklist-chr13.v2.bed",
 package = "tepr")
tmpfoldpath <- file.path(tempdir(), "tmptepr")
windsize <- 200
genomename <- "hg38"
chromtabtest <- rtracklayer::SeqinfoForUCSCGenome(genomename)
allchromvec <- GenomeInfoDb::seqnames(chromtabtest)
chromtabtest <- chromtabtest[allchromvec[which(allchromvec == "chr13")], ]
## Copying bedgraphs to the current directory
expdfpre <- read.csv(exptabpath)
bgpathvec <- sapply(expdfpre$path, function(x) system.file("extdata", x,
 package = "tepr"))
expdfpre$path <- bgpathvec
write.csv(expdfpre, file = "exptab-preprocessing.csv", row.names = FALSE,
 quote = FALSE)
exptabpath <- "exptab-preprocessing.csv"
## Necessary result to call createtablescores
allannobed <- retrieveanno(exptabpath, gencodepath, verbose = FALSE)
allwindowsbed <- makewindows(allannobed, windsize, verbose = FALSE)
blacklisthighmap(maptrackpath, blacklistpath, exptabpath, nbcputrans = 1,
 allwindowsbed, windsize, genomename = genomename, chromtab = chromtabtest,
 verbose = FALSE)
## Calling the function to test
finaltabtest <- createtablescores(tmpfold = tmpfoldpath, exptabpath,
 savefinaltable = FALSE, verbose = FALSE)

Compute ECDF for Genes Based on Expression Data

Description

This function calculates the empirical cumulative distribution function (ECDF) for expressed genes across multiple transcripts. It processes the expression data to filter out non-expressed transcripts, compute ECDF values for each transcript, and combine the results into a unified data frame. The function operates in parallel for speed optimization.

Usage

genesECDF(allexprsdfs, expdf, nbcpu = 1, rounding = 10,
showtime = FALSE, verbose = TRUE)

Arguments

Details

The function performs several steps:

1. Filters the main expression table to retain only the expressed transcripts.

2. Splits the data by each transcript.

3. For each transcript, computes ECDF values for the score columns while respecting the strand orientation ("plus" or "minus").

4. Combines the ECDF results into a final data frame.

The function uses parallel processing to compute ECDF for each transcript simultaneously, making it faster on systems with multiple CPU cores.

Value

A list containing two elements:

See Also

[averageandfilterexprs]

Examples

exppath <- system.file("extdata", "exptab.csv", package="tepr")
transpath <- system.file("extdata", "cugusi_6.tsv", package="tepr")
expthres <- 0.1
## Calculating averageandfilterexprs and countNA to call genesECDF
expdf <- read.csv(exppath)
transdf <- read.delim(transpath, header = FALSE)
avfilttest <- averageandfilterexprs(expdf, transdf, expthres,
 showtime = FALSE, verbose = FALSE)
countnatest <- countna(avfilttest, expdf, nbcpu = 1, verbose = FALSE)
## Testing genesECDF 
resecdf <- genesECDF(avfilttest, expdf, verbose = FALSE)

Calculate knee for each condition separately

Description

This function calculate knees for each condition separately

Usage

kneeallconds(alldf, expdf, expthres, nbcpu = 1, rounding = 10,
showtime = FALSE, verbose = TRUE)

Arguments

Details

The kneeallconds function calls successively for each condition:

• 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.

• 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.

Value

A data.frame with the columns transcript, gene, and strand window_size. For each condition 'cond': AUC_cond, p_AUC_cond, D_AUC_cond, MeanValueFull_cond, adjFDR_p_AUC_cond, knee_AUC_cond, and max_diff_Fx_cond.

See Also

[averageandfilterexprs()], [genesECDF()], [meandifference()], [kneeid()]

Examples

exptabpath <- system.file("extdata", "exptab.csv", package="tepr")
alldfpath <- system.file("extdata", "cugusi_6.tsv", package="tepr")
expdf <- read.csv(exptabpath)
alldf <- read.delim(alldfpath, header = FALSE)
expthres <- 0.1
kneedf <- kneeallconds(alldf, expdf, expthres, verbose = FALSE)

Identify the Knee and Max ECDF Differences for Each Transcript

Description

This function 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.

Usage

kneeid(transdflist, expdf, nbcpu = 1, showtime = FALSE, verbose = TRUE)

Arguments

Value

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.

Examples

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)
ecdf <- genesECDF(avfilt, expdf, verbose = FALSE)
resecdf <- ecdf[[1]]
nbwindows <- ecdf[[2]]
meandiff <- meandifference(resecdf, expdf, nbwindows,
 verbose = FALSE)
bytranslistmean <- split(meandiff, factor(meandiff$transcript))
## Testing kneeid
reskneeid <- kneeid(bytranslistmean, expdf, verbose = FALSE)

Split Gene Annotations into Fixed-Size Windows

Description

This functions uses the annotations filtered from gencode (see retrieveanno). It removes any ensembl names containing "PAR_Y". It filters out intervals smaller than windsize and splits each transcript into "windsize" windows.

Usage

makewindows(allannobed, windsize, nbcputrans = 1, verbose = TRUE,
 saveobjectpath = NA, showtime = FALSE)

Arguments

Details

The function filters out annotations with intervals smaller than the specified number of windows ('windsize'). It uses parallel processing to enhance performance when splitting transcripts into fixed-size windows. The result includes metadata for each window, such as its chromosome, start and end coordinates, associated gene, and the window number.

Intermediate functions, such as '.computewindflist' and '.divideannoinwindows', handle computation and validation of windows. Gene intervals with the "PAR_Y" tag are excluded from the analysis.

Value

A data frame containing the split windows for each gene annotation. The output includes fields such as 'biotype', 'chr', 'coor1', 'coor2', 'transcript', 'gene', 'strand', and 'window'.

See Also

[retrieveanno]

Examples

exptabpath <- system.file("extdata", "exptab-preprocessing.csv", package="tepr")
gencodepath <- system.file("extdata", "gencode-chr13.gtf", package = "tepr")
windsize <- 200
## Necessary result to call makewindows
allannobed <- retrieveanno(exptabpath, gencodepath, verbose = FALSE)
## Calling makewindows
allwindowsbed <- makewindows(allannobed, windsize, verbose = FALSE)

Compute Mean and Differences of Scores for Each Condition

Description

This function calculates the mean values, mean Fx (ECDF) and ECDF differences (Fx) for expression data, across different experimental conditions. If only one condition is provided, skips computation of mean differences.

Usage

meandifference(resultsecdf, expdf, nbwindows, showtime = FALSE,
verbose = TRUE)

Arguments

Value

A data frame that contains, for each condition:

• Mean values for the "value" and "Fx" columns (e.g., mean_value_ctrl, mean_Fx_ctrl).

• Differences between the Fx column and coordinate ratios (e.g., diff_Fx_ctrl).

If only one condition is provided, the differences on mean columns are not performed.

Examples

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)
countna <- countna(avfilt, expdf, nbcpu = 1, verbose = FALSE)
ecdf <- genesECDF(avfilt, expdf, verbose = FALSE)
resecdf <- ecdf[[1]]
nbwindows <- ecdf[[2]]
## Testing meandifference
meandifftest <- meandifference(resecdf, expdf, nbwindows,
 verbose = FALSE)

Plot AUC Comparison Between Conditions

Description

This function generates scatterplots comparing the area under the curve (AUC) for control and stress conditions, with an option to highlight specific genes or groups. The plot can be saved as a file or displayed interactively.

Usage

plotauc(tab, expdf, genevec = NA, auc_ctrlname = "AUC_ctrl",
auc_stressname = "AUC_HS",
pvalkstestcolname = "adjFDR_p_dAUC_Diff_meanFx_HS_ctrl",
labelx = "AUC in Control", labely = "AUC in Stress", axismin_x = -10,
axismax_x = 100, axismin_y = -10, axismax_y = 100, maintitle = "",
subtitle = "", legendpos = "bottom", formatname = "pdf", outfold = tempdir(),
outfile = "AUCcompare_pval", plottype = "pval", plot = FALSE,
universename = "Universe", groupname = "Group", verbose = TRUE)

Arguments

Details

The function supports two plot types:

• "pval": The plot highlights genes based on adjusted FDR p-values and can highlight specific genes provided in genevec.

• "groups": The plot highlights predefined groups, such as "Attenuated" and "Outgroup", within the data.

If plot = TRUE, the plot is displayed interactively. If plot = FALSE, the plot is saved to a file in the specified format and output folder.

Value

A plot comparing AUC values between control and stress conditions, either displayed or saved to a file.

See Also

[allauc]

Examples

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, expdf, 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)
resug <- universegroup(resatt, expdf, verbose = FALSE)
## Testing plotauc
plotauc(resug, expdf, plottype = "groups", plot = TRUE)

Plot Empirical Cumulative Distribution Function (ECDF)

Description

This function generates an ECDF plot to analyze transcription density relative to the distance from the transcription start site (TSS) across different conditions. The plot displays AUC values, Kolmogorov-Smirnov (KS) statistics, and knee points, with options to display or save the plot.

Usage

plotecdf(dfmeandiff, unigroupdf, expdf, genename,
 colvec = c("#90AFBB", "#10AFBB", "#FF9A04", "#FC4E07"),
 outfold = tempdir(), digits = 2, middlewind = 100, pval = 0.01,
 plot = FALSE, formatname = "pdf", verbose = TRUE)

Arguments

Details

The function processes data related to transcription levels and cumulative transcription density for a given gene across multiple experimental conditions. The ECDF plot is constructed with optional annotation of key statistics such as AUC values and significant KS test results. Knee points, representing significant changes in transcription density, are also displayed if the KS test passes the specified p-value threshold.

Colvec: The number of colors should be equal to the number of rows of expdf divided by two (a forward and reverse files are provided for each experiment).

Value

An ECDF plot showing the transcription density across windows around the TSS, with highlights for significant KS test results and knee points. The plot can either be displayed or saved as a file.

See Also

[meandifference], [universegroup]

Examples

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, expdf, 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)
resug <- universegroup(resatt, expdf, verbose = FALSE)
## Testing plotecdf
colvec <- c("#90AFBB", "#10AFBB", "#FF9A04", "#FC4E07")
plotecdf(resmeandiff, resug, expdf, "EGFR", colvec, plot = TRUE, verbose = FALSE)

Plot Histogram of Distance from TSS to Knee Point

Description

This function generates a histogram showing the distribution of the distance from the transcription start site (TSS) to the knee point for attenuated genes. The distance can be plotted either as a percentage of the gene length or in kilobases (kb).

Usage

plothistoknee(unigroupdf, plottype = "percent", xlimvec = NA,
binwidthval = NA, kneename = "knee_AUC_HS", plot = FALSE, outfold = tempdir(),
formatname = "pdf", universename = "Universe", groupname = "Group",
verbose = TRUE)

Arguments

Value

A histogram showing the distribution of the distance from the TSS to the knee point for attenuated genes. The plot can either be displayed interactively or saved to a file.

See Also

[universegroup]

Examples

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, expdf, 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)
resug <- universegroup(resatt, expdf, verbose = FALSE)
## Testing plothistoknee
plothistoknee(resug, plot = TRUE)

Plot Metagenes for Gene Groups

Description

This function plots metagene profiles based on transcript data, comparing transcription density across conditions (e.g., control vs. stress). The function allows the user to plot metagenes for different gene groups such as attenuated genes, outgroup genes, the entire universe of genes, or all genes.

Usage

plotmetagenes(unigroupdf, dfmeandiff, expdf, plottype = "attenuation",
daucname = "dAUC_Diff_meanFx_HS_ctrl", auc_ctrlname = "AUC_ctrl",
auc_stressname = "AUC_HS", plot = FALSE, formatname = "pdf",
outfold = tempdir(), verbose = TRUE)

Arguments

Details

This function summarizes mean transcription levels across genomic coordinates for different gene groups and plots the transcription density from the transcription start site (TSS) to the transcription termination site (TTS). The function can generate metagene plots for different gene groups such as attenuated, outgroup, or all genes, and compares transcription profiles between conditions (e.g., control vs. stress). The resulting plot helps visualize differences in transcriptional response between groups of genes under different conditions.

Value

A metagene plot comparing transcription density across conditions (e.g., control vs. stress) for the selected group of genes. The plot can either be displayed interactively or saved to a file.

See Also

[universegroup], [meandifference]

Examples

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, expdf, 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)
resug <- universegroup(resatt, expdf, verbose = FALSE)
## Testing plotmetagenes
plotmetagenes(resug, resmeandiff, expdf, plottype = "attenuation", plot = TRUE)

Generate all tepr plots for all experiment comparisons

Description

This function generates for all experiment comparisons contained in the object resteprmulti all plots of tepr: ECDF, auc, metagene, and histtoknee.

Usage

plotmulti(resteprmulti, expdf, ecdfgenevec, outfold = tempdir(), digits = 2,
middlewind = 100, pval = 0.01, colvec = c("#90AFBB", "#10AFBB",
"#FF9A04", "#FC4E07"), genaucvec = NA, aucaxisminx = -10,
aucaxismaxx = 100, aucaxisminy = -10, aucaxismaxy = 100, aucmaintitle = "",
aucsubtitle = "", auclegendpos = "bottom", formatname = "pdf",
uniname = "Universe", groupname = "Group", histkneexlim = NA,
binwidthvalhistknee = NA, verbose = TRUE)

Arguments

Details

The function goes through each element of resteprmulti which corresponds to a comparison of two conditions. For each element it calls the following functions: #'

• "plotecdf": The function generates a figure for each gene given in ecdfgenevec.

• "plotauc": Generates figures by groups and pval. The lattest figure is not generated if genaucvec = NA.

• "plotmetagenes": Generates the figures by attenuation, outgroup, universe, and all.

• "plothistoknee": Generate the figures by percent and kb.

Value

Nothing is returned. Figures are written to outfold in the subfolder of the corresponding comparison.

See Also

[teprmulti], [plotecdf], [plotauc], [plotmetagenes], [plothistoknee]

Examples

# Assuming resteprmulti is the object returned by the function teprmulti
# and expdf contains the necessary data:
## Not run: 
plotmulti(resteprmulti, expdf, ecdfgenevec = c("EGFR", "DAP", "FLI1"))
## End(Not run)

Preprocess Experimental Data for Genomic Analysis

Description

This function orchestrates a pipeline for preprocessing genomic data, including filtering annotations, splitting transcripts into windows, retrieving bedgraph values, and generating a final annotated table.

Usage

preprocessing(exptabpath, gencodepath, windsize, maptrackpath,
blacklistpath, genomename = NA, nbcputrans = 1, finaltabpath = tempdir(),
finaltabname = "anno.tsv", tmpfold = file.path(tempdir(), "tmptepr"),
saveobjectpath = tempdir(), savefinaltable = TRUE, reload = FALSE,
showtime = FALSE, showmemory = FALSE, deletetmp = TRUE, chromtab = NA,
forcechrom = FALSE, verbose = TRUE)

Arguments

Details

The 'preprocessing' function performs several key tasks: 1. Filters Gencode annotations to retrieve "transcript" annotations. 2. Differentiates between protein-coding (MANE_Select) and long non-coding (lncRNA, Ensembl_canonical) transcripts. 3. Splits transcripts into windows of size 'windsize'. 4. Processes bedgraph files to retrieve values, exclude blacklisted regions, and retain high-mappability intervals. 5. Generates a final annotated table with scores derived from the above steps.

Temporary files created during processing are optionally deleted at the end.

Value

A data frame representing the final table containing transcript information and scores on 'windsize' windows for all experiments defined in the experiment table (exptabpath).

See Also

[retrieveanno], [makewindows], [blacklisthighmap], [createtablescores]

Examples

## Data
exptabpath <- system.file("extdata", "exptab-preprocessing.csv", package = "tepr")
gencodepath <- system.file("extdata", "gencode-chr13.gtf", package = "tepr")
maptrackpath <- system.file("extdata", "k50.umap.chr13.hg38.0.8.bed",
 package = "tepr")
blacklistpath <- system.file("extdata", "hg38-blacklist-chr13.v2.bed",
 package = "tepr")
windsize <- 200
genomename <- "hg38"
## Copying bedgraphs to the current directory
expdfpre <- read.csv(exptabpath)
bgpathvec <- sapply(expdfpre$path, function(x) system.file("extdata", x,
 package = "tepr"))
expdfpre$path <- bgpathvec
write.csv(expdfpre, file = "exptab-preprocessing.csv", row.names = FALSE,
 quote = FALSE)
exptabpath <- "exptab-preprocessing.csv"
## Testing preprocessing
finaltabtest <- preprocessing(exptabpath, gencodepath, windsize, maptrackpath,
 blacklistpath, genomename = genomename, verbose = FALSE)

Retrieve and Combine Annotation Information

Description

This function filters gencode annotations to retrieve "transcript". It then distinguishes transcripts coming from protein coding genes (MANE_Select) and those coming from long non-coding genes (lncRNA, Ensembl_canonical).

Usage

retrieveanno(exptabpath, gencodepath, saveobjectpath = NA, showtime = FALSE,
verbose = TRUE)

Arguments

Details

The function performs the following steps: 1. Reads experimental data from the provided CSV file and validates it. 2. Reads genomic annotations from the gencode file and filters for transcripts. 3. Separately processes protein-coding and long non-coding RNA transcripts: - For protein-coding genes, selects the most representative (MANE_Select or Ensembl_canonical) transcripts. - For long non-coding RNAs, filters out transcripts with undesirable evidence levels. 4. Combines these annotations into a single data frame, labeling each transcript with its biotype. 5. Optionally saves the resulting data frame as an RDS file in the specified directory. 6. Optionally reports the total time taken for analysis.

Value

A data frame containing the combined annotation information for protein-coding and long non-coding RNA transcripts. If 'saveobjectpath' is not 'NA', the object is also saved as an RDS file in the specified directory.

Examples

exptabpath <- system.file("extdata", "exptab-preprocessing.csv", package="tepr")
gencodepath <- system.file("extdata", "gencode-chr13.gtf", package = "tepr")
## Testing retrieveanno
allannobed <- retrieveanno(exptabpath, gencodepath, verbose = FALSE)

Retrieve chromosome lengths and information for a specified genome.

Description

This function connects to the UCSC Genome Browser database using the 'rtracklayer' package to retrieve chromosome information. It returns a 'Seqinfo' object, filtering out unwanted chromosomes such as mitochondrial DNA ('chrM') and those with alternative contigs (indicated by an underscore '_').

Usage

retrievechrom(genomename, verbose, filterchrom = TRUE)

Arguments

Value

A 'Seqinfo' object containing the names and lengths of the main chromosomes for the specified genome.

Examples

# This example requires an internet connection to the UCSC database
hg19_chroms <- retrievechrom(genomename = "hg19", verbose = TRUE)
hg19_chroms

Retrieve all the comparison names from the experiment table

Description

The 'showallcomp' function build the string of all comparisons possible using the condition column of a provided experiment table.

Usage

showallcomp(expdf, verbose = FALSE)

Arguments

Value

If less than three conditions, nothing. Otherwise a character vector of all comparisons.

Examples

# Create a valid experiment table
exptab <- data.frame(
 condition = c("cond1", "cond2", "cond3"),
 replicate = c(1, 1, 1),
 direction = c("forward", "reverse", "forward"),
 strand = c("plus", "minus", "plus"),
 path = c("toto/", "toto/", "toto/"))
checkexptab(exptab)
showallcomp(exptab)

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

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)

Perform tepr differential nascent rna-seq analysis for multiple conditions

Description

This function performs tepr differential nascent RNA-seq analysis for multiple conditions. It iterates over all pairwise comparisons of conditions within the experiment table and calls the 'tepr' function for each pair.

Usage

teprmulti(expdf, alldf, expthres, nbcpu = 1, rounding = 10,
 dontcompare = NULL, replaceval = NA, pval = 0.1, significant = FALSE,
 windsizethres = 50, countnathres = 20, pvaltheorythres = 0.1,
 meancond1thres = 0.5, meancond2thres = 0.5,
 auccond1threshigher = -10, auccond1threslower = 15, auccond2thres = 15,
 attenuatedpvalksthres = 2, outgrouppvalksthres = 0.2,
 saveobjectpath = NA, reload = FALSE, showtime = FALSE, showmemory = FALSE,
 verbose = TRUE)

Arguments

Value

A list of lists. Each inner list corresponds to a pairwise comparison and contains two data frames: - 'resmeandiff_<comparison>': Results from the 'meandifference' function for the specific comparison. - 'resunigroupatt_<comparison>': Results from the 'universegroup' function for the specific comparison.

See Also

[tepr]

Examples

## Supposing the data have more than one condition
## Not run: 
 exptabpath <- "exp.csv"
 alldfpath <- "result-preprocessing.tsv"
 expdf <- read.csv(exptabpath)
 alldf <- read.delim(alldfpath, header = FALSE)
 expthres <- 0.1
 reslist <- teprmulti(expdf, alldf, expthres)
## End(Not run)

Define Universe and Group of Genes Based on Expression Data

Description

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.

Usage

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

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.

Value

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.

See Also

[attenuation]

Examples

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, expdf, 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)